Indian Institute of Technology, Bombay

Mumbai

February 2022

IIT Bombay - PoCRA MoU IV

Phase II - Delivery Report

List of Documents:

Sr.

No.

Document Report Page Number

(Center Bottom)

Water

1 Interim Report on Fieldwork for Model

Validation

1

2 Note on Changes to Water Budget Models for

the Well Beneficiary Module

48

Energy

3 PoCRA - IIT Bombay MoU IV Phase II Report -

Energy Component Objectives F and H

91

Post-Harvest

4 Post-Harvest Analysis Phase II Report 176

Interim Report on Fieldwork forModel Validation

Prepared By: Chirag MM

With Support From:

Jaydeep Tathe, Dinesh Paralkar, Vishal Mishra and Dr. Hemant Belsare

Nov 2021IIT Bombay

Mumbai

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Table of Contents

1. Introduction 3

1.1. Background 3

1.2. Objectives of Model Validation 4

1.3. Timeline for the Model Validation 4

2. Methodology 5

2.1. Model Components: Overall Approach 6

2.2. Data: Collection, Handling and Analysis 7

2.2.1. Data from Sensors and Equipments 8

2.2.2. Data from Farmers’ Narratives 9

3. Execution of Methodology 10

3.1. Selection of Study Areas 10

3.1.1. Cluster Selection 10

3.1.2. Selection of the Catchments 11

3.2. Site Selection for Installation of Instruments 12

3.2.1. Water Level Monitoring Systems on CNBs 12

3.2.2. Water Level Monitoring Systems for V-notch 14

3.2.3. Multiprofile Soil Moisture Probes 15

3.2.4. Rain Gauges 16

3.3. Preparatory Work for Installation of the Instruments 16

3.3.1. Preparatory Works for Water Level Monitoring Systems on CNBs 16

3.3.2. Preparatory Work Required for V-notch 19

3.4. Instruments Installed 23

3.4.1. Rain gauge 23

3.4.2. Water Level Monitoring System 26

3.4.3. Multiprofile Soil Moisture Probes (Soil Moisture Monitoring Systems) 33

4. Work Progress 39

4.1. Key Activities and Tasks Conducted During Monsoon Fieldwork 39

4.1.1. Fieldwork Prior to Installation of Instruments 39

4.1.2. Installation, Monitoring, and Maintenance of Instruments 39

4.1.3. Flow Measurements 40

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4.1.4. Farmer Interviews 41

4.1.5. Field Observations 42

4.2. Ongoing Work 43

4.3. Planned Work 44

4.3.1. Fieldwork 44

4.3.2. Deskwork 45

Annexure 46

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1. Introduction

IIT Bombay is in fourth MoU with the PoCRA, Government of Maharashtra. The role of the

IITB team for the last three years is as a knowledge partner to the PoCRA. This collaboration

to the date has led to development, deployment of water balance tools and their incorporation

in village water budget for NRM planning. The current MoU focuses on the model validation,

improving model outputs especially from the point of rabi planning and community

extension. The component of model validation is linked with almost all of the other key

components as field work for the model validation forms the basis for model improvements

and to some extent for community extension.

1.1. Background

Currently, model outputs are published for every PoCRA village as a chart which is displayed

in the village. It is expected that these outputs are to be used by the local planners such as

cluster assistants (CA), krushi sahayaks (KS) to plan the NRM activities and by farmers to

plan the cropping pattern at the individual and the village level. These model outputs are

hence crucial and need to be fairly reasonable and reliable. The exercise of the model

validation is therefore important to build trustworthiness of the model among the key

stakeholders including local planners, farmers, policy makers and higher rank officials from

the department of agriculture and researchers/scientists.

Last year, the IITB team attempted the exercise of the model validation for the first time. This

involved conceptualization and execution of a structured methodology, prior to which only

some of the model outputs were tested based on the anecdotes and field observations. This

study was conducted in selected catchments of PoCRA region in Hingoli and Washim district

from Marathwada and Vidarbha respectively. It was however hit by the Covid-19 outbreak

and lockdown restrictions imposed due to the same which affected the timeline of the

exercise, procurement, installation and calibration of instruments and other subsequent

processes. The findings of this exercise are documented and shared with the PMU which

were termed as satisfactory by the World Bank (WB) experts and PMU.

Apart from the satisfactory comments received from the experts, it was also suggested that

the IITB team continue the exercise of model validation with more geographical spread in the

PoCRA region. Based on the inputs from the experts and PMU, the IITB team decided to

select catchments for model validation from the four districts, two each from Marathwada

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and Vidarbha. Given the timeline proposed for the exercise of model validation, the IITB

team has planned to share the documentation and the findings of the same in the form of three

reports viz. an initial report, a report on kharif validation and a report on rabi validation cum

closure report.

1.2. Objectives of Model Validation

● To validate regional as well as farm-level water balance components (such as surface

runoff, soil moisture, crop stress) through a combination of on-field measurements,

key observations from field and farmer narratives.

● To carry out field investigations based on observations, measurements and farmer

surveys in order to understand and incorporate key phenomena such as regional

groundwater flows, baseflows, ponding, and stream proximity which will further help

in improving the soundness of the model.

● To further strengthen the validation methodology for its robustness and

trustworthiness.

● To device simple indices as proxies which can be used for testing model outputs at

scale by different agencies/stakeholders.

1.3. Timeline for the Model Validation

The stipulated time period for the model validation is of about 10-12 months. This includes

tasks such as the selection of study area, procurement of instruments, installation, data

collection, fieldwork1, data analysis and report writing. Table 1 summarizes the key activities

and timeline for the same.

Table 1: Model Validation Timeline

Activities and Tasks

Timeline

May-Sept Oct-Nov Dec-Jan Feb-Mar Apr-Jun

Review and finalization ofmethodology

Installation, monitoring andmaintenance of the sensors

1 Team members who contributed to the karif fieldwork in their best capacities include Jaydeep Tathe, DineshParalkar, Vishal Mishra, Amol Wadje, Devanand Doifode, Gopal Chavan (PhD scholar at IITB), Dr. HemantBelsare, and Chirag MM.

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Monsoon fieldwork(measurements, interviews andobservations)

Report Writing - Initial Report

Cleaning, compilation andconsolidation of the sensor data

Data analysis and comparison withwater budget results: Kharif

Report Writing - Kharif Validation

Rabi fieldwork (farmer interviews,field observations) and analysis

Report Writing - Final Report

This report is the initial report for the model validation. It has focused mainly on the

methodology proposed for the overall validation plan and the documentation of the activities

conducted upto the month of September since the beginning of the model validation exercise.

In the first chapter it briefs the objectives and tentative plan of the overall exercise. Second

chapter discusses in detail the methodology to be adopted for the model validation. Third

chapter covers the execution part including selection of study area, preparatory works and

installation of instruments. The final chapter provides an update on the work done so far

including key observations and activities conducted as a part of fieldwork, the ongoing work,

and briefs the planned work for the model validation till the month of January.

2. Methodology

The need for the model validation was evident soon after water budget results for PoCRA

villages began publishing. Even though exercise of model validation was attempted in the last

year with a structured methodology, its success was largely limited to the measurement of the

surface runoff. The IITB team felt that validation of one of the model outputs does not

necessarily guarantee overall model validation. It was discussed in multiple internal team

meetings that all of the four model outputs needed to be validated simultaneously for a given

region, say a village or a catchment.

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Therefore, this year the idea of model validation was to ensure execution of the methodology

in its totality i.e. covering measurements of other model components to the satisfactory

standards. In this chapter we discuss the method proposed for validation of each of the four

model outputs viz. AET, soil moisture, surface runoff and groundwater recharge.

2.1. Model Components: Overall Approach

The equation for the IITB-PoCRA water balance is given as follows,

Rainfall = Runoff + GW Recharge + AET + Δ Soil Moisture (at Kharif end)

The PoCRA water budget model computes point-level surface runoff, groundwater recharge,

actual evapotranspiration (AET) and soil moisture and then aggregates them to zone / village

/ cluster. In practice, soil moisture and AET are essentially farm-level attributes and can be

measured and observed at farm level. Whereas components like runoff and groundwater are

regional in nature and are typically measured / computed for a region, say a catchment or a

watershed.

Thus, we propose different geographical units for validation of different model components.

For the soil moisture, a few sample locations in the selected farms within the catchment will

be monitored so as to cover different soil types and crops. The model results will be obtained

for different combinations of soil texture, soil depth, root depth, slope etc. and will be

compared with the soil moisture for monitored locations.

Similarly, AET will be considered for validation at the farm level. The measurement of AET

requires fairly sophisticated and costly instruments which are difficult to operate on the field

for various reasons. Therefore, AET will be verified through simpler on-field proxies such as

farmer narratives about crop stress and observations on the field about crop health such as

crop height, color etc.

For groundwater recharge and runoff, a catchment will be used as the unit for validation.

Groundwater recharge results by model will be compared and cross checked with the

empirical data for groundwater extraction post monsoon season and data for well levels in the

catchment for the consistency. Similarly, flows will be measured at the outlet of the selected

catchments and at the selected farms which will be used for computation of the overall runoff

from the respective catchment or farm over a period of time.

Table 2 shows the planned measurements and proposed methods for the same.

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Table 2: Model component and proposed validation method

ModelComponent

Scale Method to be used On-field activities

Surface Runoff Farm V notch with water levelsensors

Total 7 farms to bemonitored

Regional /Catchment

Water level sensors on CNB Sensors installed at 21locations (includingcatchments and theirsub-catchments)

Soil Moisture Point /Farm

Soil moisture sensors Total 5 farms to bemonitored (2 locationsper farm)

Groundwaterrecharge

Regional /Catchment

Monitoring of well waterlevels and estimation ofgroundwater extraction basedon farmers interviews andmeasurements

About 15 wells in thecatchment to bemonitored during kharifand rabi seasons

AET (Indirect) Point /Farm

Structured interviews withfarmers

About 15 farmers in thecatchment to besurveyed in kharif andrabi seasons

For all the model components, measured and observed values will be compared with the

model results for the respective catchments for specific rainfall events and time period. Apart

from this, post monsoon water availability for the catchment will also be computed using the

model components such as groundwater recharge and soil moisture, and surface water stored

in the water conservation structures at the end of monsoon in the catchment. This will be

matched with the farmer narratives for water cropping pattern and demand for agriculture,

water provided to the crops, its adequacy and rationing practices etc.

2.2. Data: Collection, Handling and Analysis

One of the important components of the model validation exercise is data collection. The

different datasets to be used for model validation consists of both primary as well as

secondary data. The primary data includes data from different instruments the IITB team has

installed, data collected from the farmers and different measurements by the team. The

secondary datasets include data from other agencies such as the Department of Agriculture,

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Skymet and MRSAC on cropping pattern, rainfall and soil type respectively. Given the

requirement of multiple datasets for the analysis, handling of these data also becomes crucial.

Table 3 summarizes different data required for the overall model validation along with its

type and source.

Table 3: Datasets to be used for Model Validation

Dataset Type ofdata

Source of data Purpose

Rainfall Primary Rain Gauge For recording rainfall

Height ofwater column

Primary Water levelmonitoringsystems

For flow calculation at the outlet forcatchments and farm

% Soilmoisture

Primary Soil moisturemonitoringsystems

For calculation of actual / observedvolumetric soil moisture

Crop stress Primary Farmerinterviews

For comparison with model outputs onAET

Well waterlevels

Primary Farmerinterviews

For comparing timeline of differentphenomena associated with groundwaterrecharge

Croppingpattern andwaterings

Primary Farmerinterviews

For computation of extraction andestimate rationing at the catchment level

Bulk density Primary Results from soiltesting lab

For improved and accurate model inputsfor the selected location

Rainfall Secondary Skymet For comparison and study of rainfallpattern and as a standby dataset forrainfall

Soil Textureand SoilDepth

Secondary MRSAC To be used as standard model inputs

Slope andDrainage

Secondary MRSAC To be used as standard model inputs

2.2.1. Data from Sensors and Equipments

The data from water level sensors, soil moisture sensors and rain gauges will be

systematically collated, cleaned, compiled and processed (as discussed later in this report) so

as to get a meaningful comparison of the same with model results. Water level sensor data

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will be used to calculate discharge which will be further used to compute the volume of the

water that has drained from the outlet. The outlets considered will be CNB for computation of

regional runoff and V-notch for computation of the farm runoff.

Similarly, soil moisture sensors data obtained in percentage saturation will be compiled and

compared with its volumetric counterpart. These results will be then compared with the

model output for soil moisture. All this data along with rainfall data will be used for event

based validation.

2.2.2. Data from Farmers’ Narratives

The IITB-PoCRA water balance model is planned to be validated not just using the actual

measurements but also based on the farmers' narratives for the different events and

phenomena. This includes both narratives for the events during kharif as well as rabi seasons.

Narrative based model validation is important as it can be easily replicated by different

agencies at scale.

Farmers' narrative based model validation is planned using two sets of questions. One such

set of questions is used for validation of the AET and soil moisture results by the model.

These questions mainly focus on the irrigation provided by the farmers during kharif season

which is a proxy for the declined soil moisture and the corresponding crop deficit. This set of

questions also includes the impact of the dry spell on the crop yield and its comparison with

the maximum and frequently observed yield to get an idea about the crop AET.

Another set of questions deals with the narratives on the water levels for the selected wells

during kharif. These questions are aimed to understand pre-monsoon water levels, when did

the water level start rising which can be considered as a proxy for groundwater recharge and

when did wells get filled completely. These observations will be compared with the model

results to see if the model indicated occurrence of groundwater recharge around the same

time period.

Similarly, during rabi season, farmers' narratives on the number of waterings provided to the

crops will be used to estimate the actual groundwater extraction post monsoon. These

estimated figures will be compared with the groundwater recharge shown by the model.

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3. Execution of Methodology

This chapter discusses the execution part of the methodology explained in the previous

chapter. It covers the procedures followed for selection of the study areas, site selection for

installation of different instruments, preparatory and installation works involved for the same.

3.1. Selection of Study Areas

The main focus while selecting the study areas was to ensure that the selected areas fairly

represented the PoCRA region. This representativeness was based on the coverage of the

different attributes used as inputs for the model which includes rainfall, soil texture and

depth, terrain and land use. The coverage of these attributes is also important because they

affect different model outputs especially runoff, groundwater recharge and soil moisture. The

selection of the study area for the model validation exercise firstly involved selection of the

potential PoCRA clusters and then selecting appropriate catchments from these clusters.

3.1.1. Cluster Selection

Based on the past experience of the IITB team on model validation and the feedback from the

PMU, about 40 potential clusters from the PoCRA region were shortlisted. These clusters

were paired up in about 28 groups using different combinations with two clusters in each

group which can be operated from the same base location for the IITB team. The pairing was

primarily based on the logistics for the fieldwork to be conducted in these clusters. Given the

uncertainty associated with the restrictions imposed due to the pandemic and considering

factors like connectivity between the clusters and time required for the travel, an attempt was

made to pair those clusters which are not far away from each other. These clusters however

needed to be from different taluka and preferentially from different districts to have better

administrative coverage.

One of the key factors considered for cluster selection was rainfall received by these clusters

wherein normal rainfall and rainfall for the last 5 years were compared to check if the clusters

in a pair complement each other and do not show similar pattern. These selected clusters were

checked for adequate diversity and coverage of the key attributes such as soil types, and land

use. Considering above points and a suggestion of PMU to have study areas from both

Marathwada and Vidarbha region, two clusters each from the respective regions were

selected. Table 4 lists the selected clusters and Figure 1 shows location of these locations in

the project region. Ahmedpur and Karnja were the base locations for the clusters from

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Marathwada and Vidarbha region respectively where two field teams stayed for about four

months for the fieldwork during the kharif season.

Table 4 : Selected clusters for Model Validation

Cluster District Taluka

511_gv-101_03 Nanded Loha

524_mr-47_05 Latur Ahmedpur

510_wrb-1a_01 Yavatmal Ner

502_ptkp-1_03 Washim Karanja

Figure 1: Location of the selected clusters in PoCRA region

3.1.2. Selection of the Catchments

Given the large size of the clusters (more than ten thousand hectares in most of the cases) and

complications associated with the model validation exercise, it was not possible to select a

complete cluster as the study area. Therefore, only part of the cluster was selected for the

model validation. This selection was based on the attributes of soil, slope, drainage and land

use. The objective was to strike an appropriate balance of the combination of these attributes

ensuring representativeness of the study area and considering various practical limiting

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conditions. Table 5 lists the attributes and their respective variations considered for the

catchment selection among the available options.

Table 5: Catchment attributes considered while selection

Catchment

Attribute

Variation Considered

Rainfall High, Moderate and Poor rainfall

Soil Type Different soil textures with combinations of different soil depths such as

clayey very deep, clayey deep, clay loam shallow, gravelly clay deep etc.

Land use and

land cover

Different combination of land use and landcover in terms of completely

agricultural area and mix of agricultural and non agricultural area,

percentage of only kharif crop and 2-3 crops

Terrain Relatively flat, moderately hilly terrain

Based on study of different regions in the clusters for the above attributes, catchments were

selected. Although the idea was to select four main catchments and a few subcatchments for

the respective catchments, suitable main catchment could not be finalized in Karanja cluster

considering various constraints on the field. Instead, two independent small catchments

which can be considered equivalent to subcatchments were selected. The maps for the

selected main catchments such as soil maps, drainage and LULC are attached in Annexure.

These catchments were further categorized in different subcatchments and water level

monitoring systems were installed accordingly.

3.2. Site Selection for Installation of Instruments

Once catchments were finalized the next task was to select suitable locations for installation

of different instruments. This section discusses site selection for different instruments which

includes water level monitoring systems for CNBs and V-notches, multiprofile soil moisture

probes and rain gauges.

3.2.1. Water Level Monitoring Systems on CNBs

Given the large number of the water level monitoring systems planned for the installation and

its dependency on the CNBs, site selection for all these systems was one of the most crucial

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tasks. The multiple factors considered for site selection can be categorized as attributes of the

CNB catchments and of the CNB structure.

The attributes of CNB catchments to be selected included combination of soil type and soil

texture, average slope of the catchment, land use, and area of the catchment which affects

runoff generation. Some of the catchments were selected for the presence of the dominant

soil type and texture whereas others for the presence of different combinations of soil types.

Similarly, CNBs with different sizes of the catchments were selected ranging from tens of

hectares to hundreds of hectares. While selecting the site, an attempt was made to ensure that

all the CNBs selected add different aspects to the study and CNBs with almost similar

attributes for catchments (such as soil type, slope, land use) were not selected.

The attributes of CNB structure considered included CNB dimensions, silting in the CNB,

effective water holding capacity of CNB, damage to the structure and leakages if any. These

factors were considered to assess the feasibility of the CNBs to install the water level

monitoring systems. CNBs with damaged structure and with any major leakages as reported

by farmers were not selected. When CNBs were found appropriate they were assessed for

accessibility for installation of instruments to make sure that the necessary equipment can be

carried to the site and installation was planned considering availability of the electricity for

using the drilling machine.

Figure 2: Sample photos of selected CNBs

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Figure 2 shows some sample photographs of the CNBs selected for the installation and Figure

3 shows CNBs rejected due to damaged structures from the initial list of CNBs prepared

using Google Earth imagery before actual field visits.

Figure 3: Sample photos of rejected CNBs

Site selection for the water level monitoring systems were met with a few challenges on the

field. One of the main limitations was the time available to visit all the potential sites

shortlisted for installation based on satellite images and select them based on the visual

inspection and farmers narratives. Finalization of the sites at the earliest was important to

place the order for sensors of required range to the vendor based on the CNB attributes and

field conditions. Lead time for the delivery of the sensors after orders were placed also

needed to be accounted for in the installation plan. All this was required to be completed

before we proceed with the installation. Another issue was inconsistencies in the farmers'

narratives about leakage from the concerned CNB. Since there was no flowing water during

the initial inspection of the CNBs, there was hardly any reliable way to confirm the same.

3.2.2. Water Level Monitoring Systems for V-notch

As mentioned earlier in this report water level sensors were planned for installation on

V-notch to measure farm runoff. The original plan for selection of the V-notch sites was

aimed at covering different attributes of the farm which affects the runoff generation.

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However, given the limited number of V-notches scheduled for installation, soil was the only

parameter considered as it affects the runoff generation the most. The idea while selecting the

farms for V-notch installation was to cover different combinations of the soil type and soil

texture to study its impact on farm runoff.

Figure 4: Sample photos of sites selected for V-notch installation

The variation and combinations of parameters other than soil such as slope of the farm and

cropping pattern were not considered for site selection. The area of the farms to be considered

for V-notch installation was decided to be in the range of 1-2 Ha. All the V-notch sites

planned were in soybean farms with the fairly flat terrain. Except in one case, a cotton farm

with a slightly higher slope was selected.

3.2.3. Multiprofile Soil Moisture Probes

All the sites for soil moisture monitoring were selected from the same farms where V-notches

were installed. This was to make sure that we can simultaneously monitor two components of

model outputs. Given the limited number of soil moisture sensors procured, two soil moisture

monitoring setup (with multiprofile soil moisture probes) were installed per farm in five such

farms of the total seven farms where V-notch were installed. The location within the farm for

installation was selected while ensuring representativeness of the farm and hence locations

close to the outlet, extreme ends and borders were avoided. Both the soil moisture monitoring

setup installed in the farm were about 12 to 15m away from each other.

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3.2.4. Rain Gauges

As compared to other instruments, site selection for the rain gauge was comparatively easy.

The sites selected for installation of the rain gauges were required to be in the open space

without any obstruction nearby which can affect rain collected by the rain gauge collector.

All the rain gauges were installed on the rooftop of either of the public buildings in the

village such as primary school and gram panchayat office. This was to ensure the safety of

the instrument and also to create a sense of ownership among the villagers for the instrument.

While selecting the sites, it was confirmed with the farmers that the location of the rain gauge

will be fairly representative of the rainfall received by the catchment.

3.3. Preparatory Work for Installation of the Instruments

As far as preparatory works for different instruments are concerned, installation of rain gauge

and soil moisture probe involved no such work. In the case of soil moisture probes, for one of

the two types, an access tube needed to be inserted in the soil first before installation of the

actual probe. But this was a minor work and hence is not discussed in detail. The preparatory

work only for installation of water level monitoring systems is thus discussed in this section

which includes preparatory work for installation on CNBs and V-notches.

3.3.1. Preparatory Works for Water Level Monitoring Systems on CNBs

This section discusses preparatory work involved in installation of soil water monitoring

systems on CNBs. There are two options available for installation of these systems viz.

stilling well and direct mounting. Both of these methods have their pluses and minuses where

direct mounting is easier for installation, on the other hand stilling well provides more

reliable results. Although these methods require different preparatory works, they essentially

differ only in the positioning and placement of the water level sensor in the main channel and

other set up including hardware remains the same.

3.3.1.1. Stilling well

Stilling wells are cylinder-shaped chambers that are connected to, but isolated from, a

channel's main flow of water. Capturing a portion of water inside of a stilling well helps to

quiet the interfering turbulence occuring in the main channel. In our case, these stilling wells

were necessarily PVC pipes of (6 or 8 inch diameter) which were installed near the suitable

CNB side wall. The stilling well is connected to the stream channel through PVC pipes of

about 2.5 inches which maintain the stilling-well water level at the same head as in the stream

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channel. The water level sensors mounted on a platform inside a PVC pipe of 1.5 inch

diameter are installed inside this stilling well. Construction of the stilling well involved

excavation for the main pipe and the connecting pipe, assembling the various parts and

putting them all together for the actual erection of the overall setup.

Figure 5: Sample photos for stilling well setup

Figure 5 shows sample photographs during various stages of the stilling well construction.

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3.3.1.2. Direct Mounting

In direct mounting, the overall water level sensor assembly (i.e. water level sensors mounted

on a platform inside the PVC pipe of 1.5 inch diameter) is installed in the main channel with

support from the iron pole fixed in the sidewall of the CNB instead of installing it inside the

stilling well. Only precaution to be taken while installing this setup is not to install it very

close to the CNB wall so that water level recorded is not intervened due to any effect of the

CNB wall. Typically when the channel at the upstream of the CNB is not silted and well in

shape, no preparatory work is needed as such.

However, when CNB is silted to a considerable extent leading to uneven flows, this poses

challenges in computation of flows flowing in the channel. In such cases, desilting of the

CNB is required. For a couple of locations where such CNBs were selected, desilting was

carried out using a JCB machine. Given the time constraints and cost associated with this

activity, only a small area on the upstream of the CNB just sufficient for our purpose was

considered for desilting. Two such locations (one each from Marathwada and Vidarbha)

required these preparatory works for direct mounting method.

Figure 6: Sample photos of preparatory work required for direct mounting of silted CNB

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Apart from the key components (such as water level sensors, RTU and the gateway, discussed

later in this report) that constitute the water level monitoring system, hardware for these

components plays a crucial role in their smooth functioning. Installation of this necessary

hardware prior to actual installation of sensors also needs to be considered as preparatory

work. One of the most important hardware components among these included a sturdy 1 m

iron pole on which the solar plate is mounted. For the systems with ultrasonic sensors, this

pole also provides the platform on which the ultrasonic sensor setup is mounted on.

Figure 7: Sample photos of erection of iron rod

In case of direct mounting, the iron pole also serves the additional purpose of providing

support for the water level sensors to be able to withstand the force flowing water in the

stream. This pole is fixed in the ground using fasteners of about six to nine inches. When this

hardware and necessary arrangements depending on the type of installation are ready, water

level sensors can be installed.

3.3.2. Preparatory Work Required for V-notch

The preparatory work for V-notch includes two key tasks of designing the V-notch and

channel preparation.

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3.3.2.1. V-notch Design

The triangular weirs among the various different types of weir available were selected for

V-notch. The design for V-notch was based on the study of different literature available on

triangular weirs with θ = 90°. The document titled ‘Standard Test Method for Open Channel

Flow Measurement of Water with Thin Plate Weir’ published by ASTM International was

used as the primary reference for the design of V-notch.

Figure 8: Schematic representation of V-notch

H/P ≤ 1.2

H/B ≤ 0.4

P ≥ 0.3 ft (0.1 m)

B ≥ 2 ft (0.6 m)

0.15 ft (0.05 m) ≤ H ≤ 2 ft (0.6 m)

Where, H is height of the V cut (distance between base of the inverted triangle to the vertex),

B is the width of the channel, and

P is the distance from the V cut to the channel bottom.

V-notch dimensions were finalized based on the above five conditions and considering

feasibility of the design with respect to field conditions. For the optimal drainage through

V-notch, higher values of ‘H’ are desired. However, in many cases, for the suitable design of

V-notch, the channel width i.e. B, was coming out to be more than 3 ft as per the above

conditions. This is perceived as inappropriate by farmers because these channels observed on

ground are generally of lesser width and channels with greater width are considered

unnecessary. Similarly, in case of higher values of ‘P’ which were necessitated for higher ‘H’,

it meant greater storage of water (dead stock upto V-point) in the channel which was not

desired by farmers.

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Therefore, for a couple of V-notches, given the necessity of the mentioned field conditions,

there was a slight deviation for some of the dimensions in accordance with the reported

literature. Based on these designs used, corresponding standard discharge relationships will

be used to compute flow from the weir.

Figure 9: Sample photograph of V-notch fabrication

The V shape was cut out of the standard rectangular metal sheet of 6*6 feet and of about 2

mm of thickness. All the necessary fabrication works which included cutting of the metal

sheet as per required dimension (ensuring 90° angle and isosceles triangle), drill for mounting

of the water level sensor assembly and welding of the stand and necessary support to overall

V-notch were carried out locally under the supervision of the IITB team.

3.3.2.2. Channel Preparation

V-notch is to be installed in a channel that collects all the runoff generated from the farm and

drains the same. In reality, many times a farm drains runoff not from a single but multiple

outlets. Similarly, there are farm channels that drain runoff generated not just in the

concerned farm but also from the neighboring farm. Such farms are not suitable for the

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V-notch installation. The farms that have proper drainage mechanisms from a single outlet

are therefore preferred to install V-notch.

Figure 10: Sample photographs of channel preparation for V notch

For the farms where there was drainage of runoff from a single outlet but proper channels

were not in place, a new channel was prepared. Whereas for farms where the channel was

present, some cleaning and minor excavation was carried out to meet the desired dimension

as per standard designs. These channels were prepared in such a way that it carries the farm

runoff during peak events from the V-notch without causing any spillover, sideways or

otherwise.

Figure 11: Sample photographs of V notch

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When both V-notch fabrication and channel preparation are ready, V-notches can be erected

in these channels. For V-notches, the installation set up remained exactly the same except that

the iron pole on which the ultrasonic sensor and regular sensors were mounted, was erected in

the soil next to the berm using iron rods of about 1 m length as anchors.

To ensure stability of the V-notch and provide adequate support, at least 1 ft of plate was

inserted in the ground and tens of centimeters in the side berms. Similarly, these side berms

especially near the V-notches were compacted to make sure that water does not find a

sideway escape route bypassing V-notch. This compacting was crucial given the fact that

V-notches were not installed in recommended cemented channels due to limitation of

seasonal timeline, curing required for cemented structure and its acceptance among farmers.

To avoid silting of the channel during runoff events, pitching of the stones at the entry of the

channel was ensured for all the channels.

3.4. Instruments Installed

This section discusses all the different instruments that are installed on the field for the data

collection. This covers the need and importance of the instruments, working principle,

provisions for data logging and visualization and overall number and location of the

instruments installed.

3.4.1. Rain gauge

3.4.1.1. Need for the instrument

Rainfall is one of the most important inputs to the water balance model. Mathematically, it is

the sole parameter in the left hand side of the water balance equation and all of the

parameters in the right hand hand side which are model outputs are dependent on it. The

quality of the rainfall data therefore becomes crucial to get fairly accurate model outputs.

For computation of the water budget of the PoCRA villages, rainfall data used as model input

is from the skymet weather stations. These skynet weather stations are located at the revenue

circles. The IITB team has observed during different field visits and from interaction with the

farmers that overall rainfall data recorded by the skymet stations reasonably matches with the

rainfall received by the village in general. However, some of the rainfall events do not

necessarily match both in terms of the time and intensity. This is especially true when the

village is located far away from the weather circle or there is a ridge in between. It was also

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observed that for some of the skymet circles rainfall data for multiple hours in a day were not

recorded.

Since, we have now moved from daily model to hourly model, accuracy of rainfall data has

become more important. A single rainfall event of 30 mm in an hour for a day will produce

different runoff and contribute to GW in a different proportion than a scenario where a total

of 30 mm of rainfall is received in a day in multiple rainfall events. This has been well

documented in the previous reports where daily and hourly model results are compared.

Considering all of the above points, the IITB team decided to install rain gauges in each of

the selected cluster catchments to minimize the error that may arise due to inconsistency in

the actual rainfall received and the recorded rainfall data.

3.4.1.2. About the instrument: Components, Working Principle, and Data Logging

The rainfall measurement system installed has two main components. One is the mechanical

setup of the rain gauge which actually measures the rainfall and another is the electronic

component which records and logs the data. Though the system is procured from a single

vendor, there are two separate manufacturers for rain gauge and the logger.

The mechanical setup is essentially the rain gauge manufactured by Davis Instruments which

works on the principle of the tipping bucket. One of the rain gauges out of three has a tipping

bucket whereas the other two have a tipping spoon assembly as a collector. These collectors

are factory calibrated to tip for every 0.2 mm of rainfall.

Figure 12: Sample photographs of rain gauge collector

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The electronic component of the instrument is by Dataflow Systems which records each tip of

the collector which is timestamped along with the temperature recorded separately by the

logger. This recorded data gets logged and locally stored in the data logger which can be

fetched later using android phone or tablet. When an android device is connected to the

logger via bluetooth, it transfers the locally stored data to the web server which can be

viewed and downloaded from the web.

Figure 13: Sketch of data logger (a) and snippet of web interface for sample rainfall data (b)

Figure 13(b) shows the snippet of the visualization of the rainfall data on the web interface.

The only limitation of the installed rain gauge systems was the rainfall data collected was not

live on the web interface to visualize or to work on. One could only visualize or download

data only upto the point when data locally stored in the logger was pushed to the web server

using an android device. Therefore, regular visits to the rain gauge sites were needed to

ensure timely update of the data on the web for visualization.

3.4.1.3. Number and location of the instruments

All of the three rain gauges were procured from Dataflow Systems, Newzealand. This vendor

was selected for the better performance in terms of functionality provided, accuracy of the

instrument and cost affordability. These were installed in the three villages viz. Morewadi

(Ahmedpur cluster), Mangrul (Loha cluster) and Adgaon (Ner cluster).

Another rain gauge from the local vendor based on the same principle of tipping bucket was

attempted for the installation in Wai Pr. Karanja (Karanja cluster). However the idea was

dropped after facing some issues related to data logging. It was decided that for this

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catchment, rainfall data from the skymet circle will be used which was found to be

reasonably consistent with the farmers narrative and field observations for the events of the

rainfall in the catchment.

Thus, although the IITB team planned for installation of the four rain gauges, one each for a

cluster, we could successfully install only three. This was also because of the practical

limitations of logistics especially due to restriction on the movement due to pandemic,

availability of the instrument of the desired specifications with the vendor, timeline for

procurement and delivery, cost of the alternative instrument.

3.4.2. Water Level Monitoring System

3.4.2.1. Need for the instrument

As discussed earlier in the report, runoff is the only model output among others which can be

physically measured with ease. To compute the runoff generated in a region, we need flow at

the outlet of the catchment that drains water out. Water level sensors are required to be

installed at the outlet to compute these flows. Water level monitoring systems essentially

provide height of the water column at a given point of time. The water levels and their

corresponding time duration recorded by these sensors are necessary to compute the flow

from the catchment.

Water level sensors for monitoring water height of the channel were used last year as well for

flow measurement. However unlike last year, this year installation of these systems was not

limited to CNBs to measure runoff from the catchment of the CNB. This year, these systems

are also used for measuring farm runoff which are installed on the V-notch setup. Except for

the range, the measurement systems installed on CNBs and V-notch are exactly the same.

3.4.2.2. About the instrument: Components, Working Principle

These systems were procured from Green Pyramid Energy, Pune. The overall system

comprises three main components viz. Water level sensor, Remote Terminal Unit (RTU) and

Gateway. The water level sensor measures the water height, RTU locally logs and stores this

data and Gateway acts as a communicator between the RTU and the web server.

RTU is provided with four ports (called P1, P2, P3 and P4) for connection with the water

level sensors, an on off switch and a memory card slot in case data needs to be fetched

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manually. RTUs are placed at the sensor site on the iron rod and are powered by the solar

plates and additional battery backup.

Figure 14: Sample photographs for Remote Terminal Unit (RTU)(a) For regular sensors only; (b) For regular and ultrasonic sensors

The gateway is provided with an on off switch and a slot for a sim card. Similar to RTUs, the

gateways are powered by a separate solar panel and also have a battery backup. For every

water level monitoring system, separate water level sensor(s) and a RTU are required whereas

the gateway can be shared among the different systems in the nearby area. To ensure greater

coverage of the gateway, they were installed at the highest elevation possible in the

catchment such as elevated overhead tanks.

Figure 15: Sample photographs for Gateway

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The sensor consists of multiple sub-sensors connected to each other at the adjoining ends like

train coaches. The number of sub-sensors for a given sensor decides the range of water

columns that can be measured using the sensor. The order placed to the vendor for the sensors

was for 1, 1.5, 2 and 2.5 m. The number of sub-sensors in a sensor were 20, 30, 40 and 50

respectively since for each meter of sensor length 20 sub-sensors are required.

Figure 16: Sample photographs of (a) Water level sensors (b) Water level sub-sensor

Each of the sub-sensor has two points as can be seen in the figure 16 (b). These points on the

sub-sensor essentially resemble an electric circuit which is open under normal circumstances

and gets shorted when water touches both of these points of the sub-sensor. The raw data

provided by the sensor is in the form of the number of sub-sensors which are not shorted

along with its position. For example, when a sensor of 1.5m is immersed in water for about

half a meter, the raw data would indicate top 20 sub-sensors are not short or open and 10 at

the bottom are shorted. The height of the water column is basically dependent on the position

of the last shorted sub-sensor in the chain.

A water level monitoring system installed comprises two water level sensors mounted on a

platform at an offset of 2.5 cm. Having a system of such two water water level sensors

improves the accuracy of the measurement since now there are three sensing points instead of

two for the unit length of. This is also helpful in the case of events where one of the water

level sensors goes offline or stops functioning, we won’t lose complete data as the other

sensor may still provide the data. The least count of such a water level monitoring system

with two sensors is about 2.5 cm.

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3.4.2.3. Ultrasonic Water Level Sensors: Working Principle, Components

Apart from the regular water level sensors used last year, a new type of sensors were also

installed at some of the CNBs and on all the V-notches to explore new and alternative options

for water level measurement. These were ultrasonic water level sensors procured from the

same vendor and installed at the selected CNBs with the regular sensors. The idea to install

these sensors with regular sensors and not at different locations was to see if the data from

both types of the sensors reasonably matches with each other.

The ultrasonic water level sensors differ from the regular water level sensors in terms of

working principle and functionality to some extent. One of the key differences in these two

sensors is that the regular sensors need to be in contact with the water column and hence are

required to stand in the water channel. Whereas, ultrasonic sensors are not required to be

physically there in the water channel. These sensors are positioned above at a height of about

30 cm higher than the maximum possible water channel height of the flowing water.

Figure 17: Sample photographs for ultrasonic sensor setup

These sensors emit short, high-frequency sound pulses at regular intervals which propagate in

the air at the velocity of sound before they strike flowing water and gets reflected back as

echo signals to the sensor, which itself computes the distance to the flowing water from the

positioned height based on the time-span between emitting the signal and receiving the echo.

These sensors thus have a wider measurement range than that of regular sensors and can

measure water channels of about 5m height.

The other components of the water level monitoring system i.e. RTU and gateway were

needed for these ultrasonic sensors as well. Common gateways were used for both the type of

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the water level monitoring systems i.e. those with only regular sensors and those with regular

as well as ultrasonic sensors. However, different types of RTUs compatible with both regular

as well ultrasonic sensors were used as shown in the figure 14 (b). These RTUs have a

separate connection other than the four ports of the regular RTUs.

3.4.2.4. Data Logging and Visualization

As mentioned earlier, the data recorded by the sensor is logged and stored locally in the RTU.

This data for water level monitoring systems is available in real time for visualization on web

interface as well as on mobile application. These systems were also fitted with the

temperature sensors and systems with ultrasonic sensors had additional humidity sensors to

better estimate speed of the light in the air. All this data was readily available for

visualization at hourly, daily and monthly scale. Figure 18 shows snippets from the data

visualization interface for the water level monitoring systems.

Figure 18: Snippet of data visualization for a sensor

In total we installed water level monitoring systems at 28 different locations which included

20 CNBs, 1 percolation tank well and 7 V-notches. The details of the location and system

specifications are summarized in Table 6. Figure 19 shows all the different types of water

level monitoring setups installed in the field.

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Table 6: Summary for water level monitoring systems

SrNo

SystemID

Village Taluka Type ofsite

Type ofInstallation

Mountingon

Rangeofsystem(m)

Ultrasonic(Yes / No)

1 A2 Chobali Ahmedpur CNB DirectMounting

Side Wall 1.5 No

2 A3 Morewadi Ahmedpur CNB StillingWell

NA 2 No

3 A4 Morewadi Ahmedpur CNB StillingWell

NA 1.5 Yes

4 A5 Morewadi Ahmedpur CNB DirectMounting

Side Wall 2 No

5 A6 Morewadi Ahmedpur CNB DirectMounting

CNBWall

2 Yes

6 A8 Gadewadi Ahmedpur CNB DirectMounting

Side Wall 2 Yes

7 VA1 Morewadi Ahmedpur V -notch

DirectMounting

V Plate 1 Yes

8 VA2 Chobali Ahmedpur V -notch

DirectMounting

V Plate 1 No

9 L1 Mangrul Loha CNB DirectMounting

CNBWall

2 Yes

10 L2 Mangrul Loha CNB DirectMounting

Side Wall 2 No

11 L3 Mangrul Loha CNB DirectMounting

Side Wall 2 Yes

12 L4 Mangrul Loha CNB DirectMounting

Side Wall 2 No

13 L5 Polewadi Loha CNB DirectMounting

Side Wall 2 No

14 L6 Berali Loha CNB StillingWell

NA 1.5 Yes

15 L7 Polewadi Loha P. TankWell

DirectMounting

Well Wall NA Yes

16 VL1 Mangrul Loha V -notch

DirectMounting

V Plate 1 Yes

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17 VL2 Mangrul Loha V -notch

DirectMounting

V Plate 1 Yes

18 N1 Adgaon Ner CNB DirectMounting

Side Wall 2 Yes

19 N2 Adgaon Ner CNB StillingWell

NA 1.5 No

20 N3 Adgaon Ner CNB StillingWell

NA 1.5 No

21 N4 Adgaon Ner CNB StillingWell

NA 1.5 Yes

22 N5 Adgaon Ner CNB StillingWell

NA 2 No

23 N6 Umartha Ner CNB StillingWell

NA 1.5 No

24 VN3 Adgaon Ner V -notch

DirectMounting

V Plate 1 Yes

25 VN5 Adgaon Ner V -notch

DirectMounting

V Plate 1 Yes

26 K1 Wai Pr.Karanja

Karanja CNB StillingWell

NA 1.5 No

27 K2 Lohara Karanja CNB StillingWell

NA 1.5 No

28 VK1 Wai Pr.Karanja

Karanja V -notch

DirectMounting

V Plate 1 Yes

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Figure 19: Water level monitoring systems installed in the field

3.4.3. Multiprofile Soil Moisture Probes (Soil Moisture Monitoring Systems)

3.4.3.1. Need for instrument

As mentioned earlier in the report, last year when model validation was attempted, only water

level sensors were installed. For measurement of soil moisture, a dry oven method was tried.

However, this posed multiple challenges which included technical, logistics and that of

reliability. The idea then was to collect soil moisture samples from the selected farms at

regular intervals of a day or two. However, this meant that we ended up measuring soil

moisture for different locations every other time. It was realized that monitoring of soil

moisture must be done at a single location given the variation that exists in the farm for soil.

This is also important because from a validation perspective, one shall know the overall soil

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moisture profile at least for the past 5-10 days. Having soil moisture testing using the oven

dry method at the same location was not possible and hence alternate method was required.

Oven dry method also has logistic issues such as overall time consumed, availability of the

appropriate apparatus, and feasibility to transport the soil sample to the lab. The accuracy of

the method was also not satisfactory for the model validation. Therefore, this year soil

moisture sensors were installed.

Soil moisture sensors are widely available in the market which work on different principles

and have different functionality. Since different types of soil moisture sensors have their pros

and cons, and considering the limiting conditions such as urgency of the instruments to be

procured to meet the monsoon timeline, cost of the sensors, availability of the sensors etc.

two types of sensors were procured, one from Dataflow Systems, New Zealand and other

from Riot Technology, Canada. The former were procured from the manufacturer whereas

later were procured from Meatech Solutions, Chandigarh.

3.4.3.2. Data Flow Multiprofile Soil Moisture Probes

Soil moisture sensors by Dataflow Systems are capacitance based sensors where the multiple

sensors are mounted on a sensor rod. The sensors in the rod consist of two metal plates

connected to a proprietary oscillator circuit. As soil moisture in the surrounding soil changes

the electric field and hence the capacitance also changes. This change in the capacitance

affects the frequency of the circuit which is measured by the sensor over a fixed time period.

The sensor measurement diameter for this setup is up to 17 cm in soil.

