Financial and Economic Profitability of Selected

Agricultural Crops in Bangladesh

Institution: Department of Management and Finance

Sher-e-Bangla Agricultural University (SAU)

Principal Investigator: Dr Mohammad Mizanul Haque Kazal

Department of Development & Poverty StudiesSAU

Research TeamDr. Sanzidur RahmanUniversity of Plymouth, UK

Mr. Ripon Kumar Mondal, SAU

Dr. Mohammad Jahangir AlamBangladesh Agricultural University

Mr . Shah Johir Rayhan, SAU

Dr. Shaikh Tanveer HossainFIVDB

Mr. Sajeeb Saha , SAU

Background of the Study• Crop sector is the source of staple food for 150

million people• major source of livelihood for 16 million farm

households. • The crop and horticulture sector jointly

contributed US$9,643 million (11.3% of the GDP)

• Financial profitability differs from economic (social) profitability because of distortions in the factor and product markets

Introduction

• Trade and price policy • stability in food prices• input subsidy and output support • food security of the poor• strategic element for poverty

alleviation

Objectives To examine the financial and economic profitability of the

various crops including an assessment of the comparative advantage for import substitution and/or export (i.e. conduct a standard PAM analysis).

To assess the impact of fertilizer subsidies on financial profitability and production and the factors leading to differences in financial and economic profitability across different crops and across different regions for the same crop.

To explain changing patterns of agricultural land use since 2000 based on different levels of financial profitability for different crops.

Research DesignThe study was designed to conduct into three phasesPhase-I deals with farm level survey, financial and

economic cost and return analysis and assessment of comparative advantages of crops;

Phase-II deals with assessing the impact of fertilizer subsidies on profitability using experimental data as well as farm-survey data for rice only;

Phase-III measure the changing patterns of agricultural land use and identifies its socio-economic determinants using through the secondary time-series data.

Figure: Survey District

SL No. District UpazillaFarm Type

Marginal SmallMedium /

LargeTotal surveyed

Farms1 Tangail Mirzapur 35 35 35 105

2 Mymensingh Phulpur 34 36 35 105

3 Kishoreganj Karimganj 35 35 35 105

4 Netrokona Khaliajuri 21 38 46 105

5 Faridpur Bhanga 35 35 35 1056 Faridpur Boalmari 20 20 20 607 Rajshahi Charghat 35 35 35 1058 Natore Lalpur 34 35 36 1059 Sirajganj Ullapara 35 35 35 105

10 Bogra Sherpur 31 34 33 9811 Bogra Sariakandi 35 35 35 10512 Jaipurhat Kalai 35 35 35 105

13 Dinajpur Chirirbander 36 30 39 105

14 Dinajpur Birganj 70 35 35 140

15 Thakurgaon Balia Dangi 35 35 35 105

16 Lalmonirhat Hatibandha 34 34 37 105

17 Barisal Bakerganj 35 35 35 10518 Kushtia Sader 35 35 35 105

19 Sunamganj Derai 35 35 35 105

20 Habiganj Baniachang 31 38 36 105

Total 696 685 702 2083

Table: Distribution of sample according to farm type

Crops Regions Specified Character

Region wise survey districtNorth-western Central Southern South-

centralHaor

Boro High land Dinajpur, Rajshahi

Medium land Mymensingh

Kushtia

Low land Kishoreganj, Sunamganj, Habiganj, Netrokona

Aman Rainfed Dinajpur, Rajshahi

Barisal

Supplementary irrigation

Bogra, Joypurhat

Wheat Irrigated Dinajpur, Thakurgaon

Supplementary irrigation / rainfed

Rajshahi Faridpur

Maize Winter Dinajpur, Lalmonirhat

Jute Kishoreganj FaridpurLentils Natore, BograMustard Tangail,

Sirajganj

Table : Study area based on land elevation and technology

Analytical Techniques

1. Financial and economic costs and returns from crops

2. Assessment of comparative advantage of crops

Policy Analysis matrix (PAM) framework applied to measure economic efficiency and competitiveness under different production systems

Financial profitability of major crops

Cost and return analysis is the most common method of determining and comparing the profitability of different farm enterprises. In estimating the level of profitability in crop production the following formula was used:

∏ = P1Q1 + P2Q2 - ∑PiXi – TFC

Where, ∏ = Profit per hectare for producing the crop; P1 = Per unit price of the output; Q1 = Quantity of output obtained

(per hectare); P2 = Per unit price of by-product; Q2 = Quantity of by –product

obtained (per hectare); Pi = Per unit price of the ith input used for producing the crop; Xi =

Quantity of the ith input used for producing the crop; and TFC = Total fixed cost.

