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AN INTERNATIONAL

JOURNAL OF AGRICULTURE

AND ENVIRONMENTAL

RESEARCH

ISSN: 2455-6939

Volume: 06, Issue: 01

January-February 2020

www.ijaer.in

Malwa International Journals Publication

MALWA INTERNATIONAL JOURNALS PUBLICATION:

An International Journal of Agriculture and Environmental Research

Official Journal of The Malwa International Journals Publication

87, Hanuman Chouk, Garoth, Madhya Pradesh (India), Pin Code:458880 (ISSN: 2455-3969)

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EDITOR-IN-CHIEF

Dr. Gautam Singh Rathore

EDITORIAL BOARD:

Associate editors

Prof. Muhamad Mat Noor, Agriculture Engineering, Malaysia

Prof. Dong-Chul Park, Myong Ji University, Korea

Prof. Anjaiah Devineni, Manipal University, India

Prof. Mohamed Ben Haj Frej, POST University, USA

Editorial board member

Prof. Figueira, F. M. Monteiro, ULHT - Universidade Lusofona, Portugal

Mr. ROSHAN BABU OJHA, Soil Scientist,Nepal Agricultural Research Council.

Asso. Prof. L. K. Bhatiya, BHK collage of Engineering, India

Mr. BINOD GHIMIRE, Agriculture Extension Officer Ministry of Agricultural Development,

Government of Nepal, Nepal

Ass. Prof. H. A. Sayeswara, Department of Zoology, Sahyadri Science College (Autonomous),

Shivamogga-577203, Karnataka state,India

Dr. Mahadeva Swamy, Senior Research Fellow (SRF), Biopesticide Laboratory (BPL), Division of

Biotechnology, (IIHR), Hessarghatta lake post, Bangalore, India

Prof. Meltem Serdaroğlu, Engineering Faculty, Food Engineering Department,

Ege University, Turkey

Mr. Jiban Shrestha, Scientist (Plant Breeding & Genetics), Nepal Agricultural Research Council,National

Maize Research Program Rampur, Chitwan, Nepal

Prof. Muhamad Mat Noor, Agriculture Engineering, Malaysia

Prof. Dong-Chul Park, Myong Ji University, Korea

Prof. Anjaiah Devineni, Manipal University, India

Prof. Mohamed Ben Haj Frej, POST University, USA

Prof. Figueira, F. M. Monteiro, ULHT - Universidade Lusofona, Portugal

Mr. ROSHAN BABU OJHA, Soil Scientist, Nepal Agricultural Research Council.

Asso. Prof. L. K. Bhatiya, BHK collage of Engineering, India

Mr. BINOD GHIMIRE, Agriculture Extension Officer Ministry of Agricultural Development,

Government of Nepal, Nepal

Ass. Prof. H. A. Sayeswara, Department of Zoology, Sahyadri Science College (Autonomous),

Shivamogga-577203, Karnataka state, India

Dr. Mahadeva Swamy, Senior Research Fellow (SRF), Biopesticide Laboratory (BPL), Division of

Biotechnology, (IIHR), Hessarghatta lake post, Bangalore, India

Prof. Meltem Serdaroğlu, Engineering Faculty, Food Engineering Department, Ege University, Turkey

Mr. Jiban Shrestha, Scientist (Plant Breeding & Genetics), Nepal Agricultural Research Council, National

Maize Research Program Rampur, Chitwan, Nepal

Acknowledgement: The Association would like to thank the following people for contributing

to the publication of this issue of International Journal of Agriculture and Environmental

Research.

IJAER (International Journal of Agriculture and Environmental Research)

TABLE OF CONTENT

Article

No.

Title & Author name Page

1 INTRODUCTION OF SOIL CULTIVATION (HYDROPONIC SYSTEM)

TO IMPROVE QUALITY AND QUANTITY OF VEGETABLE

HORTICULTURE RESULTS

Ratna Rositawati

1-10

2 THE EFFECTS OF GOOD GOVERNANCE ON THE AGRICULTURAL

SECTOR

Marzieh Ronaghi, Sayed Saghaian, Mohammadreza Kohansal, M. Reed,

Mohammad Ghorbani

11-28

3 IMPACT OF BIOFERTILIZERS AND CHEMICAL FERTILIZERS ON

NODULATION, N UPTAKE AND GROWTH OF SOYBEAN (Glycine max

L.)

Betty Natalie Fitriatin, Rahadian Nur Prathama, Reginawanti Hindersah

29-37

4 EVALUATING THE EFFICIENCY AND RESISTANCE TOWARDS THE

ABIOTIC FACTORS IN THE NEWLY BRED VARIETIES OF THE

CEREAL CROPS

Hamlet Martirosyan, Marine Hovhannisyan, Mariam Abovyan

38-46

5 AFLATOXINS CONTAMINATION IN MAIZE- BASED FOOD AND

HUMAN HEALTH IMPLICATION IN BAFIA (CENTRE-CAMEROON)