Figure 20: Data Flow soil moisture profiling sensors(a) Schematic representation; (b) and (c) Photographs of installed sensors from the field

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These sensors come with a data logger attached to it and hence require no additional

components which makes these sensors very much affordable and easy for installation. These

sensors can continuously log the data as per the set logging intervals which get timestapped.

In our case, a logging interval of 15 minutes was set for all of these sensors i.e. soil moisture

readings along with the corresponding temperature were recorded after every 15 minutes. In

all, six such soil moisture monitoring systems were procured, out of which four were

equipped with 3 sensors (10, 30 and 40 cm) for 0.4m sensor rod and two were equipped with

5 sensors (10, 20, 40, 60, 100 cm) for 1m sensor rod.

These sensors are very much similar to the rain gauges in terms of functionality for data

logging and visualization on the web interface, discussed earlier in this chapter. The data

logger connects to the sensor rod at its tip record and logs data locally which is then pushed

to the web server through an android device. For these sensors, live data is not available for

visualization and only data logged upto the last data push from the android device can be

visualized on the web interface. In all six soil moisture monitoring systems of this kind were

installed in three farms with two systems per farm.

3.4.3.3. RiOT Technology Soil Moisture Probe

These soil moisture sensors use Time Domain Transmissometry (TDT) and patented

technology as the basis for its measurement. TDT measures the time taken for an

electromagnetic wave to propagate (travel) along a given length of a transmission line in the

soil. Moisture in the soil changes the soil’s dielectric properties, so that the electromagnetic

wave travels at different rates in wet soil compared to dry soil. Unlike previous sensors which

measured soil moisture at discrete points, these sensors measure soil moisture for a segment

of 15 cm. In all, 5 such soil moisture profiling probes were procured of which 3 probes were

with 2 segments (30 cm) and 2 probes were with 3 segments (45 cm). Although these sensors

are said to provide more accurate profiling of soil moisture, they are more expensive.

Unlike Data Flow sensors, separate data loggers of SDI-12 Protocol for serial communication

type need to be purchased for these sensors. The standard data loggers available in the market

for these soil moisture sensors were very much costly (as costly as the sensor and even more

in some cases) hence to optimize the cost, the data loggers were procured separately from

Green Pyramids Energy, Pune with only required specifications. Therefore the overall soil

moisture monitoring system consists of the soil moisture sensors by RiOT and data logger by

Green Pyramid Energy.

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Figure 21: RiOT soil moisture profiling sensors

(a) Schematic representation; (b) and (c) Photographs of installed sensors from the field

However, unlike previous cases, the data by these sensors is available live for visualization.

This visualization functionality is exactly the same as provided for the water level monitoring

sensors as discussed in the earlier section. In all four soil moisture monitoring systems of this

kind were installed in two farms with two systems per farm.

Table 7 summarizes the details for all the soil moisture monitoring systems installed in the

study areas.

7: Summary for soil moisture monitoring system

SrNo

FarmID

Village Taluka Vendor WorkingPrinciple

Range(cm)

No. ofsensors

SensorPlacementat (cm)

1 VA1 Morewadi Ahmedpur RioT TDR 45 3 15, 30, 45

2 VL1 Mangrul Loha Dataflow Capacitive 40 3 10, 30, 40

3 VL2 Mangrul Loha Dataflow Capacitive 40 3 10, 30, 40

4 VN3 Adgaon Ner Dataflow Capacitive 100 5 10, 20, 40,60, 100

5 VN5 Adgaon Ner RioT TDR 30 2 15, 30

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Thus different instruments such as rain gauge, water level and soil moisture monitoring

systems were installed at different locations in the selected study area. Table 8 summarizes

the number of instruments installed in different study clusters.

Table 8: Clusterwise summary for installed instruments

Cluster(Taluka)

Villages No. ofrain

gauges

No. ofwater levelmonitoring

systems

No. of soilmoisture

monitoringsystems

No. ofV-notches

511_gv-101_03(Loha)

Mangrul,Polewadi, BeraliKh.

1 7 4 2

524_mr-47_05(Ahmedpur)

Morewadi,Chobali,Gadewadi

1 6 2 2

510_wrb-1a_01(Ner)

Adgaon,Karkheda,Bhalki, Umartha

1 6 4 2

502_ptkp-1_03(Karanja)

Wai Pr. Karanja,Lohara

0 2 2 1

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Figure 22 and Figure 23 shows locations of the different instruments installed in the

Marathwada and Vidarbha region respectively.

Figure 22: Catchments and sensor locations for clusters from Marathwada region

Figure 23: Catchments and sensor locations for clusters from Vidarbha region

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4. Work Progress

This chapter provides an update on the work progress which includes work done till now,

ongoing work and planned work.

4.1. Key Activities and Tasks Conducted During Monsoon Fieldwork

This section discusses some of the important activities that were conducted on the field

during this monsoon season.

4.1.1. Fieldwork Prior to Installation of Instruments

The fieldwork for the model validation began with visiting different CNBs identified using

Google Earth images. These visits were meant to check if the CNBs were suitable for

installation of the water level sensors. After appropriate CNBs were finalized for sensor

installation and type of the installation (direct mounting or stilling well) decided, necessary

preparatory works were taken up. Similar process of identifying suitable locations for

V-notch installation and preparatory work for the same was initiated.

For installation of rain gauge and soil moisture sensors, no preparatory work was required

and only identification of appropriate locations within catchment (for rain gauge) and farm

(for soil moisture sensor) were needed. The details for each of the preparatory work required

for all the different sensors is discussed earlier in chapter of this report. This work dealt

mainly with site cleaning, excavation and site preparation wherever applicable and necessary.

4.1.2. Installation, Monitoring, and Maintenance of Instruments

Installation of all the instruments and sensors was completed with ease and mainly involved

erection of the hardware and its wiring and connection with the actual sensors / measuring

devices. In the case of water level sensors, calibration was an additional task that involved

communicating the actual water level in the field at the installed sensor to the vendor.

Post installation, an important task was to monitor the functioning of these different sensors.

Some of these sensors were available live for monitoring on a web while some were not. The

monitoring of the instruments involved checking for the proper inflow of the data, its quality

and physical inspection of the instruments. The water level sensors which were found to be

physically damaged or functioning improperly during such monitoring rounds were replaced

by the IITB team. However a damaged rain gauge and a soil moisture sensor could not be

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replaced due to logistic issues pertaining to timeline and transportation to and from New

Zealand.

Maintenance activities were mainly required for water level sensors only. This included

periodic checks of different wirings and connections, cleaning of water level sensors for any

silt deposition due to water and ensuring adequate clearance for ultrasonic sensors. In case of

V-notch maintenance activities were required for ensuring even and predescribed channel

dimensions. For rain gauge and some soil moisture sensors (procured from Dataflow

Systems), which did not have live visualization of data, site visits were an additional task to

the above mentioned activities.

4.1.3. Flow Measurements

Flow computation for a given catchment is required to calculate overall runoff. Though flow

computation using broad crested weir is possible and planned in the model validation

exercise, actual flow measurements wherever possible are equally important. Actual flow

measurements not only help in cross checking the results obtained using broad crested weir

formula but also in calibrating discharge coefficient value for a CNB. Thus actual flow

measurements were conducted for different CNBs using the pygmy type current meters.

These actual flow measurements were however not possible for all the CNBs, since many of

them were either dried or had water for only a couple of days. Also, only one measurement

for a CNB is neither sufficient to plot the graph for water level over CNB and discharge nor

for any calibration of the discharge coefficient for the CNB. Therefore, meaningful flow

measurements were possible only for some of the CNBs. These measurements were faced by

various limiting constraints on the field which included difficulties in accessing the CNBs

due to water logging in the nearby farms, unevenness and irregularities of the water channel

and intrinsic limitation of the instrument to measure flows with very small water height (say

about 3 cm).

The main limiting constraint however was logistical. In many of the cases, where CNBs were

overflowing, they maintained constant water level and by the time the team could reach the

CNB, after a rainfall event, water used to get back to pre-rainfall level. This meant we could

get a very small window of appropriate time to measure the flow. Most of the time, this

available window was the same for multiple CNBs and at a time only one measurement was

possible which lasted for about one hour to one and a half hours depending on the channel

profile.

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As we progressed with the number of measurements, not just the time required for the

measurement reduced but also the method got more standardized for the channels which were

not steady and typically had multiple irregularities in the channel dimension. Figure 24 shows

snippets during flow measurement with the current meter.

Figure 24: Sample photographs for flow measurements using current meter

Figure 25: Snippets from the calculation sheet for flow measurements

In some cases, where CNBs were overflowing but it was not feasible to measure flow using

the current meter, other flow measurement methods such as float method and bucket

(container) method were used to measure flow. For the float method small leaves were used

as floats. While the container method was used only a couple of times where a secondary

stream with minimal flow intervening with the main channel was to be measured.

4.1.4. Farmer Interviews

A round of farmers interviews was conducted during the fieldwork aimed at understanding

the farmers narratives on impact of dry spell on crops to get an idea about AET and soil

moisture. These narratives are later planned to be compared with the model outputs. Apart

from these narratives, some of the data collected during these interviews such as soil type and

soil depth, sowing date will also be used as model input to run the point model at sample

locations in the catchment.

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Figure 26: Sample photos of blank and filled survey forms for effect of dry spell on crop yield

The questions in the interviews dealt with occurence of dry spell with respect to crop stage,

yield loss due to crop deficit faced during dry spell, and waterings provided. Figure 26 shows

snippets of the questionnaire and some sample responses. This data is also useful for

understanding the difference in the AET for a given crop with the same rainfall but different

soil type and soil depth combinations. The detailed analysis of this data will be discussed in

the next report i.e. kharif validation report.

4.1.5. Field Observations

One of the important activities during fieldwork conducted was field observations. These

included observations about different biophysical parameters and attributes of the study areas

listed below:

- Soil type, soil depth, terrain of the region and their correlation if any

- Change in cropping pattern and yields and its relation with above mentioned attributes

- Presence of wells, borewells and their spread in the stream proximity region

These observations are not only important to understand the agricultural scenario and water

availability in the village but are also helpful to confirm reliability of the primary data

collected from farmers. Some of the key observations from the perspective of model

validation about the study areas are listed below:

● Effect of slope, soil type and depth on runoff: It was evident in the study areas that in the

region of good soil (balck soil with depth more than five feet), the occurrence of runoff

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generation was delayed as compared to light soils. This was almost always true in

Ahmedpur and Loha clusters where both of the regions showed very different patterns of

runoff generation for almost similar rainfall events. Similar observations were also

reported in the Ner cluster.

● Water overflowing over CNBs: As compared to Marathwda, CNBs in the Vidarbha

region considered for the study were flowing throughout the season once they got full.

However in Marathwada, especially in Ahmedpur cluster, CNBs hardly were full in the

season. In Loha cluster, after one cycle of filling up and getting dry during the dry spell,

CNBs were carrying water for the rest of the season, especially after the wet spell. In

Ahmedpur, CNBs were dry for most of the time and some CNBs were not overflowing

even during peak rainfall and runoff events.

4.2. Ongoing Work

The ongoing work on model validation is limited only to deskwork. It is aimed at bringing all

the different datasets in the format which can be readily used for the analysis needed for

model validation. The tasks involved are mainly related to data handling of the primary data

from the different instruments the IITB team has installed. These tasks are listed below:

- Selection of the key rainfall events in the different catchments for which runoff will be

calculated later so as to compare water budget results.

- Converting the rainfall data recorded by the rain gauges installed in the field to hourly

rainfall data which is standard input for the model. The raw data available from the rain

gauges is not in hourly format and hence can not be used directly as input.

- Cleaning of the water level sensor data (both regular sensors as well as ultrasonic

sensors) by removing data with unusual spikes of sudden rise and fall in the absence of

any rainfall events (which can be attributed to unknown field conditions), selection of

appropriate dataset from the available data set (lower, higher or consolidated sensor

reading by regular sensors and ultrasonic sensors) etc.

- Cleaning of the soil moisture sensor data by removing data during waterings provided by

the farmers if any. Preparing a simple template for converting the % saturation data as

provided by the soil moisture sensor to the % volumetric data on soil moisture.

- Compiling and consolidating all the different datasets such as rainfall data, moisture

sensor data and water level sensor data once it has been cleaned so as to bring the same

on common grounds where they can be compared and analyzed.

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4.3. Planned Work

The work planned ahead for model validation can be categorized into fieldwork which will

primarily comprise collection and triangulation of the data and deskwork which will comprise

mainly of analysis and report writing.

4.3.1. Fieldwork

- Soil texture analysis is scheduled in the coming months for the samples from the farms

where V-notch and soil moisture sensors have been installed. The IITB team shall collect

the sample using a core cutter and get soil texture analysis done externally. The results

from this testing will be used as inputs to run the model at farm level. This will be

helpful to eliminate the errors in the output due to inaccuracies in the input data for soil

texture as well as soil depth. This will be also useful to understand the extent to which

model outputs may vary for different datasets on soil texture and soil depth while other

inputs remain the same.

- Soil texture analysis at selected locations in the catchments for the dominant soil type

will also be conducted. These results are to be compared with MRSAC data so as to

check if they reasonably match with each other. In case there is significant variation in

what field data suggests and what has been reported and published by MRSAC, we may

use the field data with improved results to run the model at the catchment level as well.

- Focused group discussion of the selected farmers is planned to get an overall narrative on

the AET for kharif crops and soil moisture at the end of monsoon in the different regions

/ zones of the village so as to triangulate the data earlier collected from individual

farmers. These narratives will be then compared with AET and soil moisture at the end

of monsoon as estimated by model.

- The findings from all of the above fieldwork will be useful for the next report for model

validation i.e. report on kharif validation. Apart from this, a parallel fieldwork will be

conducted for validation of the groundwater recharge as estimated by the model. This

will include water level monitoring for observation wells, data collection on cropping,

waterings provided to crops and overall extraction during rabi. The findings from this

fieldwork however will be covered in the rabi cum closure report which will be the final

report of model validation.

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4.3.2. Deskwork

- The deskwork planned has broadly two components, analytical and report writing. The

focus however will be primarily on the analytical component only which involves

multiple tasks as listed below:

o Run the water balance model for different sets of inputs in selected catchments, sub

catchments and farms. Compare the results for the respective catchments where

different datasets have been used.

o Computation of flows from the outlet of catchments and sub catchments for different

water levels on CNBs using broad crested weir discharge formula and current meter

measurements whichever is applicable and possible. Similarly computation of flow

from farm outlets using formula for discharge from V notch. Calculation of runoff for

the respective region (catchment, sub catchment and farm) using water level sensor

data and computed flows for the corresponding water levels.

o Comparison of the runoff estimated by water balance model with the flow computed

on the field using different methods.

o Comparison of farmers narrative on the water requirement of the crops, crop stress,

yield loss etc. during kharif season with model outputs for AET and soil moisture.

o Investigation and justification of the findings emerging from the above comparison.

- The second component of report writing will be mainly limited to documenting above

findings in the kharif model validation report.

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Annexure

Soil Maps:

LULC Maps:

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Note on

Changes to Water Budget Models for

the Well Beneficiary ModulePrepared By:

Hemant Belsare

Asim R P

Nov 2021IIT Bombay

Mumbai

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Table of Contents1. Background and Objectives 2

2. Conceptual Basis 4

3. Outlining Prioritization Criteria 9

4. Quantification and Computation of Biophysical Vulnerability 13

5. Well Beneficiary Module Architecture 21

6. Results for the Sample District - Hingoli 29

7. Data Related Issues 33

8. Deployment on PoCRA Server 35

9. Conclusion and recommendations 41

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1. Background and Objectives

One of the key objectives of the PoCRA project is to enhance water security at the farm level

through different interventions to reduce risks associated with intra- and inter- seasonal climate

variability. This has been done at the village- or micro-watershed- level through natural resource

management planning as well as at the farm-level through distribution of various benefits for the

individual farmers. These farm-level interventions are done through the mechanism of Direct

Benefit Transfer (DBT). This includes a wide range of benefits which help in creating new water

sources, implementing water-saving micro-irrigation facilities, water transfer mechanisms,

enhanced farm practices, seed processing, creating resilience through post-harvest interventions

and so on. Some examples of the benefits are private wells, pumps, pipeline sets, drip / sprinkler

sets, farm-ponds, promotion of horticulture, power weeders, power tillers etc.

These benefits are granted through an elaborate process as follows. Demand from individual

farmers is noted for different components through an online process by linking of the

AADHAAR card, bank account details, land documents etc. The final beneficiaries are approved

in three steps. These are (i) basic eligibility and acceptance by the Village Climate Resilience

Management Committee (VCRMC), typically based on socio-economic attributes such as caste,

gender and land-holding, (ii) scrutiny by state personnel such as Cluster Assistant, Agriculture

Assistant, Sub-divisional Officer, Taluka Agriculture Officer, of the suitability and the location of

the component and its utility, and finally (iii) actual implementation of the component through

expenditure from farmer’s own pocket followed by inspection of the same on the farm and

transfer of the subsidy amount to farmer’s linked bank account.

About Well DBT:

Digging of the open well is one of the DBT items which is very heavily demanded by farmers.

This sub-component comes under the component for creating a new water source at the

farm-level. The main objective is to make the small and marginal farmers climate resilient by

providing them access to protective irrigation.

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However, due to financial budget constraints and other limitations, it has now been decided by

the PMU (Project Management Unit) that not all the eligible applications (i.e. the ones which

pass through the above approval process) could be granted wells. The number of well

applications to be granted may turn out to be much less than the number of eligible well

applications in a village. This is unlike other benefits (such as drip/sprinkler etc.) where all the

eligible applications are eventually granted.

Thus, specially, for the case of well DBT, in addition to the existing process of approving the

well application, there is a need for a mechanism to prioritize or rank the well applications at the

village level based on who deserves it the most. Such a mechanism is supposed to provide a

scientific and transparent basis for prioritizing more deserving applications and to give a

perception of fair-play to the VCRMC and the farmers.

The main objectives of the following report are as follows:

1. To outline a conceptual framework for the prioritization of the well applications within a

village and to introduce the concept of on-farm vulnerability as the core basis for prioritizing the

well applications.

2. To design the prioritization criteria based on the above concept and to formulate the core

functionality to translate the prioritization criteria into concrete and quantifiable indicators using

the IITB water budget model at cadastre-level.

3. To incorporate the proposed indicators into the existing framework by integrating with the

existing prioritization criteria as per the well DBT guidelines.

4. To design the architecture and develop the GIS-based well DBT prioritization module using

Python, Postgres and QGIS plugin and to create a well-tested prototype which can be deployed

on the PoCRA server by the PMU IT team.

The key outputs will be the relevant village-level maps and the list of prioritized well

applications which will aid the village-level state officials and the VCRMC to select the final

well beneficiaries.

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2. Conceptual Basis

The next section highlights some of the key issues related to water security at the farm- and

village- level and forms the conceptual basis for prioritizing well applications.

2.1 Groundwater as the primary source of irrigation and well as the primary access device:

Groundwater is the primary source of irrigation in the dryland and rainfed regions in

Maharashtra, which cover more than 70% of the cultivated area in the state.

This precious groundwater resource occurs in the hard rock, basaltic aquifers which underlay

more than 82% of the land in Maharashtra and almost 100% of the land in the PoCRA region.

The vertical profile of the land typically consists of the soil layer, followed by highly

unconsolidated and highly weathered basalt (called soft murum), followed by less consolidated

weathered basalt (called hard murum) followed by hard, compact basalts of different kinds. The

layer above the hard rock i.e. the weathered layer is called the shallow aquifer whose depth may

vary from a few meters up to 25-30 meters. This shallow aquifer holds the rain water recharged

during the monsoon season and gets replenished every year. It acts as the primary storage for the

groundwater as well as the medium through which it flows (GSDA 2014). The open dug well

which taps this shallow aquifer is the most popular and common structure that provides access to

this precious but limited groundwater resource.

Therefore, the demand for the open dug wells has always remained high. The well density has

increased manifold in the last 2-3 decades. But the limited storage capacities of the basaltic

aquifers put limits to the groundwater recharge during monsoon and hence to the available

groundwater stock for use during the post-monsoon seasons. Thus, as more and more farmers

access groundwater through wells, more is the competition for the scarce resource. More

competition leads to more uncertainty in access and even more investments in securing assured

access to water. This has been encountered and already documented by the IITB team through

numerous field visits and farmer interviews in different parts of the state.

This is also clearly evident in numerous villages in Maharashtra where the number of wells and

borewells have increased manifold over the last one or two decades, but the area under rabi crops

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as well as rabi crop yields have remained more-or-less stagnant. At the same time, the shallow

aquifers are getting dry sooner and sooner during the post-monsoon season, thus leading to rabi

water stress and subsequent drinking water stress.

With regards to groundwater planning or regulation, such a situation puts the two very important

normative concerns of equity and sustainability in conflict with one another. On the one hand,

well is the most critical asset which can provide access to protective irrigation and can elevate a

rainfed farmer to an irrigated farmer. Thus, in order to achieve equitable distribution of the

groundwater resource and its access, every farmer must be entitled to have his/her own well. But

on the other hand, more wells and consequently more competition for the limited resource means

a threat to sustainability of the resource.

One of the ways to address and resolve this tricky conflict is through collective use and better

planning of supply, demand and allocation of water at the village or watershed level. This

requires better demand-side management, use of common wells, community crop planning,

innovative rules and incentives for restrictions on groundwater extraction as well as targeted

supply-side interventions, focused farm-level interventions for vulnerable farmers etc. This

further requires better and more accurate water budgets and also comprehension of the water

budgets by the community so as to act on them. This has been the key motivation behind the

implementation of the IITB water budget model in the PoCRA project.

Most of these activities have also been explicitly or implicitly suggested in the Maharashtra

Groundwater Act of 2009, which was passed in the Assembly in 2013. However, these have not

been operationalized on the ground yet due to lack of rules or GRs to implement the Act.

Thus, currently there are no ready and viable policy answers to some fundamental questions

related to groundwater use, such as: What should be the limit on maximum number of wells

tapping an aquifer / watershed / village? How much groundwater may be extracted sustainably

during a particular year? What should be the sustainable cropping pattern for a particular

geography? How to design and impose rules and restrictions on farmers for regulation of

groundwater use without disturbing the economic returns? etc. The IITB team is currently

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working on better estimation of groundwater seasonality and flows and rabi-planning

frameworks. These are part of the MoU IV between IITB and PMU.

However, coming back to the present situation, it cannot be denied that access to groundwater

through open dug wells significantly improves the incomes of a farmer. And that the demand for

wells will keep on rising. In such a scenario, there needs to be a mechanism to decide who

deserves it the most and how to identify these deserving farmers.

2.2 Understanding the demand for wells:

In order to decide which well DBT applications need to be selected, it is important to understand

the nature of the demands for wells by different farmers, to identify which of these demands

should be considered as most deserving and to come up with a scientific and transparent basis for

selecting the most deserving applications.

Within a typical rainfed village in Maharashtra, there is a wide variation in the adaptive capacity

of the farmers to cope with the growing challenges of climate variability and increasing

competition for the limited groundwater. This wide range is owing to large variation in the

biophysical as well as socio-economic factors, which decide the farm-level water availability on

the one hand and the ability / affordability to invest in new structures for getting access to water

on the other.

Thus, farmers within the same village may be divided into different levels / stages with respect to

water availability, access to water, cropping patterns etc. as shown in the following diagram:

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Figure 1 - Irrigation infrastructure ladder

The above diagram does not depict a single farmer’s whole journey. It tries to show that at a

given time, within a typical village, different farmers may be at different stages in this ladder.

Such a conceptualization helps to highlight the importance of irrigation infrastructure and coping

mechanisms in which farmers often plan to invest in order to secure the water source.

The farmers in stage 0 are purely rainfed farmers without any source of irrigation whose

farm-livelihood totally depends on the kharif crop yields. These farmers are completely prone to

the uncertainties and vagaries of monsoon rainfall, especially a drought year or the long dry

spells during monsoon. The combination of poor biophysical factors and lack of access to water

make these farmers the most vulnerable and least resilient to climate changes and water scarcity.

One of the most critical investments which can elevate the rainfed farmer to a partly or fully

irrigated farmer is the digging of a well. It enables access to the groundwater and helps in

providing critical protective irrigation to kharif crops during long dry spells in monsoon. It also

enables one or two protective irrigations after the monsoon season for the long kharif crops such

as cotton, tur etc. or for the conventional rabi crops such as jowar, harbhara. Following are

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snippets from some of the farmer interviews which were recently carried out by the IITB team in

Adgaon, Yavatmal in October 2021.

Everything changed after the well!A rainfed farmer, Gautam Gosavi with 6 acre land having thin layer of gravelly sandy loam soildug 40-feet open well in the year 2020 on his own by saving Rs. 2-3 lakhs over 5 years. Prior todigging the well, he used to grow soybean, cotton and tuurduring kharif season and nothing during the rabi season.Now with the new dug well, he has sown 2 acre harbharaduring the rabi season along with 10-15 guntha of chilly inthe rabi season of 2021-22. He also applied for thesprinkler set through PoCRA DBT. With the sprinkler sethe can ration his water optimally for the harbhara andchilly crops and can maximize the rabi area. With thisincrease in rabi area and shift in cropping pattern he is expecting to earn at least Rs. 25-30thousand more (i.e. double) than the previous year.

Desperately need a well, but cannot invest on own!Ratnavati Dhaye is an agricultural labor who works on other farmers’ fields for plucking cottonon daily wages. Her family owns 2 acre land and has nowell or borewell. They practice rainfed farming and growSoybean and tuur with annual income from farming lessthan Rs. 30,000. They want to dig a well but do not haveenough money. A well will enable them to give protectiveirrigation to Soybean and Tuur and to grow harbharaduring rabi season. This will increase their income by Rs.20,000-25,000.

There are several such success stories of small farmers who were able to increase kharif crop

yields and take a decent rabi crop and increase farming incomes significantly after digging a

well. There are many more small and marginal farmers who are in desperate need of a water

source for getting the most critical access to protective irrigation for their kharif and long kharif

crops and for growing a decent rabi crop in a good rainfall year. This will surely help in

doubling their meager incomes. However, the biggest hurdle for them is the lack of funds to

invest in the open dug well.

It is clear from the above conceptualization that apart from such rainfed farmers, a dug well is

also demanded by a partly irrigated farmer who plans to increase area under rabi crops, or by an

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irrigated farmer who plans to invest more into remunerative water-intensive crops for fetching

higher returns. Individual wells may also be demanded by siblings in a joint family to step out of

the common family land (previously irrigated by a common well) and develop their own

land-parcels.

Thus, in the hierarchy of the farmers, right from the purely rainfed farmer without any source of

irrigation, to partly irrigated to fully irrigated to a progressive farmer taking horticulture / annual

/ exotic crops, different farmers may demand well for different purposes. While all the demands

may be justified in their own rights, the main question for a state program with limited funds is,

who needs / deserves it the most.

3. Outlining Prioritization Criteria

The key objective of PoCRA is to make small and marginal farmers climate resilient. As per the

revised guidelines for DBT issued by the PMU, the main objectives for granting private well are:

1. To strengthen and equip the small and marginal farmers in the PoCRA clusters to tackle the

negative effects of climate change

2. To enable access to protective irrigation and increase crop productivity and hence incomes.

Also, as per the same guidelines, the above component should be granted only to farmers with no

prior source or access to protective irrigation.

Thus, our conceptualization of the infrastructure ladder as well as the existing well DBT

guidelines suggest that the wells need to be granted to small and marginal farmers who do not

have access to protective irrigation and who are more susceptible to low yields and incomes due

to climate changes. We call such farmers ‘vulnerable’. The next task is to formulate a process to

identify these vulnerable farmers at the village level.

We define vulnerability as the inherent lack of adaptive capacity to resist or respond to the effects

of climate change. This lack of adaptive capacity may be due to on-farm biophysical factors or

due to socio-economic status of the farmer as mentioned above. Accordingly, there can be

different types of vulnerability as follows:

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- Biophysical vulnerability – This is the susceptibility of the farm plot to water scarcity,

poor kharif crop yields, inability to take rabi crops and eventually low farm incomes. The

key reasons for this biophysical vulnerability are the farm-plot characteristics such as soil

type, soil thickness, slope etc.

- Social / socio-political vulnerability – This is the vulnerability due to lack of adaptive

capacity due to farmer’s social status within the village i.e. caste, gender, access to and

participation in key village-level decision making bodies/committees, access to

knowledge etc.

- Economic vulnerability – This is the vulnerability due to lack of financial ability to invest

in the infrastructure for creating on-farm water storage structures or securing access to

protective irrigation such as wells, pipelines, farm ponds etc.

The main feature of the well beneficiary module is to run the point-level soil water balance for

all the cadastre polygons for the given PoCRA village in order to quantify the biophysical

vulnerability. The computed kharif crop water deficit along with the other criteria will be

compared to rank the applications and decide the priority.

We also use another factor to quantify the biophysical vulnerability (for the rabi season), which

is the ability of the farmer to take the rabi crop. The proxy used for this factor is the land-use

category (as per the MRSAC land-use land-cover map) for the specific cadastre or Gat number.

The cadasters where only kharif crop is cultivated according to MRSAC land use map are

considered to be water scarce and more susceptible to water stress during the rabi season as

compared to ones where rabi crop or rabi and summer crops are cultivated.

With regards to socio-economic vulnerability, we use the caste, gender, disability and

land-holding as mentioned in the DBT applications as the proxies.

Thus, the final prioritization criteria are as follows.

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Table 1 - Prioritization criteriaPrioritization Criteria Proxies used Method for

quantificationData sources used

Biophysicalvulnerability – kharifseason

Kharif crop waterdeficit in mm

IITB Water budgetmodel run for eachcadastre

Hourly weather data2018 – skymetSoil texture and soildepth maps – MRSACCrop - SoybeanSoil and cropproperties - FAO

Biophysicalvulnerability – abilityto take rabi crop

Land use category LULC class = “Onlykharif” category giventhe highest priority

MRSAC land use map

Socio vulnerability Caste, Gender Priority given to Caste(ST, SC, Open) ,Gender (F, M)

As entered in the DBTapplication

Socio-economicvulnerability

Land holding Lower theland-holding, higherthe priority

As entered in the DBTapplication

Rejection criteria

Apart from the prioritization criteria, there are few conditions which may be used for rejecting

the well applications based on hydrogeological attributes as suggested by GSDA and other

conditions as per the PoCRA well DBT guidelines. These are as follows:

Table 2 - Rejection criteriaRejection Criteria Proxy and Method for quantification Data sources usedGroundwateroverexploitation

Only PoCRA villages in the safewatersheds as per GSDA to beconsidered for granting wells

List of “safe” villages asprovided by GSDA for eachPoCRA district

Hydrogeologicalcriteria

Applications where distance ofproposed well to D.W. source less than500 m to be rejectedApplications where distance ofproposed well to Irrigation source lessthan 150 m to be rejected

On-field inspection byAgriculture Assistant

Existing protectiveirrigation source

Applications by farmers who alreadyhave a functional protective irrigationsource to be rejected.

On-field inspection byAgriculture Assistant

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Thus, the final workflow for the well DBT module is as follows:

Figure 2 - Well DBT process workflow

So, going ahead, the key tasks are:

A. to outline the process for quantification and computation of on-farm biophysical vulnerability

B. to design the process for ranking / prioritizing the well applications based on on-farm

vulnerability as well as socio-economic vulnerability as suggested by the current guidelines for

well DBT

C. to incorporate and automate the above processes into a well DBT module which can be

deployed on the PoCRA server for the existing DBT applications in all PoCRA districts and

which can generate village-level reports with relevant maps and lists indicating the prioritized

well applications.

The social and economic vulnerability is currently taken care of through existing PoCRA

guidelines by using indicators such as Caste, Gender, Land holding in the existing prioritization

framework. The key issue is to quantify and compute the bio-physical vulnerability and

incorporate the proxies / indicators into the existing DBT framework.

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In the next section, we begin with task A, i.e. the conceptual basis and the description of the core

soil water balance computation and the cadastre-level water budget model which will be used to

quantify the biophysical vulnerability.

4. Quantification and Computation of Biophysical Vulnerability

The main task is to identify the small holder farmers who are most prone to kharif crop failure

and do not have access to protective irrigation. This will be done by running the IITB point level

water budget model on each cadastre (survey number) in the village by using the soil map,

LULC map, slope map and the daily / hourly weather data. The core basis of using the water

budget model to indicate and quantify the vulnerability is explained below.

One of the most substantial ill-effects of climate variability and uncertainty is the intra-seasonal

variation in rainfall. This variation can manifest in terms of delayed onset of monsoon or long

breaks during the season or early withdrawal of monsoon. Several studies have shown that the

number of rainy days has reduced, while the rainfall intensity and duration of dry spells has

increased over the last 50-60 years (Singh et. al., 2010; Mishra et. al., 2014).

See below the rainfall distribution for Sangamner (Ahmednagar district) for two years 2016 and

2017.

Figure 3 - Sangamner (Ahmednagar) 2016 daily rainfall

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Figure 4 - Sangamner (Ahmednagar) 2017 daily rainfall

The total rainfall is similar during these two years, but the pattern varies. 2017 rainfall starts on

time, i.e. in the first week of June, is more distributed in nature with more number of rainy days

and less number of long dry spells, the 2016 rainfall begins late (i.e. 3rd week of June), has less

number of rainy days and a long dry spell lasting for more than a month during August and

September months which are crucial for crop growth.

In case of dryland agriculture, soil moisture is the main source of water and hence the occurrence

of dry spells has a large impact on the crop productivity and hence on the farm incomes. Thus,

the farmers who solely depend on the kharif crop suffer badly due to such dry spells. See below

for example the entire crop cycle for soybean crop for the above monsoon rainfalls for

Sangamner of 2016 and 2017. The IITB water budget model is used to compute the daily /

hourly soil moisture changes based on the weather data, the daily / hourly crop water demand

and the geographical factors such as soil texture, soil depth, slope and land-cover. The model

gives us the total soil moisture availability at each time step and tells us whether the crop water

demand for that time-step could be met with the available moisture in the soil layer. If the

demand is not met, it causes a deficit. The aggregation of these deficits over the whole crop cycle

gives us the total crop water deficit during the season. The details about the model could be

found at

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https://www.cse.iitb.ac.in/~pocra/Phase%20III%20Plugin%20description%20document.pdf.

Let us now see how this water budget model gives the soil moisture availability, the crop water

demand and how the crop water deficits vary throughout the monsoon season for different soil

types when run for the different rainfall patterns for the Soybean crop.

First, let us see the Soybean crop deficits for both the above rainfall patterns for deep clayey

loam soil type.

Figure 5 - Soybean PET vs. AET - Sangamner (2016) for clayey loam soil

Figure 6 - Soybean PET vs. AET - Sangamner (2017) for clayey loam soil

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The x-axis in the above graphs is the days, starting from 1 i.e. June 1 to 154 i.e. October end. The

orange bars show daily rainfall. Daily rainfall (right hand side y-axis) varies from 0 mm to about

60 mm in this case. The green line shows the daily crop water requirement for soybean crop from

sowing to harvest (left hand side y-axis). It ranges from 1.5 mm to about 5 mm per day

depending on various crop growth stages. The total crop water demand is close to 400 mm. The

blue line depicts the water available for the crop through soil. If the rainfall amount and its

distribution is adequate, the blue line will always coincide with the green line and there will be

no deficit.

In the above cases we see the gaps between green and blue lines during dry spells. This is the

crop water deficit. It can be seen that during 2016 there was a long dry spell and the crop water

deficit was about 100 mm i.e. almost 25% of the crop requirement. This happened at the crucial

stage of the crop growth when more water was required. This has a large impact on the crop

productivity, especially if it occurs during crucial crop growth stages such as budding or

flowering.

In the year 2017, the total rainfall was similar to last year’s, but there were two short dry spells

instead of a long dry spell. In this case the crop water deficit was around 50 mm i.e. half that of

previous year.

Thus, uneven distribution of rainfall within season and the presence of dry spells lead to crop

productivity loss. But as mentioned above, this loss is not equally experienced within the village.

Some farmers can cope with the dry spells and suffer mildly while others suffer badly. This

depends on the natural / geographical factors like soil types, location of the farm (slope, nearness

to stream etc.) and on socio-economic and infrastructural factors like having a well,

drip/sprinkler sets, ability to transfer water from long and short distances, ability to buy water

during water stress periods etc.

Here we are focusing more on the geographical / natural factors which decide crop productivity

and farm incomes. As we know, some soils like clay, clay loams etc. with good soil thickness are

good for moisture retention while soils like sandy, gravelly etc. with less soil thickness cannot

retain water for longer duration. Thus, during a dry spell, a deep clay loam can hold water for a

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few more days than a shallow gravelly sandy soil (Barron, et. al., 2003). This helps the crop to

survive more and reduce loss in productivity.

Now we consider the same Sangamner rainfall for the year 2016 when there was a long dry spell

and see its effects on a clay loam soil with thickness 1 m, and a gravelly sandy loam soil with

thickness less than 30 cm.

Figure 7 - Soybean PET vs. AET - Sangamner (2016) for clayey loam soil

Figure 8 - Soybean PET vs. AET - Sangamner (2016) for gravelly sandy loam soil

Above two graphs clearly show that for the same crop, i.e. soybean and for the same rainfall, the

two soils, deep clay loam and shallow gravelly sandy loam behave differently and lead to

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different results as far as crop water deficit and crop productivity are considered. The soybean in

gravelly sandy loam suffers 200 mm of water stress i.e. almost half the crop water requirement.

Most of the stress occurs during the long dry spell of about 35-40 days during August and

September. The crop gets no water for around 25 days which can lead to serious reduction in

yield and even complete crop failure.

The soybean crop in clayey loam also suffers around 100 mm of water stress, however, there is

not a single day when the crop gets no water from the soil. This results in some productivity loss

but less as compared to soybean in gravelly sandy soil.

Soybean crop yields from IITB field surveys in Osmanabad (2019)

Following survey data demonstrates the variance in theyields for few villages in two PoCRA clusters inOsmanabad and Beed districts respectively. It can beclearly seen that soil type is one of the key descriptorsfor explaining the variance in yields. Thesoil type mentioned in the plots below is as describedby the farmer during the interview.

Bavi, Mandva – Monsoon 2019

At the same time, it was also evident that protectiveirrigation during the dry spell avoided the probableloss in yield by 1 or 2 quintals per acre. Theimportant condition was the availability of water andaccess to water. The farmers with a well or borewelland the micro-irrigation set (sprinklers) were able toprovide irrigation during the mid-season dry spells.Sangaon, monsoon 2020

The above point water balance model can be run for every cadastre polygon within the village to

compute the farm-level water budget and distribution of rainfall into surface runoff, crop water

demand (or crop PET), actual water taken up by crop (i.e. AET), daily / hourly changes in soil

moisture and resultant groundwater recharge (i.e percolation of water below the soil layer) if we

get cadastre-level soil texture and soil depth maps and relevant secondary information required

such as soil properties (hydraulic conductivity of the soil, bulk density, available water holding

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capacity etc.) for each soil type. Such information is available through MRSAC (Maharashtra

Remote Sensing Applications Center) maps and FAO, and daily / hourly weather data for the

given village. Such a farm-level water budget would further give us the crop water deficit for

each cadastre or Gat number or farm in the village. The variation in the crop water deficit thus

computed gives us the biophysical vulnerability of different farm plots against variation in

rainfall pattern and dry spells owing to their soil texture and soil depth. We call such maps as

vulnerability maps.

Such vulnerability maps would prove useful for agriculture and water resource planners to target

regions within the village which seem to be more prone to climate changes.

Let us see an example for a village in Hingoli district with significant spatial variation in soil

types and depths. Following is the soil texture and depth map of the village. The southern part of

the village has a very thin layer of gravelly clay loam soil which is prone to soil moisture stress

during the dry spells in the monsoon season. The northern part has thicker clayey soils which are

good at holding soil moisture.

Figure 9 - MRSAC soil map (Waychal Pimpari, Hingoli)

Following is the daily rainfall pattern for the nearest skymet rain circle, Goregon for the year

2018. The total monsoon rainfall was 858 mm. But as we can see, the distribution of the rainfall

was uneven. 75% of the total rainfall occurred in the first 50 days of the season i.e. before 19th

July. This was followed by a small dry spell in the last week of July and 1st week of August and a

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big dry spell in late August with very early withdrawal of monsoon by 1st week of September.

Thus, there was no rainfall in the crucial periods of crop growth i.e flowering, harvesting etc.

Figure 10 - Daily rainfall (2018) - Goregaon skymet circle

Following is the vulnerability map for the same village. This map helps us to show how the

above rainfall pattern affected crop water deficit for Soybean for different soil textures and depth

within the village.

Figure 11 - Vulnerability map (Waychal Pimpari, Hingoli)

The above map shows that the southern region of the village with poor soils faced a crop water

deficit of greater than 150 mm, which is around 40% of the total crop water requirement for

Soybean. At the same time, the deficit in the northern region (with good soils) was much less i.e.

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less than 50 mm. This shows how uneven monsoon rainfall can affect the kharif crop yields in

different regions within the same village owing to spatial variation in biophysical factors. Thus,

such a map may help in indicating or pointing the regions within a village which are more

vulnerable to climate changes. These vulnerability maps may be used by local planners to

identify farmers more prone to kharif crop failure.

We plan to use the point-level water budget model explained above to compute kharif crop water

deficit at the cadastre level and generate such vulnerability maps, which will aid the process of

well beneficiary prioritization.

Thus, for using vulnerability in the well DBT module, we need to fix the crop and the reference

year for which the crop water deficit will be computed. We choose the major kharif crop in the

PoCRA region which is Soybean.

The reference monsoon year should be chosen such that it would reflect the spatial variation in

the crop water deficit due to biophysical factors within the village. The monsoon year with

erratic rainfall distribution and long dry spells will reflect this variation the best. Thus, we

choose the reference year as 2018, as this was the rainfall year with significant dry spells and

early withdrawal of monsoon which would have affected the kharif crop.

The next task is to incorporate the above computation of vulnerability in the well DBT module

and use it along with other criteria to rank the well DBT applications at village level. In the next

section we will see the architecture of the well DBT module.

5. Well Beneficiary Module Architecture

Thus, the IITB point-level water budget model is the key feature of the well DBT module which

introduces, quantifies and computes the biophysical vulnerability in terms of kharif crop water

deficit in mm and generates a vulnerability map for a village. The next step is to design a

stand-alone well DBT module which integrates the above vulnerability computation in the

existing DBT workflow. This would require following key components:

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i) database design to store and process all the required data such as villages in safe watersheds,

DBT application data, data required for computing water budget at cadastre-level etc.,

ii) sanity and validity checks for ensuring that the data is accurate,

iii) incorporating water budget model, joining the water budget and vulnerability results with the

DBT data and

iv) producing priority as per the decided prioritization criteria.

Based on consultation with the PMU team and above components following is the detailed flow

chart of the well DBT prioritization process which was decided:

The module will be run at the district level, i.e. for all the PoCRA villages in the safe watersheds

in the district. This will be done in three parts: i) screening and validity check, ii) water budget

computation for each valid cadastre, and iii) prioritization and report generation.