This analyses was done by using two different approaches such as

(1) by using the experimental data from BRRI, and

(2) by using farm-survey data collected in Phase 1.

Approach 1: Using experimental data from BRRI

First step is to find the yield / profit maximizing level of N fertilizer use.

Approach 2: Using farm-survey data for rice crops only

A profit function approach will be adopted to examine the impact of fertilizer subsidies on profitability of rice farming. The general form of the translog profit function, dropping the subscript for the farm, is defined as:

4

1

4

1

4

1

4

1

4

121

0 ln'ln'ln'ln'ln'lni i h i k

kiikhiihii ZPPPP

4

1

4

1

4

121 )1(lnlnln

k k jjkkjkk ZZZ

The corresponding factor share equations are expressed as,

)2(ln'ln'ln

'ln

'

4

1

4

1

h k

kikhihii

iii ZP

P

XPS

4

1

4

1

4

1

4

1

4

1

)3(ln'ln1ln

'ln1

' i ik

kikh

hih

ii

y

yyy ZP

P

XPS

Phase 3: Socio-Economic and Environmental Determinants of Crop Diversity in Regions of Bangladesh (1990-2008)

In this phase, First, it was estimated the rate of change of individual crop area over time.

Next, it has been identified the determinants of land use of each crop over time.

)1(ln 0 ititit TA

The study was computed growth rate of area cultivated for individual crop using semi-log trend function as follows:

The study were used a model of crop choice in a theoretical framework of the farm household model applying a micro-econometric approach.

Items RevenueCosts

ProfitTradable inputs Domestic factors

Private prices

62150 2216 25621 34313

Social prices 72835 4530 21665 46640Divergences -10685 -2314 3956 -12327

Item ValueNominal Protection Coefficient on Output (NPCO)

0.853 (<1)

Nominal Protection Coefficient on Input (NPCI)

0.489 (<1)

Effective Protection Coefficient (EPC) 0.877 (<1)Domestic Resource Cost (DRC) 0.317 (<1)Private Cost Ratio (PCR) 0.427 (<1)

Policy Analysis Matrix for rainfed Aman rice in southern region of Bangladesh (Average of 2010 and 2011):

Variables HYV AMAN

model

HYV Boro

model

HYV Aus model

Mean Standard deviation

Mean Standard deviation

Mean Standard deviation

Experimental P 11.18 2.18 20.71 6.04 13.43 5.05

Experimental K 40.71 6.56 51.88 12.31 44.93 10.60

Experimental N 75.42 17.79 125.71 16.97 66.08 20.76

Optimum N 128.26 102.97 232.38 17.42 47.93 42.49

Optimum urea (10% rise in urea price

127.22 102.96 232.12 17.42 47.96 42.48

Optimum urea (20% rise in urea price

126.17 102.95 231.86 17.41 47.98 42.48

Optimum urea (30% rise in urea price

125.13 102.95 231.60 17.41 48.01 42.48

Optimum urea (40% rise in urea price

124.09 102.94 231.34 17.41 48.04 42.48

Optimum urea (50% rise in urea price

123.04 102.94 231.07 17.41 48.07 5.05

Table. Actual and economic optimum levels of urea fertilizer per hectare

Variables Parameter HYV AMAN model

HYV Boro model HYV Aus model

Coefficient t-ratio Coefficient t-ratio Coefficient t-ratioConstant 3536.1430*** 14.85 -968.9645 -0.28 3326.3390*** 6.57