Figure 12 - Well DBT module - key steps

The key steps involved in the above process are explained in detail as follows:

Step 1 - Populate villages in “Safe Watersheds”

- Get data from GSDA

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- Check for sanity (VINCODE must be present)

- Reject villages in non-safe watersheds as given by GSDA

Step 2 - Validity check for cadastral maps

- Identify insignificant cadastre polygons (streams, roads, tiny ones etc.) and discard them

- Identify duplicates / problematic cadastre polygons and flag them

- Discard problematic cadastres

- Discard any village with at least one DBT application contains problematic cadastre

Step 3 - Validity check for DBT data

- Populate data for all well applications with stage = Pre-sanction Desk 2, status = Pending

- For all the valid villages from above, verify if Gat no. for all applications exists in

cadastral maps

- Identify Gat numbers with problematic DBT application data.

- Discard any village with at least one Gat no. with problematic DBT application data.

Step 4 - Resolve the data issues and re-run the steps 1 to 3 (for PMU)

Step 5 – Load all the maps and data required for computation of water balance

- Land use Land cover map

- Soil texture and depth map

- Slope map

- Weather data for the reference year (2018 in this case)

Step 6 - Run water balance for all clean villages

- For each cadastre polygon in the clean villages, compute the water balance for the

monsoon of year 2018, for Soybean crop using IITB water budget model

- LULC for the cadastre –

o Intersect LULC layer with the cadastral layer

o Divide LULC classes into following categories

▪ Kharif – LULC = Only Kharif

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▪ Rabi – LULC = Rabi, Cropped two or more than two seasons, Summer,

Zaid etc.

▪ Other green – LULC = Fallow, Scrub, Forest etc.

▪ Other – LULC = Wasteland, Forest, Habitation, Water body etc.

o Compute the area of each LULC category for each cadastre polygon

▪ Area under each of the above categories within each cadastre polygon is

computed and stored. This parameter is used for prioritizing the well

applications based on the land-use class, where “kharif” class is given the

highest priority.

▪ The LULC class which is passed to the

o Soil texture and soil depth class for the cadastre

▪ Intersect MRSAC soil map with the cadastral map.

▪ The area under different intersected soil-depth and soil-texture classes is

computed for each cadastre polygon.

▪ The soil depth and texture class with the largest area within the cadastre

polygon is considered to be the soil-texture and soil-depth of the polygon.

These are then passed to the point water budget model for computing the

crop water deficit for the polygon.

o Weather data for the cadastre

▪ 10 nearest weather stations are assigned for each safe PoCRA village

using the AWS Locations and PoCRA villages layer.

▪ For any village, if the weather data for the ith weather station contains at

least one blank row, the weather data for the (i+1)th weather station is

fetched. This is iteratively done until the weather station contains all

non-blank rows i.e. there is non-blank weather data for each and every

hour from 1st June to 31st October i.e. the period for which the water

budget is computed.

▪ All the cadastres in a particular village use the weather station assigned to

the village through the above process.

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- Store the Gat-wise i.e. cadastre-wise water budget results and crop water deficit in mm.

Following are the columns generated through above process

o Monsoon year – 2018 in this case

o Village census code

o PIN – the Gat no. or the key which identifies cadastre polygon

o Rainfall, Runoff, GW recharge, Soil moisture, AET, PET (in mm) – water budget

results

o Kharif crop water deficit i.e. PET – AET for the period 1st June to 31st October (in

mm)

o Kharif area, Rabi area, Other Green area, Other area as assigned as per above

steps

o Soil depth and soil texture classes assigned and used for water budget

computation

Step 7 - Well-beneficiary prioritization

- Join the “PIN” from the cadastre-wise water budget results with the “Use712No” column

of the well DBT applications. The matching DBT applications will thus have following

attributes which will be used to prioritize the applications:

o Crop water deficit (in mm)

o Kharif area, Rabi area, Other green area, Other area

o Caste (from DBT application data)

o Gender (from DBT application data)

o Land holding ((from DBT application data – usedHectare + usedAre)

- Order all the applications according to the above order for each census code and assign

priority according to the sorted order in a separate column.

Step 8 - Generate well-beneficiary report for each village

○ For all the clean villages, generate a PDF report with following pages

■ Land use map highlighting the well beneficiaries

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■ Vulnerability map colored according to kharif crop water deficit

■ Cadastral map indicating priority of the well applications

■ List of prioritized well applications along with guidelines for local

officials

The above module will be run in two parts:

A. The water budget module which runs for each cadastre for all “Safe” PoCRA villages. This

module stores the water budget results in a postgres table which contain crop water deficit in mm

and details of each cadastre (such as area, land-use etc.) which will be later used by the DBT

module to prioritize the applications.

B. The prioritization and report generator module which takes the cadastre-level deficit and

land-use details as computed in A. and joins them with the well DBT application data for the

concerned villages to produce the priority list. It also generates the PDF report as explained

above.

One of the tricky parts in the well DBT module is the assigning of land use and soil type to the

cadastres. This is explained in detail below.

Assigning land-use and soil type to cadastres

The IITB point-level model needs a single value of land use, soil depth and soil texture to run the

water budget. Thus, to compute the water budget for each Gat no. or cadastre polygon, a single

value of LULC, soil depth and soil texture needs to be generated. In the original logic, this was

done by computing centroids for all the cadastre polygons using the QGIS plugin. However there

are few obvious problems with this method. It is possible for some peculiar shapes of the

polygons where the centroid lies outside the polygon. Such a centroid will not represent the land

use or soil types in the cadastre polygon of interest. With cadastre polygons having multiple land

use types of multiple soil types, the centroid may not always represent the most dominant type

covering the polygon.

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Thus, for assigning soil type to the polygon, after intersecting soil layer with the cadastral layer,

the soil type which covers the largest area in the polygon is assigned to the cadastre and is sent to

the point model to compute water budget.

For example, in the example shown, the cadastre polygon

with Gat no. 104 is fully covered with soil type “Clayey –

Very deep (> 100cm)”. So there would be no problem in

assigning soil type to this polygon. For Gat no. 103, if the

soil type is assigned according to centroid-method, the soil

type “Clayey – Deep (50 – 100 cm)” will be assigned

which is smaller in area compared to “Clayey – Very deep

(> 100 cm)”. Thus, soil types are assigned according to the proportion of soil type categories

within the cadastre polygon. This will give us the closest representing soil type of the polygon.

In case of land-use type, it is a little more tricky. The assignment of land-use is significant for

assigning priority of the well applications, as land-use class is an important component of the

prioritization criteria. Well application from a cadastre with land-use class assigned as “kharif”

will be given higher priority than the cadastre with land-use class as “rabi”. An important point

to note here is that there can be many farmers within the same cadastre with different individual

land-use types, with some farmers able to take rabi crops while some who may have to depend

on the kharif crop for their livelihood. There is no mechanism currently to identify the exact

farm-plot on the cadastral map of the farmer who has applied for a well. So there is a problem

with assigning land use type according to the largest area as done for the soil type.

For instance, say a cadastre polygon contains both “kharif” and “rabi” land-use types and say the

“rabi” land-use covers a larger area. Thus, as per the method used for soil type, this cadastre will

be labeled as “rabi”. But now, if there is a small and marginal farmer whose whole land falls in

the “kharif” part of the polygon and has applied for the well, then his application will be assigned

the wrong priority.

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To get around this issue, it is decided to use the following logic of labeling the land-use type to

the cadastre polygon:

- if there is any small non-zero area under “kharif” land-use

class in a polygon, the cadastre will be labeled as “kharif”. See,

for example the land-use classification of Gat no. 313 in the

adjacent figure.

- if the area under

“kharif” land-use class is

zero, and if there is any

small non-zero area under “rabi” class, then the cadaster is

labeled as “rabi”. For example, the land-use class for the

Gat no. 15 is assigned as “rabi” as shown in the adjacent

figure.

- if both, the area under “kharif” and “rabi” are zero and if there is any small non-zero area under

“scrub”, “forest”, “fallow” land use types, then the cadastre is labeled as “other green” for which

the water budget will be computed considering the non-agricultural vegetative land-use type. In

case there is any well application from this cadastre, it will be assigned priority lower than

“kharif” and “rabi”.

- if the area under all the vegetative land-use types, i.e. “kharif”, “rabi”, “scrub”, “fallow”,

“forest” is zero, then the non-agricultural, non-vegetative land-use class, named “other” will be

labeled to this cadastre suggesting that water budget will not be computed for this cadastre. This

“other” land-use type includes “habitation”, “waterbodies”, “mining” etc. If there are any well

applications from such cadastres, those will not be considered for prioritization. Such

applications need to be considered separately.

Such a method will ensure that in the absence of individual farmer-level land-use data (which is

typically available on saat-baaraa document), a well application of a kharif farmer will never be

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treated as one of a rabi farmer, although vice-versa is possible. Thus, for example, a farmer

taking a kharif crop in Gat no. 313 above will be rightly prioritized, but the farmer taking rabi

crop in the same Gat will be given higher priority than what he/she deserves because his/her land

use class will be labeled as “kharif”.

6. Results for the Sample District - Hingoli

The well DBT module has been successfully deployed on the PoCRA server and has been run for

the sample district i.e. Hingoli. Following are some of the key summary data for the Hingoli

district.

1) All PoCRA villages in Hingoli district:

Following map shows all the villages in PoCRA clusters in Hingoli district.

Figure 13 - PoCRA villages - Hingoli

2) PoCRA villages in the safe watersheds as categorized by GSDA in Hingoli:

Following map shows villages in PoCRA clusters which are categorized as “safe” by GSDA.

There are 144 “safe” villages. The well DBT module will run only on these 144 villages.

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Figure 14 - "Safe" PoCRA villages - Hingoli

3) PoCRA safe villages with well DBT applications in Hingoli:

Following map shows the “Safe” PoCRA villages which have well DBT applications. There are

103 “safe” villages where farmers have applied for wells. In the remaining 41 villages there are

no well applications. Thus, the well DBT module will now run for the 103 villages with well

applications only.

Figure 15 - "Safe" PoCRA villages with well applications - Hingoli

4) PoCRA safe villages with valid well DBT applications in Hingoli:

Following map is the result of the validity check for the DBT applications. There are some DBT

applications in which the entered Gat no. (i.e. the column “use712No”) does not match with the

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Gat no. (i.e. the column PIN) in the cadastral maps. These unmatched applications are considered

as invalid. There are 31 “safe” villages which have at least one or more than one such invalid

applications. The well DBT module is applied for the remaining 72 villages. There are a total

572 valid well DBT applications which are finally considered for prioritization.

Figure 16 - "Safe" PoCRA villages with valid well applications - Hingoli

Well DBT report:

Once the initial screening for the cadastral maps and validity of the DBT applications is

successfully completed, the well DBT report generator module is run for the villages which have

all valid well applications (572 valid well applications in 72 “safe” villages in Hingoli as shown

above). For each such selected village, a PDF report is generated as shown below. The report has

following pages:

Page 1 – Land use Land cover map for the village overlayed over cadastral map with cadastre

polygons with well applications highlighted

Page 2 – Vulnerability map as generated by the IITB water budget model which colors the

cadastres according to the Kharif crop water deficit in mm

Page 3 – Prioritization map which highlights and shades the well applications as per the

prioritization criteria. The criteria used is displayed in marathi in the page-footer

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Page 4 – Table displaying list of prioritized well applications for the village. The columns are

“priority”, “Registration no.”, “full name in marathi” and “Gat no.”. This list may extend to more

than one page in case there are many applications in a village.

Last page – This page gives some instructions / guidelines for the local officials for using the

above maps and priority list in selection of final well applications.

All the above pages are displayed for a sample village Waychal Pimpari in Sengaon taluka of

Hingoli district.

Figure 17 - Well DBT report (Waychal Pimpari, Hingoli)

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7. Data Related Issues

DBT data issues

a. Separators used in 7/12 nos.

In case of some applications, multiple Gat nos. have been used for the single benefit

There is no fixed or consistent format for using separators in case of multiple Gat nos. Following

are some of the separators used

“/” “+” “,” “.” “ “ “v” “ and ” “&” and so on..

The most tricky is the “/” separator, as it is sometimes used to separate different Gat nos. while in

other cases it forms a part of the single Gat no.

E.g. 112/1 is a single Gat no. whereas 112/113 indicates two separate Gat nos. 129 and 130.

How to get around this issue?

What does multiple Gat nos. in the same application (separated by above separators) mean in

case of application for well? How can a well be constructed in multiple Gat nos. How to resolve

this data issue?

Figure 18 - DBT data issue

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Cadastre data issues

1) Tiny cadastres

While processing the cadastral maps, it was found that there are several cadastre polygons, which

are very very small in area, say 50 sqm, 10 sqm or even less. Such cadastre polygons do not

represent any administrative Gat nos. but have been generated due to errors in geo-referencing

and converting cadastral maps to shape files in the past. Such cadastres (whose area is less than

100 sqm) have been discarded by the well DBT module in the initial screening process itself.

2) Multiple villages with same census codes:

It is assumed that the village census code (VINCODE) is the unique primary key which

identifies a revenue village in the PoCRA region. However, it was noticed in some cases that

multiple villages (such as Savli and Savli Tanda in Hingoli district) have the same census code.

Such villages may create problems in prioritizing well applications and must be treated urgently

by the PMU.

3) Multiple Survey numbers within the same village:

It is assumed that the PIN (along with PIN1, PIN2 etc. in some cases) is the unique primary

identifier of a survey Gat within the village. However, it has been found in some villages that

such duplicate cadastres exist. This is a highly important issue because such duplicate cadastres

may lead to granting of well to wrong applicants.

Following is the snapshot of the data with some villages having duplicate cadastres in Hingoli

district:

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Figure 19 - Duplicate cadastre issue

There are 72 such villages. The screening methodology rejects any “safe” PoCRA village from

the well DBT module which has a well application from any of a duplicate cadastre. Fortunately,

for Hingoli district there were no well applications for any of the above duplicate cadastres, and

hence no village was rejected due to duplicate-cadastre issue. However, this may not happen for

other districts, and hence this issue needs to be looked into by the PMU urgently.

8. Deployment on PoCRA Server

The IIT team has developed the well DBT module through consultation, study of existing

guidelines, formulation of the problem and design of the architecture. The model code will be

handed over to the PoCRA IT team in the form of plugin, python scripts and postgres SQL

scripts. The IIT team will hand-hold and assist the PoCRA IT team to deploy and run the code on

the PoCRA servers and to run the module for a sample district, say Hingoli.

Once the code is deployed and run successfully for the sample district, the PoCRA IT team will

run the module for the remaining districts.

For deploying the module on any machine, following steps need to be followed:

8.1. Install required software packages

- Quantum GIS (QGIS) version 3x

- PostgreSQL version 13 or higher with PostGIS extension installed

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8.2. Prepare raw input datasets

The following datasets are required to be resting in the folders as mentioned below:

- PoCRA cluster-wise data

All the cluster-wise geometry files for all the PoCRA clusters need to be stored in the

“Release8May” folder. This folder must contain district-wise sub-folders which in turn must

contain cluster-wise sub-folders of all the clusters in the respective districts.

The cluster-wise sub-folder must contain following geometry files:

Table 3 - Input data requiredFile Columns requiredCadastral.shp D_NAME – District name

T_NAME – Taluka nameLOCATION – Village namePIN – Gat no. / Survey no.

Soil.shp DEPTH – soil depth categoryTEXTURE – soil texture category

LULC.shp Class – Land-use Land-cover classDrainage.shp Required to display on the output mapVillages.shp <optional>Slope.tif Slope map of the cluster

- PoCRA region data

Apart from the above cluster-level data, other data required is as follows:

Table 4 - Input data required (contd.)Content Columns required DescriptionAWS_Locations.shp Location (name of the village in

which Skymet station is located)point layer of all locations ofthe Skymet weather stations inthe PoCRA region

PoCRA_Villages.shp VINCODE (village census code –primary key), District, Taluka,VIL_NAME, Mini_Water(Cluster name)

village boundary layer of allthe villages in the PocRAregion

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dbt_applications.csv VillageCode (equivalent toVINCODE), Use712No(equivalent to PIN), FullName,FullName_marathi,RegistrationNo, Gender,Category, LandStatus,UsedHectare, UsedAre, District,Taluka, VillageName, ActivityGroup, Status, Stage

All the latest well DBTapplication data containing allthe mentioned fields, asdownloaded from thedbt.mahapocra.gov.in webportal

gsda_safe_watersheds.csv District, Taluka,VINCODE (village census code)

List of PoCRA villages whichfall in the “safe” or“semi-critical” watersheds ascategorized by GSDA – mustcontain VINCODE column foreach village in the csv file

hourly_weather.csv district, taluka, rain_circle, lat,lon, rain_year,for_date, for_hour, rain,temp_min, temp_max, temp_avg,"rh_min, rh_max, rh_avg,wind_min, wind_max, wind_avg

Contains hourly weather datafor all the AWS weatherstations (rain circles) which thedistrict in the mentioned formatas downloaded fromgis.mahapocra.gov.in. Eachrain circle must contain 365 x24 records for the given rainyear.

8.3. Obtain source code

The source code for the well DBT module can be accessed from the following links:

Link 1 - gis-data.git (https://gitlab.com/pocra-iitb/gis-data)

This repository contains python and SQL scripts as well as GIS data needed to run QGIS plugins

such as the water budget model, cadastral vulnerability, beneficiary prioritization and so on.

The SQL scripts in this directory need a PostgreSQL database with PostGIS extension installed

as mentioned in 4.1. The scripts in this directory either (1) load raw data into a PostgreSQL

database or (2) process the loaded raw data and perform operations on one or more PostGIS

tables.

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Link 2 - kharif_multicrop_plugin_ponding.git

(https://gitlab.com/pocra-iitb/kharif_multicrop_plugin_ponding)

This is the source code for the QGIS plugin which computes water budgets for all the cadastral

polygons in the given region and generates village-wise vulnerability results.

8.4. Load data

Run the shell script "gis-data/load_postgis_layers.sh" by providing following input arguments:

- name of the database to be created,

- path to data directory containing AWS_locations.shp, PoCRA_Villages.shp,

DBT_Applications.csv and GSDA_safe_watersheds.csv as mentioned in 4.2,

- path to district-wise cluster data folder containing Cadastral.shp, Drainage.shp, Soil.shp,

LULC.shp, slope.tif, Villages.shp as mentioned in 4.2

The shell script essentially loads all the above geospatial and CSV data into corresponding

PostGIS tables. It first runs the “postgis_layers_schema.sql” script which creates schema for all

the PostGIS tables. Then it loads the above data to these tables with some processing required in

some cases, as follows:

- Load the cluster-wise shape files directly to PostGIS tables

- Load the AWS locations and PoCRA villages directly to PostGIS tables

- Run the villages_with_weather.sql script – This script essentially determines 10 nearest

rain circles for each village and creates a new table pocra_villages_with_rain_circles with

same columns as that of PoCRA_Villages and an additional column “weather_st” which

contains “|” separated tuples of “<rain_circle_name>,<distance>”. This script associates a

rain circle with each village. This saves time during water budget computation as the

weather data now needs to be fetched only once per village instead of once per cadastral

polygon. At the same time, this script gives an option to select the next-nearest rain circle

(up to 10th nearest rain circle) in case the weather data of the nearest rain circle is

incomplete or invalid.

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- Assign VINCODE to each cadastral polygon in the “cadastres” table. Presently the

cadastral.shp does not contain the unique village identifier i.e. the VINCODE. Adding

this column to the “cadastres'' saves time and space.

- Load DBT_applications.csv to PostGIS table

- Load GSDA_safe_watersheds.csv to PostGIS table

- weather_stations_by_district.sql: This is the SQL script to get a list of all the weather

stations catering to a given district.

- fetch_weather.py: This is a python script to run weather_stations_by_district.sql for the

specified district. For each weather station returned by this SQL query, its hourly

weather data is obtained for the specified year, by making HTTP requests to the PoCRA

server to fetch the weather data through an API provided by the PMU IT team.

- DBT_Validation.sql: This script creates a materialized view “dbt_cadastres_villages”

which helps validate the DBT applications data. It does a left outer join between the

“DBT_applications” table and the “Cadastres” table. This allows easy identification of

the DBT applications for which no cadastre polygon (i.e. no Gat number) exists. This

essentially means that the Gat number entered in the DBT application data is invalid. The

script also filters only Well DBT applications (i.e. where Activity Group = “Well”) which

have “stage = Pre-sanction Desk 2” and “status = pending”.

- Invalid_cadastres.sql: This script helps remove cadastre polygons having duplicate PIN

in the same village. This view is used to filter out invalid cadastres and villages that

contain at least one invalid cadastre from which well DBT application is made.

8.5 Open QGIS and install multicrop plugin from "kharif_multicrop_plugin_ponding.git"

directory (obtained from git in step 4.3)

8.6 Run the “kharif_multicrop_plugin_ponding” plugin from QGIS main window.

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This will open a dialog box which contains following sections:

- PostgreSQL connection parameters

- Data source parameters

District name --> <the district for which we want to run the well DBT>

Other parameters --> Database table names and “geom” columns of the data

loaded in step 4.4 [no need to change the default values]

- Model parameters

Crop --> soybean [no need to change]

Year --> 2018 [no need to change]

Output directory --> all the CSVs and the final PDF for the entered district will be

generated in this directory

8.7 Click "Ok" on the dialog box

This will run the water budget model for all the cadastres in the safe PoCRA villages which

contain all valid DBT data and valid cadastres. The module will give following outputs:

-- Safe-watersheds summary: msgbox [district, no_of_pocra_villages,

no_of_safe_villages]

-- Invalid cadastres: CSV [district, taluka, village_name, vincode,

no_of_invalid_cadastres]

-- village-wise well DBT: CSV [district, taluka, village_name, vincode,

no_of_pending_well_dbt, invalid_dbt]

-- district summary: msgbox [district, no_of_safe_villages, no_of_safe_valid_villages,

no_of_safe_valid_villages_withValidDBT]

-- Vulnerability output: CSV [“Year”, “Crop”, “Vincode”, “Village name”, “Taluka”,

“district”, “Gat no.”, “Rainfall mm”, “PET mm”, “AET mm” and “deficit (PET-AET) mm”

“LULC class”, “Soil texture class”, “soil depth class”

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-- Village-wise Well DBT prioritized: CSV ["village_name", "vincode", "taluka",

"district", "Gat no.", "deficit mm", "LULC class", "Soil texture class”, “soil depth class”,

"DBT_registration_no", "fullname", "birth_category", "gender", "landholding(ha)"] -- filtered as

per the criteria

-- village well DBT summary: PDF [(i) Village well applications map, (ii) village LULC

map, (iii) village vulnerability map (iv) selected well applications map and (v) list of selected

and prioritized well DBT applications with details]

9. Conclusion and recommendations

Thus, the well DBT module will help in creating concrete mechanisms for mapping vulnerability

in a format that is easily comprehended by villagers as well as program officials in order to bring

greater accountability in the targeting of interventions. The module will also incorporate

necessary checks and balances to ensure that the targeting of the project beneficiaries is in line

with the project objectives and not appropriated by other vested interests.

This module provides a robust design and planning framework for targeting the beneficiaries

which may also be applied to other benefits in PoCRA or even to other watershed programs in

the future. Such a module may also be enhanced further to incorporate other key attributes such

as on-farm seasonal groundwater availability, stream proximity and so on, as more key

farm-level and village-level data becomes available in the GIS framework.

Recommendations

1. Common wells – one of the solutions to the problem of addressing the conflict of

sustainability and equity in access to groundwater has been the use of common wells (or

“samayik wihiri”). Although this is a traditional and old practice and is now diminishing, there

need to be creative and innovative mechanisms backed by concrete and transparent data and

clear, unbiased rules which may be useful for the farmers as well as be sustainable in the long

run.

2. Crop planning, regulation of extraction according to Groundwater Act – The IITB water

budget model may be used effectively for crop planning and regulation of groundwater use at the

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village -level. These are one of the important recommendations of the Maharashtra Groundwater

Act 2009 which have not been implemented on the ground yet due to lack of clear and

transparent rules. The water budget framework may effectively provide the groundwork for

delineating and implementing these rules.

3. Community ban on borewells – There are some success stories and exemplary villages such as

Ralegan Siddhi and Hiware Bazar which have formulated and have been collectively practicing

the ban on borewells. Looking at the data from interviews carried out by IITB, the expenditure

on borewells by individual farmers has grown manifold, especially on failed borewells. Thus,

digging deeper and deeper borewells in desperate search of water is a risky game and needs to be

addressed through innovative and incentivizing schemes at the village level.

5. Application of this module to other benefits and its incorporation into DBT portal – This

module and the IT stack can be used effectively to create similar tools and modules for other

benefits and even for other watershed programs at the state level.

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PoCRA - IIT Bombay MoU IVPhase II Report - Energy Component

Objectives F and H

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Table of Contents

Table of Contents 2

1. Objective F: Selection of 4 Distribution Transformer User Groups (DTUGs), and DT andvillage profiles 3

Methodology for selection of DTUGs 3Village profiles 4

2. Objective H: Selection of feeders for MLP based Energy Estimation tool 25Methodology for selection of Feeders 26Village profiles 33

Makani Feeder (Ahmadpur, Latur) 33Morewadi 35Makani 40Chopali/Chobali 45Fulsewadi 50

Malakoli Feeder (Loha, Nanded) 53Mangrul 55Polewadi/Policewadi 59Khedkarwadi and Ramachiwadi (Non-PoCRA) 63Malakoli and Ghughewadi (Non-PoCRA) 68

Sohal Feeder (Karanja, Washim) 71Kinhi Rokade 73Gaiwal 76Sohal 79

3. Distribution Transformer (DT) selection for DT meter installation 83

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1. Objective F: Selection of 4 Distribution Transformer User Groups

(DTUGs), and DT and village profiles

Methodology for selection of DTUGs

The selection criteria for DTUGs included the following:

● DT selection will be done at first based on quality supply i.e., overloading and low

voltage profile of DT.

● Secondly, farmers must take interest in conducting the proposed demand side activities of

load scheduling and capacitor installation.

● After DT selection, detailed surveys are to be conducted for collecting data on individual

farmer load, cropping pattern, irrigation method, irrigation behavior, and to come up with

a feasible sample load management schedule to avoid DT overloading and to prevent low

voltage issues.

● LT distribution network data of the DTs will be collected and mapped. The selected DTs

are to be metered to monitor the usage pattern and loading on the DT. Farmers will be

encouraged and supported to install capacitors.

● Hand Holding of farmers will be done regularly during peak Rabi season to create load

management schedules suited to the farmer requirements, to monitor whether the load

schedules are being followed, to identify and rectify problems occurring while following

the load management schedule.The exercise will be used to develop a set of guidelines

for functioning of, and assistance to, such DT User Groups (DTUGs).

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Village profiles

There are four different locations selected for 4 DTUGs as shown in Fig. 1.1. Village level and

DT level details of these villages are given in Table 1.1:

Fig. 1.1: Selected Locations for DTUGs in District Map.

.Fig. 1.2: Selected Location for DTUG in Ahmadpur, Latur

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Fig. 1.3: Selected Location for DTUG in Loha, Nanded.

Fig. 1.4: Selected Location for DTUG in Karanja, Washim.

Table 1.1: Details of the location of selected DTs for DTUGs.

DT Name Village VillageCode

Cluster Code Feeder Substation Taluka District

Ambedkar UmbardaBazar 530952 502_wrb-3_04 Umbarda Umbarda Karanja Washim

Ballinge Manbha 530948 502_wrb-1a_01 Umbarda Umbarda Karanja Washim

Somvanshi UmargaYelladevi 560401 524_mr-47_05 Ajansonda Chapoli Ahmedpur Latur

WaterSupply

DhanoraMakta 545275 511_gv-101_03 Sunegaon Loha Loha Nanded

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1. Dhanora Makta :

Village Dhanora Makta Total geog. area 1199 ha Rainfall (mm) 718.3 mm

Taluka Loha Cultivable area - in 2019 (mm) 957.9 mm

District Nanded Irrigated area - in 2020 (mm) 799 mm

Region Marathwada Land holdings - in 2021 (mm)as on 22nd Oct 900 mm

Cluster Code 511_gv-101_03

Stakeholders met:

MSEDCL Assistant Engineer Krishi Sahayak, Cluster Assistant, Sarpanch, Up-Sarpanc’shFather-in-Law, Farmers

Description of meetings and procedure of selection:

2nd August 2021:

The village and DT was identified with the help of MSEDCL officials. Meetings were held at

MSEDCL Sub division office Loha (Nanded) and Shirur(Latur) with Executive Engineer (EE)

and Deputy Executive Engineers where a brief overview of the research work done so far by the

PoCRA-IIT Bombay team was given. Plans to do demand side interventions to improve

electricity supply for irrigation were discussed, and help in identifying an overloaded DT in a

village with cooperative farmers was requested. MSEDCL officials at Loha Sub Division

suggested Dhanora Makta village for the load management exercise based on requirements given

to them. A meeting was organized on 2nd August 2021 by the Assistant Engineer Mr. Waghmare

(Loha Section MSEDCL) at the village and after discussions with the farmers as shown in Fig.

1.5, Water Supply DT was selected based on the response from the farmers and as suggested by

the AE based on the past history of complaints received by him at MSEDCL office. Mainly the

presence and weight of Mr. Waghmare convinced the villagers. In addition, the villagers

suggested one more DT while in the meeting, but MSEDCL officials said to leave it for the

moment. We attempted to follow up with this DT later, but the farmers did not respond.

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Fig. 1.5: 1st discussion with farmers for Village overview and DT selection on 2nd August 2021.

2nd September:

The meeting was arranged by contacting Sarpanch and local contacts in the village. The meeting

was organized to have a discussion with the Water Supply DT and Patil DT farmers on load

scheduling and capacitor installation. Initially, due to heavy rainfall during the day, our contact

from the village cautioned us that the IIT team may have trouble returning if the rainfall cuts off

the village later that evening. However, we insisted on meeting with the farmers and reached the

village. In the village, Mr. Ram Kadam, announced the public announcement (PA) system for the

farmers of both DTs to come to the Gram Panchayat office for the meeting. After some time, the

farmers started gathering at the venue. The turnover was more than 40 persons including a lot of

young men. We were informed that the good turnover was due to farmers returning from their

fields early because of chances of heavy rainfall. However, there were few farmers only whose

connections were on the two DTs. We were informed that many farmers had already left for the

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local bazaar nearby before they could be informed about the meeting.

The farmers and other villagers present at the venue seemed interested in the discussion about

capacitors and load scheduling. During the discussion on capacitor installation, we showed them

a video on the uses of capacitors and various aspects related to capacitor selection and

installation. The video generated further interest in the farmers and villagers present at the

meeting. It also increased the turnover as more villagers joined the meeting because of the visual

medium. We further explained about the load scheduling exercise to reduce DT tripping and

burnout issues. We showed them another video explaining a few ways in which load scheduling

could work. The farmers were initially skeptical about whether everyone can irrigate their fields

without overloading the DT. In order to overcome their skepticism, we showed them a

presentation where we prepared a tentative irrigation schedule for Umarga Yelladivi village in

Latur. Again, the visual medium helped with retaining the farmers’ interest in the discussion. We

showed them using calculations that it was practically possible to prepare a schedule where every

farmer can irrigate their crops without overloading the DT. They seemed more convinced after

looking at the final results from the scheduling exercise. We urged them to make a similar

schedule with the other farmers on their DT and we would assist them with the same. Since a lot

of the farmers on the two identified DTs (Water Supply DT and Patil DT) were not present

during the meeting, the farmers asked us to visit the village on the next morning at 8 am as the

farmers are usually available at that time before they go for work.

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Fig. 1.6: Follow-up meetings with Water Supply DT farmers on 2nd and 3rd September 2021.

3rd September:

As per the direction of the Water supply DT and Patil DT farmers present in the meeting on 2nd

September, we visited Dhanora village on 3rd September at 8 am. Mr. Ram Kadam again

announced on the PA system for the farmers of both DTs to come to the village temple for the

meeting. Most farmers from Water Supply DT and few farmers from Patil DT gathered for the

meeting by about 8:45.

Since most of the farmers in the meeting were not present at the last meeting, we decided to

explain everything once again. We started by giving an introduction to the farmers about our

work and then showed them the capacitor video. At the end of the video, we helped the farmers

distinguish between the capacitors (three phase Agricultural capacitors for power factor

improvement) that we were recommending them to install and the capacitors (locally called

condensers) that they were already using for one/two phase to three phase conversion. The

farmers were also skeptical about who is going to get benefitted by the capacitor sales as

MSEDCL had also conducted capacitor purchase drives in the past as well as made it mandatory

for the farmers to purchase capacitors. We clarified their doubts by first explaining that we’re

neither from a private company nor from MSEDCL. Further, we also explained that we are not

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recommending any particular company/brand for capacitors. The farmers can purchase any ISI

mark capacitor from the vendor of their choice who can provide them capacitors at a competitive

price. This helped build trust with the farmers. After the farmers were fairly convinced of

purchasing capacitors for their pumps, we explained to them about DTUG and load scheduling

exercise that would help reduce issues such as DT tripping and burnout. We showed them the

presentation we prepared using Umarga Yelladevi farmers’ data on how the load management

could be done so that everyone got their irrigation chance without overloading the DT.

We asked them to fill their details (names and pump HPs) in a load scheduling sheet we had

printed for them. Some farmers were hesitant in providing their actual pump HP details since

their official connections were registered for a lower HP pump. We explained to them first that

the data we’re collecting is only for our internal use to assist them with preparing a schedule.

These details will not be shared with MSEDCL. This helped reduce the reluctance in the farmers.

Further, we explained to them that without knowing the exact pump specifications (HPs), the

schedule will not be able to incorporate the additional load on the DTs from such pumps that are

not registered in the official database. Slowly, the farmers agreed to provide the details of all

their pumps (both registered and unregistered) and even entered the data into the sheet by

themselves after multiple discussions among themselves. This was a positive outcome for us as

cooperation among the farmers is the building block of this intervention. The farmers asked us to

come before the Soybean harvest (at the end of September) for carrying out load scheduling

exercise with them using the data they provided to us in the meeting.

Water sources:

Dhanora Makta is the PoCRA village near Loha on Sunegaon Feeder and there is 1 major stream

(4th order) that flows through the village from South towards the North. Along with this 4th order

stream there are some 2nd order streams flowing across the village. A total of 9 CNB's have been

constructed on the 4th order stream and 7 CNB’s on 2nd order streams .

Elevation difference is between 433 (West) to 376 (North-East) m as shown in Fig. 1.7.

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Fig. 1.7: Elevation Map of Dhanora Makta.

Typical water sources are open wells, borewells, CNB’s and rivers (perennial and/or seasonal).

Open well depths are in the range of 40-50 ft. In most of the open wells, water is available till

March-April (5-10 ft. water column height). Borewells are 300 to 450 ft. deep and water is

available till April-May.

Water transfers from BW to OW are also seen but in limited cases and it starts from January

onwards. Pipe diameters are 2-2.5” and pump HP’s are 3 HP and 5 HP. BW to field irrigation is

also very rarely seen.

Soil and Agriculture:

Soil type is clayey (good) mostly about 90% and gravelly clay loam soil (lighter) about 10%.

Soil depth varies from 4 ft. to 10-12 ft. (near the stream) as shown in Fig. 1.8.

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Fig. 1.8: Dhanora Makta Soil Map.

Kharif crops are Soybean, Tur, Cotton, Moong and Urad. Rabi crops are Wheat, Jowar, Turmeric

and Gram. Soyabean is mostly a rainfed crop, however, farmers having access to irrigation tend

to irrigate the crop once during a dry spell of more than 15 days. Sprinkler irrigation method is

used for this. 8 to 12 sprinkler nozzles are connected in 1 shift of irrigation covering one acre.

For Tur, 2 irrigations are done after monsoon till January with sprinkler as well as border/ furrow

method.

Farmers having access to irrigation (OW, BW) have sprinklers and irrigate Soybean and Gram.

Wheat is irrigated using the furrow method, while sprinkler + furrow method is used by the

remaining farmers.

Electricity scenario:

There are 14 Ag DTs in Dhanora Makta (including 2 HVDS), of which Water supply DT 100

kVA, and remaining of the capacities 63 kVA and 100 kVA

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Water Supply DT Profile:

Major crops:

Kharif: Soybean, Cotton, Tur.

Rabi: Wheat, Jowar, Gram & Turmeric.

Water sources:

Open wells, borewells, CNBs, river (seasonal).

DT Detail:

The Water Supply DT(100kVA) selected is heavily overloaded (38 pumps) and the location is

shown in the village soil map in Fig. 1.8, Pump capacities are in the range 3HP to 5 HP and

farmers irrigate the crops, wheat, gram, jowar and turmeric during peak demand in the rabi

months.

2. Umarga Yelladevi :

Village UmargaYelladevi Total geog. area 819 ha Rainfall (mm) 747.4 mm

Taluka Ahmedpur Cultivable area - in 2019 (mm) 671.1 mm

District Latur Irrigated area - in 2020 (mm) 763.4 mm

Region Marathwada Land holdings - in 2021 (mm)as on 22nd Oct 820.1 mm

Cluster Code 524_mr-47_05

Stakeholders met:

Sarpanch, Krishi Sahahyak, Cluster Assistant, Farmers

Description of meetings and procedure of selection:

30th July 2021:

The village was identified from village level discussions with several villages as elaborated

below.

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During the village level meetings at villages on Makni feeder & Malakoli Feeder in Latur &

Nanded districts respectively, discussions were put forth regarding capacitor installation by all

farmers. The importance of capacitors as a low-cost alternative to significantly improve supply

by reducing the load on the DT and increasing voltage at the pumps as well as to avoid loss of

irrigation due to DT and pump burnouts were discussed. The awareness video was shown during

the meeting and circulated among farmers. Emphasis was given on getting all farmers on the DT

to install capacitors to achieve the desired results.

In 6 villages among the 10 villages visited which showed interest in the capacitor initiative,

trying out load management to overcome the supply issues associated due to overloading was

suggested. Among these villages, Umarga Yelladevi farmers agreed to try out load management

strategies to improve their supply hours and quality in the first meeting itself which was held on

30th July 2021 along with Sarpanch, Krishi Sahayak, Cluster Assistant and Farmers. Others were

reluctant as they were doubtful and in some cases sure that farmers won't follow a set schedule

and everyone would want to get their irrigations done first as the supply hours are not reliable.

Supply hours get further reduced in these villages as the electric lines are poorly maintained

leading to feeder tripping and DT tripping due to faults at the LT lines.

Among the overloaded DTs in the village, the Sarpanch suggested Somvanshi DT (Sarpanch’s

DT) for initial study as it's easier to get farmers together (as very few in no. i.e., has greater

average landholdings) to create and adhere to a schedule. The farmers on this DT are mostly a

homogenous population (same community or family) and leadership and influence of the

Sarpanch would help in continuing the demand-side interventions in the future. It seemed that

the presence and interest of the Sarpanch on the DT, ensured that all farmers agreed to try out the

load management.We had individual discussions with all farmers on the DT about load

management during surveys, Either the farmers themselves felt the initiative might help them

and one of them responded "if sarpanch asks me to follow the schedule I will".

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Fig. 1.9: Follow-up meeting with farmers on Somvanshi DT on 31st July 2021.

31st July 2021:

Then during a follow up meeting on 31st July 2021 as shown in Fig. 1.9, detailed surveys of

cropping patterns, water sources, and irrigation methods etc. on Somvanshi DT in particular were

noted.

Water sources:

Umarga Yelladevi is the PoCRA village near Shirur Tajband on Chapoli Feeder and there are 2

major streams (2nd order) that flows through the village from West towards the East. Along with

these 2nd order streams there are several 1st order streams flowing across the village. A total of 8

CNB's have been constructed on these 2th order streams and very few of them are in working

condition according to farmers', discussed during the village overview meeting.

Elevation difference is between 620 (West) to 560 (East) m as shown in Fig. 1.10.

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Fig. 1.10: Elevation Map of Umarga Yelladevi.

Typical water sources are open wells, borewells, and CNB’s. Open well depths are in the range

of 40-50 ft. In most of the open wells, water is available till Feb-March (5-10 ft. water column

height). Borewells are 300 to 500 ft. deep and water is available till April-May.

Water transfers from BW to OW are also seen but in limited cases and it starts from January

onwards. Pipe diameters are 2.5-3” and pump HP’s are 3 HP and 7.5 HP. BW to field irrigation is

also very rarely seen.

Soil and Agriculture:

Soil type is clayey (good) mostly about 80% gravelly sandy loam soil (lighter) about 15% and

gravelly clay loam around 5%. Soil depth varies from 1-2 ft. to 4-6 ft. (near the 2nd order

streams) as shown in Fig. 1.11.

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Fig. 1.11: Umarga Yelladevi - Fulsewadi Soil Map.

Kharif crops are Soybean and Tur. Rabi crops are Wheat, Jowar, and Gram. Also, some farmers

do annual crops as well like Sugarcane but these are very few.

Electricity scenario:

There are a total 10 Ag DTs in Umarga Yelladevi (including 2 HVDS), of which all Ag DT’s are

of capacity 63 kVA.

Somvanshi DT Profile:

Major crops:

Kharif: Soybean and Tur

Rabi: Wheat and Gram

Annual: Sugarcane

Water sources:

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Open wells and borewells

DT Detail:

The Somvanshi DT (63 kVA) selected is heavily overloaded (18 pumps = 93 HP) and located in

village soil map as shown in Fig. 1.11, pump capacities are in the range 3HP to 7.5 HP and the

farmers irrigate for the crops, wheat, gram and sugarcane during peak demand.

3. Umbarda Bazar :

Village Umbarda Bazar Total geog. area 1434.93ha

AverageRainfall (mm) 722.6 mm

Taluka Karanja Cultivable area - in 2019 (mm) 632.2 mm

District Washim Irrigated area 191 ha in 2020 (mm) 865.7 mm

Region Vidharbha Land holdings - in 2021 (mm)as on 22nd Oct 1524.25 mm

Cluster Code 502_wrb-3_04

Stakeholders met:

Sarpanch, Krishi Sahahyak, Cluster Assistant, Farmers

Description of meetings and procedure of selection:

The first interaction with farmers of Ambedkar DT occurred as part of the village level energy

infrastructure study (MoU III) in Umbarda Bazar. The DT was selected mainly because of the

history of load management once tried out by the farmers.

The DT being overloaded resulted in frequent trippings during peak season and low voltage

issues at the pumps located far from the DT. Farmers had themselves tried and failed operating

partial loads at a time taking turns among themselves. This was done without any formal

arrangements or schedules being made. The practice stopped as all farmers were not involved

and some started irrigating in slots not allocated to them. Farmers collect money for transformer

maintenance before peak season and don't allow illegal ‘hooking’ on their DT during peak

seasons. Hence it seems like there is some history of community action here which may allow us

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to leverage it.

Farmer meetings are to be organized for Ambedkar DT farmers to get all of them on board for

load management to work and to assist in creating a load management schedule as per their

irrigation requirements. Umbarda Sarpanch promised his full support in organizing meetings and

agreed to circulate the awareness video made by PoCRA among farmers.

Water sources and Cropping Pattern:

The typical water sources in the region are wells and bore-wells. The well depths are roughly

60ft and bore-wells are around 200-300 ft whereas some are even as deep as 400 ft. However,

about 70% farmers are rain-fed according to the villagers.

Fig. 1.12: Umbarda Bazar Soil Map.

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Fig. 1.13 Umbarda Bazar Elevation Map.

Kharif crops are Soybean, Cotton and Tur. Rabi crops are Wheat and Gram.

Ambedkar DT Profile:

Soil Type:

The Ambedkar DT command area North-East of the transformer (See Fig. 1.12) has shallow

medium soil with low water retention capabilities, also the wells in this region dry up sooner

being at higher altitudes and due to slope of the land. The south west region has mostly black soil

and water sources with water available till summer months.

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Fig. 1.14: Location of Ambedkar DT.

Major crops:

Kharif: Soybean, Cotton and Tur

Rabi: Wheat and Gram

Water sources:

Open wells and borewells

DT Detail:

The Ambedkari DT (100 kVA) selected is heavily overloaded (28 pumps = 134 kVA) and located

in village google map as shown in Fig. 1.14, pump capacities are in the range 3HP to 7.5 HP and

the farmers irrigate for the crops, wheat, and gram during peak demand.

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4. Manbha :

Village Manabha Total geog. area 1779.88ha Rainfall (mm) 1084.5 mm

Taluka Karanja Cultivable area - in 2019 (mm) -

District Washim Irrigated area - in 2020 (mm) 1524.25 mm

Region Vidharbha Land holdings - in 2021 (mm)as on 22nd Oct -

Cluster Code 502_wrb-1a_01

Stakeholders met:

Sarpanch, Krishi Sahahyak, Cluster Assistant, Farmers

Description of meetings and procedure of selection

Ballinge DT is located towards the farther end of the feeder and receives low voltage at the DT

itself. A further voltage drop occurs due to 2km long LT lines and overloading. Frequent tripping

occurs at the DT. Even though farmers use local pumps operational at low voltages, effective

supply hours available are less. As the DT has poor supply conditions, the willingness of farmers

for trying out load management was enquired during individual farmer interactions, to which

only some gave a positive response. A couple of farmers offered to help persuade other farmers

to take part in the initiative if some formal arrangements can be made to legitimize the load

management schedules once it is made. With the push from the village sarpanch and a local

person of influence on the DT, the farmers have expressed their readiness to get capacitors

installed at their pumps. The DT was selected mainly because of the local support available.

In MOU III, Both Ambedkar and Balinge DT had been selected for a detailed

irrigation-network analysis, hence a fair bit of interaction between the IIT team and the farmers

had already taken place and some rapport developed.

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Elevation difference is between 405 (West) to 3750 (East) m as shown in Fig. 1.15.

Fig.1.15 Elevation and drainage map of Manbha.

Fig. 1.16 Soil map of Manbha.

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Balinge DT Profile:

Soil Type:

The Manbha DT command area has mostly clayey and somewhat gravelly clay loam soil with

moderate water retention capabilities.

Major crops:

Kharif: Soybean, Cotton and Tur

Rabi: Wheat and Gram

Annual/Parennial: Horticulture (Orange and Sweet Lemon)

Water sources:

Open wells and borewells

DT Detail:

The Balinge DT (100 kVA) selected is heavily overloaded (32 pumps = 126 kVA), pump

capacities are in the range 3HP to 7.5 HP and the farmers irrigate for the crops, wheat, orange,

sweet lemon and gram during peak demand.

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2. Objective H: Selection of feeders for MLP based Energy

Estimation tool

In MoU III, a framework was developed to estimate the energy and water consumption by crops

in a group of village(s) under one feeder based on MLP data, secondary datasets, and some

primary surveys in the villages. This framework is being developed into a tool to estimate energy

infrastructure sufficiency in a village. The tool output will be an indication of energy

infrastructure status to indicate overloading or insufficiency, the effect of various crops, irrigation

methods, and water transfers. Since the energy consumption is currently measured at the

substation for each feeder, the smallest unit for validation of this framework is a feeder.

Thus, in MoU IV, we select 3 feeders in three clusters for the implementation and validation of

the energy estimation tool. In addition, to better understand variations in energy consumption due

to effect of various crops and irrigation methods, 12 energy meters will also be installed at

selected Distribution Transformers (DT) to correlate cropping, agro-climatic and other data

which are inputs to the tool, and energy consumption pattern at the DT. Out of the total 12 DT

meters, 8 will be installed on the three selected feeders. And 4 will be installed on the DTUGs.

The locations of the selected feeders are provided in Fig. 2.1.

Fig. 2.1: Locations of the selected feeders for MLP based energy estimation tool.

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Methodology for selection of Feeders

The selection criteria for feeders included the following:

● The villages on the selected feeders should be PoCRA villages (as many as possible) to

increase the likelihood that the feeders are representative of the PoCRA project area.

● The selected feeders should cover villages that overlap with water team’s selected

villages for intervention (as many as possible) to have a better convergence of water and

energy related interventions.

● The villages should be representative of the taluka and the district in terms of cropping

pattern, soil type, and rainfall.

● The irrigation intensity in the villages should be representative of the taluka and the

district, and there should not be a concentration of perennial water sources in the villages,

as that would not be representative of the PoCRA project area.

● The feeder should not have too many villages as it increases the likelihood of too many

variations in the irrigation practices across the villages. These variations do not show up

in the Energy Estimation tool at the feeder level and makes the deviations difficult to

track.

Selection of feeder in Ner - Dist. Yavatmal, or Karanja - Dist. Washim

As the first step, villages where water based interventions were carried out were identified in

Karanja taluka in Washim district and Ner taluka in Yavatmal district. However, the two villages

selected for water based interventions in each taluka were connected to very long feeders

consisting of 10 - 15 villages each. Further, most of the villages on those feeders were

non-PoCRA villages. Thus, due to the likelihood of too many variations, these feeders and

subsequently the villages, were not selected for the intervention. The details of the two feeders

are as follows:

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Details of villages covered by Wai Ag Feeder:

Villagename

PoCRAvillage

Cluster code Selectedby watergroup

Fieldvisit/VCRMC meetingcarried out

Data collected

Wai Pr.Karanja

Yes 502_ptkp-1_03

Yes Yes Village overview - croppingpattern, soil, water sources,irrigation, electricity scenario

Lohara Yes 502_ptkp-1_03

Yes Yes Village overview - croppingpattern, soil, water sources,irrigation, electricity scenario

Koli No N/A No No N/A

Tapowan No N/A No No N/A

Sheluwada No N/A No No N/A

Murambi No N/A No No N/A

Kisan Nagar Yes 502_ptkp-1_03

No No N/A

Wadhavi Yes 502_ptkp-1_03

No No N/A

Shevti No N/A No No N/A

Poghat No N/A No No N/A

Shivni No N/A No No N/A

Details of villages covered by Lohi Ag Feeder:

Village name PoCRAvillage

Clustercode

Selectedby watergroup

Fieldvisit/VCRMCmeetingcarried out

Data collected

Adgaon Yes 510_wrb-1a_01

Yes Yes Village overview - croppingpattern, soil, water sources,irrigation, electricity scenario

Umartha Yes 510_wrb-1a_01

Yes Yes Village overview - croppingpattern, soil, water sources,irrigation, electricity scenario

Sawangi No NA No No N/A

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Hatola No NA No No N/A

Budhegaon No NA No No N/A

Ramgaon No NA No No N/A

Changarwadi No NA No No N/A

Karkheda No NA No No N/A

Parajana No NA No No N/A

Satefal No NA No No N/A

Waiparaj No NA No No N/A

Mahajanpur No NA No No N/A

Lohi No NA No No N/A

Hence, per the aforementioned criteria, other feeders were identified in the two talukas using

data from MSEDCL. Subsequently, three feeders namely Sohal feeder in Karanja taluka, and

Sawargaon and Kharadgaon feeder in Ner taluka were shortlisted. Sohal feeder covers three

villages (2 PoCRA villages) and has more than 22 Ag DTs, Sawargaon feeder covers 4 villages

(all PoCRA villages) and has more than 35 Ag DTs, and Kharadgaon feeder covers 6 villages (5

PoCRA villages) and has more than 55 Ag DTs.

Further, the rainfall data for the two circles, namely Kekatumara (close to Sohal feeder) and

Shirajgaon (close to Sawargaon and Kharadgaon feeder), for 2020-21 indicated 1047 mm and

848 mm respectively. Thus, the three feeders were comparable in terms of rainfall. However,

looking at the satellite maps of villages covered by these feeders, we could see the villages under

the Sawargaon feeder (as shown in Fig. 2.2) and the Kharadgaon feeder (as shown in Fig. 2.3)

were both served by large reservoirs/irrigation tanks. On the other hand, the villages under the

Sohal feeder (as shown in Fig. 2.4) have only one smaller reservoir (close to one of the villages).

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Fig.2.2 : Sawargaon feeder Google Satellite map.

Fig. 2.3: Kharadgaon feeder Google Satellite map.

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Fig.2.4: Sohal feeder Google Satellite map.

Additionally, Rabi cropping pattern for the villages covered by the three feeders was as follows:

For Sawargaon feeder, as shown in Fig. 2.5, it was observed that Brahmanwada (P) had thrice the

percentage of wheat as compared to the other villages on the feeder as well as the taluka (Ner).

On the other hand, Brahmanwada (P) and Pandhari had half the percentage of Gram as compared

to the other villages on the feeder as well as the taluka.

For Kharadgaon feeder, as shown in Fig. 2.6, it was observed that Kanhergaon, Khalana, and

Ramgaon had two to three times the percentage of wheat as compared to the other villages on the

feeder as well as the taluka (Ner). On the other hand, Ramgaon also had more than twice the

percentage of Gram as compared to the other villages on the feeder as well as the taluka.

For Sohal Feeder, as shown in Fig. 2.7, it was observed that Gaiwal had twice the percentage of

wheat as compared to the other villages on the feeder as well as the taluka (Karanja). On the

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other hand, the cropping pattern for gram was similar for all three villages on the feeder as well

as the taluka (Karanja).

Fig. 2.5: Rabi 2020-21 cropping pattern of villages on Sawargaon feeder as % of cropped area.

Fig. 2.6: Rabi 2020-21 cropping pattern of villages on Kharadgaon feeder as % of cultivable area.

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Fig. 2.7: Rabi cropping pattern of villages on Sohal feeder as % of cropped area.

Based on the aforementioned observations it could be concluded that while all three feeders were

comparable in terms of rainfall and representation of PoCRA villages, Sohal feeder has the least

concentration of large reservoirs among the three feeders. Also, the villages on the feeder have

the least variation in terms of Rabi cropping pattern and are more representative of the taluka’s

cropping pattern. Thus, Sohal feeder in Karanja, Washim was selected for MLP based energy

estimation tool.

Two other feeders were selected in the two other districts: Malakoli feeder in Loha, Nanded and

Makani feeder in Ahmadpur, Latur. In both these locations, we selected feeders where the

villages on the feeder have an overlap with the villages selected for water based interventions.

The two feeders have 4-5 villages each which have minor variations in terms of irrigation

practices and irrigation intensity. One more reason why we selected a different feeder in

Karanja/Ner area is because we felt all the feeders that the water group had selected villages had

plenty of irrigation. Hence, we selected the Sohal feeder in Karanja because it seemed to have

comparatively lesser irrigation.

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Village profiles

Makani Feeder (Ahmadpur, Latur)

Substation: Shirur Tajband.

Villages on Makani feeder: Fulsewadi, Makani, Chopali, and Morewadi.

No. of DTs: 32+

Feeder Type: Mixed feeder (Agriculture + Residential).

Supply schedule: 7 days daytime supply, 7 days nighttime supply.

Fig. 2.8: Villages on Makani feeder represented on Latur district map.

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Fig. 2.9: Villages on Makani feeder.

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Morewadi

Village details:

Village Morewadi

Taluka Ahmedpur

District Latur

Region Marathwada

Cluster Code 524_mr-47_05

Stakeholders met:

Krishi Sahayak, Cluster Assistant, Sarpanch, Farmers

Water sources:

The village has a total geographical area of 290 ha out of which 284 ha is cultivable area. The

village receives average annual rainfall of 1046 mm. However, the rainfall received by the

village in 2020 was 688 mm which was 34% lower than the average. This is the closest rain

gauge for all villages on the feeder.

Morewadi is the second village in catchment and there is 1 major stream (3rd order) that flows

through the village. 4 Cement Nala Bunds (CNBs) have been constructed on this stream.

Elevation difference ranges from 568 m (north) to 536 m (south) as shown in Fig.2.10.

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Fig. 2.10: Morewadi village Elevation map.

Typical water sources are open wells (47-50) and borewells (more than 100). Open well depths

are in the range of 40 - 70 ft. In most of the open wells, water is available till March (5 - 6 ft.

water column height). Borewells depths are in the range of 250 to 500 ft. and water is available

in borewells till April-May.

Water transfers from borewells (BW) to openwells (OW) are common and start from January.

These water transfers are primarily done for Sugarcane. BW to field irrigation is also a common

practice. About 10 farmers lift water from CNBs and transfer it over a distance of less than 200

ft. directly to their fields. Further, a group of 5-6 farmers have laid pipelines over a distance of 5

km from Kharabwadi dam/irrigation tank. Common pipe diameters are 2.5” and 3” as observed

from the farmer surveys.

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Soil and Agriculture:

Soil type is mostly clayey while some areas have gravelly clay loam soil as shown in Fig.2.11.

Soil depth varies from 1 ft. (generally) to 7-8 ft. (near the stream). Approximately 15% farmers

in the village have done soil restoration using clayey soil.

The area is dominated by Sugarcane. Kharif crops are Soybean, Tur, and Sugarcane. Rabi crops

are Wheat, Jowar, and Gram. Soybean is mostly a rainfed crop, however, farmers having access

to irrigation tend to irrigate the crop once during a dry spell of more than 15 days using

sprinklers. For Tur, two irrigations are done post monsoon with sprinkler as well as

border/furrow method.

Fig. 2.11: Morewadi village Soil map.

Farmers having access to irrigation, irrigate Wheat, Jowar, and Gram using the sprinkler method

as well as border/furrow method. Some farmers who have less water in OW and do not have

another source do all the irrigations using the sprinkler method.

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Approximately 50% of the cropped area under sugarcane is on drip irrigation. During the

monsoon season, if there is a gap of 3-4 days between rainfall events, these farmers irrigate

sugarcane using drip. Even if the drip laterals are laid on the field, few irrigations post monsoon

are done using the furrow method. This is because these farmers have enough water in their open

wells (as observed from farmer surveys).

Area to the North-East of the village is mostly rainfed with fewer open wells in the region. Thus,

only late kharif crops such as Tur are irrigated in this region. A few farmers also grow Jowar

and/or Gram in this region.

Electricity scenario:

There are 6 Agriculture (Ag) Distribution Transformers (DTs) in Morewadi namely Water

Supply DT (near Gaothan), Chame DT (near Chopali border), Abande DT and Moghe DT (near

the stream), Wadekar DT (on Udgir road), and Ghurme DT (in Shirur village). Ghurme DT is a

shared DT with some connections in Shirur. All the DTs are of 63 kVA capacity.

Water supply DT has a total of 25 connections, out of which 7-8 connections operate all year

round while others are seasonal. Initially the DT was of 100 kVA capacity, but after 1st

breakdown it was replaced with a 63 kVA capacity DT due to unavailability of 100 kVA capacity

DT. After replacement, the DT has broken down several times (6-7 times since last replacement

8 months ago).

There are 6-7 connections from Morewadi on the Ghurme DT. The LT lines are 1.5 km long as

the DT is in Shirur village.

2-Phase to 3-Phase conversion:

The feeder that supplies electricity to the village is a mixed feeder (residential and agricultural).

Thus, the mode of operation of electricity supply is such that during Ag supply (8 hours), all

three phases are supplied, while during non-Ag hours one or two phases are supplied so that the

agricultural pumps are not able to run (since they require three phase supply to run). However,

because of electricity supply issues and because 8 hours is insufficient for their needs, the

farmers use 32 to 120 µF capacitors to convert the single/two phase supply to three phase supply

to run their pumps and meet their irrigation requirements during non-Ag supply hours.

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As per our primary surveys, this is a common practice in the region. In all villages farmers said

that 90% farmers with irrigation pumps do it with their dugwell pumps. It began two years ago.

This is also around the time that MSEDCL began to supply energy in two phases in non-Ag

hours. Before that they used to supply one phase, but the load became high and hence they

started to supply power on two phases in non-Ag hours.

This practice is extremely detrimental to their pumps, and they fail frequently. Farmers don’t use

this on borewell pumps since those are hard to remove for repair etc. It is also very energy

inefficient.

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Makani

Village details:

Village Makani

Taluka Ahmedpur

District Latur

Region Marathwada

Cluster Code 524_mr-47_05

Stakeholders met:

Krishi Sahayak, Cluster Assistant, Upsarpanch, Farmers, KrishiMitra

Water sources:

The village has a total geographical area of 745 ha out of which 728 ha is cultivable area. The

village receives average annual rainfall of 1046 mm, same as Morewadi and Chopali because of

the same rain circle. The rainfall received by the village in 2020 was 688 mm which was 34%

lower than the average.

One major stream (3rd order) flows through the Makani village from West to East. One CNB is

constructed on this stream.

Elevation ranges from 590 m (North-West) to 540 m (South-East) as shown in Fig.2.12.

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Fig. 2.12: Makani village Elevation map.

Typical water sources are open wells (200-250) and borewells (150-200). Open well depths are

in the range of 60 - 80 ft. In most of the open wells, water is available till March (5 -10 ft. water

column height). Open wells near the irrigation tank have water available till April. Borewells

depths are in the range of 300 to 500 ft. and water is available in borewells till April-May.

The village also has a percolation tank (old Makani tank), and an irrigation tank. The percolation

tank, situated upstream of the village as shown in Fig.2.13, has water available till

January-February. Whereas the irrigation tank, situated downstream, has water available

throughout the year (water level drops towards May end). This irrigation tank is shared by 4

villages (Makani, Bhakarwadi, Kharabwadi and Gadewadi) and it has been in use for the past 10

years. Additionally, there are 2 unlined farm ponds in the village.

Water transfers from BW to OW are common and start from January. These water transfers are

done primarily for Sugarcane. Some farmers have laid pipelines over a distance of 2.5 to 3 km

from the irrigation tank to irrigate their fields. Common pipe diameters are 3” and 4”.

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Fig. 2.13: Makani village Land Use Land Cover (LULC) map.

Soil and Agriculture:

Soil type is mostly clayey, as shown in Fig.2.14, while the area to the south-west of the village

has gravelly clay loam soil. Soil depth varies from 1 ft. to 7-8 ft.

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Fig. 2.14: Makani village Soil map.

The area is dominated by Sugarcane. Kharif crops are Soybean, Tur, and Sugarcane. Rabi and

summer crops are Wheat, Gram, Groundnut, Moog, Jowar, etc. Soybean is mostly a rainfed crop,

however, farmers having access to irrigation tend to irrigate the crop once during a dry spell of

more than 15 days using sprinklers. For Tur, 2 irrigations are done post monsoon with sprinkler

as well as border/furrow method. Approximately 30% to 40% farmers have sprinklers and

irrigate Soybean, Wheat, Jowar, Gram, and Groundnut using the sprinkler method. These farmers

irrigate Wheat and Jowar with sprinklers during initial 2-3 irrigations while Groundnut and Gram

are irrigated only using the sprinkler method. The farmers provide 2-3 irrigations for Gram,

10-11 irrigations for Wheat and Groundnut, and 5-6 irrigations for Jwar.

Approximately 60-70% of the cropped area under Sugarcane is on drip irrigation. During the

monsoon season, if there is a gap of 3-4 days between rainfalls, these farmers irrigate sugarcane

using drip. Even if the drip laterals are laid on the field, few irrigations post monsoon are done

using the furrow method. This is because these farmers have enough water in their open wells

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and the irrigation tank. This irrigation tank has led to suboptimal use of water (as observed from

farmer surveys).

The North-West part of the village is mostly rainfed with fewer open wells in that region. The

region has black soil and farmers in the region have done soil restoration from the percolation

tank. In this region, water is available in open wells till January.

The south-west part of the village has medium soil (gravelly clayey loam/sandy loam). It has

open well depths upto 50 ft. and the borewell depths are in the range of 300 - 400 ft.. In most of

the open wells, water is available till March (5 - 6 ft. water column height). This region has 10

acres of Sugarcane but the water is transferred from the irrigation tank with pipeline lengths of 2

to 2.5 km.

Electricity scenario:

There are more than 20 Ag DTs in Makani. Out of these 8-10 are HVDS transformers while

other DTs are of 63 kVA capacity. The DTs near the irrigation tank are overloaded and have more

than 15 connections per DT. The DT in the north-west region are not overloaded due to more

area under rainfed crops.

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Chopali/Chobali

Village details:

Village Chopali

Taluka Ahmedpur

District Latur

Region Marathwada

Cluster Code 524_mr-47_05

Stakeholders met:

Krishi Sahayak, Cluster Assistant, Sarpanch, Farmers

Water sources:

The village has a total geographical area of 604 ha out of which 604 ha is cultivable area. The

rainfall received by the village in 2020 was 688 mm which was 34% lower than the average.

Two major streams (2nd order) flow through the Chopali village. There are 6-7 CNBs on the

longer 2nd order stream and 2 CNBs on the shorter 2nd order stream.

Elevation ranges from 585 m (North-West) to 550 m (South-East) as shown in Fig.2.15.

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Fig. 2.15: Chopali village Elevation map.

Typical water sources are open wells (50-55) and borewells (less than 10). Open well depths are

in the range of 40 - 60 ft. In most of the open wells, water is available till January-February (5-10

ft. water column height). Open wells near the irrigation tank have water availability till March.

Borewell depths are in the range of 300 to 350 ft. and water is available in borewells till March.

The village also has two 2 percolation tanks/irrigation tanks as shown in Fig.2.16. The larger

percolation tank is situated upstream of the village and has water availability till

January-February. Whereas the smaller percolation tank situated North-East of the village

habitation has water availability till the end of monsoon. This percolation tank is not used for

irrigation. Additionally, there are 2 lined farm ponds - one is situated downstream to the larger

percolation tank in an area of 1 acre, and the second one is situated near the smaller percolation

tank.

Water transfers from BW to OW are not common in the village. 2-3 farmers transfer water from

BW to OW during monsoon. Other water transfers include one BW to farm pond transfer.

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Common pipe diameter observed was 3” and typical pumps in the village are of 3 - 5 HP

capacity (as observed from the farmer surveys).

Fig. 2.16: Chopali village LULC map.

Soil and Agriculture:

Soil type is mostly clayey, while some area to the North-East of the village has gravelly clay

loam and gravelly sandy loam soil, as shown in Fig.2.17. Soil depth varies from 1 ft. to 4 - 5 ft.

10 - 15 farmers upstream to the larger percolation tank have done soil restoration using clayey

soil.

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Fig. 2.17: Chopali village Soil map.

The area is dominated by Soybean. Kharif crops are primarily Soybean, and Tur. Cropped area

under Cotton and Sugarcane is marginal. Rabi crops are Wheat, Gram, and Jowar. Soybean is

mostly a rainfed crop, however, farmers having access to irrigation tend to irrigate the crop once

during a dry spell of more than 15 days using sprinklers. For Tur, 2 irrigations are done post

monsoon with sprinkler as well as border/furrow method. Approximately 50% to 60% farmers

have sprinklers and irrigate Soybean, Wheat, Jowar, and Gram using the sprinkler method. These

farmers irrigate Wheat and Jowar with sprinklers during initial 2-3 irrigations while Gram is

irrigated only using the sprinkler method. Rabi crops are primarily grown alongside the two 2nd

order streams but the cropped area is marginal. The farmers provide 2-3 irrigations for Gram,

10-11 irrigations for Wheat, and 5-6 irrigations for Jwar.

Approximately 75 - 80% of the cropped area under Sugarcane is on drip irrigation. During the

monsoon season, if there is a gap of 3-4 days between rainfalls, these farmers irrigate sugarcane

using drip. Even if the drip laterals are laid on the field, few irrigations post monsoon are done

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using the furrow method. This is because these farmers have enough water in their open wells (as

observed from farmer surveys).

Area to the North-East of the village has medium soil (Gravelly clay/sandy loam). This region

has open well depths in the range of 50 - 60 ft. and the borewell depths are approximately 350 ft.

Water availability in this region is till February-March. This region has Soybean, Tur, Jowar,

Gram, and 10 acres of Sugarcane (near the smaller percolation tank).

Electricity scenario:

There are 5 - 6 Ag DTs in Chopali village. Out of these 1 - 2 are HVDS transformers and other

DTs are of 63 kVA capacity. The DTs near the larger percolation tank are overloaded and have

more than 25-30 connections, while the DTs in the North-East region are not overloaded due to

more area under rainfed crops. The LT lines on a few DTs are extended to about 1 - 1.5 km and

low voltage is a common problem in the village.

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Fulsewadi

Village details:

Fulsewadi is a small habitation (30-35 households) situated to the North of Umarga Yelladevi

village as shown in Fig.2.18.

Water sources:

Fig. 2.18: Fulsewadi village Elevation map.

Two major streams (2nd order) flow through Fulsewadi as shown in Fig.2.18. Elevation ranges

from 573 m (North-West) to 555 m (South-East).

Typical water source is the percolation tank (old Makani tank), few open wells and borewells.

Open well depths are in the range of 40-50 ft. In most of the open wells, water is available till

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February (5-10 ft. water column). Open wells near the percolation tank have water available till

March. Borewells are 300 to 400 ft. deep and water is available in borewells till April.

Percolation tank is downstream of the habitation and has water available till January-February.

As the region is on higher elevation, very few open wells have water availability after

January-February. Thus, Fulsewadi is mostly rainfed. Only some farmers cultivate Rabi crops.

Soil and Agriculture:

Fig. 2.19: Fulsewadi village Soil map.

Soil type is clayey (good) as shown in Fig.2.19. Soil depth varies from 3 ft. to 5-6 ft.

The area is dominated by Soybean. Kharif crops are primarily Soybean and Tur. Rabi crops are

Gram and Jowar. Soybean is mostly a rainfed crop, however, farmers having access to irrigation

tend to irrigate the crop once or twice during a dry spell, depending on the extent of the dry spell.

For Tur, 2 irrigations are done post monsoon with border/furrow method. Gram and Jowar are

irrigated using sprinkler method by the farmers who are in the vicinity of the Old Makani tank.

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The farmers provide 2-3 irrigations for Gram and 1-2 irrigations for Jowar due to poor electricity

supply despite having water availability.

Electricity scenario:

There is only one Ag DT in Fulsewadi which is of 100 kVA capacity. This DT is fully

overloaded with an LT line extended till 1.5 km to 2 km with more than 30-35 pump connections

in peak season.

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Malakoli Feeder (Loha, Nanded)

Substation: Malakoli.

Villages on Malakoli feeder: Malakoli, Khedkarwadi, Mangrul, Policewadi (in order).

No. of DTs: 15+

Feeder Type: Mixed feeder (Agriculture + Residential).

Supply schedule: 7 days daytime supply, 7 days nighttime supply.

Fig. 2.20: Villages on Malakoli feeder represented on Nanded district map.

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Fig. 2.21: Villages on Malakoli feeder.

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Mangrul

Village details:

Village Mangrul

Taluka Loha

District Nanded

Region Marathwada

Cluster Code 511_gv-101_03

Stakeholders met:

Krishi Sahayak, Cluster Assistant, Sarpanch, Gram panchayat Sewak, Farmers

Water sources:

The village has a total geographical area of 383 ha out of which 193 ha is cultivable area. The

rainfall received by the village is 856.25 mm. Till July 2021, the village has received 613 mm of

rainfall (72% of its annual average).

Two major streams (3rd order and 4th order) flow through Mangrul. There are 7 CNBs

constructed on these streams and Earthen Nala Bunds are constructed on the lower order (2nd

order) streams.

Elevation ranges from 502 m (South) to 411 m (North) as shown in Fig.2.22.

Typical water sources are open wells (47-50) and borewells (more than 100). Open well depths

are in the range of 40-70 ft. In most of the open wells, water is available till March (5-6 ft. water

column height), while there are a few open wells near streams having all year good water

availability (10-15 ft. water column height). Borewells are 250 to 500 ft. deep and water is

available till April-May. One of the streams is generally dry until the water overflows from the

percolation tank present on the boundary shared by Mangrul and Policewadi.

Apart from this there are 2 farm ponds (unlined) for open well recharge. CNB to field irrigation

is not common in Mangrul.

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Water transfers from BW to OW are not very common either, but there are farmers (15-20%)

who start these water transfers from February. They are done majorly for Groundnut, Turmeric,

and Wheat. BW to field irrigation is also a common practice. Common pipe diameters are 2.5”

and 3” as observed from the farmer surveys.

Fig. 2.22: Elevation (in meters) map (Mangrul).

Soil and Agriculture:

Soil type is clayey (good) mostly and gravelly sandy clayey soil (lighter) is present towards the

southern side hilly area (see Fig. 2.23). However, on-field observations show that there is

gravelly sandy clay soil (lighter/ murum) in the regions where clayey soil is shown in the

MRSAT data. Soil depth varies from 1 ft. to 7-8 ft. (near the stream). 20% of the farmers have

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done soil restoration from the Policewadi percolation tank 5-10 years ago. This layer is placed

with a depth of generally 1-2 ft.

Kharif crops are Soybean, Tur, Cotton, Turmeric, Jowar, and Vegetables. Soybean is mostly a

rainfed crop, however, farmers having access to irrigation tend to irrigate the crop once during a

dry spell of more than 15 days. Sprinkler irrigation method is used for this. For Cotton and Tur,

2-3 irrigations are done after monsoon till January. ~15% Cotton area is on drip and irrigation

practice is generally the same as border/ furrow. Dry spell irrigation is either using a drip or

sprinkler method. 2 types of crop spacing for Cotton 3 x 3 ft. and 4 x 1-1.5 ft.

Turmeric is another major crop in the area and almost 90% of the Turmeric variety is Salem.

About 75% of the Turmeric area is on drip irrigation. Initial 1-2 irrigations during sowing or dry

spells are done with drip or sprinkler and the rest are done using drip or furrow. A common

practice in the area is to irrigate the Turmeric on the day of Dasara (in October).

Apart from this, summer Groundnut (10-12 acres) and Vegetables (5-6 acres) are cultivated by

10-15 farmers in Mangrul. Farmers with access to more than 1 water source cultivate Groundnut

and Vegetables. Common vegetables grown are Tomato, Chilli, Okra, etc. Sprinkler irrigation for

Groundnut and Jowar and mixed irrigation method (surface + drip) for vegetables.

From the farmer surveys, it was understood that farmers having farms at multiple locations with

OW and/or BW at one of the gat numbers and having financial constraints, generally cultivates

Soybean+Tur and/or Cotton+Tur in the gat where water source is not present. Hence, this is

generally rainfed.

Area to the south of the village (ridge), approximately 92 ha, is wasteland.

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Fig. 2.23: Soil map with locations of surveyed farmers (Mangrul).

Electricity scenario:

There are 3 Ag DTs in Mangrul, Hake DT (near the entry road), Gaothan DT (near Gaothan), and

Shinde DT (on the boundary of Mangrul and Berali kh.). All DTs are of 100 kVA capacity. Out

of these, Shinde DT and Hake DT are overloaded (25-32 pumps) while Gaothan DT has only 8-9

connections. Hake DT has LT line length over 1 km distance and few of the farmers face low

voltage issues. Shinde DT is shared between Mangrul (15) and Berali (12) farmers and the

supply is from Sawargaon feeder. Along with these, there are 2 HVDS transformers in the

village. Shinde DT also has an oil leakage problem and has failed 2 times in the last 2 years.

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Polewadi/Policewadi

Village details:

Village Polewadi/Policewadi

Taluka Loha

District Nanded

Region Marathwada

Cluster Code 511_gv-101_03

Stakeholders met:

Krishi Sahayak, Cluster Assistant, Sarpanch, Gram panchayat Sewak, Farmers, MSEDCL lineman

Water sources:

The village has a total geographical area of 476 ha out of which 300 ha is cultivable area. The

rainfall received by the village is 856.25 mm, same as Mangrul and Khedkarwadi because of the

same rain circle. Till July 2021, the village has received 613 mm of rainfall (72% of its annual

average).

There are 2 major streams that flow through Policewadi/Polewadi (see Fig. 2.24). 4 CNBs have

been constructed on these streams and earthen nala bunds are present on the lower order streams.

One of the streams forms a boundary between Mangrul and Policewadi.

Elevation difference 502 m (south) to 430 m (north-west) (see Fig. 2.24).

Typical water sources are open wells (27), borewells (39), a percolation tank, and CNB. Open

well depths are in the range of 40-60 ft. In most of the open wells, water is available till March

(5-6 ft. water column height), while there are few open wells near streams having all year good

water availability (10-15 ft. water column height). Borewells are 250 to 300 ft. deep and water is

available till April-May. Borewells are more in the western region of the village.

Apart from this there are 2 farm ponds as per the farmers (could not meet the FP farmers).

Water transfers from BW to OW are common, and farmers (~50%) start the transfers from

December. These are done majorly for Turmeric, Wheat, etc. BW to field irrigation is also a

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common practice. About 5-6 farmers near CNBs lift water and transfer it over to a distance of

120 ft.-600 ft. directly to the fields. This is mostly done for Jowar.

Percolation tank has water available till February-March and after that it is reserved for drinking

water purposes only. It is not allowed to lift water from the tank, but 10-12 farmers near the tank

transfer water either to their open wells or directly to the field in Nov to Jan. One of the farmers

transfers water from an open well to a tank in monsoon i.e. pumping out to avoid water logging

in the farm and in Rabi again transfers water from tank to the farm.

Common pipe diameters are observed to be 2.5” and 3”. 2-3 farmers with land in multiple gat

numbers have laid pipelines up to 6000 ft. All these farmers grow Turmeric and cultivate some

Rabi crops.

Fig. 2.24: Elevation (in meters) map (Policewadi).

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Soil and Agriculture:

Soil types are clayey (good), gravelly clay (lighter), and gravelly sandy loam soil (murum)

(present towards the southern side hilly area). Soil near the streams is clayey (see Fig. 4). Soil

depth varies from 1 ft. to 6 ft. (near stream). Almost 90% of the farmers have done soil

restoration from the Policewadi percolation tank. This layer is placed with a depth of generally

1-2 ft.

Kharif crops are Soybean, Tur, Cotton, Turmeric, Jowar, and Vegetables. Soybean is mostly a

rainfed crop, however, farmers having access to irrigation tend to irrigate the crop once during a

dry spell of more than 15 days. Sprinkler irrigation method is used for this. For Cotton and Tur,

2-3 irrigations are done after monsoon till January. Border/ furrow is a common practice for

Cotton. Dry spell irrigation is either with a sprinkler or border/ furrow method. 2 types of crop

spacing for Cotton 3 x 3 ft. and 4 x 1-1.5 ft. were generally seen.

Turmeric is another major crop in the area and almost 90% of the Turmeric variety is Salem.

Close to 50% of the Turmeric area is on drip irrigation. Initial 1-2 irrigations during sowing or

dry spells are done with drip or sprinkler and the rest are done using drip or furrow. A common

practice in the area is to irrigate the Turmeric on the day of Dasara (in October). Farmers with

water/ more than 1 water source does the last irrigation using furrow. Few of the farmers connect

the drip system to the borewell. Mixed practice of drip irrigation for Turmeric was observed,

some farmers irrigate for 8 hours stretch and repeat after 8-10 days gap, while some farmers

irrigate for shorter durations (2-3 hours) every 3-4 days.

Apart from this, Vegetables (10-12 acres) are cultivated by 10-15 farmers in Policewadi. Farmers

with access to more than 1 water source cultivate Vegetables and other Rabi crops. Common

vegetables grown are Tomato, Chilli, Okra, etc. Sprinkler irrigation for Jowar, Gram, Wheat, and

mixed irrigation method (surface + drip) for vegetables.

From the farmer surveys, it was understood that farmers having farms at multiple locations with

OW and/or BW at one of the gat numbers and having financial constraints, generally cultivates

Soybean+Tur and/or Cotton+Tur in the gat where water source is not present. Hence, this is

generally rainfed.

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Area to the south-east of the village (ridge) about ~56-60 ha is wasteland/ forest.

Fig. 2.25: Soil map with locations of surveyed farmers (Policewadi).

Electricity scenario:

There are 5 Ag DTs in Policewadi, and out of these, Gaothan DT (75 kVA) has Ag as well as

residential connections. All DTs are of 63 kVA capacity. Oil theft, Overloading of DT near the

tank, frequent tripping in Rabi peak months is common.

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Khedkarwadi and Ramachiwadi (Non-PoCRA)

Village details:

Village Khedkarwadi

Taluka Loha

District Nanded

Region Marathwada

Cluster Code N/A

Stakeholders met:

Krishi Sahayak, Sarpanch, Gram panchayat Sewak, Farmers

Water sources:

The village has a total geographical area of 600 ha out of which 193 ha is cultivable area. Till

July 2021, the village has received 642 mm of rainfall.

There are 2 percolation and minor irrigation tanks in Khedkarwadi one is upstream of the village

and the other is downstream shared by Ramachiwadi village. 2 CNBs have been constructed on

one of the streams.

Elevation difference 520 m (south) to 462 m (north-west) (see Fig. 2.26).

Typical water sources are open wells (20-25), borewells (~10), percolation and minor irrigation

tanks. Open well depths are in the range of 30-40 ft. In most of the open wells, water is available

till February-March (5-6 ft. water column height). Borewells are 250 to 300 ft. deep and water is

available till March-April. Apart from this there are about 5 farm ponds as told by farmers. 2

farm ponds are lined and the rest are unlined.

Water transfers from BW to OW are rare (~10 borewells) and farmers start the transfers from

December. These are done majorly for Turmeric and Jowar. Percolation tank (upstream) and

irrigation tank (downstream) has water available till February-March. 10-12 farmers near the

tank transfer water directly to the field in Nov to Jan. Rabi crops are concentrated near both these

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tanks. Farmers with land holding in multiple gat numbers have laid pipelines up to 2400 ft. All

these farmers grow Turmeric and cultivate some Rabi crops.

Common pipe diameters are observed to be 2.5” and 3”.

Area to the south-east of the village is mostly rainfed, and there is no LT network in the region.

One farmer uses a diesel pump to irrigate Cotton and Jowar.

Fig. 2.26: Elevation (in meters) map (Khedkarwadi).

Soil and Agriculture:

Soil types are clayey (good) and gravelly clay (lighter) as shown in Fig.2.27. Soil depth varies

from 1 ft. to 4 ft. Village has flat-hilly terrain.

Kharif crops are Soybean, Tur, Cotton, Turmeric, Jowar, and Vegetables. Soybean is mostly a

rainfed crop, however, farmers having access to irrigation tend to irrigate the crop once during a

dry spell of more than 15 days. Sprinkler irrigation method is used for this. For Cotton and Tur,

2-3 irrigations are done after monsoon till January. Border/ furrow is a common practice for

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Cotton. Dry spell irrigation is either with a sprinkler or border/ furrow method. 2 types of crop

spacing for Cotton 3 x 3 ft. and 4 x 1-1.5 ft. were generally seen.

Turmeric is another major crop in the area and almost 90% of the Turmeric variety is Salem.

Close to 50% of the Turmeric area is on drip irrigation. Initial 1-2 irrigations during sowing or

dry spells are done with drip or sprinkler and the rest are done using drip or furrow. A common

practice in the area is to irrigate the Turmeric on the day of Dasara (in October). Farmers with

water/ more than 1 water source does the last irrigation using furrow. Few of the farmers connect

the drip system to the borewell. Mixed practice of drip irrigation for Turmeric was observed,

some farmers irrigate for 8 hours stretch and repeat after 8-10 days gap, while some farmers

irrigate for shorter durations (2-3 hours) every 3-4 days.

Sprinkler irrigation for Jowar, Gram, and Wheat.

Fig. 2.27: Soil map with locations of surveyed farmers (Khedkarwadi).

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Electricity scenario:

There are 4 Ag DTs in Khedkarwadi, and 2 HVDS transformers. Khedkar DT is of 100 kVA and

rest all DTs are of 63 kVA capacity. Maximum number of pumps on a DT are 12-15. No

overloading issue. One DT breakdown last year.

Ramachiwadi

Ramachiwadi is a small habitation near Khedkarwadi village. The maps for Ramachiwadi are

given below:

Fig. 2.28: Ramachiwadi village Elevation map.

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Fig. 2.29: Ramachiwadi village Satellite map.

Fig. 2.30: Ramachiwadi village Soil map.

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Malakoli and Ghughewadi (Non-PoCRA)

Village details:

Village Malakoli and Ghughewadi

Taluka Loha

District Nanded

Region Marathwada

Cluster Code N/A

Stakeholders met:

Krishi Sahayak, Sarpanch, Gram panchayat Sewak, Farmers

Maps for Malakoli and Ghughewadi, two non-PoCRA villages on the feeder are given below:

Fig. 2.31: Elevation (in meters) map (Malakoli).

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Fig. 2.32: Elevation (in meters) map (Ghughewadi).

Fig. 2.33: Soil map (Malakoli).

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Fig. 2.34: Soil map (Ghughewadi).

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Sohal Feeder (Karanja, Washim)

Substation: Koli/Poha.

Villages on Sohal feeder: Kinhi Rokade, Sohal, Gaiwal (in order).

No. of DTs: 22+

Feeder Type: AG feeder (Agriculture).

Supply schedule: 3 days daytime supply, 4 days nighttime supply.

Fig. 2.35: Villages on Sohal feeder represented on Washim district map.

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Fig. 2.36: Villages on Sohal feeder.

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Kinhi Rokade

Village details:

Village Kinhi Rokade

Taluka Karanja

District Washim

Region Vidharbha

Cluster Code N/A

Stakeholders met:

Krishi Sahayak, Panchayat Samiti Chairman, Ex-Sarpanch, Farmers

Water sources:

The village has a total geographical area of 581 ha out of which 499 ha is cultivable area. The

rainfall received by the village in 2020 was 1047.4 mm.

Kinhi Rokade is the non-PoCRA village on Sohal Feeder and there is 1 major stream (2nd order)

that flows through the village North-West to South East. No CNBs have been constructed on this

2nd order stream.

Elevation difference is between 435 (North-West) to 408 (South-East) m as shown in Fig. 2.37.

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Fig. 2.37: Elevation Map of Kinhi Rokade.

Typical water sources are open wells (90-100) and borewells (near about 30). Open well depths

are in the range of 40-70 ft. In most of the open wells, water is available till Feb-March (5-6 ft.

water column height). Borewells are 300 to 500 ft. deep and water is available till April-May.

Water transfers from BW to OW are also seen but in limited cases and it starts from January

onwards. Pipe diameters are 2-2.5” and pump HP’s are 3 HP and 5 HP. BW to field irrigation is

also very rarely seen.

Soil and Agriculture:

Soil type is clayey (good) mostly about 90% and gravelly clay loam soil (lighter) about 10%.

Soil depth varies from 2 ft. to 4-5 ft. (near the stream).

Kharif crops are Soybean (50-55%), Tur (8-10%), Cotton (15-20%), Moong-Urad (4-5%)

Vegetables (5-6%) and others (3-4%). Rabi crops are Wheat and Gram. Soyabean is mostly a

rainfed crop, however, farmers having access to irrigation tend to irrigate the crop once during a

dry spell of more than 15 days. Sprinkler irrigation method is used for this. 8 to 12 sprinkler

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nozzles are connected in 1 shift of irrigation covering one acre. For Tur, 2 irrigations are done

after monsoon till January with sprinkler as well as border/ furrow method.

Farmers having access to irrigation (OW, BW) have sprinklers and irrigate Soybean and Gram.

Wheat is irrigated using sprinkler (by some farmers having less water in OW), while sprinkler +

furrow method is used by the remaining farmers.

Electricity scenario:

There are 6 Ag DTs in Kinhi Rokade, of which Old Water supply DT (near Gaothan) 100 kVA,

Old Gaothan DT of 100kVA, and again 2 of 100 kVA (location needs to confirm). Also, 2 DTs

are of 63 kVA capacity.