X1 (N) β1 1.2377** 2.39 51.4000* 1.80 5.1078 0.53

X1 x X1 (N x N) γ11 0.0001 0.15 -0.0852 -1.45 -0.0650 -0.662002 529.1220* 1.73 -219.2515 -0.82 -493.4921 -1.052003 25.9761 0.11 -232.3814 -0.90 -885.7895* -1.742004 652.8753** 2.16 -234.2008 -0.74 -- --2005 395.1257 1.36 -797.7428*** -2.69 -- --2006 60.0974 0.26 -468.5997** -2.34 -298.6252 -0.552007 561.9247** 2.28 -325.8483 -1.242008 90.5782 0.37 -84.5475 -0.39 -235.4953 -0.622009 120.2893 0.45 -555.7791** -2.43 137.4528 0.272010 327.9542 1.38 -878.4116*** -4.05 1087.6020** 2.542011 362.7055 1.52 -366.4178* -1.68 828.0839** 2.44Gazipur -413.2901*** -3.26 -55.2649 -0.36 300.3102 0.59Sylhet 284.0253 0.86 797.4019*** 3.80 44.0407 0.08Kushtia 48.9909 0.25 512.1128 1.93 -156.4561 -0.33Rajshahi 249.6528 1.58 103.9682 0.46 -100.1389 -0.20Khula 313.4603** 2.25 -381.4991** -2.25Barisal 23.7443 0.16 -106.0647 -0.58Dhaka 49.4871 0.16 -195.5642 -0.38Dinajpur 161.1312 0.50 -392.8672 -0.83Rangpur 80.1611 0.40 -324.2421 -1.44Noakhali -14.8516 -0.08 -1197.7840*** -3.06Faridpur 939.0849*** 3.83 1606.6420*** 7.24Mymensingh -150.1463 -0.53 -309.2255 -0.86Jessore -131.6670 -0.32 409.4536 0.99Bogra -302.4152 -0.65 -- --Model diagnosticsAdjusted R-squared 0.14 0.17 0.53F – value 6.68*** 8.74*** 7.22***Sample size 884 918 72

Table. Yield response function of rice using economically optimum dose of urea fertilizer

Variables Parameter HYV AMAN model

HYV BORO model

HYV AUS

modelProduction elasticity of N*

η 0.04 0.48 -0.10

(0.03) (0.17) (0.17)

Table. Production elasticity of optimum dose of urea fertilizer.

Results indicate that the experimental level of urea fertilizer use is far lower than the economically optimum level of urea fertilizer for Aman and Boro seasons but higher for Aus season. The discrepancy is highest for HYV Boro rice where the profit maximizing level of N fertilizer dose is 232.4 kg/ha as compared to only 125.7 kg/ha. Also, production elasticity of HYV Boro rice is highest at 0.48, implying that a one percent increase in the optimum dose of N fertilizer will increase rice yield by 0.48% which is substantial. Changes in price of urea will exert some reduction in the optimum doses of urea fertilizer only in Aman season with no noticeable effect on Boro and Aus season.

Regions Average annual compound growth rateLocal rice HYV rice Minor cereals Pulses Oilseeds Spices Jute Sugarcane Vegetables

Barisal -0.010*** 0.069*** -0.028* -0.062*** -0.117*** 0.043*** -0.012* -0.094*** 0.040***

Bogra -0.075*** 0.024*** -0.008 -0.145*** 0.060*** -0.019*** -0.058*** -0.034*** 0.086***

Chittagong -0.075*** 0.021*** 0.103*** -0.066*** -0.020*** 0.031*** NG 0.012*** 0.049***

Chittagong Hill Tracts-0.040*** 0.050*** -0.013 -0.054*** -0.098*** 0.074*** -0.226*** 0.017*** 0.065***

Comilla -0.082*** 0.020*** -0.038*** -0.069*** -0.081*** 0.027*** -0.060** 0.005 0.027***

Dhaka -0.082*** 0.044*** -0.026*** -0.085*** 0.029 0.050*** -0.047*** -0.021*** 0.042***

Dinajpur -0.087*** 0.062*** 0.022*** -0.130*** -0.017 0.038*** -0.023*** -0.005*** 0.066***

Faridpur -0.058*** 0.057*** -0.005 -0.060*** -0.046*** 0.064*** 0.027*** -0.027*** 0.043***

Jessore -0.099*** 0.035*** -0.023** -0.072*** -0.016** 0.063*** -0.013 -0.030*** 0.045***

Khulna -0.063*** 0.065*** -0.041*** -0.044** -0.012 0.027*** -0.025** -0.012* 0.054***

Kushtia -0.105*** 0.048*** 0.009*** -0.065*** 0.031*** 0.087*** 0.010 -0.025*** 0.058***

Mymensingh -0.082*** 0.045*** -0.081*** -0.121*** -0.069*** 0.058*** -0.058*** -0.029*** 0.039***

Noakhali -0.025*** 0.013*** -0.078*** -0.075*** -0.098*** 0.038*** -0.073*** -0.035*** 0.040***

Pabna -0.057*** 0.054*** -0.027** -0.053*** -0.001 0.105*** -0.006 0.012** 0.009**