Water supply DT has a total of 35-40 pumps and all are seasonal ones. Hence during season

DT failure, Feeder tripping and frequent tripping at DT is seen. There are 1.5-2 km long LT line

from Water supply DT. Hence low voltage is also a major problem.

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Gaiwal

Village details:

Village Gaiwal

Taluka Karanja

District Washim

Region Vidharbha

Cluster Code 502_pga-2_03

Stakeholders met:

Krishi Sahayak, Cluster Assistant, Up-Sarpanch, Farmers

Water sources:

The village has a total geographical area of 456 ha out of which 352 ha is cultivable area. The

village receives average annual rainfall of 1524.25 mm, same as Kinhi Rokade and Sohal

because of the same rain circle. The rainfall received by the village in 2020 was 1047.4 mm

which was 30% lower than the average.

Gaiwal is the 1st village on Sohal AG Feeder and there is 1 major stream (4th order) that flows

through the village North-West to South East. And few 2nd and 3rd order streams also flow

across the territory. 12 CNBs have been constructed on these streams. Also 3 percolation tanks

are available of which 1 is used for direct lift irrigation and around 15-20 farmers are irrigated by

this lift irrigation method.

Elevation difference is between 435-440 (North-West) to 405 (South-East) m as shown in Fig.

2.38.

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Fig. 2.38: Elevation Map of Gaiwal.

Typical water sources are open wells (80-85) and borewells (< 10-12). Open well depths are in

the range of 40-60 ft. In most of the open wells, water is available till April-May (5-10 ft. water

column height). Borewells are 300 to 450 ft. deep and water is available all round the year.

Water transfers from BW to OW are not seen here. Pipe diameters are 2”, 2.5” and 3” pump HP’s

are 3 HP and 5 HP. And one farmer has a 12.5 HP pump on Wasteland DT.

Soil and Agriculture:

Soil type is clayey (good) mostly about 50-60% along the 4th order stream and gravelly clay soil

(lighter) about 40-50% along north and south village boundary. Soil depth varies from 1.5-2 ft.

for gravelly clay to 4-5 ft. for clayey (near the stream).

Kharif crops are Soybean (45-50%), Tur (6-8%), Cotton (8-10%), Moong-Urad (3-5%),

Horticulture (10-15%) and others (5-10%). Rabi crops are Wheat and Gram. Soyabean is mostly

a rainfed crop, however, farmers having access to irrigation tend to irrigate the crop once during

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a dry spell of more than 15 days. Sprinkler irrigation method is used for this. 8 to 12 sprinkler

nozzles are connected in 1 shift of irrigation. For Tur, 2 irrigations are done after monsoon till

January with sprinkler as well as border/ furrow method.

Fig. 2.39: Land Use Land Cover (LULC) map of Gaiwal.

Farmers having access to irrigation (OW, BW) have sprinklers and irrigate Soybean and Gram.

Wheat is irrigated using sprinkler (by some farmers having less water in OW), while sprinkler +

furrow method is used by the remaining farmers. Some farmers use drip for Cotton.

Electricity scenario:

There are 6 Ag DTs and 3 HVDS in Gaiwal, out of which Waste Land DT (near Gaothan) is of

100 kVA capacity while all other DTs are of 63 kVA capacity.

Waste Land DT has a total of 20-25 pumps and all are seasonal ones, mostly 5 HP pumps along

with one 12.5 HP. Hence during season DT failure, Feeder tripping and frequent tripping at DT is

seen.

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Sohal

Village details:

Village Sohal

Taluka Karanja

District Washim

Region Vidharbha

Cluster Code 502_pga-2_03

Stakeholders met:

Krishi Sahayak, Sarpanch, Farmers

Water sources:

The village has a total geographical area of 1286 ha out of which 1256 ha is cultivable area. The

rainfall received by the village in 2020 was 1047.4 mm which was 30% lower than the average.

Sohal is the 2nd village on Sohal Feeder and there is 1 major stream (4th order) that flows

through the village West to South East. And few 2nd and 3rd order streams also flow across the

territory. 7 CNBs have been constructed on these streams. Also 2 percolation tanks are available

and 3 KT weir are also available. 8-10 Farmers also irrigate using water from the 4th order

stream.

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Fig. 2.40: Elevation Map of Sohal.

Elevation difference is between 443 (North-West) to 394 (South-East) m as shown in Fig. 2.40.

Typical water sources are open wells (125-130) and borewells (10-12). Open well depths are in

the range of 45-55 ft. In most of the open wells, water is available till April-May (10-15 ft. water

column height). Borewells are 200 to 250 ft. deep and water is available all round the year.

Water transfers from BW to OW are not seen here. Pipe diameters are 2”, 2.5” for 3 HP and 5

HP Pumps respectively.

Soil and Agriculture:

Soil type is clayey (good) mostly about 60-70% along the 4th order stream and near Gaothan.

and gravelly clayey soil (lighter) about 30-40% (North-West). Soil depth varies from 1.5-2 ft. for

gravelly clayey, to 10-15 ft. for clayey (near the stream).

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Kharif crops are Soybean (55-60%), Tur (8-10%), Cotton (8-10%), Moong-Urad (3-4%),

Horticulture (15-20%) and others (5-10%). Rabi crops are Wheat and Gram. Soyabean is mostly

a rainfed crop, however, farmers having access to irrigation tend to irrigate the crop once during

a dry spell of more than 15 days. Sprinkler irrigation method is used for this. 8 to 12 sprinkler

nozzles are connected in 1 shift of irrigation covering one acre. For Tur, 2 irrigations are done

after monsoon till January with sprinkler as well as border/furrow method.

Fig. 2.41: Land Use Land Cover (LULC) map of Sohal.

Farmers having access to irrigation (OW, BW) have sprinklers and irrigate Soybean and Gram.

Wheat is irrigated using sprinkler (by some farmers having less water in OW), while the

sprinkler and furrow method is used by the remaining farmers. Some farmers use drip for Cotton.

They also use drip for Horticulture.

Electricity scenario:

There are 10 Ag DTs and 5 HVDS (Not working yet) in Sohal, out of which Dharanavarachi DT

100 kVA having 35-40 pump connections mostly 5HP pumps, Gitabaichi DT of 63 kVA, and

again 2 of 100 kVA and 6 of 63kVA DT (location needs to be confirmed).

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Dharanavarachi DT has a total of 30-35 pumps and all are seasonal ones, mostly 5 HP pumps

and 4-5 are 7.5HP pumps. Hence during season DT failure, Feeder tripping and frequent tripping

at DT is seen.Low voltage is also one of the major problems during peak season.

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3. Distribution Transformer (DT) selection for DT meter installation

To ensure a good analysis for development of the energy estimation tool, in addition to the three

feeders, energy meters were installed on select DT - 8 in addition to the DTUG transformers.

This would help us fine tune the methodology further, since a feeder is quite large. The DT

selection criteria and high level data on the select DT has been documented in this chapter.

Selection Criteria:

Distribution Transformer selection criteria for DT meter installation is Distribution transformer

should have representative Cropping pattern of that village. There should not be major water

resources like dams, Major streams etc. Cropping pattern of the selected DT is also nearly the

same as the cropping pattern of that district (If possible).

List of DTs:

Kinhi Rokade (Washim):

1) Water Supply DT (100kVA).

2) Smashanbhumi DT (100kVA).

In Kinhi Rokde we identified two potential DTs for DT meter installation after discussing with

farmers. Typical water sources on these DTs are open wells. No dams, rivers near these DTs.

Major Kharif crops on this DTs are soybean tur, cotton & Rabi crops are gram and wheat. 2-3

hectares of horticulture on both of these DTs as per discussion. Water Supply DT has 30-35

connections. One DT meter will be installed in Kinhi Rokade.

Gaiwal (Washim):

1) Waste Land DT (63 kVA).

Wasteland DT has a total of 20-25 pumps and all are seasonal ones, mostly 5 HP pumps along

with one 12.5 HP. No dams, rivers near this DT. Typical water sources of irrigation on this DT

are wells. Kharif crops are soybean, tur, cotton, moong-urad. Rabi crops are wheat and gram.

Only one farm of 1-1.5 hectares on this DT. One DT meter will be installed in Gaiwal.

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Sohal (Washim):

1) Dharanavarachi DT (100kVA).

2) Geetabai DT (63kVA).

Dharanavarachi DT has a total of 30-35 pumps and all are seasonal ones, mostly 5 HP pumps

and 4-5 are 7.5HP pumps. Kharif crops are soybean,tur,cotton,turmeric and horticulture. Rabi

crops are gram,wheat. Few farmers on this DT irrigate in summer (As per discussion with

sarpanch). As the name suggests it is located near Dam.

Geetabai DT has 20-25 connections,and No Dams or rivers near this DT. Cropping pattern is

similar to Dharanavarachi DT.

One DT meter will be installed in Sohal.

Polewadi (Nanded):

1) Bajgir DT (100kVA).

2) Shendge DT (100kVA). Near Talav.

Bajgir DT has 27 pumps of 20 farmers.Kharif crops are soybean, tur, cotton,turmeric, kharif

jowar. Rabi crops on the Bajgir DT rabi crops are rabi jowar and chilly.gram which is usually the

main crop of rabi in vidarbha not observed on this DT. Major water sources are borewell and dug

wells. Shendge DT has 19 pumps for 12 farmers.it is located near the Talav.cropping pattern of

Shendge DT is similar to the Bajgir DT with 1-2 hectares of vegetable.

Morewadi(Latur):

1) Abande DT (100kVA).

2) Water Supply DT (100kVA).

3) Shirur DT (Details not available).

Abande DT has 17 pumps for 12 farmers. Kharif crops are soyabean,sugarcane,tur. Rabi crops

are wheat and gram. Sugarcane is the major crop on this DT. Water supply DT has 25 pumps of

19 farmers. Kharif crops are similar to the abande DT but in rabi crops are gram and jowar.major

water sources are wells and borewell no dam and talav is nearby. Shirur DT is located on the

border of Morewadi. In this DT region availability of water is very less. It has 24

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connection.Kharif crops are soybean and tur and rabi crops are jowar,gram & wheat. Typical

water sources are wells.

DT Meters

Genus Sampoorna Energy meters have been installed on all DTs selected for the energy

estimation tool as well as DTUG DTs. These meters measure Apparent Energy, Demand,

Voltage, Current, Power factor, Active Power, Reactive Power, Frequency. This meter also has an

internal battery back-up for meter reading in the event of power outage. Data is logged every 15

minutes. For communication purpose and data downloading meter has a Galvanically isolated

Optical/GSM/GPRS compatible port for local and remote communication. For remote

communication, SIM cards may be used but these meters are only Airtel enabled by the vendor

hence some meters need manual downloading every month. BY the time the vendor informed us

of this limitation it was too late to change the meter or do anything about the technology. Due to

paucity of time

The DTs in Latur and Nanded are mostly 63 kVA except for two which are 100 kVA. As

mentioned earlier, the farmers in these areas use capacitors to convert two phase to three phase in

non-Ag hours and use the motors. This practice results in high currents. The meter has a limit of

200 A, and it is not certain that the current will stay within this limit. A 100 kVA DT generally

has a fuse that allows 160 A current so it must, but there are practices where farmers have

bypassed fuses. To protect the meter, only one branch out of the two emanating from the DT,

with half the consumers, was connected to the meter.

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Post-Harvest Analysis

PHASE - II REPORT

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Contents

Section 1: ONION STORAGE STRUCTURE

1 Introduction ........................................................................................................................ 5

2 Visit to MahaFPC onion storage facility ........................................................................... 6

3 Visit to FPCs dealing with onion commodity in Jalna district ........................................ 13

4 Techno-economic feasibility study .................................................................................. 16

4.1 Parameters ................................................................................................................. 16

4.2 Costs .......................................................................................................................... 18

4.3 Analysis ..................................................................................................................... 20

4.4 Concluding remarks .................................................................................................. 26

5 Way Ahead....................................................................................................................... 26

Section 2: VALUE ADDITION

6 Background ...................................................................................................................... 28

7 Methodology .................................................................................................................... 28

8 Screening of FPC for field work ...................................................................................... 30

9 Potential value added products for PoCRA region .......................................................... 37

10 Feasibility report on Poultry Feed manufacturing Unit ................................................... 38

10.1 Introduction ........................................................................................................... 38

10.2 Poultry feed status in India .................................................................................... 40

10.3 Project description (TEA) ...................................................................................... 44

11 Feasibility report of Soy milk and tofu processing unit ................................................... 62

11.1 Soybean as a commodity ....................................................................................... 62

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11.2 Proposed value added product ............................................................................... 65

11.3 Techno-economic analysis .................................................................................... 66

11.4 Conclusion ............................................................................................................. 75

12 Feasibility report of Turmeric powder and Curcumin extraction .................................... 76

12.1 Turmeric as commodity ......................................................................................... 76

12.2 Proposed value added product ............................................................................... 79

12.3 Techno-economic analysis of turmeric powder ..................................................... 81

12.4 Techno economic analysis of curcumin extraction plant ...................................... 88

12.5 Conclusion ............................................................................................................. 94

13 Future Work .................................................................................................................... 95

14 Work Plan for the year 2021-2022 ................................................................................... 96

Appendix A ............................................................................................................................ 100

Appendix B ............................................................................................................................ 105

Appendix C ............................................................................................................................ 138

Categorisation of value-added products for FPC based on quantum ................................. 141

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Organization of the report

The Second phase report on Post-harvest component is divided in two parts. The first part covers

‘Onion Storage structure’ and the second part covers ‘Value addition component’.

‘Onion storage structure’ part involves the details of visits and survey carried out with FPCs and

farmers involved in onion storages. Observations for the same are recorded in the systematic

manner. All the storage structures covered in the second phase of the report are open ventilated

storage structure. The report focuses on the critical review of such storages in helping farmers in

curbing losses while understanding farmers’ perception to such large storage systems. Finally, first

part concludes with outcomes from the visits and strategy for going ahead with other available

‘Controlled atmosphere’ storage structure and their feasibility analysis.

Second part of the report deals with ‘Value addition’ of agri-commodities. During inception phase,

strategy for selecting FPCs for visits and interviews was proposed. In current phase of the work,

the FPCs were screened and several of them were visited. The report covers description and

feasibility of four value added products namely poultry feed, soy milk/Tofu, turmeric powder and

curcumin. Their process flow diagrams and financial analysis is presented in this report

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SECTION-1:

ONION STORAGE STRUCTURE

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1 Introduction

The second part of the ‘Post Harvest’ component deals with the ‘Climate agnostic Onion storage

structure and its appropriateness among other available storage options’. The work started with the

identification and definition of the problem (which later became specific objectives for the

project). It further involves beneficiary analysis, feasibility of the intervention, techno-economic

study and implementation of the intervention.

During the previous phase of the project (Inception phase), It was decided to visit the MahaFPC

(MahaOnion) onion storage structures and also to meet and understand the requirements of the

FPCs from Jalna who were interested in working with the onion storage. Virtual meetings were

held to understand the perspective and the requirements form onion growers as well as FPC

directors. With the help from the PMU office (PoCRA), field visits were organized. In the 2nd

phase of the project, emphasis was given to the outcomes from the visits and surveys.

Figure 1 : Locations of field visits to onion storage facilities (West and North Maharashtra)

First field visit and survey were carried out in two locations of the onion storage facility established

by Maha Onion (Joint venture between MahaFPC and NAFED). Purpose of the field visit was to

evaluate the performance of large sized onion storage structures and document its usefulness in

reducing onion losses and the drudgery in the onion value chain. Two distinct locations were

chosen to check for the effect of geographical and climatic changes on the working of the storage

system.One of the locations was at Vaijapur, Aurangabad whereas other was at Shikrapur near

Pune.

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Second field visit was organized at Jalna, where farmers (FPC members) from 6-7 villages were

gathered. Purpose of the visit was to discuss the condition of the existing onion storage facilities

and opinions of the farmers on the adoption of the Climate agnostic storage system by IIT Bombay.

Visits and surveys were successfully conducted and outcomes are presented as below -

2 Visit to MahaFPC onion storage facility

MahaOnion is the venture between MahaFPC and NAFED. ‘Agronirmiti Farmer producer company

limited, Shikrapur’ is a member of MahaOnion and has established a 1000 MT storage structure with

the subsidy and other support from NAFED as a part of MahaOnion initiative. Mr. Aditya Khandale

is director of the FPC and looks after all the operations of the onion storage structure.

Figure 2: Glimpses of visit to onion storage facility at Shikrapur, Pune

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Specifications and technical details

The figure above shows the internal arrangements for the storage of onions inside MahaOnion

storage structure. The storage structure is open ventilated. Steel mesh (chain link) is used for

manufacturing containers to store onions. Usually one such container can store up to 25 MT of

onion by mere staking. Approximately 1.5 ft space is left at the bottom of these meshed containers

for ventilation and cleaning purpose. To avoid physical damage of onion at the bottom, bamboo

sticks are used as flooring of containers. Containers are set up in pairs with 2 feet gap between

them. All such pairs are organized approximately 8 ft away from each other for ease of

loading/unloading.

At the time of loading, onions are poured in the container from the top until the container gets fully

stacked. Hinged windows/openings are provided on sides of the container to unload the onion.

Onions were stored in the above shown storage facility at the start of May 2021 (+-15 days) and

they were in storage for more than 3 months (till the day of the visit).

Upon asking about the efficacy of the storage facility, Mr. Aditya Khandale said that almost 40%

of the onions are degraded (either completely or partially rotted and sprouted) during the last 3

months. Following different reasons were stated during the survey.

Figure 3: IIT Bombay team with Mr.Vijay Dukare at MahaOnion onion storage faciity at

Vaijapur, Aurangabad

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Poor quality onion seed

One of the major reasons as told by farmers was the use of poor quality (low quality and

un certified) seeds which has resulted in onion with less storability properties. This

emphasizes on the parameters that need to be taken into account for the storability of the

onions, and the response of open ventilated as well as climate agnostic storage structure

for storing such already degraded quality of the onion.

Mixed variety of onion

Due to improper sorting of the onion, there are always chances of mixing of two different

varieties. Onion with poor storability properties have great chances to spoil over the period

of time under uncertain climatic condition and do to mixing of such onion bulbs with other

Heavy and uncertain rain and exposure to direct sunlight

Due to heavy rainfall in the months of July and August, there were high humidity

conditions which is not good for the storability of onions. As it is seen in the picture, onions

have undergone sprouting and rotting to a significant level. There was no mechanism to

control the humidity and temperature of the surrounding (inside storage structure) and

hence farmers were left with no choice than watching their onion getting rotted and

damaged.

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Figure 4: Excessive rotting and spoilage of onion at one of the bins inside MahaOnion onion

storage facility

Figure 5: Exposure to direct sunlight results in degradation of outer layer of onion and causes

excessive PWL

Lack of proper/forced ventilation

While roaming inside the storage, one can easily feel the smell of rotten onion and other

gases which makes oneself uneasy. It was found that though it was called Open ventilated

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storage structure, sufficient channels were not provided to take out hot, moist air and gases

from inside the storage structure.

Spread of rotting due to heavy staking

The containers which were used for storing onion were made of steel (channels and C-

section as a support while chain link for covering the sides. Onion is a commodity which

easily undergoes physical damage even when subjected to moderate pressure. Here, in

containers, onion was staked in bulk for the height of 5 feet and length almost 50 feet. So,

the bottom layer of onion was subjected to almost 25 MT load over it for more than 3

months. This highly damages the onion and reduces its life. Such bulk staking without

providing any space for decent ventilation makes the situation even more vulnerable. Lack

of provision of fresh air and removal of gases produced during respiration leads to growth

of microorganisms (black mould, fungus) and bugs which speed up the rotting. Closely

packed stacking also leads to spread of rotting to nearby locations which has potential to

spoil the major portion of the container

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Figure 6: Figure depicting the risk of propagation of rotting due to heavy staking which leaves

no room for proper ventilation

Accumulation of the dew on the surface of roof

It was also found that due to heat pouring in the structure, air was getting warmer and

started moving upwards. As it was mentioned earlier, due to lack of opening at the roof,

this saturated air upon striking the roof, condensed and the resulting water fell down on the

onion. Onion upon getting wet starts the cycle of sprouting and rotting.

Costly and time-consuming loading/unloading, sorting operations

Through observations and discussion with the farmers, it came to notice that there is no

provision for reducing the cost and drudgery of loading and unloading of the onion stored

in the storage facility. Operation is fully manual and it amounts to a lot of labour effort

despite having less accuracy in sorting and grading the onion. Labour workers expresses

their concerns over continuous exposure to odour and gases from rotted onion without

proper safety equipment.

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Figure 7: Excessive odour and drudgery involved in loading/unloading and sorting operation

(Photograph taken at Onion storage structure near Jalna)

Defected onion bulbs due to improper curing

Curing is considered to be one of the important and first stages after harvesting onion from

the field. But in general, less attention is paid to this process and the same was observed

during the survey. Onions were cut improperly during manual curing operation and hence

it obviously affected the post-harvest shelf life of the onion.

Uncontrolled used of powder for preventing sprouting

It is usual practice to spread powder (mention name here) over the onion surface in order

to keep them dry and less vulnerable to sprouting and rotting. Though it helps to achieve

the intended result, it also becomes the topic of investigation for excessive use of such

materials and its unintended effects on quality of onion and safety of consumers.

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Figure 8: Excess use of powder to keep onion dry and to prevent further sprouting and rotting

3 Visit to FPCs dealing with onion commodity in Jalna district

During the virtual meeting with FPCs from Jalna, it was decided to meet the farmers in-person and

discuss various issues with onion cultivation and its storage. There were 6 FPC directors who were

confirmed to meet at ‘Hariyali green veg producer company Ltd., lakhamapuri, Ambad’ which

was a central place near Jalna where one of the onion storage facilities was established.

Figure 9: Spread of rotting due to uncertain rain and exposure to high humidities for longer time

(Hariyali FPC, Ambad, Jalna)

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Figure 10: Discussion on onion storage losses with FPC members (Hariyali FPC, Ambad,Jalna)

Problems discussed during the visit

During the visit to various locations of large onion warehousing systems (500 MT and above) in

Jalna district, it was found that the owner of the system has employed his own understanding along

with the traditional design. It involved alterations in dimensions, materials and methods. Though

it came from their own understanding of the system, little came from the advice of the experts.

Extreme rotting and sprouting of the onion: Due unscientific design, the onion was exposed to the

extreme and uncertain rain that caused direct immediate damage and later resulted in extreme

rotting and sprouting of the onion. The important thing observed was that there is no designated

agency to check and approve the designs for such warehouses (though some government

departments like NABARD use designs suggested by ICAR-DoGR).

During the discussion with the farmers, it was observed that many farmers are interested in

converting their existing storage facility in the Climate agnostic storage system. When it comes to

installing storage, a system developed by IIT Bombay which has high capital investment but

comparatively good returns, there is still a long way to go to convince farmers as they have

developed the feeling that whatever we (engineers or innovators) do, onion will get damaged for

sure. To overcome this reluctance of farmers, there is a need for exposure to such storage systems.

Important observations from the visit and interviews

It was observed during the discussion that farmers were more focused on saving on initial costs of

the structure than understanding the effect of design parameters on Storage efficiency as well as

operational costs of the storage structure. Budget constraints of FPCs, Cap on subsidies on storage

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structure, focus on more storage capacity with less investment at cost of quality were some of the

reasons for poor functioning of onion storage structures.

The current NAFED model

Most of the MahaOnion supported storages are running on the ‘NAFED model’ where risks of

onion spoilage and subsequent financial loss to owner are highly reduced. Owner has to make sure

that 75% of the onion stored at the start of rabbi onion harvesting period (or period designated by

NAFED) will be available for dispatch during the period of approximately 6 months (with intervals

and quantum of each dispatch being decided by NAFED). Owner of the storage is paid a rent

amount of 1.25 Rs per KG for the entire period of the storage. In case onion quantity goes below

75% (65% good quality onion and 10% average quality onion), the owner is accounted for

replenishment of the onion beyond the above limit.

Though this model works well for farmers, this reduces focus from reduction in losses and 25%

losses is assumed at the start. In such cases, the owner has more focus on enhancing storage

capacities in order to earn more rent (profits) and the novel objective of ‘reducing storage losses

is ignored.

Major objective of NAFED behind this initiative is not to reduce losses but to stabilize prices in

the market through high stocking of the onion.

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4 Techno-economic feasibility study

4.1 Parameters

Following parameters are considering for performing Techno economic analysis of the Onion

storage structure

Table 4:1:Parameters for TEA

Variable IITB CA

Storage

MahaOnion

Storage

TATA Steel

NestIn Unit

Onion Procurement cost 8 8 8 Rs/Kg

Selling price 20 20 20 Rs/Kg

Storage structure Life 15 15 15 years

Storage duration 6 6 6 Months

Losses during duration of

storage 15 35 30 %

Discount Rate 10 10 10 %

Salvage Value of Storage

Structure 15 15 15

15% of

initial

Difference in Prices 12 12 12 Rs/Kg

Price increment rate for

OP_cost 4 4 4 %

Loan Interest rate 10 10 10 %

% Subsidy 0 0 0 %

% Loan of capital inv (exc

subsidy) 75 75 75 %

4.1.1 Initial Unit Price

This is wholesale price of onion per Kg at which procurement of onion in bulk is being done. From

the analysis of data from APMCs and published researches, these prices are considered. Initial

Unit price is market gate price which is to be paid to the farmers.

This is reoccurring cost and hence associated with the operating costs in the analysis.

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4.1.2 Final Unit Price

This is wholesale price of onion per Kg at which selling of onion in bulk is being done. These

prices are taken into account from the analysis of data from APMCs and published researches.

Final Unit price is Warehouse/storage gate price which is paid to storage structure owner and goes

directly into revenue.

4.1.3 Storage structure Life

Storage structure life depends mainly on quality of its material and the atmospheric conditions it

is expected to face. Considering standard warehousing & Storage norms and data from on field

survey, storage life of 15 years is considered for all the three types of storage structures.

4.1.4 Storage duration

It has been calculated from the agronomic data of rabbi onion which are primarily being stored in

the storage structures. Market price fluctuation is also considered for the right time of the sale of

the onion and possibilities of sustained shelf life. Storage of Onion starts from the month of May

and storage structure gets unloaded by the month of November. So, the period of 6 months is

considered for the TEA.

4.1.5 Loss after given Storage Duration

This is one of the major deciding factors for calculating efficacy of the storage structure. From

experimental results form IITB CA Storage structure, It was seen that losses in the storage can be

prevented well below 10% with precise control of inside atmosphere. Considering the scale of the

Storage and uncertainties involved, safety factor of 1.5 is considered (with worst case scenario in

mind) and hence losses are considered as 15% for the analysis.

For MahaOnion storage structure (and similar Open ventilated storage structures), losses vary

between the range of 30% to 70% (huge uncertainty!). Again, the best-case scenario is considered

for Open ventilated MahaOnion storage structure and Losses are considered to be 35%. This will

help us understand the performance of IIT Bombay CA storage structure in worst possible cases.’

In similar way, Tata Steel onion storage structure uses evaporative cooling method and blowers to

bring down the temperature in summer but used the same internal staking as that of MahaOnion

storage structure. It is on field experience and data that this storage structure doesn’t perform under

high humidity conditions during rainy season which is major reason for sprouting and rotting.

Unappropriated usage of technology does not significantly reduce the losses. Losses up to the

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degree of 40-45% are observed in the Tata Steel storage structure but loss value of 30% is

considered for the analysis.

4.1.6 Discount Rate

It is the opportunity cost rate which can be seen as the gains we would have received of we had

invested in any traditionally safe option. Here Discount rate of 10% per annum is considered for

the analysis.

4.1.7 Salvage Value of Storage Structure

It is depreciation cost after the standard life of storage structure gets over. 15% salvage value is

considered as standard in storage and warehousing industry.

4.1.8 Price inflation rate

All the future Net revenue values are inflated with the inflation rate of 4%.

4.1.9 Loan Interest rate

Loan interest rate of 12% is considered in case, part of capital costs is to be covered with the Loan.

As the amounts are huge, FPOs has to opt for loan or subsidies from the governments to set up the

storage facilities.

4.1.10 % Subsidy

It is subsidy given for setting up storage structure. For the preliminary analysis, no subsidy is

considered as It won’t reflect the real business dynamics. Options with the subsidy can be worked

out in DPR.

4.2 Costs

4.2.1 Capital costs (Capital Investments)

Capital costs for setting up the storage facilities for the different storage structures are listed in the

table. These costs are inclusive of Installation and taxes. This is are the final amounts FPCs need

to pay in order to buy (build in case of this project) the Storage facilities. MahaOnion builds only

1000 MT of storages (modern ones) and hence values for other capacities are extrapolated. TaTa

steel Nest In has recently produced storage facility for 500 MT and same market price is considered

for the analysis.

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IIT Bombay CA storage structure costs are calculated after detailed design and inviting quotations

from material providers, equipment providers and fabricators to custom build the storage structure.

Table 4:2:Capital costs of Storage structures

Capacity (MT) MahaOnion TaTa Steel IITB

100 20 25 30

200 35 40 50

500 80 100 120

1000 140 180 210

*Values are in Lakh Rs

Figure 11: Graphical representation of capital costs for Storage structure

4.2.2 Operational Costs

Operational costs are deciding factor in the feasibility analysis of the Onion storage structures.

Following table depicts the annual operational costs for all three storage structures. Details of the

operational costs can be found in Annexure.

0

50

100

150

200

250

100 200 500 1000

Co

st o

f th

e st

ora

ge in

Lak

h R

s

Type of the Storage with capacity in MT

IITB CA Storage TATA Steel Storage MahaOnion

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Table 4:3 : Operational costs (in Lakh Rs)

Capacity in

MT

IITB CA

Storage

TATA Steel Storage MahaOnion

100 3.96 3.16 2.69

200 4.99 4.79 4.05

500 8.85 11.56 10.14

1000 12.74 19.73 17.37

Figure 12 : Operational costs for various types of storage

4.3 Analysis

4.3.1 Labor costs as a fraction of total operational costs

It is evident from the data presented above that labor and related costs are major concern for the

operational costs. Due to use of automatic Loading, unloading and sorting operations, time and

efforts (and drudgery too) involved in the operations significantly reduces.

Table 4:4 :Percentage of Operational costs constituting labour costs

Capacity in

MT

IITB CA Storage TATA Steel Storage MahaOnion

100 3% 38% 45%

200 5% 50% 59%

-

5.00

10.00

15.00

20.00

25.00

100 200 500 1000Op

erat

ion

al c

ost

s(an

nu

al)

in L

akh

Rs

Type of storage structures with capacity in MT

IITB CA Storage TATA Steel Storage MahaOnion

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500 7% 52% 59%

1000 9% 61% 69%

Figure 13: Share of labour cost in operational costs

For IIT CA Storage structure, fraction of costs is below 10% for operational costs. As we can see,

this fraction goes on increasing for higher capacities due difference in increment of static and

dynamic operational costs.

4.3.2 Net Present Value

Net Present Value is valuable indicator to decide the profitability of the intervention or business.

It shows the money that can be produced over the period of business activity (in terms of its present

value).

Table 4:5: Net Present Value for three storage structures

Capacity in

MT

IITB CA

Storage

TATA Steel

Storage MahaOnion

100 17.05 0.82 0.99

200 73.93 9.76 20.64

500 225.91 73.06 59.53

1000 533.47 201.30 169.45

0%

10%

20%

30%

40%

50%

60%

70%

80%

100 200 500 1000% o

f o

per

atio

n c

ost

nee

ded

fo

r la

bo

ur

Types of Storage structures in with capacities in MT

IITB CA Storage TATA Steel Storage MahaOnion

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22

Figure 14 : Comparison of storage structures based on NPV (in Lakh Rs)

If we observe the above shown graph, we could understand that NPV for TaTa Steel storage

structure is negative and negligible at lower capacities. Though positive and better in comparison

to the Tata Steel Nest in Storage structure, MahaOnion storage structure generates lesser NPVs for

the given investments. As these are absolute values and doesn’t necessarily give comparative

picture, lets look at other comparative indicator called Benefit Cost ratio or Benefit Cost Factor

(BCR)

4.3.3 Benefit Cost Ratio (BCR)

Benefit Cost Ratio below 1 is indicator of loss in the business. It gives us the picture of every rupee

invested in the business and respective profit earned in terms of present value (Considering

Discount rate as well as inflation rate)

Table 4:6 BCR for all three storage structure

Capacity

in MT

IITB CA

Storage

TATA Steel

Storage MahaOnion

100 0.57 0.03 0.05

200 1.48 0.24 0.59

500 1.88 0.73 0.74

1000 2.54 1.12 1.21

-

100.00

200.00

300.00

400.00

500.00

600.00

100 200 500 1000

NP

V in

lakh

Rs

ype of storage structures with capacity in MT

IITB CA Storage TATA Steel Storage MahaOnion

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Values in the above table are visualize through graphical representation in Figure 15. It is evident

from the analysis that BCRs are comparatively much higher for IITB CA Storage structure

compared to other two storage structure. But here, we have restrictions over capacities. We have

to build Storage facility of capacity 500 MT or higher if want to realize higher BCRs.

Figure 15 : Comparison of BCR for all three storage strcutures

4.3.4 Risks associated with price fluctuations

Fluctuation in procurement and selling price significantly affects the profit margins and hence Cost

Benefit Ratio. Following Scenario analysis gives a glimpse of what happens when prices vary on

both sides. Both procurement and selling prices are mentioned in terms of Rs/kg.

a. Effect of Selling price of onion on BCR of the Storage structure chosen for the

intervention

When we fix the procurement cost at 8 Rs/kg and start to increase the selling price from 20

Rs/kg to 26 Rs/Kg, value of BCR starts to change. Cells marked with green in the following

table shows the value of BCR more than 1 (i.e, the value above which business is

considered to be profitable).

In the case of IITB Storage structure, storages with the capacity 200 MT and more are

profitable even for the least values of selling prices. whereas, for other two storages, we

have to receive better selling prices for storage structure of same capacity to show profit.

Even within the green cells, values of BCR for IITB onion structures are more than that of

other two storage structures.

-

0.50

1.00

1.50

2.00

2.50

3.00

100 200 500 1000

Ben

efit

Co

st R

atio

Types of storage structures (in MT)

IITB CA Storage TATA Steel Storage MahaOnion

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Table 4:7 Variation of BCR for varying selling prices for a fixed procurement cost

b. Effect of Selling price of onion on DPBP (Discounted Payback Period) of the Storage

structure chosen for the intervention

When we fix the procurement cost at 8 Rs/kg and start to increase the selling price from 20

Rs/kg to 26 Rs/Kg, value of DPBP starts to change. Cells marked with green in the

following table shows the value of DPBP less than 4 years (i.e, the value below which

business is considered to be profitable).

In the case of IITB Storage structure, storages with the capacity 200 MT and more are

profitable for values of selling prices more than 25 Rs/Kg. whereas, for other two storages,

even the selling price of 26 Rs/Kg do not provide DPBP below 4 years. Even within the

green cells, values of DPBP for IITB onion structures are less than that of other two storage

structures.

Table 4:8 Variation of DPBP for varying selling prices for a fixed procurement cost

c. Effect of varying selling price of onion on IRR of the Storage structure chosen for the

intervention

When we fix the procurement cost at 8 Rs/kg and start to increase the selling price from 20

Rs/kg to 26 Rs/Kg, value of IRR starts to change. Cells marked with green in the following

table shows the value of IRR more than 10% (i.e, the value above which business is

considered to be profitable).

200

25

Though, in all the cases, IRR is well above 10%, In the case of IITB Storage structure, Its

value is better compared with the other two interventions.

Table 4:9 Variation of IRR for varying selling prices for a fixed procurement cost

With few differences, selling price restriction is quite tight in case of MahaOnion and TATA Steel

storage structure.

Finally, when we count the number of green (Positive BCR) cells for all the three tables, IITB CA

Storage counts for maximum. It denotes the ranges of lower selling prices it can withstand without

going into loss.

4.3.5 Consolidated results

IIT Bombay CA Storage strucuture

MT 100 200 500 1000

NPV (Rs) 17,05,194 73,92,734 2,25,91,142 5,33,46,704

IRR (%) 19% 30% 35% 42%

DPBP (Yrs) 11.2 7.9 6.9 5.7

BCF 0.57 1.48 1.88 2.54

MahaOnion Storage Structure

MT 100 200 500 1000

NPV (Rs) 98,670 20,64,387 59,52,979 1,69,44,550

IRR (%) 12% 19% 21% 27%

DPBP (Yrs) 14.6 11.3 10.6 8.7

BCF 0.05 0.59 0.74 1.21

TATA Steel NestIn Onion storage structure

MT 100 200 500 1000

NPV (Rs) 81,650 9,76,411 73,06,050 2,01,29,826

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IRR (%) 12% 15% 21% 26%

DPBP (Yrs) 14.8 13.3 10.6 9.0

BCF 0.03 0.24 0.73 1.12

4.4 Concluding remarks

Techno-Economic Analysis is evidently speaking about the efficacy of going for controlled

environment storages. We compared the three potential storage options on every technical and

financial front and came to conclusion that choosing the CA storage structure for onions above

specified capacities would help in reduction of losses during storage as well as improve the

profitability of the business for FPOs working with smallholder onion grower farmers.

Though this is preliminary analysis and detailed analysis will uncover many more interesting

patterns and tradeoffs in the Detailed Project Report (DPR), we can surely choose the direction of

work to achieve the objectives of the project

5 Way Ahead

Till now, we visited the following locations. Intention of the visits was to understand the current

scenarios and efficacy of naturally ventilated onion storage ecosystem. Surveys with major

stakeholders were carried out and observations have been noted.

As the next part of the task, a visit will be planned to controlled atmosphere storage structures at

Nasik (TATA-Steel Sahyadri initiative) and two controlled conditioned storage structures at

DoGR Pune in order to understand the economics and feasibility of these storage structures from

FPC’s point of view.

Then, visits will be followed by comparative analysis of all the onion storage structures available,

selection of optimum option available for the intervention and selection of FPC for the

intervention.

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SECTION-2:

VALUE ADDITION

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6 Background

In line with the vision of promoting value addition opportunities for FPCs, the current

component aims at exploring and assessing feasible value addition routes around commodities.

As a deliverable, a DPR would be developed for potential products which would enable the

FPC to take informed decision to enter new ventures of value addition.

In the previous report (inception report), a methodology to screen FPCs for initiating field work

was presented. Based on the feedback received from PMU, rather than the top, the top 3

districts in terms of production for each commodity (soybean, maize, turmeric) are now

considered. The current report presents the list of screened FPCs and primary data (mainly

pertaining to quantum of commodities) of the visited FPCs so far. Further, a preliminary

feasibility of four products namely poultry feed, soymilk/tofu, turmeric powder and curcumin

processing plants is presented.

7 Methodology

Figure 16 presents a general methodology used to develop DPR. The methodology include

steps for screening FPCs for field work, primary data collection, preliminary feasibility/techno-

economic feasibility study and DPR preparation.

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Figure 16: Flow chart showing general methodology used to develop DPR

Techno-economic feasibility study:

The collected data will be used to identify FPCs based on geography, principal crops,

quantum dealt with, willingness for value addition etc. The technical feasibility and financial

Screening of FPC for field work

•Objective : To select FPCs for primary data collection

•Criteria for selecting district : Top 3 districts in production of Soybean, Maize andTurmeric

•Criteria for selecting FPC: i) Age of FPC ii) Principal crops iii) Number ofstakeholders iv) Rating

Field Visit to FPC

•Objective: Primary data collection for recording number of commodities,quatum, existing practices & willingness for value addition

•Visit to around 10 selected FPCs in each selected district

Preliminary feasibility/Techno-

economic feasibility study

•Objective : To check technical feasibility and financial viability of followingproducts derived from Soybean, maize and turmeric

• i) Poultry feed unit

• ii) Soy milk and Tofu unit

• iii) Turmeric powder

• iv) Circumin extraction

• Cost-benefit analysis and break-even analysis

Detailed Project Report

•Objective : A project report that will enable the FPC to take decision to enter new ventures related to above mentioned value added products

•Cost estimates of plants set up with different capacities, value chain proposition,opportunity, the operating plan, the marketing plan into anticipated financialresults with other relevant information in financial decision making.

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viability of poultry feed processing unit, soymilk/tofu unit, turmeric powder and considering

the factors influencing the supply chain for soybean, maize and turmeric. The cost-benefit and

break-even analysis for the proposed intervention will be done for the identified FPC who have

appropriate quantum for various value added products.

Detailed Project Report:

The report will include cost estimates of plants set up with different capacities, value

chain proposition, the operating plan, and the marketing plan including potential buyers based

on current demand of the product and a list of potential forward linkages. The detailed project

report will enable the FPC to take decision to enter new ventures of value addition.

8 Screening of FPC for field work

The purpose of screening was to select certain FPCs with whom preliminary field surveys and

interactions could be initiated. A list of 1451 FPCs which belonged to various districts in the

PoCRA region was received from the PMU. Top three districts with highest production in each

Soybean, Maize and Turmeric were identified and FPCs from these districts were screened.

Figure 17: Distribution of soybean production in PoCRA districts

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31

Figure 18: Distribution of maize production in PoCRA districts

Figure 19: Distribution of turmeric production in PoCRA district

Figure 17, Figure 18 and Figure 19 represent the distribution of production among districts in

Soybean, Maize and Turmeric respectively. It could be observed in Figure 17 that Latur,

Buldana and Washim are the top three producing districts for Soybean. Similarly, Figure 18

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32

clearly depicts that Jalgaon, Aurangabad and Jalna are the top three districts for Maize while

Hingoli, Washim and Yavatmal were top three producing districts in Turmeric as depicted in

Figure 19. The FPCs in selected districts were screened using certain criteria that were derived

using data received from PMU. The criteria were as follows:

o Age of FPC -

▪ FPCs operating for more than one years from the date of registration

were considered

o Principal crops-

▪ FPCs solely dealing with commodities that were out of scope of this

project such as cotton, milk, poultry, etc. were screened out

o Number of stakeholders-

▪ FPCs having several stakeholders below 10 were screened out

o Rating:-

▪ Assessment of FPCs was done by PMU based on several criteria such

as organizational, administrative, financial, infrastructural, and

managerial performances. After putting the above criteria

▪ The rating data was sorted in descending order and top score FPCs were

selected

The FPCs screened using the above criteria are presented in Table 8:1: FPCs selected in top

three districts for soybean, maize and turmericTable 8:1. The field visit comprised of interviews

with the FPCs director mainly using a semi-structured survey form which included questions

related to principal commodities, quantum of commodities, current activities, infrastructure

related to current activities and willingness for value addition interventions. Figure 20 shows

the location of screened FPCs and the FPCs already visited so far. Description and quantum

summary of FPCs visited in Aurangabad, Buldana and Washim are given in Appendix A. The

description of screened FPCs of other selected districts will be included in the next phase report.

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33

Table 8:1: FPCs selected in top three districts for soybean, maize and turmeric

Soybean Maize Turmeric

Latur Buldana Washim Jalgaon Aurangabad Jalna Hingoli Washim Yavatmal

Bhogeshwar

Agro Producer

Company Ltd

Sonpaul

Farmers

Producer

Company Ltd

Nardas Farmer

Producer

Company

Padmalaya

Farmer

Producer Co

Ltd

Akash Farmers

Producer

Company Ltd,

Silod

Krushiputra

Farmer

Producer

Company

Limited

Shishiwar

Shetakari

producer

Company.

Ltd

Nardas Farmer

Producer

Company

Jiavanonnati

Mahila Fpc Ltd

Shivhar Agro

Producer

Company

Limited

Ruj Farmers

Producer

Company Ltd

Hari Om Agro

Producer

Company

Chinawal

Farmers

Producer Co

Ltd

Krushi Kranti

Hitech Agro

Producer

Company Ltd

Aamhibaliraja

Producer

Company

Limited

Mishrilal

Food

Producer

Company

Ltd

Hari Om Agro

Producer

Company

Indujaa Mahila

Milk Producer

Co.Ltd.

Tivatghal

Agriculture

Producer

Company

Rajashree

Farmers

Producer

Company Ltd.

Shendurjana

Farmer

Producers

company

Kashtkari

Farmers

Producer Co

Ltd

Ghrushneshwar

Shetkari

Utpadak

Company Ltd.

Dyanjyoti

Mahila

Farmers

Producer

Company

Limited

Shrisant

Namdev

Maharaj

Farmer

Producer

Company

Ltd.

Shendurjana

Farmer

Producers

company

Rivagro Fpc.Ltd.

Jangave Agro

Producer

Company

Limited

Laxminarayan

Farmers

Producer

Company Ltd.

Ayush Farmer

Producer

Company

Reva Valley

Agro

Producer Co

Ltd

Pinakeshwar

Shetkari

Producer

Company

Limited

Tukoba Agro

Producer

Company

Limited

Appaswami

Shetakari

Utpadak

Company

Ltd

Ayush Farmer

Producer

Company

Ghatanji Mahila

Producer

Company Ltd

Ltr agro foods producer

Sant Gajanan

Agri Development

Krushisamrajya Farmer

Development

agro vision farmers

Karmad Farmer Producer

Purna Kelna

Producer Company Ltd

Anukaran

Farmer Producer

Krushisamrajya Farmer

Vadal Fpc Ltd

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34

company

limited

Farmers

Producer

Company Ltd.

Producer

Company

producer

company

limited

Company

Limited

Company

Ltd

Producer

Company

Satyaai agro

producer

company ltd.

Shemba Kranti

Shetkari

Utpadak

Company Ltd.

Krushi Mauli

Shetkari

Utpadak

Company Ltd.

Anjani khore

farmer

producer com

ltd.

Jai Siddheshwar

Krishi Producer

Company

Limited

Jadaimata

Producer

Company Ltd

Surya

Farmers

Producer

Company

Ltd

Krushi Mauli

Shetkari

Utpadak

Company Ltd.

Yavatmalkrushi

samurdhi trading

and prosessing

producer

company limited

Katpur agro

producer

company ltd

katpur tq.latur

Kulbhushan

Shetkari

Utpadak

Company Ltd.

Sant

Dnyaneshwar

Shetkari

Utpadak

Company Ltd.

Tapi farmers

producer

company

limited

Mandana

producer

company

limited

Walsavangi

Agro Producer

Company Ltd

Shree

Faleshwar

Maharaj

Farmer

Producer

Company

Ltd

Sant

Dnyaneshwar

Shetkari

Utpadak

Company Ltd.

painganga agro

producer co.ltd.

Lokmauli agro

producer

company ltd.

Jay Sardar

krushi vikas

Shetkari

Utpadak

Company Ltd.

Parivartan

Organic

Shetkari

Utpadak

Company Ltd.

Sant

Changdev

Tapi Purna

Farmer

Producer

Cimpany ltd

Mhasrul

Farmers

Producer

company Ltd

Pradnyashil

Taruna

Farmers

Producer

Company

Ltd

Parivartan

Organic

Shetkari

Utpadak

Company Ltd.

sweekar agro

produser

company limited

Panagro

services

producer

company ltd

Kelvad

Shetkari

Utpadak

Company Ltd.

Krushideep

Agriculture

Producers

company.

Aadishakti

muktai krushi

vikas farmers

producer

company

limited

Bhudan Agro

Producer

Company Ltd

Godavary

Valley

Farmers

Producer

Company ltd

Krushideep

Agriculture

Producers

company.

Vasant-sudha

Farmers

Producer

Company

Limited

Shivneri agro

prod.company

lmt.shivani

Vidarbha

Samruddhi

Shetkari

Utpadak Company Ltd.

Greenza

Producers

company ltd.

Dhayanai

punyai agro

farmer

producer company

Kisan Disha

Farmers

Producers

Company ltd.

Greenza

Producers

company ltd.

bumitra self

reliyant farmers

producer farmers

producer company

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35

Agrotech agro

producer

company ltd

Figure 20: Location of selected FPCs in the PoCRA region

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36

(a) (b)

(c) (d)

Figure 21: Field visit photos.(a)- Steam distillation unit at Nardus FPC, Washim (b)- Grading

machine at Krushi Mauli FPC, Washim (c)- Cleaning grading sorting unit at Jai Siddheshwar

FPC, Aurangabad (d) Seed processing and Warehouse at Sonpaul, Buldana

Challenges during field visits

Data was received based on discussion with either FPC directors or senior members,

however the accuracy of data was solely dependent on the respondent’s heuristics and

could not be validated using any other media. In fact, there were some dissimilarities

in the data received from PMU and field visit data.

FPCs assumed that we were representatives of PoCRA and had expectations that the

team would provide them with immediate solutions or grant approvals for certain

projects. When the respondent realised that the purpose of current field visits was data

collection for study and research, the attitude of respondent changed.

For Washim district, the contact details of FPCs were inaccurate. The actual details

were acquired from district coordinators and local sources.

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37

9 Potential value added products for PoCRA region

Considering that the PoCRA districts have large quantum of soybean, maize and turmeric

production, their lies a potential for value addition intervention in these three commodities.

Currently the FPCs in PoCRA are mainly into trading and generate low profit margins,

therefore a profitable and sustainable business venture pertaining to processing of agri-

commodities will be well accepted by the FPCs.

The following value added products pertaining to soybean, maize and turmeric processing are

proposed:

1. Poultry feed

2. Soy Milk and Tofu

3. Turmeric power

4. Curcumin

These products are chosen based on the observations (from field visits) of raw material

availability in the PoCRA region. Moreover, the proposed products have a well-established

market, therefore forward linkages/marketing of the products would be convenient. The

profitability of the processing would depend on the economics of scale therefore a feasibility

study is necessary to determine the optimal plant capacity considering all practical variables.

The following sections discusses the preliminary feasibility of each product by analysing the

economics of processing.

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10 Feasibility report on Poultry Feed manufacturing Unit

10.1 Introduction

Across the world poultry market, India ranks sixth (using FAOSTAT rankings). The domestic

poultry industry is the fastest growing segment with a compound growth rate of 18%. As per

Agricultural and Processed Food Products Export Development Authority (APEDA) India has

become the world’s fifth largest egg producer. Egg production has increased quadrupled in

two decades in our country (30 billion in 2000 to 114 billion in 2020). Similarly, poultry meat

production growth, is also very significant, crossing 4.3 MMT in 2020 (www.indiastat.com).

It is projected that egg production may reach 136 billion eggs by 2023, with poultry meat

production to total 6.2 MMT.

Andhra Pradesh is the country’s largest egg producing state. Besides Andhra Pradesh, Tamil

Nadu, Telangana, West Bengal, Karnataka, Harayana, Maharashtra, Punjab, Uttar Pradesh and

Bihar are major egg producers. In case of poultry meat, Haryana tops the list followed by the

West Bengal and Uttar Pradesh. The government of India fixed targets for annual production

of poultry with a view to ensure availability of eggs and broilers both to meet domestic

consumption as well as export. With this projected development of the poultry industry, the

demand for production of balanced poultry feed has become imperative.

Poultry sector in India is largely an organized commercial sector with about 80% of the total

market share. The unorganized sector (largely backyard poultry that supplements income

generation and family nutrition) has about 20% of the total market share.

In 2020, India’s consumption of poultry meat was over 3.9 million metric tons, still quite

limited relative to the overall population size. Demand for protein rich food, combined with

improved consumer purchasing power is spurring increased poultry meat consumption.

Egg offers as a low cost, highly nutrient dense food which includes a wide variety of essential

micronutrients. Eggs can supplement household plant based diets. In the last two decades, per

capita availability of eggs have more than doubled in the country (Figure 23). Of course, this

may not be proportional to the population of the states.

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39

Figure 22: Top 10 states in egg production in India (FY 2019-20).

Figure 23:Per capita eggs availability per annum in India

There is no uniformity in terms of size and housing environment of poultry farms. It may vary

from 200 birds to more than 50,000 birds. Typically, small poultry are open sheds while only

a few large poultry integrators have controlled-environment housing with automatic feeding

and drinking systems. For small farmers, poultry business poses various challenges due to high

capital cost requirement which restrict them to adopt sophisticated housing system for better

performance of poultry and high price of feed which accounts for more than 80 percent of the

total production cost.

0

5

10

15

20

25

Bill

ion

egg

s p

er a

nn

um

0

20

40

60

80

100

19

50

-19

51

19

68

-19

69

19

80

-19

81

19

83

-19

84

19

86

-19

87

19

89

-19

90

19

92

-19

93

19

95

-19

96

19

98

-19

99

20

01

-20

02

20

04

-20

05

20

07

-20

08

20

10

-20

11

20

13

-20

14

20

16

-20

17

20

19

-20

20

Per Capita Eggs Availability

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40

10.2 Poultry feed status in India

The current demand for poultry feed in India is ~25 million tons. Poultry segment dominates

the market due to the growing meat consumption leading to the higher demand for poultry feed.

Increasing per capita income, and rising awareness of healthy prodcuts among consumers

quality poultry products have significantly contributed to the rising demand for poultry feed in

India. According to the 19th Livestock Census by Department of Animal Husbandry, growing

demand for poultry products will further increase to drive growth in India poultry feed market

in the coming years.

The predominant feed grain used in poultry feeds worldwide is maize. The plant protein source

traditionally used for feed manufacture is soybean meal, which is the preferred source for

poultry feed. Feed supplements like probiotics, vitamins, minerals, amino acids, mold

inhibitors, enzymes, preservatives, coccidiostats, antioxidants etc. are mostly imported. Feed

represents the major cost of poultry production, constituting about 80 percent of the total cost

and about 65-75% of total cost is shared by maize and soymeal.

10.2.1 Maharashtra Scenario

On the geographical front, South India represents the leading market for animal feed,

accounting for the largest market share. In recent years, the market has witnessed

growth in Andhra Pradesh, Karnataka and Tamil Nadu, owing to the rise in the

manufacturing of poultry products. While poultry integrators are much stronger in regional

pockets of Andhra Pradesh, Karnataka and Tamil Nadu, the much larger landscape for the

poultry industry and its expansion beyond these belts provide ample opportunity for standalone

feed players. The demand is expected to grow by 7-8 percent in near term.

The demand of maize depends largely on demand as feed for poultry and livestock, and

partially on its direct demand for human food and industrial uses. Maize is the preferred energy

cereal used in poultry feed formulations because of its high energy, low fibre and the presence

of pigments and essential fatty acids. Consequently because it is a primary source of energy,

due to its higher level of inclusion in poultry diets (60-70%), it contributes approximately 30%

of the protein requirement of poultry. However, maize, like other cereals, is deficient in certain

essential amino acids, such as lysine and tryptophan. Soymeal, a byproduct of soybean oil

industry is a common plant protein source, which contain about 44- 45% crude protein. The

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41

protein in soybean provides the building blocks for muscles, organs, feathers, and eggs. Maize

and soymeal have been considered as the primary feed ingredients in the poultry diets.

Maharashtra holds a great potential to become a hub for poultry feed production as it has

significant production of soybean and maize. Maharashtra is second largest producer of

soybean in India after Madhya Pradesh. As shown in the maps below, PoCRA districts in

Maharashtra contribute significantly to the production of soybean and maize. It should be noted

that with the announcement of new biofuels policy in 2018, cropping pattern under maize will

increase significantly in these districts in the next few years. It has been estimated that maize

productivity will also increase significanty, thus improving the chances of farmers to diversify

their market portfolio. Bihar, a major producer of corn in India, accounting for 8 per cent of

the national production of corn in 2019-20, has come up with a state-level policy on ethanol

production (Government of Bihar, 2021). This could be seen as an opportunity for states like

Maharashtra to emerge as alternative markets in poultry feed industry.

The layer industry alone creates the feed demand of about 12 million tonnes with 5-6 percent

CAGR. In the near term, significant opportunities exist in layer industry for compound feed

demand. With farms consolidating and growing in size in long term, layer farmers will be

integrated backwards into feed milling.

Total maize production in Maharashtra is about 2.3 million tonnes (FY 2020). The cumulative

production of maize in the 15 districts of POCRA region is about 7 lakh tonnes of which

Jalgaon, Aurangabad, Jalna, Buldhana and Amravati contributes to 95% of total production.

Similarly, soybean production in POCRA districts is also quite significant. Cumulative

production of thse districts FY 2020 was 39 Lakh tonnes.

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42

(a)

(b)

Figure 24 : Maize production in POCRA districts of Maharashtra

0

0.5

1

1.5

2

2.5

3

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43

(a)

(b)

Figure 25:Soybean production in POCRA districts of Maharashtra

In this context, feasibility analysis of a poultry feed manufacturing business has been

done for POCRA districts that carry a great potential for supplying significant quantum

of feed to the state and neighbouring states.

0

1

2

3

4

5

6

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44

10.3 Project description (TEA)

Considering the quantum of raw material availability in the region, this project has been

proposed for establishment of poultry feed unit of 1 ton per hour. The proposed project will

offer flexibility to produce –

Prestarter feed

Crumbs (started feed)

Pellet Feed (Finisher feed)

Since Pellet feed manufacturing is most exhaustive process which subsumes above two

processes, all calculations presented in the feasibility analysis are for pellet feed.

10.3.1 Poultry Feed production process

10.3.1.1 Raw material procurement

For commercial poultry farming, feed serves as the largest cost of the operation.

Therefore, sourcing high quality raw material is of utmost importance for the

success of the business. This mixture of various concentrate feed ingredients in

suitable proportion is known as compound feed.

Considering that Farmer producer companies in the region are going to be the direct

stakeholder of the project, procurement of maize and soymeal (largest cost

contributor) can be managed well within 100 kilometers radius of the project site.

Even though procurement of raw material can happen from multiple suppliers,

consistency of feedstock can be ensured by utilizing selected varieties to minimize

variations in proximate composition. The low variability in the unit value

realization in case of poultry feed demands greater incentives for the processing

sector.

10.3.1.2 Weighing and quality check:

Raw materials stored in storage area are sent for weighing. High degree of accuracy

and precision is required for weighing. After that ingredients are sent to laboratory

for analysis. After acceptance from feed laboratory, these ingredients are sent for

grinding with the help of equipment’s like conveyor and elevators.

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45

10.3.1.3 Grinding:

Size reduction is an important unit operation of feed manufacturing process. The

grinding improves feed digestibility, acceptability, mixing properties, palatability,

and increases the bulk density of some ingredients. In a commercial poultry feed

mill, hammer mills are the most popular.

The raw materials are grinded in grinding machine to obtain appropriate size of

grains. The end product is in form of pellet or mesh. So grinding is done

accordingly. Grinded materials are further separated by means of a sieve, and then

stored in the assorting tanks according to the kind of raw materials.

10.3.1.4 Mixing:

The raw materials are mixed by means of a feed mixer. In this process, fatty

ingredients are added to the materials in order to raise the nutritional value of the

feed. The feed obtained from the mixer is blended with molasses. Proper mixing is

crucial for uniformity of composition of product. Double ribbon blender is used to

mix all ingredients after grinding.

10.3.1.5 Conditioning:

Direct and indirect injection of steam in feed mix for 10-50 seconds is done.

Conditioner should have provision for varying conditioning time as per formulation

requirement. It adds moisture content of feed to 17-18%.

10.3.1.6 Pelletization:

In this process, blend of raw material put into a Pelleting machine.

Pellets are made using extrusion principle with the use of temperature, moisture

and high pressure. The heat generated in conditioning and pelleting makes the

feedstuffs more digestible by breaking down the starches. Pelleting minimizes

waste during the eating process. Pellet size may vary from 1.8 mm to 10 mm

diameter as per the need. The positive effects of pelleting are higher feed density,

no feed ingredient separation, better bacteriological quality, easier ingestion,

improved growth and feed conversion ratio. Pelleting of meal leads to hardness and

increased durability of the feed meal.

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10.3.1.7 Cooling:

From the pellet machine chamber, the pellets normally flow by gravity into a device

for cooling and drying of the pellets. Pellets will leave the pellet mill at

temperatures as high as 90°C and moisture contents are high as 20%. For proper

storage and handling of the pellets, their moisture content must be reduced to less

than 10%. This is to be accomplished by passing a stream of air through a bed of

pellets. This evaporates the excess moisture, causing cooling both by the

evaporation of water and by contact with the air. The counter flow pellet cooler has

automatic control for optimum cooling. Its air flow opposite to movement of hot

pellets results in fast cooling and removal of moisture.

10.3.1.8 Product Quality Inspection:

Proximate composition of pellet feed is done in the lab. In general practice,

protein content 22% minimum, fibre maximum 10%, fat 5% minimum, maximum

2% ash and moisture content should be maximum 10% in the pellet.

10.3.1.9 Weighing and Packing:

Cattle feed is weighed with the help of electronic balance and packed suitably in a

poly bag.

10.3.1.10 Storage

Packed cattle feed stored in cool and dry place and deliver as per demand.

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47

Figure 26: Process flow diagram of poultry feed pellet production

10.3.2 Feed Composition

The feed consists of three macronutrients: Carbohydrates, proteins and lipid, together with

molasses, and micronutrients (minerals and vitamins). Major raw materials required for the

manufacture of poultry feed are maize, soymeal, molasses, salt, limestone (ground), other

grains (optional), meat bone meal, vitamins, amino acids and minerals. A ration of corn and

soybean meal is recognized as technically superior for raising broilers, but other ingredients

are sometimes substituted based on availability and price. Animal feed for modern high-

performance breed is blend of grains, protein meals, vitamins, minerals and a number of feed

additives pelleted and crumbled to suit ingestion by different age of birds. The feed

composition may vary depending upon the age of the bird and end use. Largely, the change is

observed in the protein content of feed composition. In the analysis, two different compositions

based on protein content were evaluated as follows –

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48

Table 10:1: Feed compositions for 1 TPH feed model

S.No. Ingredients

Composition 1

(%)

Composition 2

(%)

1 Maize 65 55

2 Soya meal 23 33

3 Dicalcium phophate 1 1

4 Meat bone meal 2 2

5 Mustard DOC 2.35 2.35

6 Soybean Oil 1 1

7 Mineral and Vit. mixture 0.2 0.2

8

Methionin +

Tryptophane 0.3 0.3

9 Lysine 0.15 0.15

10 Rice bran deoiled 3 3

11 Molasses 1 1

12 limestone 1 1

10.3.3 Economic analysis

10.3.3.1 Capital Investment Cost

In this study, an economic analysis was conducted to estimate the NPV, IRR, and PBP,

respectively which is based on the capital investment, and on operating costs of the refinery.

Model for integrated biorefinery was constructed in Superpro designer software. Capital

investment costs are estimated based on the purchased costs of each piece of operating

equipment (Table 10:1). The purchased costs for the major equipment items were based on

budgetary quotations from equipment suppliers. In those instances where the capacities of the

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49

equipment in the model vary from the equipment, costs are adjusted for capacity using standard

engineering scaling factors. The mass and energy balance outputs from the processing model

were used to evaluate the capital and operating costs. Equipment cost information was derived

from literature, equipment suppliers.

Direct Fixed Capital Cost (DFC) is a sum of Direct Cost (DC), Indirect Cost (IC), and

contingency. The DC estimated is based on total equipment purchase cost (EPC). Theplant

considered here is assumed to be financed with 75% loan and 25% equity. The plant has a 15-

year lifetime with 5 % salvage value at the end.

Table 10:2 shows economic evaluation parameters considered for the base case i.e. 1 ton per

hour (TPH). The annual depreciation cost is calculated via the straight-line method.

Table 10:2 : Economic evaluation parameters for 1 TPH Poultry feed model.

Time parameters Value

Financing

parameters Value

Analysis year 2022 Equity and loan 25% and 75%

Project life 15 Depreciation method Straight line

Construction period

(months) 12 Depreciation period 10 years

Start up period (Months) 1 Income tax 35%

Inflation rate (%) 6 Discount rate (%) 10

Operating parameters Construction plan Value

Annual operating time

(days) 300 1st year (% DFC) 75

Start up cost (% DFC) 5 2nd year (% DFC) 25

Salvage Value (%DFC) 5

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50

Table 10:3: Summary of equipment list for 1 TPH feed model

Description Unit Cost (INR) Cost (INR)

Receiver Tank 200,000 200,000

Vessel Volume = 481.97 L

Screw Conveyor 74,000 74,000

Pipe Length = 15.00 m

Grinder 200,000 200,000

Rated Throughput = 433.50 kg/h

Cyclone 74,000 74,000

Rated Throughput = 433.78 L/h

Silo/Bin 100,000 100,000

Vessel Volume = 873.26 L

Generic Box 400,000 400,000

Rated Throughput = 668.04 kg/h

Bucket Elevator 50,000 50,000

Elevator Length = 10.00 m

Blending Tank 200,000 200,000

Vessel Volume = 818.45 L

Screw Conveyor 143,000 143,000

Pipe Length = 15.00 m

Extruder 500,000 500,000

Screw Diameter = 9.56 cm

Blending Tank 100,000 100,000

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51

Vessel Volume = 831.85 L

Generic Box 400,000 400,000

Rated Throughput = 679.23 kg/h

Generic Box 800,000 800,000

Rated Throughput = 433.50 kg/h

Unlisted Equipment

360,000

TOTAL 3,601,000

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52

Table 10:4: Fixed capital estimate summary

4A. Total Plant Direct Cost (TPDC) (physical cost)

1. Equipment Purchase Cost 3,601,000

2. Installation 907,000

3. Process Piping 0

4. Instrumentation 720,000

5. Insulation 0

6. Electrical 1,440,000

7. Buildings 1,080,000

8. Yard Improvement 360,000

9. Auxiliary Facilities 720,000

TPDC 8,829,000

4B. Total Plant Indirect Cost (TPIC)

10. Engineering 883,000

11. Construction 2,649,000

TPIC 3,531,000

4C. Total Plant Cost (TPC = TPDC+TPIC)

TPC 12,360,000

4D. Contractor's Fee & Contingency (CFC)

12. Contractor's Fee 0

13. Contingency 1,236,000

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53

CFC = 12+13 1,236,000

4E. Direct Fixed Capital Cost (DFC = TPC+CFC)

DFC 13,596,000

Table 10:5:Summary of raw material cost (Composition 1)

Bulk

Material

Unit Cost

(INR)

Annual

Amount

Annual Cost

(INR) %

Fines 18.00 74,412 kg 1,339,416 1.01

Maize 18.00 2,715,444 kg 48,877,992 36.99

mixture 50.00 576,000 kg 28,800,000 21.79

soy meal 50.00 1,060,301 kg 53,015,040 40.12

Water 120.00 995 MT 119,367 0.09

TOTAL

132,151,815

100.00

Table 10:6: Summary of raw material cost (Composition 2)

Bulk

Material

Unit

Cost

(INR)

Annual

amount

Cost

(INR) %

Fines 18 69,602 kg 1,252,843 1.01

Maize 18 2,297,009 kg 41,346,158 36.99

mixture 50 576,000 kg 28,800,000 21.79

soy meal 50 1,520,640 kg 76,032,000 40.12

Water 120 956 MT 114,743 0.09

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54

TOTAL 147,545,744 100

Table 10:7:Summary of utilities cost for 1 TPH poultry feed model..

Utility

Unit

Cost

(INR)

Annual

Amount

Ref.

Units

Annual Cost

(INR) %

Std Power

10.00 409,181 kW-h 4,091,813 86.73

Steam 888.00 596 MT 528,958 11.21

Chilled Water 5.00 19,397 MT 96,985 2.06

TOTAL

4,717,756 100.00

Table 10:8:Annual operating costs for (A) Composition 1 and (B) Composition 2

Item Composition 1 Composition 2

Cost (INR) Contribution

(%) Cost (INR) Contribution (%)

Raw Materials 132,152,000 91.07 147,546,000 91.93

Labor 2,680,000 1.85 2,680,000 1.67

Facility Dependent 2,707,000 1.87 2,707,000 1.69

Laboratory/QC/QA 402,000 0.28 402,000 0.25

Utilities 4,718,000 3.25 4,718,000 2.94

Advertising/Selling 2,445,000 1.68 2,445,000 1.52

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Total 145,104,000 100 160,498,000 100

Table 10:9:Summary of project economics for feed compositions.

Composition 1 Composition 2

Total Capital Investment ( INR) 28,911,000 30,388,000

Annual operating cost (INR) 145,104,000 160,491,000

Net Unit Production cost (INR/kg) 29.67 32.66

Product Selling price

Pellets (INR/kg) 35 35

Net Profit

Pellets ( INR/year) 16,940,000 7,460,000

IRR % (after taxes) 27.1 51.9

Payback period (years) 1.7 4.1

10.3.3.2 Sensitivity analysis

It is evident through analysis that raw material is the predominant contributor in deciding the

fate of the project. Likewise, the market price of feed product would also strongly affect the

economic viability of the process. Likewise, plant capacity, days of plant operation etc. will

have bearing on the economic viability of the plant. The sensitivity bounds are chosen based

on what is expected due to market fluctuations. This was accomplished by evaluating NPV

after changing one parameter keeping other parameters constant. To test the sensitivity of

results, tornado charts were constructed for baseline scenario and associated variables

sensitivities.

10.3.3.3 Raw material price

Base case Scenario: Maize- Rs. 18/kg, Soymeal - Rs. 50/kg and Feed mixture - Rs.50/kg

Low price: Maize- Rs. 15/kg, Soymeal - Rs. 40/kg and Feed mixture - Rs.40/kg

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56

High price: Maize- Rs. 21/kg, Soymeal - Rs. 60/kg and Feed mixture - Rs.60/kg

10.3.3.4 Effect of soymeal price on NPV

Historical data shows that soymeal price was considered quite stable and it would hover

between 20 to 30 rupees per kg. Last year, unprecedented increase in Soybean price had rattled

the animal feed sector. Since soybean meal is a major constituent of poultry feed, volatility in

soymeal would directly affect poultry feed cost of production.

As mentioned above, for base case scenario, soymeal price is considered INR 50 /kg. If price

moves towards right by 10%, NPV for composition 2 becomes Zero, which implies that project

is not viable. However, if proportion of soymeal is kept below 25% in the final feed

composition for the same price hike, project may turn out to be profitable (Figure 27) for 180

days and 300 days of operation.

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57

(a)

(b)

Figure 27: Effect of operating days on NPV for both feed compositions

-10

-5

0

5

10

15

20

30 35 40 45 50 55 60NP

V (

In C

rore

ru

pe

es)

Soymeal price (Rs./kg)

300 days operation

Composition 1

Composition 2

-6

-4

-2

0

2

4

6

8

10

12

30 35 40 45 50 55 60NP

V (

in C

rore

ru

pe

es)

Soymeal price (Rs./kg)

180 days operation

Composition 1

Composition 2

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58

10.3.3.5 Effect of plant capacity on NPV

Figure 28: Effect of plant capacity on NPV for both feed compositions

10.3.3.6 Conjoint analysis

(i) Effect of raw material price and no. of days of operation on NPV

It is evident from the Figure 29 that raw material price inflation would directly affect project

economics. Project viability comes out to be negative for 180 days of operation.

Figure 29: Effect of raw material price and operating days on NPV

-10

-5

0

5

10

15

20

25

0 0.5 1 1.5 2

NP

V (

in C

rore

ru

pe

es)

Plant capacity (tonnes per h)

Composition 2

Composition 1

-5

0

5

10

15

20

180 days 240 days 300 days

NP

V (

In C

rore

ru

pe

es)

Low

Base case

High

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59

10.3.3.7 Effect of raw material price and pellet selling price on NPV

The most important factors affecting the project economics is variability on raw material and

selling price of pellets.

Figure 30:NPV w.r.t Pellet price

10.3.3.8 Benefit Cost Ratio (BCR):

(i) Effect of plant capacity and feed compositions on BCR

It is apparent from the above Table 10:10 that project economics is viable at 1 TPH capacity

for both feed compositions. Scaling down the plant capacity to 0.5 TPH is still a profitable

venture for 1st feed composition whereas project economics for 2nd feed composition doesn’t

seem viable.

Table 10:10: Effect of plant capacity and feed compositions on BCR

0.25 TPH 0.5 TPH 1 TPH

Composition 1 -1.14 1.01 3.14

Composition 2 -2.33 -0.59 1.05

-15

-10

-5

0

5

10

15

20

25

30

35

Low Base case HighNP

V (

In C

rore

ru

pe

es)

30 INR

35 INR

40 INR

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60

(ii) Effect of operating days and feed compositions on BCR (for 1 TPH model)

For feed composition 1, all operating days (180 days through 300 days) present a great potential

for the business viability as BCR is more than 1.0 in all three cases. However, for feed

composition 2, reducing no. of days of operation from 300 days to 240 days or lower adversely

affects the project economics (Table 10:11). It is therefore suggested to run the plant for 300

days if all other parameters are constant.

Table 10:11: Effects of operating days and feed composition on BCR (for 1 TPH model)

BC ratio 180 days 240 days 300 days

Composition 1 1.6 2.4 3.14

Composition 2 0.3 0.7 1.06

(iii) Effect of pellet selling price and feed composition on BCR (for 1 TPH and 300

days model)

Current market price of poultry pellet is varying between 40 and 42 rupees per kg. It is apparent

from the Table 10:12 that project economics is favourable wen pellet selling price is ≥ 35

rupees.

Table 10:12: Effect of pellet selling price and feed composition on BCR (for 1 TPH and 300

days model)

BC ratio INR 30 INR 35 INR 40

Composition 1 -0.3 3.1 4.3

Composition 2 -3 1.06 3.33

10.3.3.9 Project viability:

The Internal Rate of Return (IRR) of the project is 140% and 41%, for two compositions, which

are significantly higher than the bank return rate of 10%. Analysis of BCR ratio under various

conditions revealed that project is viable for feed composition 1, even under less operating days

and reduced plant capacity. For feed composition 2, project is viable when plant capacity is

kept at 1 TPH or higher. In addition, plant has to operate for 300 days.

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61

Hence, the project is financially viable. The NPV of the project is positive at a discount factor

of 10% during the period of operation considered. This implies that the project generates

sufficient funds to cover all its cost, including loan repayments and interest payments during

the period.

The situation may change further depending upon the selling price of the pellet. In this project,

pellet selling price of 35 rupees per kg is a very conservative figure. As stated above, current

wholesale market price is hovering between 40 and 42 rupees. This may change the overall

scenario for the feed composition 2. A detailed uncertainty analysis using Monte Carlo method

will help us understand the probability of success of the venture.

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62

11 Feasibility report of Soy milk and tofu processing unit

11.1 Soybean as a commodity

Glycine max (L.) Merrill is a self-pollinated diploid annual legume. It is thought to have been

domesticated for food around three thousand years ago in eastern China from its viny wild

relative, Glycine soja. Most soybean seeds, unlike Glycine soja, do not have a dormancy phase

following harvest, and hence rely on human agriculture. The soybean is a tall, branching plant

that can grow to be more than 2 metres (6.5 feet) tall. Soybeans may be grown in a variety of

soil types, but they thrive in sandy loam that is warm, productive, and well-drained. Soybean

flowers are white or purple, and seeds can be yellow, green, brown, black, or bicolored,

however most commercial cultivars have brown or tan seeds. Each pod contains one to four

seeds.

Because of its high productivity, profitability, and critical contribution to soil fertility, soybean

occupies a significant position in the world's oilseed farming situation. The crop is also the

world's most important seed legume, contributing 25% of worldwide vegetable oil production,

nearly two-thirds of the world's protein concentrate for cattle feeding, and is a valuable element

in formulated poultry and fish diets.

11.1.1 Composition of Soybean

Soybeans are abundant in protein and have a high nutritional value. It contains around 19% oil

and 36% high-quality protein (as against 7.0 per cent in rice, 12 percent in wheat, 10 per cent

in maize and 20 to 25 per cent in other pulses). Soybean protein is high in the essential amino

acid lycine (5%), which is lacking in most cereals. It also has a lot of minerals, salts, and

vitamins (thiamine and riboflavin), and its sprouting grains have a lot of Vitamin C. Vitamin

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63

A is present in the form of precursor carotene, which is transformed into vitamin A in the

intestine.

Figure 31 : Composition of Soybean grain (Source-https://www.nopa.org)

11.1.2 Production of soybean in PoCRA district

Production of Soybean in India has increased at a CAGR of 9.60 per cent while a convincing

growth of 43% in the annual production is observed in Maharashtra in the previous decade

(43.16 lakh tonnes in 2010-11 to 62.01 lakh tonnes in 2020-21). Over the decade, an average

annual production of soybean in Maharashtra has been 62.01 lakh tonnes wherein a major

contribution has been from the PoCRA district (39.3 lakh tonnes). That means, around 63% of

the state’s soybean production has come from the PoCRA district. Figure 32 shows the

distribution of Soybean production in the PoCRA district. The three major producing districts

are Buldana, Latur and Washim.

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64

Figure 32: Production of Soybean in PoCRA districts

11.1.3 Quantum of Soybean in visited FPCs

The field work suggested that the quantum of soybean in FPCs was variable and a summary of

the observed quantum is presented in Table 11:1. Four categories of quantum being

<10MT/annum, 10-100MT/annum, 100-500MT/annum and >500MT/annum was made. Most

of the visited FPCs dealt in >100MT/annum. The purpose of field visit as stated in section 7

was to understand ground realities and current practices of FPCs. Moreover, the field work was

a sample survey comprising of a small sample size, therefore generalization of quantum based

on geography, capacity of FPCs etc would be inappropriate. (Turmeric row in the below table

could go in the turmeric part)

Table 11:1: Quantum of Soybean in visited FPCs

<10

MT/annum

10 to 100

MT/annum

100 to 500

MT/annum

> 500

MT/annum

Soybean 7

(B-2,J-3, L-1,

Y-1)

6

(B-1, H-3, J-1, Y-1)

10

(B-1, H-1, J-1, L-4,

W-2, Y-1)

8

(B-1, H-2, L-3,

W-2)

A- Aurangabad, B- Buldana, H- Hingoli, J- Jalna, L- Latur, Y- Yavatmal , W- Washim

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65

The current soybean related activities in most of the FPCs comprised on cleaning, grading,

packaging and trading. Based on discussions with the FPC directors, it was calculated that the

soybean grain trading provided them a profit of around 2%. Certain FPCs were involved in

soybean seed processing which generated an average profit of 15%. However rejection rate in

seed processing was high and the rejected soybean would be sold as grain in market. Pertaining

to soybean, no other processing activities were observed during field visits.

11.2 Proposed value added product

Given the numerous benefits of soybean consumption, it is past time to promote soybean

consumption as a food component. When processed into edible forms, soybean can replace

traditional diets due to its high nutritional value. In daily dietary systems, it can be used in the

form of soymilk and milk products such as tofu / soy paneer.

11.2.1 What is Soymilk?

Soymilk is prepared by soaking and crushing soybeans in water to produce a creamy, milk-like

beverage. In mainland China, soymilk has been consumed for centuries. Soymilk is an

economical, lactose-free, highly digestible, and nutritious alternative to a dairy and meat-based

diet, in addition to being high in protein, vitamins, and minerals. It can perform nearly all of

the functions of bovine milk. It is a cholesterol-free product with a low fat content and a high

concentration of polyunsaturated phospholipid fatty acids, particularly lecithin and linolenic

acid. Soymilk typically has a total solids content of 7-8 percent. When 3-4 percent sugar and

around 0.05 percent salt are added, it reaches a sugar, salt, and total solids level that is similar

to toned (2 percent fat) cow's milk, i.e. about 12-13 percent total solids. This can be consumed

as such or after sweetening and diluting, alternatively, it can be made into yogurt (curd) or tofu

(paneer).

11.2.1.1 Health benefits and comparison to dairy milk

Table 11:2 illustrates that soymilk has a nutritional content that is nearly equal to or better than

human and cow milk. Lactose intolerance affects around half of India's adult population. They

get sick, bloated, have abdominal pain, and have gas after drinking milk. Lactose intolerance

occurs in humans when the capacity to digest lactose, the carbohydrate component of

cow/buffalo milk, is lost. The majority of people who have this problem are unable to notice

signs when they consume dairy products. They simply refuse to consume milk. For children

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66

and adults who are lactose intolerant or allergic to bovine milk, soymilk is the effective

alternative.

Table 11:2: Composition of Soy milk as compared to other milks

Human Cow Buffalo Soybean Moisture 87.43 87.20 82.76 93.00 Fat 3.75 3.70 7.38 2.00 Protein 1.63 3.50 5.48 3.00 Lactose 6.98 4.90 5.48 0.00 Ash 0.21 0.70 0.78 0.20

Other carbohydrates 0.00 0.00 0.00 0.00

11.2.2 Market demand and Potential in PoCRA region

With the increasing health consciousness among the general people, the use of Soyabean is

getting acceptance in the form of Soya milk, Tofu and Soya curd etc. Since India is mainly a

country of vegetarians, India has high potential for Soya products. Soy products are already

penetrating in the Indian markets and the soy milk and soy drinks category is forecasted to

grow at a CAGR of 10.6 % between 2018 to 2023 (Fnbnews, 2019). The characteristics of soy

milk being lactose free makes it a superior alternative for lactose tolerant population. Soymilk

and soy products has the potential to be competitive in the functional food market which is

constant growing due to the health awareness and rising incomes of the Indian populations.

Since PoCRA region has large soybean production, the availability of soybean as raw material

should be convenient. Currently, the market of soy beverage is already well established in Tier-

1 cities and due to the growing trend of health consciousness in Tier-2 and Tier-3 cities, the

demand of soy milk and tofu is expected to rise in PoCRA region as well. The soy milk

intervention could be setup even at small scale. The processing technology is simple and ready

available in the Indian markets. Details of soy milk processing and economics of manufacturing

is discussed in following section.

11.3 Techno-economic analysis

The following section describes the process and financial analysis of soy milk and tofu

manufacturing plant.

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67

11.3.1 Process flow diagram

The soymilk is produced in the processor by cold grinding of properly soaked soybeans in

water without air, pressure cooking the resulting slurry with culinary steam and separating the

soymilk from the undissolved solids (okara) in a filter press (Figure 33). The basic soymilk

thus obtained is absolutely free from any chemical impurity and can be easily formulated into

tasty cold or hot drinks, or further processed to produce tofu, yogurt, frozen desserts and a

variety of other products. The list of equipment required are Grinder, Cooker; Steam generator

(Boiler); and Tofu press. The production of tofu consists of two main steps: 1.The preparation

of soy milk. 2. The coagulation of this soymilk to form curds which are then pressed to form

tofu cakes. In general, 1 kg of soybean produced around 7.5 litre of soy milk while 1 litre of

soy milk produced around 0.2 kg of Tofu after processing soy milk with coagulant.

Figure 33: Process flow diagram of Soy milk and Tofu processing

11.3.2 Financial analysis

Table 11:3 presents the financial analysis for a soy milk and tofu processing unit of 350 ltr/hr

capacity. The assumptions and costs are considered after through study of literature and contact

with manufacturers/vendors. The analysis has been done considering the 200 days of

operations. A work shift of 8 hours is used for the analysis and based of these considerations,

the annual raw material requirement (raw soybean) is estimated to be around 75 tonnes. The

fixed cost (capital investment) include the cost of machinery, land, civil construction, taxes and

Soybean (1kg)Cleaning

(Clean7-8 times in water)

Soaking (4 hrs in summer and 6 hr

in winters

Wet grinding (Add 10 litres per

kg of SoybeanSoy slurry

Cooking or steaming

Filtration Soymilk (7.5 kg)Soy Panner

(Tofu) (1.5 kg)

Drain

water

Add Coagulant (CaSO4), 0.005kg/ltr

Okara

(2 kg)

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68

pre-operative expenses. The operating or variable cost include the salaries of staff, cost of raw

material, power/electricity, fuel, maintenance and contingency. The costs of individual items

is mentioned in Table 11:3.

It is considered that of the total production, soy milk is 60% while Tofu is 40%. That means,

that the 40% milk is converted into tofu. As mentioned in the process flow diagram (Figure

33), 1 litre of milk produces 0.2 kg of tofu. Therefore the annual production of the plant at full

capacity and 60-40% distribution of milk and tofu is 336000 litres and 44800 kg respectively.

Assuming a wholesale selling price of soy milk and tofu as Rs. 30 litre and Rs. 120/kg, the

annual income of the plant is estimated as Rs. 1,54,56,000. Considering the life of plant as 10

years, the Net Present Value (NPV) is calculated to be Rs 1,25,26,666 at a discount rate of

10%. The calculation indicate an internal rate of return (IRR), benefit to cost ratio (BCR) and

discounted payback period (DPBP) as 36%, 1.67 and 3.13 years respectively. Since, the value

of IRR is in the acceptable range while BCR is more than one, it could be inferred that the soy

milk and tofu processing unit of the proposed capacity is convincingly profitable.

Table 11:3:Financial summary of Soy milk and Tofu processing unit

Value addition intervention –

Soy milk/Tofu processing unit

Remarks/Details Values

A. Machine Capacity In litre/hour 350

A.1 Number of operating days 200

A.2

Raw material requirement per

annum

(at full capacity)

In kg

74667

B. Capital Investment

B. l

Cost of Machine excluding

taxes & duties

(Grinder, Cooker, Manual

Boiler, Tofu box, Tofu Press)

385467

B.2 Accessories

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69

B.2.1 Containers and

Utensils

100000

B.2.2 Vacuum packing

machine 70000/unit x 2

140000

B.2.3 Pouch sealing machine 12000/unit x 2 24000

B.2.4

Tofu slice/cutting

machine (Cap-100

kg/hr)

15000/unit x 2

30000

B.2.5 Deep freezer cost

(500kg/hr)

Seven days storage post Tofu

production 169641

B.3

Land (plant area) In sqft (square feet) 2000

B.3.1 Land cost

(ownership/leased) 1500/- sqft including taxes

3000000

B.3.2

Civil Work including

water tank and

electrical work

Construction cost 1200/sqft + utility

cost 300/sqft (Electrical) 3000000

B.4

Pre-Operational Expenses

B.4.1 GST on machines 18% 152839

B.4.2 Licencing and

registration fees 300000

B.4.3 Training, Installation

and delivery charges 10% of equipment cost 70911

B.4.4 Office Furniture &

Equipment 50000

B.4.5 Miscellaneous 50000

B.5 Total Capital Investment

(B.l+B.2+B.3+B.4)

7472858

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C. Annual Expenses

C.1 Interest on Loan@ 10%pa Considering 40% of capital cost is

loaned by FPC 298914

C.2 Manpower Cost 3 Workers @

10000/- per month

400000/- marketing expenditure per

annum 760000

C.3

C.3.1 Raw soybean 60 Rs/kg 4480000

C.3.2 Coagulant (CaSo4) 25 Rs/kg 70000

C.3.3

Packaging material

C.3.3.1 Milk packing

material Tetra pack (200 ml) - 5 Rs/unit

1678320

C.3.3.2 Tofu packing

material 250 gram pieces - 2.5 Rs/unit

111888

C.4

Power Consumption

C.4.1 Unit consumed per

annum

107461

C.4.2 Cost of Electricity @

Rs. 10/kWh Industrial power supply- 10Rs/kWhr

1074610

C.5 Cost of Water RO water - 0.4 /litre 2240000

C.6 Maintenance 20000

C.7 Fuel-LPG 900 Rs/cylinder 44053

C.8 Contingency 5% of total fixed cost 373643

C.9

Depreciation

C.9.1 Depreciation on

Furniture at 10%

5000

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71

C.9.2 Depreciation on

Machines at 10%

74911

C.9.3 Depreciation on Civil

work at 10%

300000

C.10 Total Annual Expenses

(C1:C9)

11531340

D. Total production per annum Distribution of production

D.1 Soy milk (Plain) 60% of total production 336000

D.2 Soy Tofu 40% of total production 44800

E. Cost of production

E.1 Soy milk (Plain) 20.59

E.2 Soy Tofu 102.96

F. Annual Income (Full capacity)

F.1 Soy Milk (Plain) Soy milk selling price -30 Rs/ltr 10080000

F.2 Soy Tofu Soy Tofu selling price -120 Rs/ltr 5376000

F.3 Total income 15456000

G. Economic Indices

Plant life : 10 years.