Rajshahi -0.107*** 0.051*** 0.023*** -0.083*** -0.008 0.092*** -0.039 -0.004 0.075***

Rangpur -0.108*** 0.043*** -0.011** -0.091*** -0.037*** 0.019*** -0.045** -0.043*** 0.100***

Sylhet -0.042*** 0.043*** -0.104*** -0.053*** -0.071*** 0.034*** -0.042* 0.003 0.024**

Bangladesh -0.063*** 0.038*** -0.016*** -0.068*** -0.033*** 0.049*** -0.038*** -0.017*** 0.051***

Table. Trends in cultivated area under different crop groups in Bangladesh

Regions Mean index 1990 level 2008 level % change Diversity Average non-cereal share in GCA (%)

Barisal 0.95 0.97 0.86 33.07 ↓ 13.89

Bogra 1.06 1.27 0.85 19.35↓ 11.62

Chittagong 0.88 0.93 0.75 20.35↓ 06.83

Chittagong Hill Tracts

1.58 1.72 1.37 11.34 ↓ 39.77

Comilla 1.33 1.43 1.07 25.17↓ 14.41

Dhaka 1.59 1.76 1.38 21.59↓ 25.16

Dinajpur 1.40 1.42 1.05 26.06↓ 12.79

Faridpur 1.77 1.70 1.78 -4.71 ↑ 34.20

Jessore 1.44 1.62 1.13 30.25 ↓ 24.09

Khulna 1.07 0.80 1.09 -36.25 ↑ 09.33

Kushtia 1.65 1.80 1.34 25.56↓ 29.83

Mymensingh 1.30 1.38 1.05 23.91↓ 13.23

Noakhali 1.07 1.03 0.99 3.88↓ 10.08

Pabna 1.65 1.76 1.51 14.20↓ 22.23

Rajshahi 1.35 1.50 1.13 24.67↓ 16.63

Rangpur 1.27 1.39 0.96 30.94↓ 15.53

Sylhet 0.88 0.75 0.80 -6.67 ↑ 03.69

Bangladesh 1.27 1.32 1.09 17.42 ↓ 30.77

Table. Shannon index crop diversity in Bangladesh

Figure. Shannon index of regional crop diversity in Bangladesh

Variables Random effects GLS modelCoefficients z-value

Constant 1.5932 8.78Prices (Normalized by rice price)Urea 1.4823*** 5.33TSP 0.0580 0.92MP -0.0558 -0.67Jute 0.0438 0.93Sugarcane -0.9112*** -4.63Pulses -0.0345 -1.31Vegetables 0.3123*** 4.35Spices -0.0042 -0.25Oilseeds -0.0100 -0.65Socio-economic factorsExtension expenditure per farm 0.0018** 2.08Animal power per farm -0.0486*** -3.20Labour per farm 0.0245*** 2.75Share of irrigated area in GCA -0.1548 -0.87Average farm size 0.0080 0.22R&D investment 0.0267** 2.31Average literacy rate -0.0169*** -6.34Climatic factorsTotal rainfall -0.0015 -1.46Temperature variability 0.0248** 2.19Model diagnosticsR-sq within regression 0.6098R-sq between regression 0.2021R-sq overall 0.2569Sigma_u 0.1185Sigma_e 0.0720Rho (fraction of variance due to ui) 0.7306Wald Chi-squared (18) 474.20***

Table. Determinants of crop diversity in Bangladesh

Results demonstrate that other than area under modern rice, vegetables and spices, all other crop areas experienced significant decline at variable rates over time. The level of crop diversity over time declined for most regions except Khulna and Sylhet. In identifying the determinants of crop diversity, the results clearly reveal that a host of price and non-price factors influence farmers’ decision to diversify. Among the prices, an increase in the relative prices of urea fertilizer and vegetables will significantly increase crop diversity. In other words, a rise in urea price and vegetables relative to other prices will shift farmers to diversify their cropping portfolio.

Both extension expenditure and R&D investment significantly positively increases crop diversity which is very encouraging indeed and the government should seriously increase investment in these two policy amenable instruments. A decline in wealth in terms of livestock induces farmers to switch to non-cereals that are not heavily dependent on draft power as these are grown on small scale by individual farms. Switching to a diversified cropping system is labour intensive and our results show that increase in labour stock per farm allows farms to diversify. Farmers also seem to respond to climate change as we see that variation in temperature as well as a reduction in total annual rainfall induces farmers to diversify their cropping system.