Capacity Utilization :

Year 1- 50% , Year 2 – 65%,

Year 3 – 80%, Year 4 onwards 100%

G.1 Net present value (NPV) In Rs. 12,52,6666

G.2 Internal rate of return (IRR) % 35.98

G.3 Benefit to cost ratio (BCR) 1.676

G.4 Discounted payback period In years 3.136

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11.3.3 Sensitivity analysis

To understand the impact of fluctuation of variables on the returns, a sensitivity analysis is

done. Those variables are chosen which might vary in real time situations. These variables are

cost of raw soybean, cost of water, cost of packaging material, soy milk selling price, soy tofu

selling price, capital investment, operating days, production distribution and plant capacity. A

variation of ± 30% in the variables is considered for this analysis while its impact on the BCR

is studied. The plant capacity is varied at 200 lph, 350 lph (base case) and 500 lph while

scenarios for production distribution are 80-20, 60-40 and 40-60 (Soy milk- Tofu). The analysis

is done by changing one variable at a time while keeping others constant.

Figure 34 shows the results of the sensitivity analysis. The base case-350lph is taken as

benchmark to understand the variation due to each variable. As seen in Figure 34, soy milk

selling price is the most sensitive variable as it causes the highest variation. Similarly, in the

order of sensitivity, operating days, tofu selling price and cost of raw soybean are the next three

sensitive variables. Production distribution turns out to be the least sensitive, meaning that by

changing the production distribution pattern from 60-40 to 80-20 doesn’t affect the BCR

significantly as compared to other variables. It could also be observed in Figure 34 that the

BCR in certain scenarios is less than 1, suggesting that those scenarios should be avoided to

prevent losses.

Figure 34: Sensitivity analysis of Soy milk and Tofu processing

-1 -0.5 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5

Cost of raw soybean

Cost of water

Cost of packaging material

Soy milk selling price

Tofu selling price

Capital Investment

Operating days

Production distribution (80-20, 60-40, 40-60)

Capacity (200lph, 350lph, 500lph)

BCR

Var

iab

les

30%

-30%

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73

Figure 35: NPV and BCR vs Soy milk selling price

Since soy milk selling price is observed to be the most sensitive parameter, another study is

performed to understand the variation of soy milk selling price with NPV and BCR. The

purpose of this study is to identify threshold values of soy milk selling price, below which the

soy milk should not be sold to avoid losses. The cost of production of soy milk is estimated as

Rs. 20.59/ltr which means selling soy milk higher than Rs. 20.59/ltr would be profitable.

However, as suggested in Figure 35, for positive NPV, the milk selling price should be above

Rs. 23. Also, considering a BCR more than 1, the minimum value for selling milk should be

more than Rs. 27.15. For better scenarios such as a selling price of Rs. 40, the BCR could be

as high as 4.03. It could be inferred that an appropriate price for selling the soy milk should be

above Rs. 27.15/kg while to achieve a BCR of 2 and 3, the prices should be Rs. 31.4/kg and

Rs. 35.6/kg.

11.3.4 Preliminary comparison with Poultry feed unit

As the raw material for both poultry feed unit (as discussed in section 10.3.3) and soy milk unit

is soybean, a preliminary comparison is conducted to study the choice of value addition

proposition for soybean. For this comparison, base cases described in section 10.3.3 and section

11.3.2 are considered. Table 11:4 presents certain economic parameters for the two

propositions.

-1

0

1

2

3

4

5

-100

-50

0

50

100

150

200

250

300

350

18 20 22 24 26 28 30 32 34 36 38 40 42

BC

R

NP

V (

in la

khs)

Soy milk selling price (Rs/ltr)

NPV BCR

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Table 11:4: Preliminary comparison of Poultry feed and Soymilk/tofu unit (Base cases)

Parameter for comparison Poultry feed unit Soy milk and Tofu unit

Plant Capacity 1 TPH 350 lit/hr

Direct capital investment (in Rs.) 13,596,000 7,472,858

Total Operational Cost (in Rs.) 145,104,000 11,531,340

Soybean associated raw material

cost as the percent of total operating

cost

40.12% 38.8%

NPV (in Rs.) 90,000,000 12,526,666

IRR 27.1 35.98

BCR 3.14 1.67

PBP 1.7 3.13

It is evident that the poultry feed proposition require higher capital investment and operating

cost as compared to soy milk/tofu unit, however, the returns in terms of NPV, BCR and PBP

suggest that the poultry feed proposition is more profitable since it has higher NPV and BCR

and low PBP. For both the case, the raw material cost (soymeal and raw soybean) contribute

to around 40% in the operational cost. A 10% increase in soymeal cost reduces the NPV of

poultry feed unit by 23% while in case of soy milk/tofu unit, an increase of 10% in raw soybean

cost reduces the NPV by around 19%. On the other hand, if the selling price of poultry pellet

is increased by 16%, then the NPV increases by around 108%. In soy milk/tofu unit, an increase

of 16% in selling price of soy milk increases the NPV by 70%. Considering the uncertain nature

of the soybean cost and selling price of the finished products, poultry feed unit is slightly more

volatile than soy milk/tofu unit. The current comparison is preliminary and a rigorous

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75

comparison is needed to appropriately choose among the two value added propositions in

different scenarios.

11.4 Conclusion

In this section, soy milk and tofu processing unit is introduced and its potential in the PoCRA

district is discussed. Based on the techno-economic analysis, it is understood that the soy milk

and tofu plant of the proposed capacity is profitable with a quantum requirement of around 75

MT/annum. Based on the field visit experience, majority of the FPCs have sufficient quantum

to venture into the soy milk business. Additionally, it is observed in the analysis that as the

capacity of the unit is increased, the profitability increases therefore FPCs with large quantum

could plan higher capacity processing plants. As compared to the current activities of FPCs,

soy milk processing plant could provide a profit of around 135% while trading and seed

processing could generate merely 2% and 15% respectively. Therefore soy milk and tofu

processing unit could be seen as highly profitable value addition intervention for the PoCRA

region.

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12 Feasibility report of Turmeric powder and Curcumin extraction

12.1 Turmeric as commodity

Curcuma Longa L. is the scientific name for turmeric. It is a member of the "Zingiberaceae"

family. It's a South East Indian and Indonesian native. It's a common ingredient in foods,

pharmaceuticals, and other products. It's also used in the textile business to make oils,

ointments, and poultices, as well as in cosmetics to make natural and herbal creams, lotions,

and hair dye. Turmeric, the principal spice powder in Indian cuisine, is often regarded as the

world's most powerful herb for combating and possibly reversing disease. Turmeric is an

annual crop, although it is produced as an erect perennial crop. It's widely utilized in the food,

textile, pharmaceutical, and cosmetic sectors. Turmeric is grown in both tropical and

subtropical climates.

12.1.1 Composition of turmeric

The detail chemical composition is mentioned in Table 12:1

Table 12:1: Composition of Turmeric

Principle Nutrient Value Percentage of

RDA

Energy 354 Kcal 17%

Carbohydrates 64.9 g 50%

Protein 7.83 g 14%

Total Fat 9.88 g 33%

Cholesterol 0 mg 0%

Dietary Fiber 21 g 52.5%

Vitamins

Folates 39 μg 10%

Niacin 5.140 mg 32%

Pyridoxine 1.80 mg 138%

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Riboflavin 0.233 mg 18%

Vitamin A 0 IU 0%

Vitamin C 25.9 mg 43%

Vitamin E 3.10 mg 21%

Vitamin K 13.4 μg 11%

Electrolytes

Sodium 38 mg 2.5%

Potassium 2525 mg 54%

Minerals

Calcium 183 mg 18%

Copper 603 μg 67%

Iron 41.42 mg 517%

Magnesium 193 mg 48%

Manganese 7.83 mg 340%

Phosphorus 268 mg 38%

Zinc 4.35 mg 39.5%

Composition & Nutritive Value of Turmeric (per 100 g of edible portion), fresh weight basis

Source: USDA National Nutrient Database

12.1.2 Production of turmeric in PoCRA district

The production turmeric in PoCRA region has increased significantly in last 5 years from 1.23

lakh metric tons in 2016-17 to 2.71 lakh metric tons in 2020-21. The area under cultivation of

turmeric in the region has increased from 7.12 thousand hectare to 52.16 thousand hectare in

2020-21. In last 5 years, the average annual production of turmeric in PoCRA region has been

3.52 lakhs tons. Table 12:2 shows major species grown the PoCRA region and their range of

curcumin content.

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Table 12:2: Spices of turmeric in PoCRA region

Species of

turmeric

Approx curcumin

content

Pratibha 3.5-7.7 %

Selam 2.2-5.9 %

Rajapuri 2.8-4.4 %

Krishna 1.6-3.5 %

Figure 36 shows the distribution of turmeric in PoCRA region. The three major producing

districts are Hingoli, Washim and Yavatmal. Since the quality of turmeric is dependent on its

curcumin content, the species that are grown in the region need to be considered.

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Figure 36: Production of Turmeric in PoRCA districts

12.1.3 Quantum of turmeric in visited FPCs

The field work suggested that the quantum of soybean in FPCs was variable and a summary of

the observed quantum is presented in Table 12:3. Four categories of quantum being

<10MT/annum, 10-100MT/annum, 100-500MT/annum and >500MT/annum was made. Most

of the visited FPCs dealt in >100MT/annum. The purpose of field visit as stated in section 7

was to understand ground realities and current practices of FPCs. Moreover, the field work was

a sample survey comprising of a small sample size, therefore generalization of quantum based

on geography, capacity of FPCs etc would be inappropriate.

Table 12:3: Quantum of Turmeric in visited FPCs

Quantum

Commodity

<10

MT/annum

10 to 100

MT/annum

100 to 500

MT/annum

> 500

MT/annum

Turmeric 2

(H-2)

1

(H-1)

2

(A-1 , H-1, L-1, W-

1)

1

(H-1)

A- Aurangabad, H- Hingoli, , L- Latur, W- Washim

The current soybean related activities in most of the FPCs comprised on trading. No other

processing activities were observed during field visits.

12.2 Proposed value added product

Given the high medicinal value and multiple uses in various industries such as nutraceuticals,

textile, food etc. turmeric and its value added products have the potential to seek high prices as

well as have high demand in the market. Figure 37 shows the potential value added products

of turmeric rhizomes. The most commonly used is turmeric powder. Other popular products

are curcumin powder, oleoresin and volatile oil. The subsequent sections describe processing

of turmeric powder and curcumin powder along with oleoresin from dried turmeric rhizomes.

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80

Figure 37: Potential value added products of turmeric rhuizome

The turmeric is most commonly consumed as its powder. The detail process of getting turmeric

powder from the cultivated rhizomes along with the financial and sensitivity analysis is given

in the following section.

12.2.1 Market demand and Potential of Turmeric powder and Curcumin

India is the world's leading producer, consumer, and exporter of turmeric. Turmeric

was grown on 1.94 lakh hectares in India in 2016-17, with a production of 10.51 lakh tonnes.

Turmeric production is estimated to be over 11 lakh tonnes per year worldwide. India leads the

global production scenario with 78 percent, followed by China (8 percent), Myanmar (4

percent), and Nigeria and Bangladesh, which together account for 6% of global production.

With a share of around 76 percent of total worldwide output and 90 percent of global trade,

India is effectively a monopolistic provider to the world. Among Indian states, Maharashtra is

second top producer and contributes around 18.57% in the total turmeric production.

The increasing urbanization offers huge market for readily available Turmeric powder

packaged attractively and merchandised in organized urban platforms such as departmental

stores, malls, super markets. Moreover, the increasing demand for natural products as food

additives makes turmeric powder an ideal candidate as a food colorant, thus increasing demand

for it

Used in foods Turmeric rhizomes

Turmeric powder

used in food and ceremonies

Drying and grinding of turmeric rhizomes

Volatile oil

Used for aroma in foods

Steam distillation of oils from ground

turmeric rhizomes, condensation

Oleoresin

Used for flavors in foods

Solvent extraction of turmeric

rhizomes/powdered turmeric, evaporative

removal of solvent

Curcumin powder

Used for color in food preparation

Precipitation of curcumin from

oleoresin using a hydrocarbon solvent.

Washing and polishing

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Curcumin has been shown to lower blood cholesterol in studies conducted over the

previous five decades. The major yellow bioactive component of turmeric, curcumin

(diferuloylmethane), has been proven to have a wide range of biological activities. Its

anticancer activity is primarily mediated by apoptosis induction. Curcumin's potential as a

therapy for Alzheimer's disease, viral infections, inflammation, malignancies, gastrointestinal

disorders, and other conditions has prompted much research and development. It is certainly

clear that the medicinal properties of curcumin generates its huge demand in the pharmaceutical

industry. India is the world's largest producer of curcumin, accounting for more than 80% of

global production. The worldwide curcumin showcase measure is anticipated to reach USD

99.3 million by 2024 and USD 151.9 million by 2027, growing at a CAGR of 12.7%. The

pharmaceutical application segment led the market in 2020 with the highest revenue share of

more than 51%. The segment is estimated to expand further at the fastest CAGR from 2020 to

2028. For many centuries, curcumin has been widely used in traditional Asian herbal medicines

to treat infections and inflammation. The cosmetics application segment is estimated to have

significant growth over the forecast period.

Since, PoCRA region, especially Hingoli has recently become the epicentre of turmeric trade

in the state, availability of raw material for processing should be comfortable. Also, the

established demand of turmeric powder and curcumin in regional, national and international

markets make turmeric powder and curcumin proposition advantageous for the FPCs.

12.3 Techno-economic analysis of turmeric powder

The following section describes the process and financial analysis of turmeric powder

manufacturing plant.

12.3.1 Process flow diagram

The process involved in manufacturing turmeric powder are as follows: boiling, drying,

polishing, grinding, sieving and packaging. Figure 38 presents the process flow diagram.

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Figure 38: Process flow diagram of turmeric powder processing

Boiling is the first post-harvest operation to be performed during turmeric powder processing

which involves cooking of fresh/wet rhizomes in water until soft before drying. Boiling

destroys the vitality of fresh rhizomes, avoids the raw odour, reduces the drying time and yields

uniformly coloured product. An effective cooking time of 45 to 60 minutes for fingers and 90

minutes for mother rhizomes is considered essential at around 80-100o C. The next process is

drying which involves removal of moisture from cooked rhizome. Different technologies for

drying are available such as vaccum drying, microwave drying and solar drying. The choice of

dryer depends on the economics of the plant. Usually, at farm level, the most common drying

technique is sun drying however in a processing unit sophisticated drying such as vaccum and

microwave is preferred. Dried turmeric has poor appearance and rough dull outer surface with

scales and root bits.

The appearance is improved by smoothening and polishing the outer surface by manual or

mechanical rubbing. Usually 5 to 8%of the weight of turmeric is the polishing wastage during

full polishing and 2 to 3% during half polishing. The polished turmeric fingers are subjected to

grinding. Grinding is one of the most common operations used to prepare turmeric powder for

consumption and resale. The main aim of particular spice grinding is to obtain smaller particle

sizes, with good product quality in terms of flavour and color. There are different ambient

Boiling (80-100o C)

1 hourDrying

Polishing (20-30 min)

Grinding & Seiving

Packaging

Cooked rhizome Dried Rhizome

Wet rhizomes

(1 kg)

+

Water

(1.5 litre)

Soil and solid waste

0.2 kg of dried turmeric

powder

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grinding mills and methods available for this process; such as hammer mill, attrition mill and

pin mill. Ground spices are size sorted through screens, and the larger particles can be further

ground. The screens usually used are 60 - 80 mesh size.

The turmeric powder is packed in packaging materials that deal with the common deteriorating

factors of turmeric powder such as hygroscopicity, loss of aroma/ flavour, discoloration, insect

infestation and microbial contamination. The volatile oil present in the spice product has a

tendency to react with the inner/ contact layer of the packaging material, at times leading to a

greasy and messy package with smudging of the printed matter.

12.3.2 Financial analysis

Table 12:4 presents the financial analysis for a Turmeric powder processing unit of 200 kg/hr

capacity. The assumptions and costs are considered after through study of literature and contact

with manufacturers/vendors. Since, the availability of wet rhizome is limited to 3 to 4 months

per year, therefore this analysis considers 120 days of plant operation per year. To effectively

utilize the plant capacity (since it is operating for 120 days only), two work shift of 8 hours

each is used for the analysis and based of these considerations, the annual raw material

requirement (wet rhizomes) is estimated to be around 384 tonnes. The fixed cost (capital

investment) include the cost of machinery, civil construction, taxes and pre-operative expenses.

It is assumed that the land is already available with the FPC and only civil construction cost is

applicable. The operating or variable cost include the salaries of staff, cost of raw material,

power/electricity, maintenance and contingency. The costs of individual items is mentioned in

Table 12:4.

As mentioned in the process flow diagram in Figure 38, 1 kg of wet rhizome produces 0.2 kg

of turmeric powder. Therefore the annual production of the plant at full capacity is 76.8 tonnes.

Assuming a wholesale selling price of turmeric powder as Rs. 160 /kg, the annual income of

the plant is estimated as Rs. 1,22,88000. Considering the life of plant as 10 years, the Net

Present Value (NPV) is calculated to be Rs ₹ 1,07,00,670 at a discount rate of 10%. The

calculation indicate an internal rate of return (IRR), benefit to cost ratio (BCR) and discounted

payback period (DPBP) as 33%, 1.49 and 3.37 years respectively. Since, the value of IRR is in

the acceptable range while BCR is more than one, it could be inferred that the turmeric powder

processing unit of the proposed capacity is convincingly profitable.

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84

Table 12:4: Financial summary of turmeric powder processing unit (2q/hr)

Value addition intervention-

Turmeric powder processing unit Remarks/Details Values

A. Plant capacity per annum (MT) 384

A.1 Plant capacity per hr (kg) 200

A.2 Number of operating days

120

A.3 Number of shifts per day 8 hrs per shift 2

A.4 Raw material input per annum 384000

B. Capital Investment

B.1

Cost of machine excluding taxes

(Washer,curing boiler, dryer, polisher,

grinder, siever)

3626100

B.2

Accessories

B.2.1 Packing machine 574350

B.2.2 Weighing scale 20000

B.2.3 Utensils 114900

B.3

Land (plant area-sqft) Land already available with

FPC-Assumed 2500

B.3.1 Civil Work including water

tank and electrical work

Construction cost 200/sqft +

utiltity cost 300/sft (Electrical) 1250000

B.4

Pre-Operational Expenses

B.4.1 GST 18% 5115670

B.4.2

Licencing, registration,

documentation, accountant

fees 300002

B.4.3 Training, Installation and

delivery charges 10% of equipment cost 433531

B.4.4 iii. Office Furniture &

Equipments 50000

B.4.5 iv. Miscellaneous 50000

B.5 Total Capital Investment 7199204

C. Annual Expenses

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85

D.1 Interest on Loan@ 10%pa Considering x% of capital cost is

loaned by FPC 287968

D.2

Salaries 247000

D.2.1 Manpower cost

3 per shift-Rs.8000/month,

1 manager-Rs.15000/month 207000

D.2.2 Marketing cost per annum 40000 per annum 40000

D.3

Raw Material cost

D.3.1 Raw Turmeric cost 5760000

D.3.2 Packaging material 1/- per kg of produce 384000

D.4 Power Consumption

D.4.1 Unit consumed per annum 100363

D.4.2 Cost of Electricity Rs. 10/kWhr 1003635

D.5 Cost of water

1 kg rhizome = 1.5 litre water,

Plain water at Rs. 0.12/litre 69120

D.6 Maintenance 30000

D.7 Contingency 5% of total fixed cost 359960

D.8 Depreciation

D.8.1 Depreciation on Furniture at 10% 5000

D.8.2 Depreciation on Machines at 10% 422044

D.8.3 Depreciation on Civil work at 10% 125000

D.9 Total Annual Expenses (D.1 : D.8) 8693727

E. Total production per annum 20% recovery from wet rhizomes 76800

F. Cost of Production Rs/kg 113.19

G. Annual Income (Full capacity) Turmeric powder selling price -

Rs. 160/kg 12288000

H. Economic Indicators

Plant life : 10 years.

Capacity Utilization : Year 1-

50% , Year 2 – 65%, Year 3 –

80%, Year 4 onwards 100%

G.1 Net present value (NPV) In Rs. ₹ 1,07,00,670

G.2 Internal rate of return (IRR) % 33

G.3 Benefit to cost ratio (BCR) 1.49

G.4 Discounted payback period In years 3.37

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12.3.3 Sensitivity analysis

To understand the impact of fluctuation of variables on the returns, a sensitivity analysis is

done. Those variables are chosen which might vary in real time situations. These variables are

cost of wet rhhizome, cost of packaging material, turmeric powder selling price, capital

investment, operating days and plant capacity. A variation of ± 30% in the variables is

considered for this analysis while its impact on the BCR is studied. The plant capacity is varied

at 100 kg/hr, 200 kg/hr (base case) and 300 kg/hr. The analysis is done by changing one variable

at a time while keeping others constant.

Figure 39 shows the results of the sensitivity analysis. The base case-200 kg/hr is taken as

benchmark to understand the variation due to each variable. As seen in Figure 39, turmeric

powder selling price is the most sensitive variable as it causes the highest variation. Similarly,

in the order of sensitivity, wet rhizome cost, operating days and plant capacity are the next

three sensitive variables. Cost of packaging material turns out to be the least sensitive, meaning

that by changes in cost of packaging material doesn’t affect the BCR significantly as compared

to other variables. It could also be observed in Figure 39 that the BCR in certain scenarios is

less than 1, suggesting that those scenarios should be avoided to prevent losses.

Figure 39: Sensitivity analysis of Turmeric powder processing unit

-2 -1 0 1 2 3 4 5

Wet Rhizome cost

Cost of packaging material

Turmeric powder selling price

Capital Investment

Operating days

Capacity (110kg/hr,200kg/hr, 300kg/hr)

BCR

Var

iab

les

30% -30%

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87

Figure 40: NPV and BCR vs Turmeric powder selling price

Since turmeric powder selling price is observed to be the most sensitive parameter, another

study is performed to understand the variation of turmeric powder selling price with NPV and

BCR. The purpose of this study is to identify threshold values of turmeric powder selling price,

below which the turmeric powder should not be sold to avoid losses. The cost of production of

turmeric powder is estimated as Rs. 113.19/kg which means selling turmeric powder higher

than Rs. 113.19/kg would be profitable. However, as suggested in Figure 40, for positive NPV,

the turmeric powder selling price should be above Rs. 133.47/kg. Also, considering a BCR

more than 1, the minimum value for selling turmeric powder should be more than Rs. 151.3/kg.

It could be inferred that an appropriate price for selling the turmeric powder should be above

Rs. 151.3/kg while to achieve a BCR of 2, 3 and 4 the prices should be Rs. 169/kg, Rs. 187/kg

and Rs. 205/kg respectively.

As mentioned above, turmeric is also processed to get curcumin powder and oleoresin which

contribute as the mail component which give medicinal properties to the commodity. The

following section explains detail techno-economic analysis of curcumin extraction plant along

with financial analysis.

-1

0

1

2

3

4

5

-100

-50

0

50

100

150

200

250

300

350

110 130 150 170 190 210

BC

R

NP

V (

Lakh

s)

Turmeric powder selling price (Rs/kg)

NPV BCR

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88

12.4 Techno economic analysis of curcumin extraction plant

The following section describes the process and financial analysis of curcumin extraction plant.

12.4.1 What is curcumin?

Curcumin is an orange–yellow crystalline powder essentially insoluble in water. Curcumin is

yellow in color shade and is most precious constituent of turmeric. Curcumin is one of the three

curcuminoids that appear in turmeric, the other two being desmethoxycurcumin and bis-

desmethoxycurcumin. These curcuminoids allow turmeric its yellow color and curcumin is

utilized as a yellow food colorant and additive. Curcumin is extracted from the dried rhizome

of the turmeric plant, which could be a lasting herb that is cultivated majorly in south and

Southeast Asia. The rhizome or the root is processed to create turmeric which contains 2% to

5% curcumin. Curcumin is the most naturally dynamic photochemical compound of Turmeric.

12.4.2 Process flow diagram

The curcumin extraction unit describes here is based on solvent extraction method. The raw

materials required for the plant are dried turmeric rhizomes, solvent (ethanol) and

isopropanol.

Industrial scale extraction of curcumin analyzed in this work can be represented in five steps:

1. Extraction of curcumin from turmeric using a solvent (ethanol).

2. Separation of curcumin-laden solvent from soaked rhizomes.

3. Recovery of solvent and concentration of extracted solution using evaporation.

4. Separation of curcumin from the oleoresin via crystallization.

5. Drying to obtain curcumin powder.

The primary step is to add cleaned turmeric rhizomes in a percolator tank. After the rhizomes

are added, the solvent is added into the percolator chamber for almost 6 hours. This operation

time of 6 hours is evaluated with regard to residence time of 4 hours. A fluid extract or

curcumin loaded solvent is obtained. This liquid is then pumped and after that filtered to isolate

the insoluble impurities such as skin, rootlets, rhizome particles etc. with the help of a

centrifuge. This decontaminated fluid extract is at that point concentrated using an evaporator

to a wanted concentration. The evaporator boils the ethanol solvent and water from the blend,

taking off an oily residue with high curcumin concentration called oleoresin. The oleoresin

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89

contains fixed oil, curcuminoids (generaly from 20-60%), together with some amounts of

ethanol and water.

The oleoresin is cooled to room temperature using a heat exchanger. Within the base-case

design, half of the oleoresin is collected as a product, and the curcumin from the remaining

oleoresin is crystallized utilizing isopropanol as solvent at low temperatures for higher yields.

Amid centrifugation, settled oils alongside isopropanol clears out as mother alcohol, and the

precipitate is collected. At last, the solids from the centrifuge are dried in a vacuum tray dryer

to get dried curcumin which can be powdered and packed. The process flow diagram in shown

in Figure 41

Figure 41: Process flow diagram of Curcumin

12.4.3 Financial analysis

Table 12:5 presents the financial analysis for a curcumin processing unit of 10 tonnes per day

capacity. The assumptions and costs are considered after through study of literature and contact

with manufacturers/vendors. The analysis has been done considering the 300 days of

operations. Based of these considerations, the annual raw material requirement (dried

rhizomes) is estimated to be around 3000 tonnes. The fixed cost (capital investment) include

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90

the cost of machinery, land, civil construction, taxes and pre-operative expenses. The operating

or variable cost include the salaries of staff, cost of raw material, power/electricity, fuel,

maintenance and contingency. The costs of individual items is mentioned in Table 12:5.

It is considered that of the total curcumin content present, 50 % is in curcumin powder and

50% is in oleoresin. As mentioned in the process flow diagram, 1 kg dried turmeric rhizomes

will yield 85g oleoresin while 17.7 g curcumin powder. The annual production of the plant at

full capacity is 34 tons curcumin powder and 160 tons oleoresin. Assuming the wholesale price

of curcumin powder as Rs.5000/kg, that of oleoresin as Rs.200/kg, the annual income of the

plant is estimated to be Rs. 20,20,00,000. Considering the life of plant as 10 years, the Net

Present Value (NPV) is calculated to be Rs 2,83,14,382 at a discount rate of 10%. The

calculation indicate an internal rate of return (IRR), benefit to cost ratio (BCR) and discounted

payback period (DPBP) as 29.05%, 1.17 and 3.62 years respectively. Since, the value of IRR

is in the acceptable range while BCR is more than one, it could be inferred that the curcumin

processing unit of the proposed capacity is convincingly profitable.

Table 12:5: Financial Summary of curcumin extraction unit (10 TPD)

Value addition intervention- Curcumin

extraction unit Details Values

A. Plant capacity per day (MT) 10

A.1 Number of operating days 200

A.2 Raw material input per batch

A.2.1 Dried Turmeric rhizomes in kg 10,000

A.2.2 Solvent (Ethanol) in kg 1,00,000

A.2.3 Isopropanol in kg 6,000

B. Capital Investment

B.1

Cost of Machine excluding taxes &

duties

(Grinder,Percolator, Centrifuge,

86,15,000

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91

Pre-heater, Evaporator, Cooler,

Crystallizer, Dryer)

B.2

Solvents

B.2.1 Solvent (Ethanol) Rs. 60/kg 60,00,000

B.2.2 Isopropanol Rs. 130/kg 7,80,000

B.3

Land (plant area) In sqft (square feet) 2,000

B.3.1 Land cost

(ownership/leased) 2000/- sqft including taxes 30,00,000

B.3.2

Civil Work including

water tank and electrical

work

Construction cost 200/sqft +

utiltity cost 300/sft

(Electrical)

10,00,000

B.4

Pre-Operational Expenses

B.4.1 GST on machines 18 % 27,71,100

B.4.2

Licencing, registration,

documentation, accountant

fees

3,00,000

B.4.3 Training, Installation and

delivery charges 10% of equipment cost 15,39,500

B.4.4 iii. Office Furniture &

Equipments

50,000

B.4.5 iv. Miscellaneous 50,000

B.5 Total Capital Investment

(B.l+B.2+B.3+B.4) 2,41,05,600

C. Annual Expenses

C.1 Interest on Loan@ 10%pa Considering 40% of capital

cost is loaned by FPC

9,64,224

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92

C.2

Manpower Cost 3 Workers @

10000/- per month and 1 supervisor

@ 30000/- per month and

200000/- marketing

expenditure per annum 9,20,000

C.3

C.3.1 Raw Turmeric cost Rs. 75/kg 15,00,00,000

C.3.2 Solvent (Ethanol) 2% losses per batch 2,40,00,000

C.3.3 Isopropanol 2% losses per batch 31,20,000

C.3.4 Packaging material 20/- per kg of produce 38,80,000

C.4

Power Consumption

C.4.1 Unit consumed per annum 4,40,000

C.4.2 Cost of Electricity @ Rs.

10/KW

Industrial power supply-

10Rs/kWhr 44,00,000

C.5 Cost of water 1 kg rhizome = 10 litre water 24,00,000

C.6 Maintenance 20,000

C.7 Contingency 5% of total fixed cost 12,05,280

C.8 Depreciation

C.8.1 Depreciation on Furniture at 10% 5,000

C.8.2 Depreciation on Machines at 10% 5,66,500

C.8.3 Depreciation on Civil

work at 10% 1,00,000

C.9 Total Expenses (C.3 + C.12) 19,15,81,004

D. Total production per annum

D.1 Curcumin Powder in kg 34,000

D.2 Turmeric oleoresin in kg 1,60,000

F. Annual Income (Full capacity) Selling price of curcumin :

Rs. 5000/kg 20,20,00,000

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Selling price of oleoresin :

Rs. 200/kg

G. Economic Indices

Plant life : 10 years.

Capacity Utilization :

Year 1- 50% , Year 2 – 65%, Year 3 – 80%,

Year 4 onwards 100%

G.1 Net present value (NPV) IN Rs. 2,83,14,382.08

G.2 Internal rate of return (IRR) % 29.05%

G.3 Benefit to cost ratio (BCR) 1.17

G.4 Discounted payback period In years 3.62

It was observed during the financial analysis that the minimum threshold capacity for viable

curcumin extraction plant was 10MT/day. A plant below 10MT/day capacity produced

negative NPV and therefore is not recommended based on the considerations in Table 12:5.

12.4.4 Sensitivity analysis

A sensitivity analysis is done by creating scenarios of the dried turmeric rhizome cost,

curcumin powder selling price and oleoresin selling price. The sensitivity of NPV and BCR is

studied under three different scenarios. Following are the three scenarios.

Base case Scenario: Dried turmeric rhizomes cost- Rs. 75/kg, Curcumin powder selling price-

Rs. 5000/kg and Oleoresin selling price - Rs.200/kg

Low price: Dried turmeric rhizomes cost- Rs. 70/kg, Curcumin powder selling price- Rs.

4000/kg and Oleoresin selling price - Rs.150/kg

High price: Dried turmeric rhizomes cost- Rs. 80/kg, Curcumin powder selling price- Rs.

6000/kg and Oleoresin selling price - Rs.250/kg

It is clearly visit in Table 12:6, that the scenarios drastically affect the overall economics of the

curcumin extraction unit. A high raw material cost negatively affects the economics while in

case of high price scenario, along with a high raw material cost, the selling price of curcumin

powder and oleoresin as also kept high and it could be observed that the NPV is almost seven

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94

times higher than the case. The high volatility of curcumin plant for raw material cost and

selling price is evident through this analysis.

Table 12:6: Sensitivity of NPV and BCR with different cases of raw material cost and selling

prices

12.5 Conclusion

In this section, curcumin extraction unit is introduced and its potential in the PoCRA district is

discussed. Based on the techno-economic analysis, it is understood that the curcumin extraction

plant of the proposed capacity is profitable with a quantum requirement of around 3000

MT/annum. Based on the field visit experience, majority of the FPCs have sufficient quantum

to venture into the curcumin business. Additionally, it is observed in the analysis that as the

capacity of the unit is increased, the profitability increases therefore FPCs with large quantum

could plan higher capacity processing plants. As compared to the current activities of FPCs,

curcumin extraction plant could provide a profit of around 129% while trading of turmeric

could generate merely 2%. Therefore curcumin extraction unit could be seen as highly

profitable value addition intervention for the PoCRA region.

-7

-5

-3

-1

1

3

5

7

9

-20

-15

-10

-5

0

5

10

15

20

25

Low Base High

BC

R

NP

V (

cro

re R

s)

Sensitivity of curcumin

NPV BCR

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13 Future Work

● Uncertainty/Risk analysis of the proposed products

● Understand market/forward linkages for the proposed value-added products, details of

food safety measures and regulatory aspects

● Prepare a DPR for the selected FPC

***

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14 Work Plan for the year 2021-2022

Tasks completed

Tasks ongoing

Tasks planned

Section 1: Technology Intervention to reduce post-harvest losses of onions

Tasks

A

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Preliminary Report (Overall: Field & Desk)

1. Matrix Development

1.1 Mapping of Onion FPC within PoCRA-

1.2 Total Production/Productivity/Area Sowing & Harvesting Schedule, Variety of onion.

1.3 Current Practices (Selling in Market/Processing), Mode of Selling, Any Current Value

Addition & Storage, Seasonality

1.4 FPC Portfolio (No of Farmers associates, variety of onion, Revenue, Profit

1.4.1 Identifying potential buyers based on the current demand of products (Onion). Prepare

a list of potential forward linkages.

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1.5 Identification turn- key providers for onion storage intervention (Pre fab structure, cooling

system, sensors & controls)

1.6 Screening of FPCs from PoCRA project list for Technological Intervention based 1.

1.7 Detail Market Analysis of Onion/Onion based products resulting from technological

intervention (seasonality-based Market Demand, Export, Price trends, Profit margins

2. Match Making with FPCs with Technological Intervention

3. Financial Viability Model

4. Installation & Commissioning

4.1 Selection of vendors from 1.5

4.2 Installation and post Installation support (As per MoU)

4.3 Final Impact report preparation

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Section 2: Technology Intervention for value addition of agriculture produce via processing

Tasks Aug Sep Oct Nov Dec Jan Feb

Preliminary Report (Overall: Field & Desk)

1. Matrix Development

1.1 Mapping of Crops FPC within PoCRA

1.2 Total Production/Productivity/Area Sowing & Harvesting Schedule, Variety

1.3 Current Practices (Selling in Market/Processing), Mode of Selling, Any Current Value

Addition

1.4 FPC Portfolio (No of Farmers associates, variety of crops, Revenue, Profit

1.5 Screening of FPCs for Technological Intervention based 1.4

1.6 Ranking/Screening of Crops based on (1.1-1.4) Economic Value, Market

Integration/Demand

1.6.1 Identification of a list of technological interventions which are feasible (based on Shelf

life, scale & economics) eg: Soybean to protein, soya oil etc.: Turmeric to curcumin etc.

1.6.2 Identifying potential buyers based on current demand of products. Prepare list of

potential forward linkages.

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1.7 Identification turnkey providers for screened technological interventions

1.8 Detail Market analysis of product resulting from technological intervention (Market

Demand, Export, Domestic Demand, Price trends, Profit margins)

1.9 Logistics of processed products (Storage + Transport)

2. Match Making with FPCs with Technological Intervention

3. Financial Viability Model

4. Details food safety measures & regulatory aspects.

5. Preparation DPR and necessary revisions

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Appendix A

Template of the Cost structures

IIT Bombay CA Storage strucuture MahaOnion Storage Structure TATA Steel

MT 100 200 500 1000 100 200 500 1000 100 200 500 1000

Rs

NPV

27,17,897

90,80,572

2,58,91,954

5,93,10,624

7,73,805

32,45,874

94,03,520

2,31,70,497

1,75,569

8,26,681

99,31,726

2,39,56,043

% IRR 9% 21% 23% 30% -1% 9% 15% 22% -11% -8% 9% 12%

Yrs

DPBP 8.5 5.8 5.5 4.5 11.6 8.4 6.9 5.6 #REF! 14.3 8.5 7.6

BCF 0.91 1.82 1.99 2.64 0.39 0.93 1.34 1.93 0.05 0.14 0.90 1.14

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101

Template of the Analysis

500 MT Loan Amount 97,50,000.00 Self Investment 32,50,000.00 Subsidy -

Year Cap_Inv OP_Cost Revenue Net FP_1 Loan Payment Net FP Net Debt_FP Net PV Cum_PV Balance_PV Net Debt_PV

1,30,00,000

- -

-1,30,00,000

1

48,85,339 85,00,000 36,14,661.0 37,59,247 12,81,869 24,77,378 -1,87,18,792 22,52,161.92 22,52,162 22,52,162 -1,56,94,008.62

2

48,85,339 85,00,000 36,14,661.0 39,09,617 12,81,869 26,27,748 -1,48,09,175 21,71,692.57 44,23,854 44,23,854 -1,22,40,446.72

3

48,85,339 85,00,000 36,14,661.0 40,66,002 12,81,869 27,84,133 -1,07,43,173 20,91,760.11 65,15,615 65,15,615 -88,66,817.29

4

48,85,339 85,00,000 36,14,661.0 42,28,642 12,81,869 29,46,773 -65,14,531 20,12,685.46 85,28,300 85,28,300 -55,72,262.50

5

48,85,339 85,00,000 36,14,661.0 43,97,788 12,81,869 31,15,918 -21,16,743 19,34,740.22 1,04,63,040 1,04,63,040 -23,55,652.96

6

48,85,339 85,00,000 36,14,661.0 45,73,699 12,81,869 32,91,830 24,56,956 18,58,152.21 1,23,21,192 1,23,21,192 7,84,368.58

7

48,85,339 85,00,000 36,14,661.0 47,56,647 12,81,869 34,74,778 72,13,603 17,83,110.52 1,41,04,303 1,41,04,303 38,49,348.42

8

48,85,339 85,00,000 36,14,661.0 49,46,913 12,81,869 36,65,044 1,21,60,517 17,09,770.00 1,58,14,073 1,58,14,073 68,40,987.75

9

48,85,339 85,00,000 36,14,661.0 51,44,790 12,81,869 38,62,920 1,73,05,306 16,38,255.33 1,74,52,328 1,74,52,328 97,61,112.40

10

48,85,339 85,00,000 36,14,661.0 53,50,581 12,81,869 40,68,712 2,26,55,888 15,68,664.59 1,90,20,993 1,90,20,993 1,26,11,646.32

11

48,85,339 85,00,000 36,14,661.0 55,64,605 12,81,869 42,82,735 2,82,20,492 15,01,072.57 2,05,22,066 2,05,22,066 1,53,94,588.21

12

48,85,339 85,00,000 36,14,661.0 57,87,189 12,81,869 45,05,319 3,40,07,681 14,35,533.60 2,19,57,599 2,19,57,599 1,81,11,991.14

13

48,85,339 85,00,000 36,14,661.0 60,18,676 12,81,869 47,36,807 4,00,26,357 13,72,084.25 2,33,29,683 2,33,29,683 2,07,65,944.71

14

48,85,339 85,00,000 36,14,661.0 62,59,423 12,81,869 49,77,554 4,62,85,780 13,10,745.54 2,46,40,429 2,46,40,429 2,33,58,559.57

15

48,85,339 85,00,000 36,14,661.0 65,09,800 12,81,869 52,27,931 5,27,95,581 12,51,525.10 2,58,91,954 2,58,91,954 2,58,91,953.99

NPV Rs. 2,58,91,953.99

1,92,28,040 5,60,45,581

IRR 23%

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102

DPBP 5.46

BCF 1.99

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103

Price fluctuations analysis (Pune APMC)

2002-

03

2003-04 2004-05 2005-06 2006-07 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19 2019

March 56% 43% 104% 62% 72% 38% 96% 76% 71% 58% 62% 48% 60% 93% 42% 86% 24%

April 97% 54% 84% 68%

39% 81% 65% 70% 64% 57% 56% 56% 97% 41% 73% 34%

May 70% 61% 86% 70% 65%

74% 68% 73% 67% 65% 63% 73% 103% 35% 71% 44%

June 100% 93% 105% 82% 84% 50% 102% 85% 103% 83% 98% 109% 100% 127% 48% 115% 67%

July 128% 95% 98% 123% 80% 80% 98% 84% 121% 95% 138% 125% 146% 126% 55% 134% 71%

August 162% 98% 102% 146% 95% 97% 85% 105% 143% 103% 280% 110% 270% 106% 145% 129% 102%

Septembe

r

175% 102% 96% 198% 100% 100% 96% 155% 145% 97% 299% 105% 286% 89% 108% 101% 146%

October 219% 154% 108% 250% 112% 126% 181% 165% 126% 121% 272% 99% 232% 87% 168% 143% 105%

Novembe

r

215% 142% 111% 282% 126% 132% 240% 243% 124% 164% 241% 101% 206% 130% 227% 139% 163%

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104

December 97% 125% 102% 116% 208% 132% 272% 377% 97% 193% 102% 103% 100% 122% 160% 99% 286%

January

135% 87% 84% 234% 157% 187% 272% 62% 177% 64% 85% 91% 87% 135% 76% 158%

February 70% 122% 73% 61% 207% 130% 145% 95% 51% 170% 41% 85% 55% 80% 92% 49% 98%

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Appendix B

This section presents a brief description of FPCs visited in three districts (Aurangabad, Washim

and Buldana). The description of FPCs of remaining district will be covered in the next stage

report/DPR.

Jai Siddheshwar:

Jai Siddheshwar FPC is located in Silod taluka of Aurangabad district. It has ~1300

shareholders among which ~700 are small and marginal holders. Based on the rating given by

PMU, Jai Siddheshwar FPC has a score of 68 which is the best score in Aurangabad district. It

has the potential to deal in commodities such as maize, cotton, jowar, bajra, onion and toor.

Currently, they are involved in cleaning, grading and packaging of grains with 4 TPH capacity

machines. Jai Siddheshwar FPC has a grain storage of 500 MT capacity. According to Devidas

Dhakar (Director), the FPC could manage a quantum of 10000 MT of maize, 1000MT of Jowar,

350MT of Bajra and 200MT of Toor. They have procurement and direct selling licences,

FSSAI certification and they also own their own brand. They are willing to invest into new

processing/value addition units. However, they would prefer a small scale unit due to

constraints in self-investments.

Godavari valley FPC (Karmad FPC)

Karmad FPC which is now renamed as Godavari valley FPC is situated in Aurangabad district

and has 509 shareholders with around 80% small and marginal holders. Karmad FPC is about

4 years old and has a score of 60 which is fourth from top in Aurangabad district as per the

evaluation done by PMU in January, 2021. Currently, they are dealing in Maize, ginger,

pomegranate, gram, onion and toor. Based on the discussion with Bharat Sapkal of the FPC,

they could manage a quantum of 2000 MT of maize, 500 MT of ginger, 100 MT of

pomegranate and 1000 MT of onion. They have cleaning, grading, storage and packaging

facilities for grains with a storage capacity of 1000 MT. Karmad FPC is involved in the seed

program of onion with a coverage of around 150 acre. They can increase their quantum for

ginger from 500 MT to 5000 MT if they find cost-effective and profitable business

interventions. Other than ginger, Mr. Sapkal has shown interest in processing of B and C grade

onion for better net returns. They aspire to cultivate Geranium and initiate essential oil

business. Also, they would prefer technical and marketing related support and are open to value

addition business in multiple commodities.

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Grishneshwar FPC

Grishneshwar FPC is located in Silod block of Aurangabad district. The FPC is 6 years old and

has 1500 shareholders with around 35% small and marginal holders. Based on the rating given

by PMU in, Grishneshwar FPC has a score of 63. They deal in Maize, gram, toor, jowar and

wheat with an average quantum of 750 MT, 10 MT, 10 MT, 200 MT and 300 MT respectively.

Currently, they are involved in cleaning, grading, storing and packaging of grains with a current

storage capacity of 500 MT while a warehouse of 2500 MT capacity has been sanctioned under

SMART scheme. According to Mr. Dadasaheb Jagtap, the director of the FPC, they have

proposed an onion storage structure and aspires to start a cattle feed business. Their existing

idea is to use maize plants and waste from cleaning maize as a cattle feed. They are willing to

invest in reasonable value addition interventions.

Pinakeshwar FPC

Pinakeshwar FPC is situated in Vaijapur taluka of Aurangabad district. It is a 3 year old

company having 425 shareholders. It has a score of 58 as per PMU rating (Jan 2021) which is

fifth from top in Aurangabad district. Pinakeshwar FPC majorly deals in Maize only with a

quantum of 200MT. They have cleaning, grading and storing facilities and aspire to expand

their commodity base into ginger, onion and chilli. The potential quantum for ginger, onion

and chilli is 3000 MT, 500 MT and 1000 MT respectively. According to Mr. Suresh Kate, the

director of the FPC, the FPC is interested in processing ginger, onion and chilli if they get

technical and marketing related support.

Krushi Kranti FPC

Krushi Kranti FPC is located in Kannad taluka of Aurangabad district. It is a 6 year old

company having 508 shareholders. As per PMU rating, it has a score of 68 which is the top

score in the evaluation done during April, 2018. Krushi Kranti is actively involved in drying

ginger, turmeric and onion. The drying happens in solar dryers which are sourced from S4S

technologies. The dried produce is collected by S4S and the FPC is paid on a per kg basis. The

quantum of drying is 500 MT of ginger, 500 MT of onion and 500 MT of turmeric. Apart from

drying activities, Krushi Kranti FPC deals in Maize, bajra and toor. They have cleaning,

grading and storage facility and currently deal in 1000 MT of Maize and 20 MT of each bajra

and toor. Krushi Kranti FPC is open from other value addition interventions in multiple

commodities.

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Akash FPC

Akash FPC is located in Silod taluka of Aurangabad district. It is 11 year old and has a score

of 65 which is the second best in Aurangabad district as per evaluation done by PMU in January

2021. Akash FPC deals in Maize, ginger, gram, wheat and chilli. Their quantum of dealing is

300 MT, 4000 MT, 6000 MT and 4000 MT of maize, ginger, wheat and chilli respectively.

They have cleaning, grading and storing facility for grains and plan to venture into flour mill

with a capacity of 2500 MT/year. They are large capacity FPC and could invest if appropriate

and manageable value addition interventions are proposed.

Krushi Samrajya FPC

Krushi Samrajya FPC is around 5 years old and located in Mangrulpir taluka of Washim

district. It has 260 shareholders among around 100 being small and marginal farmers. It has a

rating of 60 which is third best in the Washim district as per evaluation done by PMU in January

2021. Currently they deal in only Soybean with a quantum of 80 MT and they would like to

expand their quantum to 130 MT of soybean and 50 MT in gram. According to Mr Devman

Gahure of the FPC they aspire to have a besan mill, NAFED agency, magnet de-stoner for

cleaning and grading. They have financial problems for planning new interventions but they

could take up new interventions if financial support is provided.

Krushi Mauli FPC

Krushi Mauli FPC is around 5 years old and located in Mangrul pir taluka of Washim district.

It has a rating of 59 which is third best in the Washim district as per evaluation done by PMU

in April 2018. The FPC is primarily involved in neem oil extraction. They have a neem oil

extraction plant of 2TPH capacity benefitted through PoCRA. They extract oil with about 2%

oil recovery of about 10 MT dried neem seeds annually which they procure from Karnataka

and MP. The residue is used for making neem cake. According to Mr. Dilip Fuke currently

they deal in about 500 MT soybean, 400 MT gram, 100 MT toor, 10 MT udit, 10 MT mung,

and 300 MT wheat. Of the procured quantity, about 300 MT soybean, 20 MT of Toor and 300

MT of gram is being used for the seed program. They are interested in turmeric processing,

organic production of cereals and pulses, soybean oil production, wheat flour, machines for

packing, cold storage, cultivation of baby corn and establishing their own brand.

Parivartan FPC

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Parivartan FPC is around 5 years old and is located in Karanja Lad taluka of Washim district.

It has a rating of 70 which is the top score as per PMU’s evaluation done during April, 2018.

Currently, they deal in soybean, gram, toor and wheat. Their existing quantum is as follows:

600 MT of soybean, 300 MT of gram and 200 MT of wheat. They have cleaning, grading,

storage and packaging facility with cleaning and grading of 2TPH, warehouse of 250 MT and

two vehicles of total 21MT capacity. They are NAFED agents and also provide Soybean to

NCDEX. According to Mr. Dnyaneshwar Dhekre of the FPC, they plan to expand their soybean

quantum by 400 MT, gram by 2500 MT and start a seed program for gram in around 100 acre.

They want to venture into cold storages for citrus fruits, tomato, and colour sortex, turmeric

processing and organic vegetable production.

Nardus FPC

Nardus FPC is around 3 years old and is located in Karanja Lad taluka of Washim district. It

has a rating of 63 which is the best score in the Washim district as per PMU’s evaluation.

Nardus FPC is currently involved in essential oil business wherein they extract oil from

turmeric leaves, citronella, lemon grass, Palma Rosa and geranium. Their current raw material

quantum is 500 MT of turmeric leaves, 250 MT citronella, 1500 MT lemongrass and Palma

Rosa and 500 MT of geranium. Turmeric leaves are procured free of cost (labour cost

applicable) and an oil recovery of 0.9% is obtained. In case of citronella, lemon grass, Palma

Rosa and geranium, the oil recovery rate is 1&, 0.8-0.9%, 0.6-0.8% and 0.1%

respectively. According to Mr. Kuldeep Khdase, in future, they aspire to by-products (essential

oil business) such as perfumes and incense stick. Also, they would like a testing lab, distillation

unit, gas chromatograph and drying unit.

Greenza FPC

Greenza FPC is about 7 years old and is located in Karanja Lad block of Washim district. It

has a rating of 60 as per PMU’s evaluation done during April, 2018. They have 104

shareholders of which about 80% are small farmers. They are majorly involved in custom

hiring centre and soil testing lab. According to Mr. Nilesh Heda, they are interested for value

addition of turmeric and chilli.

Krishidip FPC

Krishidip FPC is located in Malegaon taluka of Washim district. It is 7 years old and has 894

shareholders among which 310 are small and marginal. As per PMU rating in April, 2018, it

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has a score of 60 which is the second best score in Washim district. Krishidip FPC majorly

deals in Gram (Chickpea) and their previous quantum were 200 ton in 2020 and 180 ton in

2021. The FPC is currently involved in cleaning, grading, sorting, bagging, packaging and

certification of gram seeds. The FPC presently have 2TPH seed processing plant, 250 MT

warehouse, two vehicles of total 11 ton capacity. FPC also has 1 Q/hr turmeric processing unit

but currently not working. The FPC has a big well to supply domestic water in the village.

According to Mr. Vasanta Ashruji Landkar of FPC they plan to expand their gram quantum by

250 ton. They are interested in dealing and processing of wheat and want to create their own

brand. They are also interested in spices processing for example making of turmeric powder,

chilli powder and integrated spices.

Aayushya FPC

Aayushya FPC is located in Risod Taluka of Washim district. It is a 2 year old company having

450 shareholders and among which 250 are small and marginal farmers. As per PMU rating in

January, 2021, it has a score of 55 which is the fifth best score in Washim district. The FPC is

mostly involved in the organic dal preparation of pulses. Currently FPC is actively involved in

cleaning, grading, sorting, and dal preparation of pulses but it doesn’t own any dal mill or

cleaning grading unit. It carry out all its operations through renting equipment through other

FPC (Bhutekar fpc agro plant Dal mill).The FPC is currently dealing in the chickpea(gram),

wheat, toor, udid ,mung the quantum of above commodities is as follows: 5 MT, 40 MT, 5

MT, 1 MT, and 1-1.5 MT respectively. The FPC is dealing in small scale quantum because of

lack of infrastructure and facilities. According to Mr. Madan Baban Shrikhande the recovery

of dal from above commodities is equal for all commodities that is 75% while rest of the part

goes waste during processing. According to Mr. Madan Shrikhande the FPC deals in organic

dal preparation and the area of shareholders is around 20% organic and they gain the rate for

organic dal for toor, chickpea, udid mung and for sortex wheat as follows 130 Rs/kg having

1kg and 5kg of packings, 120 Rs/ kg, 140 Rs/ kg, 140/ kg and for wheat as 35 Rs/ kg. They

home deliver in Washim, Risod, and Malegaon talukas. They also use channels of ATMA for

marketing of dal. According to Mr. Madan Shrikhande, they want to expand their quantum and

scale up their own business and make around 200 acre of shareholders land organic and also

establish their own brands in organic dal.

Hariom FPC

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Hariom FPC is located in Risod Taluka of Washim district. It is a 5 year old company having

311 shareholders among which 150 are small and marginal farmers. As per PMU rating in

January, 2021, it has a score of 60 which is the third best score in Washim district. They mostly

deal in soybean, Gram, wheat and Toor. Their existing quantum is 110 MT, 120 MT and 15

MT for soybean, gram and toor while they have 20 ha of wheat shareholders area. The FPC is

involved in cleaning, grading, packaging, and packaging of seeds. It is also involved in seed

certification and is an authorised district seed certification agency. The FPC has a cleaning,

grading and seed processing unit of 4TPH capacity and 500 MT storage. The company had

NAFED procurement centre in 2017-18 but unavailable currently. FPC is involved in seed

processing and they make 3300 bags of soybean seeds each of 30kg, 3600 Gram (chickpea)

bags each of 30kg and wheat and 200 bags each of 40kg. Having selling prize 2700/ bag 1900/

bag and 2000/ bag for soybean, chickpea and wheat respectively. According to Mr. Gajanan

Awchar they have plans to expand their quantum by 160 MT, 160 MT, and 20 MT for soybean,

gram, wheat respectively. FPC is interested in vegetables onion seed production, turmeric

processing and soybean oil extraction.

Rajshree FPC

Rajshree FPC is located in Lonar taluka of Buldhana district. It is a 2 year old company having

1320 shareholders of which about 1020 are small and marginal farmers. Based on the rating

given by the PMU in Jan 2021, Rajshree FPC has a score of 59 which is third best in the district.

Currently the FPC is actively involved in the seed program of soybean, toor, wheat and gram.

The FPC is using infrastructure for cleaning, grading, bagging, packaging and storage of

Sonpaul FPC which is about 20 km away. The FPC deals with 100 MT of soybean, 3 MT of

toor, 200 MT of gram and 6 MT of wheat. The FPC is also a NAFED agent. According to Mr.

Jitendra Sanap of Rajshree FPC, they are willing to build their own infrastructure for cleaning,

grading, packing and storage and expand their seed program.

Sonpaul FPC

Sonpaul FPC is located in Lonar taluka of Buldhana district. It is a 3 year old company having

1010 shareholders of which 670 are small and marginal farmers. Based on the rating given by

the PMU in Jan 2021, Sonpaul FPC has a score of 71 which is the best in the district. Currently

the FPC is actively involved in the seed program of soybean, toor, wheat and gram. The FPC

deals with about 700 MT soybean, 500 MT gram, 375 MT toor and 135 MT wheat. The FPC

has a cleaning grading unit of 4 tph and storage facility of 3000 MT capacity. According to Mr.

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Pimparkar, secretary of the FPC, they have proposed a dal mill of capacity 10 MT per day, a

warehouse of 2000 MT under SMART scheme and a cold storage of 2000 MT under MAGNET

scheme. The FPC is interested in processing of turmeric, flour making and dal mill.

Ruj FPC

Ruj FPC is located in Mehkar taluka of Buldhana district. It is a 3 year old company having

252 shareholders of which 116 are small and marginal farmers. Based on the rating given by

the PMU in Jan 2021, Ruj FPC has a score of 61 which is second best in the district. Currently

the FPC is actively involved in the seed program of soybean and gram. The FPC deals with

about 51 MT soybean and 17 MT gram. The FPC has a cleaning grading unit of 4 tph and

storage facility of 3000 MT capacity. According to Mr. Yoganand Deshmukh of the FPC, they

are interested in establishing an oil mill, cold storage and pulp extraction unit.

Jay Sardar FPC

Jay Sardar FPC is located in Malkapur taluka of Buldhana district. It is a 3 year old company

having 1049 shareholders of which 750 are small and marginal farmers. Based on the rating

given by the PMU, the FPC has a score of 70 which is the best in the district. Currently the

FPC is actively involved in the seed program of soybean, toor, wheat and gram. They have a

cattle feed unit. They have dealership of various agro input commodities such as tarpaulin

sheets, spray pumps etc. They are NAFED agent. The FPC deals with about 10 MT soybean,

2000 MT maize, 15 MT gram and 250 MT wheat. They have a storage of 1800 MT capacity.

They are NAFED agent. They have a vermi compost making unit under which they sell about

1100 MT compost at about ₹10/kg. The FPC has a cleaning grading unit of 4 tph and storage

facility of 3000 MT capacity. The FPC has market linkage with ITC ltd, Kargil Indian, Ltd,

and Kamatan ltd for the supply of soybean, maize, wheat and gram. They sell various pulses

under the brand name ‘Santusta’. The FPC has a weighing scale of 80 MT capacity. The FPC

was in news last year when the PM mentioned the work of FPC in ‘Man ki baat’ since it was

one of the very few FPC who gave dividends to their shareholders from the profit earned. The

FPC is linked with Krushi Vikas Va Gramin Prashikshan Sanstha, an NGO who acts as training

and capacity building institute for FPCs across the nation. According to Mr. Amit Nafde,

director of the FPC, they have plans to start soil testing labs and advisory for various crops, dal

mill mill, custom hiring centre, maize processing unit for cattle feed, cold storage for vegetable

and chilly storage, ‘Khapli’ wheat and turmeric. The FPC is interested in using various digital

tools for management of operations of the FPC.

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Sant Gajanan FPC

Sant Gajanan FPC is located in Motala taluka of Buldhana district. It is a 8 year old company

having 596 shareholders of which about 70% are small and marginal farmers. Based on the

rating given by the PMU in Jan 2021, Sant Gajanan FPC has a score of 50 which is fifth best

in the district. Currently the FPC is actively involved in procurement and trading of soybean,

toor, wheat, maize and gram. The FPC is a NAFED agent. The FPC is interested in cultivation

of genarium (aromatic oil), establishing a shed net for organic vegetable cultivation.

Shemba Kranti FPC

Shemba Kranti is located in Motala taluka of Buldhana district. It is a 2 year old company

having 310 shareholders of which about 60% are small and marginal farmers. Based on the

rating given by the PMU in Jan 2021, Shemba Kranti FPC has a score of 36. Currently the FPC

is actively involved in oil extraction. The FPC has a wooden oil ghani of 25kg/hr capacity. The

FPC deals with 50 MT groundnut, 2 MT sesame, 1 MT castor and 1 MT coconut. Out of the

various oil seeds dealt with, groundnut and sesame are grown and procured locally. Other oil

seeds are bought from various other regions. The oil is sold under the brand name of ‘Tuljai’.

The FPC is interested in expanding the oil extraction unit and also interested in setting up a

ginning and pressing unit.

Kulbhushan FPC

Kulbhushan is located in Nandura taluka of Buldhana district. It is a 2 year old company having

319 shareholders. Based on the rating given by the PMU in Jan 2021, Kulbhushan FPC has a

score of 47. Currently the FPC is actively involved in oil extraction. The FPC has two wooden

oil ghani of 25kg/hr capacity. The FPC deals with 50 MT groundnut, 1 MT sesame, 4 MT

castor, 1 MT coconut, 2 MT of sunflower. Out of the various oil seeds dealt with, groundnut,

sesame and sunflower are grown and procured locally. Other oil seeds are bought from various

other regions. The oil is sold under the brand name of ‘Sadguru’. The FPC is interested in

expanding the oil extraction unit and also interested in setting up a cold storage and establish

goat farm.

Kelvad FPC

Kelvad FPC is located in Chikhali taluka of Buldhana district. It is a 6 year old company having

726 shareholders of which about 75% are small and marginal farmers. Based on the rating

given by the PMU in 2018, Kelvad FPC has a score of 63 which is fourth best in the district.

288

113

Currently the FPC is actively involved in cleaning, grading, bagging and packaging of soybean,

toor, udit and gram. It has cleaning grading unit of 2 TPH. The FPC is a NAFED agent. The

FPC deals in 150 MT soybean, 25 MT gram, 25 MT Toor and 100 MT Udit. The FPC has

custom hiring centre and deals with multiple agri input commodities such as gunny bags,

tarpaulin sheets, sprinkler and drip irrigation sets, and insecticides. According to Mr. Ram

Wani, the FPC is interested to expand their operation of custom hiring centre, establish

dehydration unit for drying chilly, fenugreek, coriander, turmeric etc. The FPC plans to open

‘Farm mall’ or outlet for various agri input commodities and products.

Laxminarayan FPC

Laxminarayan FPC is located in Khamgaon taluka of Buldhana district. It is a 2 year old

company having 250 shareholders of which about 75 are small and marginal farmers. Based

on the rating given by the PMU in 2021, Kelvad FPC has a score of 51 which is fourth best in

the district. Currently the FPC is actively involved in procurement and trading of soybean, toor,

wheat, onion and gram. The FPC is a NAFED agent and has purchased 250 MT gram for

NAFED last year. According to Mr. Tejendrasingh Chauhan, the FPC is interested to initiate

seed program by establishing cleaning and grading facilities, warehouses of about 1000 MT

capacity. They plan to cultivate sorghum especially for the consumption of green sorghum

grains (hurda) as snack. The FPC is willing to initiate cultivation of baby corns.

Vidharba Samruddhi FPC

Vidharba Samruddhi FPC is located in Khamgaon taluka of Buldana district. It has 385

shareholders in which 160 are small and marginal farmers. It has a rating of 38 according to

the evaluation done by PMU in January 2021. Currently, Vidharba Samruddhi FPC deals in

Soybean, gram and toor with a quantum of 30 MT, 1430 MT and 100 MT of toor respectively.

They are NAFED agents for above commodities. They aspire to have a seed program in 10

acre of soybean, 100 acre of gram, 800 acre of wheat and 50 acre of toor. They have built a

storage facility of 1000 MT using PoCRA funding. Based on the interaction with Santosh

Sakhre, they want to establish a dal mill, flour mill, cold press oil ghani and spice grinder in

future.

The quantum of various commodities for all FPCs (covered during field visit) is summarised

in a tabular form in Table 0:1,

289

114

Table 0:2 and Table 0:3.

290

115

Table 0:1: Current and proposed quantum of commodities in surveyed FPCs in Aurangabad

Name

of

FPC

Type of

activity

Current Quantum (MT/annum) Potential / Proposed Quantum (MT/annum)

Willing

ness/Abi

lity to

invest

Ma

ize

Gin

ger

Pomogr

anate

Tur

meri

c

Gra

m

On

ion

B

aj

ra

Too

r

Jo

wa

r

Wh

eat

Ch

illi

Ma

ize

Gin

ger Onion

Tur

meri

c

Wh

eat

Chil

li

Essential/Ar

omatic

Akash

FPC

Cleaning

+

Grading

30

0

600

0

Yes

(Large

capacity,

simplifie

d

technolo

gy)

Storage

+

Trading

30

0

400

0

600

0

40

00

Seed

program 800

Other

processin

g act.

250

0

291

116

Jai

Siddh

eshwa

r

Cleaning

+

Grading

Yes

(Small

scale)

Storage

+

Trading

35

0 200

10

00

100

00

Seed

program

100

acre

Other

processin

g act.

Goda

vari

valley

FPC

(Karm

ad

FPC)

Cleaning

+

Grading

20

00

Yes

(Essenti

al oil

business

,

processi

ng of

B,C

Storage

+

Trading

20

00 500 100

NA

FE

D

Age

nt

10

00

NA

FE

D

Age

nt

500

0

292

117

Seed

program

15

0

acr

e

grade

onion)

Other

processin

g act.

Op

en

to

all

pla

ns

Proces

sing

of B

and C

grade

Essential oil

extraction

Ghrus

hnesh

war

FPC

Cleaning

+

Grading

75

0 10 10

20

0 300

Yes

(Also

intereste

d cattle

feed

business

- waste

of maize

cleaning

grading)

Storage

+

Trading

75

0 10 10

20

0 300

Onion

storag

e

struct

ure

Seed

program

293

118

Other

processin

g act.

Pinak

eshwa

r FPC

Cleaning

+

Grading

20

0

Intereste

d for

processi

ng of

chilli,

onion,

and

ginger

Storage

+

Trading

20

0

300

0

500

MT

onion

storag

e

100

0

Seed

program

Other

processin

g act.

Krush

i

Cleaning

+

Grading

10

00 20 20

Yes for

multiple

294

119

Kranti

FPC Storage

+

Trading

10

00

commod

ities

Seed

program

Other

processin

g act.

(Drying

S4S)

500 500 50

0

295

120

Table 0:2: Current and proposed quantum of commodities in surveyed FPCs in Buldana

Name of

FPC

Type of

activity

Current quantum (MT/annum) Potential / Proposed

Willingness/A

bility to

invest

Soybea

n

Sunflo

wer

Coco

nut

G

N

ut

Seas

ame

Cas

tor

Maiz

e Gram

U

dit Toor

Jowa

r

Whea

t

Soyb

ean

Gr

am

Wh

eat

To

or

Rajshree

FPC

Cleanin

g +

Grading

10 35 3 1.2 100 200 6 1.5 own

infrastructure

for cleaning,

grading,

packing and

storage and

expand their

seed program

Storage

+

Trading

10 35 3 1.2 100 200 1.5

Seed

progra

m

10 35

Other

processi

ng act.

296

121

Sonpaul

FPC

Cleanin

g +

Grading

700 500 15

0 375 135

proposed a dal

mill, a

warehouse of

2000 MT a

cold storage.

interested in

processing of

turmeric, flour

making and

dal mill

Storage

+

Trading

700 500 15

0 375 135

Seed

progra

m

700 500

Other

processi

ng act.

Ruj FPC

Cleanin

g +

Grading

51 17

Interested in

establishing an

oil mill, cold

storage and

pulp extraction

unit

Storage

+

Trading

51 17

297

122

Seed

progra

m

51 17

Other

processi

ng act.

Jay Sardar

FPC

Cleanin

g +

Grading

10 2000

NAFE

D

Agent

15 250

Plans to start

soil testing

labs and

advisory for

various crops,

dal mill mill,

custom hiring

centre, maize

processing

unit for cattle

feed, cold

storage for

vegetable and

chilly storage,

‘Khapli’ wheat

and turmeric.

Storage

+

Trading

10 2000 15 250

Seed

progra

m

Other

processi

ng act.

298

123

interested in

digital tools

Sant

Gajanan

FPC

Cleanin

g +

Grading

NAF

ED

Agen

t

NAFE

D

Agent

NAFE

D

Agent

NAF

ED

Agen

t

NAF

ED

Agen

t

interested in

cultivation of

genarium

(aromatic oil),

establishing a

shed net for

organic

vegetable

cultivation

Storage

+

Trading

Seed

progra

m

Other

processi

ng act.

Shemba

Kranti

FPC

Cleanin

g +

Grading

Interested in

expanding the

oil extraction

unit and also

interested in

setting up a

Storage

+

Trading

299

124

Seed

progra

m

ginning and

pressing unit

Other

processi

ng act.

(Oil

Extracti

on)

1 5

0 2 1

Kulbhush

an FPC

Cleanin

g +

Grading

Interested in

expanding the

oil extraction

unit and also

interested in

setting up a

cold storage

and establish

goat farm

Storage

+

Trading

Seed

progra

m

Other

processi

ng act.

2 1 5

0 1 4

300

125

(Oil

Extracti

on)

Kelvad

FPC

Cleanin

g +

Grading

150 25 25

Interested to

expand custom

hiring centre,

establish

dehydration

unit for drying

chilly,

fenugreek,

coriander,

turmeric etc.

‘Farm mall’ or

outlet for

various agri

input

commodities

and products

Storage

+

Trading

150 25 25

Seed

progra

m

150

Other

processi

ng act.

(Oil

Extracti

on)

Laxminar

ayan FPC

Cleanin

g +

Grading

50

acre

150

acr

e

Start seed

program

establish

301

126

Storage

+

Trading

NAFE

D

Agent

cleaning and

grading

facilities,

warehouses

cultivate

sorghum for

the

consumption

as snack.

Cultivation of

baby corns.

Seed

progra

m

Other

processi

ng act.

(Oil

Extracti

on)

Vidharba

Samruddh

i FPC

Cleanin

g +

Grading

Yes, in flour

mill and dal

mill, cold

press, spice

grinding

Storage

+

Trading

30

(NAF

ED)

1430

(NAF

ED)

100

(NAF

ED)

10 100

302

127

Seed

progra

m

10

acre

100

acr

e

800

acr

e

50

acr

e

Other

processi

ng act.

(Oil

Extracti

on)

Table 0:3: Current and proposed quantum of commodities in surveyed FPCs in Washim

Name

of FPC

Type

of

activity

Current quantum (MT/annum) Potential / Proposed Willingness/

Ability to

invest Soybea

n Turmeric Gram

U

dit

Mu

ng Toor

Whe

at

Essential

oil/Aromatics

Soybea

n

Gra

m

Turme

ric

Whe

at

To

or

Krushi

Samrajy

a FPC

Cleanin

g +

Proposing

Besan mill,

NAFED,

303

128

Gradin

g

Magnet de-

stoner but

financial

problem

Storage

+

Trading

80 50 50

Seed

progra

m

Other

process

ing act.

(Oil

Extracti

on)

304

129

Krushi

Mauli

FPC

Cleanin

g +

Gradin

g

500 400 10 10 100 300 600 500 350 12

0

Brand

establish,

turmeric

processing,

organic

production of

cereals and

pulses,

soybean oil

production,

wheat floor,

machines for

packing, cold

storage, baby

corn

Storage

+

Trading

500 400 10 10 100 300 600 500 350 12

0

Seed

progra

m

300 330 20

Other

process

ing act.

(Dal

making

)

70 10 10

305

130

Parivart

an FPC

Cleanin

g +

Gradin

g

600

300

(NAFE

D)

NAF

ED 200 1000

3000

(Naf

ed)

200

Cold storage

for citrus

fruits, tomato;

color sortex,

turmeric

processing,

organic

vegetable

Storage

+

Trading

600

(NCD

EX)

300

(NAFE

D)

200

Seed

progra

m

100

acre

(80

MT)

Other

process

ing act

Nardus

FPC

Cleanin

g +

Gradin

g

Byproducts

such as

perfumes,

incense stick,

306

131

Storage

+

Trading

labs for

testing.

distillation,

gas

chromatograp

h, drying unit

Seed

progra

m

307

132

Other

process

ing act

(oil

extracti

on)

500MT

procurem

ent

(Rs500/k

g)(0.9%

oil

recovery)

(oil

extraction

from

leaves)

Citronel - 150 acre

(2500 MT/ year) (3

times harvest/year)

(15-20 MT/acre/year)

(1% oil recovery)

(residue as

firewood/compost)

(Rs2000/kg) (3000-

4000/MT to farmer)

Lemon grass -

(1500MT/year)(Rs150

0/kg)(0.8-0.9% oil

recovery)((3000-

4000/MT to

farmer)(15-

20MT/acre/year)

Alma Roza -

(1500MT/year)

(Rs2000/kg) (0.6-0.8%

recovery) (3000-

4000/MT to farmers)

(15-20MT/acre/year)

Geranium -

308

133

(500MT/year) (0.1%

oil recovery)

(Rs5000/kg)

(Rs5000/MT to

farmers) (10-

15MT/acre/year)

Greenza

FPC

Cleanin

g +

309

134

Gradin

g

Storage

+

Trading

Seed

progra

m

Other

process

ing act

(Soil

testing)

Krushid

eep

FPC

Cleanin

g +

Gradin

g

200

Turmeric

processing in

future

Storage

+

Trading

200

310

135

Seed

progra

m

200 250

Other

process

ing act

1 Q/hr

(Turm

eric

powde

r)

Ayushu

sya

FPC

Cleanin

g +

Gradin

g

40 50

Increase

quantum of

existing

processes

Storage

+

Trading

200 acre

organic toor

gram udit

mung wheat

Seed

progra

m

Brand

establishment

311

136

Other

process

ing act

(Dal)

5 1 1.5 5 6

Hari

Om

FPC

Cleanin

g +

Gradin

g

110 120 20 150 160 160 200

Interested in

onion seeds

processing

and soybean

oil extraction

and turmeric

processing

Storage

+

Trading

110 120 20 150 160 160 200

They want to

avail govt

schemes

Seed

progra

m

100 T

seeds

108 T of

seeds

20

hect

8 T

of

seed

s

Other

process

312

137

ing act

(Dal)

313

138

Appendix C

Table 0:1 mentions the potential commodities for value addition and their value added products.

The information mentioned in Table 0:1 is based on preliinary knowledge and experience in food

processing industry.

Table 0:1: Potential commodities for value addition and their value added products

Commodit

y

Potential value added

products Remarks

Soybean

Soy oil Viability -Solvent extraction -150T/day and

Mechanical Extraction > 20T/day

Soybean protein

Protein isolate

Soybean atta

Soy milk Highly perishable

Soy tofu Highly perishable

Animal feed (Okara)

Fermented soy food

(Soy sauce)

Soya snacks (namkeen,

sticks, chunks)

Maize

Corn flour

Corn flakes Cereal and namkeen. General viability > 2T/day

Corn starch Residue could go as poultry feed

Glucose Economics of scale is critical

314

139

Protein rich poultry feed

Pop corn Based on variety

corn snacks Chips and extruded snacks markets are upcoming

Silage

Ginger

Dried ginger powder

(Sunth)

Ginger oil

Pickle Unsure of market

Ginger extract

Turmeric

Turmeric powder Depends on the curcumin content

Curcumin extraction Residue is starch

Essential oil extraction

Gram

Dal mill

Dal mixture/snacks

Protein (Depending on

quality)

Besan

Tur Dal mil Better value for unpolished dal

Soybean is known for its high protein and no lactose content, therefore soybean milk and tofu

becomes a prevalent alternative to dairy milk, hence suggesting its high demand in the market.

However, due to the high perishability of soy milk and tofu, it requires appropriate cold chain

technologies. The bi-product in soy milk processing is Okara which could be converted into animal

feed. Soy oil is also a popular value added product and its processing generally requires a minimum

quantum of 150T/day and 20T/day in case of solvent extraction and mechanical extraction

315

140

respectively. The oil extraction efficiency in mechanical extraction is lower than solvent extraction

however, recently, the demand for mechanically pressed oils has increased in the market due to its

higher purity. Therefore mechanical pressed oils could be proposed for FPCs which have smaller

quantum yet are interested in soy oil processing. The residue for soy oil extraction could be

converted in soy protein/isolate which is again well accepted product in the fitness industry. Lately,

the concept of multigrain flour for regular use is emerging and soy flour remains an important

ingredient due to its protein and fibre content. Similarly, soy namkeen/snacks is a healthy

alternative to refined flour snacks and products such as soy sticks have become popular in urban

and semi-urban areas. The government nutrition programs such as mid-day meals could be

benefitted by the use of soy products due to its rich nutritional profile. Moreover, inclusion of soy

products in government programs would promote local soybean processing and revenue

generation.

Products such as corn flour, corn flakes, popcorn etc. are popular value added products of Maize.

Corn flour is an essential ingredient in many Indian recipes and therefore has high demand in local

and national markets. Similarly, corn flakes are common in namkeen and breakfast cereals which

is found in many urban and semi-urban households. A general corn flakes processing unit requires

a minimum quantum of 2 T/day for its economic viability. Popcorn is a common snack and its

production is highly dependent of the variety of maize. Another prevalent product of maize is the

corn starch which has household as well as industrial application in food processing, paper making,

adhesive, cosmetic industries etc. The residue of corn starch processing could be converted into

poultry feed which is high in protein content. Glucose is another product from maize, however the

facility of enzyme treatment is expensive therefore glucose extraction from maize is critical of

economics of scale. Corn snacks in the form of chips (nachos) and extruded items are upcoming

snacks in the national market as it is looked upon as a healthier option to potato chips.

Potential ginger products are dried ginger powder (Sunth), ginger oil, ginger extract and pickle.

While all the ginger products go well in food, pharmaceutical and cosmetic industries, the market

for ginger pickle is niche and need to be explored further. Turmeric on another essential spice

widely used in the domestic and international market. Turmeric in the form of dried powder is

regularly used by Indian household and is heavily in demand throughout the year. Curcumin

content in the turmeric determine the quality of turmeric powder. Alternatively, curcumin could

316

141

also be extracted from turmeric for wide scale application such as dietary supplement, flavouring

and colouring agents and cosmetic industries. Turmeric essential oil is another value added product

finding its application in pharmaceutical and cosmetic industries. Gram and tur have similar value

added products which include milled dal. Other than dals, besan could be made from Gram which

is in heavy demand in Indian market. Depending on the quality of gram, protein could be extracted

using wet processes. Gram and tur dal could also be used in the package of mixed dal which is a

popular Indian recipe.

Categorisation of value-added products for FPC based on quantum

The following section presents the potential value added products (Table 0:2) for FPCs visited in

Aurangabad, Buldana and Washim only. The categorisation of screened FPCs of other district will

be done in the next phase report.This preliminary analysis is based on their current quantum of

dealing and a general knowledge of economics of scale. Table 0:3, Table 0:4, Table 0:5, Table 0:6,

Table 0:7 presents the potential products for FPCs dealing in soybean, maize, ginger, turmeric,

gram and tur respectively.

Solvent extraction method for soybean oil extraction is an efficient process but requires huge

quantum for economic feasibility and the visited FPCs did not deal in sufficient quantum of

soybean for solvent extraction. Therefore, if the FPCs want to venture into soy oil extraction,

mechanical soy oil extraction process is suggested if they are dealing in quantum more than 500MT

considering they process 2T/day for around 250-300 days. Processing of soy atta, snacks, soy milk

and allied products is feasible at smaller quantum (~50MT) while a combination of these products

is suggested if the quantum exceeds above 150MT depending on the marketing ability of the FPCs.

In case of Maize processing, glucose extraction is expensive due to the enzyme facility, therefore

only the FPCs dealing in large quantum such as 2000MT are recommended glucose processing as

seen in Table 0:3. Another option for large quantum FPCs could be a combination of starch

extraction, glucose, protein rich poultry feed/cattle feed, corn flakes and corn flour depending on

the marketing channels and forward linkages. For FPCs with maize quantum around 1000MT,

starch extraction and protein rich poultry feed/cattle feed would be more profitable than corn flakes

and corn flour options (which are suggested for FPC with quantum in the range of 200MT).

317

142

The value added products for other commodities such as ginger, turmeric, gram and tur (suggested

in Table 0:1) could be recommended for all the visited and respective FPCs irrespective of their

quantum. However, higher quantum of production would give FPCs an advantage for economics

of scale and marketing from value added products.

Table 0:2 : FPCs dealing in Soybean, their quantum, current activities and potential value added

product

Name of

FPC Location

Current

Quantum

(MT) Current activities

Potential value added

products

Rajshree

FPC Buldhana 10

Seed program and

trading Soybean atta

Sonpaul

FPC Buldhana 700

Seed program and

trading

Soybean tofu, snacks, atta,

oil (Mechanised), protein

Ruj FPC Buldhana 51

Seed program and

trading Soybean atta and snacks

Jay Sardar

FPC Buldhana 10

Seed program and

trading Soybean atta

Kelvad

FPC Buldhana 150

Seed program and

trading

Soybean tofu + snacks +

atta (Together)

Krushi

Mauli FPC Washim 500

Seed program and

trading

Soybean milk, tofu, snacks,

atta, oil (Mechanised),

protein

Parivartan

FPC Washim 600

Seed program and

trading

Soybean milk, tofu, snacks,

atta, oil (Mechanised),

protein

Hari Om

FPC Washim 110

Seed program and

trading

Soybean tofu + snacks +

atta (Together)

318

143

Bhanudas

FPC Washim 250

Seed program and

trading

Soybean tofu + snacks +

atta (Together)

Table 0:3: FPCs dealing in Maize, their quantum, current activities and potential value added

product

Name of FPC

Locat

ion

Quantu

m

(MT) Current activities

Potential value added

products

Akash FPC

Aura

ngaba

d 300

Cleaning grading and

trading Corn flour, snacks

Godavari valley

FPC (Karmad

FPC)

Aura

ngaba

d 2000

Cleaning grading and

trading

Starch, glucose, poultry

feed, corn flakes, corn

flour

Ghrushneshwar

FPC

Aura

ngaba

d 750

Poultry and cattle feed,

Cleaning grading and

trading Corn flour, snacks

Pinakeshwar FPC

Aura

ngaba

d 200

Cleaning grading and

trading Corn flour, snacks

Krushi Kranti

FPC

Aura

ngaba

d 1000

Poultry and cattle feed,

Cleaning grading and

trading

Starch, protein rich

poultry feed

Renukamata FPC

Aura

ngaba

d 1000

Cleaning grading and

trading

Starch, protein rich

poultry feed

319

144

Jay Sardar FPC

Buldh

ana 2000

Poultry and cattle feed,

Cleaning grading and

trading

Starch, glucose, poultry

feed, corn flakes, corn

flour

Table 0:4: FPCs dealing in Ginger, their quantum, current activities and potential value added

product

Name of FPC

Locati

on

Quantum

(MT)

Current

activities

Potential value added

products

Akash FPC

Aurang

abad 4000

Storing and

trading

Dried ginger powder, oil,

pickle, ginger extract

Godavari valley FPC

(Karmad FPC)

Aurang

abad 500

Storing and

trading

Dried ginger powder, oil,

pickle, ginger extract

Krushi Kranti FPC

Aurang

abad 500

Storing and

trading

Dried ginger powder, oil,

pickle, ginger extract

Table 0:5: FPCs dealing in Turmeric, their quantum, current activities and potential value added

product

Name of

FPC

Locatio

n

Quantum

(MT) Current activities

Potential value

added products

Krushi

Kranti FPC

Aurang

abad 500 Drying of rhisome

Turmeric powder,

essential oil

Nardus FPC Washim 500

Trading of rhisome, Essential

oil from leaves Turmeric powder

320

145

Table 0:6: FPCs dealing in Gram, their quantum, current activities and potential value added

product

Name of

FPC

Locat

ion

Quantu

m (MT) Current activities

Potential value added

products

Akash FPC

Auran

gabad 800 Seed Program

Dal mill, Dal

mixture/snacks, Protein,

Besan

Ghrushneshw

ar FPC

Auran

gabad 10

Cleaning, grading, storage,

trading

Dal mill, Dal

mixture/snacks, Protein,

Besan

Rajshree FPC

Bulda

na 35

Cleaning, grading, storage,

trading, seed program

Dal mill, Dal

mixture/snacks, Protein,

Besan

Sonpaul FPC

Bulda

na 500

Cleaning, grading, storage,

trading, seed program

Dal mill, Dal

mixture/snacks, Protein,

Besan

Ruj FPC

Bulda

na 17

Cleaning, grading, storage,

trading, seed program

Dal mill, Dal

mixture/snacks, Protein,

Besan

Kelvad FPC

Bulda

na 25

Cleaning, grading, storage,

trading

Dal mill, Dal

mixture/snacks, Protein,

Besan

Vidharba

Samruddhi

FPC

Bulda

na 1430 Storing and trading

Dal mill, Dal

mixture/snacks, Protein,

Besan

321

146

Krushi Mauli

FPC

Washi

m 400

Cleaning, grading, storage,

trading, seed program, Dal mill

Dal mill, Dal

mixture/snacks, Protein,

Besan

Parivartan

FPC

Washi

m 300

Cleaning, grading, storage,

trading

Dal mill, Dal

mixture/snacks, Protein,

Besan

Krushideep

FPC

Washi

m 200

Cleaning, grading, storage,

trading, seed program

Dal mill, Dal

mixture/snacks, Protein,

Besan

Ayushusya

FPC

Washi

m 5 Dal mill

Dal mill, Dal

mixture/snacks, Protein,

Besan

Hari Om FPC

Washi

m 120

Cleaning, grading, storage,

trading, seed program

Dal mill, Dal

mixture/snacks, Protein,

Besan

Table 0:7 : FPCs dealing in Toor, their quantum, current activities and potential value added

product

Name of

FPC

Locati

on

Quantum

(MT) Current activities

Potential value

added products

Jai

Siddheshwar

Aurang

abad 200

Cleaning grading, trading and

seed program Dal mil

Ghrushnesh

war FPC

Aurang

abad 10

Cleaning grading and trading and

Dal mill Dal mil

Krushi

Kranti FPC

Aurang

abad 20

Cleaning grading and trading and

Dal mill Dal mil

322

147

Renukamata

FPC

Aurang

abad 20

Cleaning grading, trading and

seed program Dal mil

Rajshree

FPC

Buldha

na 3

Cleaning grading, trading and

seed program Dal mil

Sonpaul

FPC

Buldha

na 375

Cleaning grading, trading and

seed program Dal mil

Kelvad FPC

Buldha

na 25 Cleaning grading and trading Dal mil

Jay Sardar

FPC

Buldha

na 15

Cleaning grading, trading seed

program and Dal mill Dal mil

Krushi

Mauli FPC

Washi

m 100 Cleaning grading and trading Dal mil

Ayushusya

FPC

Washi

m 5 Cleaning grading and trading Dal mil

Hari Om

FPC

Washi

m 20 Cleaning grading and trading Dal mil

323