Evelyne Nguegwouo, Emmanuel Ediage Njumbe, Patrick Berka Njobeh, Gabriel

Nama Medoua, Zachee Ngoko, Martin Fotso, Sarah De Saeger, Elie Fokou and

Francois-Xavier Etoa

47-61

6 IMPACT OF CARBON DIOXIDE EMISSIONS ON ECONOMIC

GROWTH AMONG DIFFERENT REGIONS OF WORLD

Sana Iftikhar, Muhammad Abdul Quddus

62-80

7 EFFECT OF DIFFERENT MULCHES ON GROWTH AND YIELD OF

TOMATO

M. R. Islam, M. G. Kibria, A. K. Das and S. D. Setu

81-84

8 ANALYSIS OF THE EFFECT OF CREDIT ON PER CAPITA ANNUAL

FARM INCOME OF RICE FARMERS; BENEFICIARIES OF SACCO

CREDIT IN BENUE STATE NIGERIA

Okolo Samson Ayegba, Olotu Olafemi Ayopo

85-

101

International Journal of Agriculture and Environmental Research

ISSN: 2455-6939

Volume: 06, Issue: 01 "January-February 2020"

www.ijaer.in Copyright © IJAER 2020, All rights reserved Page 1

INTRODUCTION OF SOIL CULTIVATION (HYDROPONIC SYSTEM)

TO IMPROVE QUALITY AND QUANTITY OF VEGETABLE

HORTICULTURE RESULTS

Ratna Rositawati

Faculty of Agriculture, Wisnuwardhana University Malang, Indonesia

ABSTRACT

Hydroponic techniques are horticultural plants of leafy vegetables, fruit, ornamental plants,

landscaping, and medicines. The key to successful hydroponic farming lies in choosing the right

system and intensive care, from nursery to harvest. In general, not many people, especially

housewives, know and understand hydroponic farming techniques, so a deeper introduction is

needed so that housewives can find out the real benefits. Many benefits can be taken from

hydroponic cultivation. Communities need knowledge in order to know and understand these

methods in-depth. Besides that, it can find out the benefits obtained if planting in hydroponics

when compared to planting using soil media. The byproducts of hydroponic farming can get

more additional income if managed properly because planting a hydroponic system does not

require large tracts of land so as to maximize yields.

Keywords: Plant Cultivation, Hydroponic Systems, Vegetable Horticulture

1. INTRODUCTION

The hydroponic planting system is a method of cultivation without soil media but instead uses

water media. Plants that can be cultivated with hydroponic techniques are horticultural plants of

leafy vegetables, fruit, ornamental plants, landscaping, and medicines. In hydroponic techniques

the treatment is given from the nursery to harvest, using the only nutrient solution as a planting

medium. The hydroponics that is carried out indoors require special lighting to replace the sun's

rays, so that the humidity will remain controlled and the problem of the emergence of bacteria

does not occur.

Commercially, hydroponics provides many advantages including saving water use up to 90%,

suitable to be applied in areas that are difficult to water, can use narrow land and do not know the

season. Profits Plants that are produced will be better, cleaner, healthier, safer and more practical

because vegetables that are harvested are free of pesticides, so vegetables that are harvested and


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then washed can be eaten immediately. Easier maintenance of vegetables, faster harvest time and

save labor.

The key to successful hydroponic farming lies in choosing the right system and intensive care,

from nursery to harvest. By routinely monitoring the need for nutrient water discharge in storage

tanks and measuring the suitability of the concentration of nutrient solutions (ppm levels) that

will be given to the type and age of plants planted to remain stable. Furthermore, maintaining

and maintaining the plant environment is always clean to support its growth.

While the tool used is Total Diluted Solid (TDS) meters, producing units of ppm (parts per

million). TDS is used to measure the number of dissolved solids in a liquid (water), both organic

and inorganic (mineral). With a TDS meter, it can measure water temperature and estimate the

amount of nutrient adequacy/concentration of the solution needed by plants.

In general, not many people, especially housewives, know and understand hydroponic farming

techniques, so a deeper introduction is needed so that housewives can find out the real benefits,

namely by providing counseling and training. By inviting housewives to grow hydroponically, it

is hoped that in the neighborhood, the residents can meet their food needs, especially vegetable

horticulture and fruits that can be planted using hydroponic techniques.

How to grow hydroponics to use land that is not too broad, not much done. Seeing the number of

narrow land such as the plots of land in the surrounding neighborhoods that have not been

utilized maximally, it is very necessary to provide guidance so that the surrounding communities

can utilize their plots to grow hydroponics while at the same time as entrepreneurs. This

hydroponic system can increase the benefits of housewives in increasing the quality and quantity

of vegetable and fruit horticultural crops in urban areas while maximizing the existing plots of

land. Based on the background above, it will be possible to study several objectives which are

the subject of this paper: (1) Provide an overview of the hydroponic system, (2) Explain and

apply methods of growing hydroponically (3) Provide an overview of the benefits and

hydroponic cultivation deficiencies (4) Describe and introduce the best way of hydroponic

systems to housewives in order to provide maximum benefits and benefits.

2. HYDROPONIC SYSTEMS

2.1. Definition of Hydroponics

Hydroponics (hydroponics) comes from the Latin language, hydro means water, and phonos

means workmanship so that hydroponics is water that works. "Hydroponics is a farming activity

carried out using water as a medium to replace soil. This water contains nutrients needed for

plant growth and roots to develop in nutrient solutions (Lingga, P., 2011). This nutrient solution


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contains mineral nutrients needed by plants. Besides hydroponic planting can also use husk

charcoal, gravel, coarse sand, or coconut fiber.

2.2. Hydroponic Systems Engineering

There are two main hydroponic system techniques in its development: the Hydroponic Substrate

System (open system) is the way to plant it almost the same as conventional farming, using pots

and solid media that can absorb or provide nutrients for water, oxygen and a little organic

material, planting media used are made from artificial planting media such as husk charcoal,

Rockwool, cocopeat, hydroton and sawdust, the nutrition provided by this technique is done by

drip irrigation which is wetting some areas around the plant. Non-Substrate Hydroponic System

(closed system), in non-substrate hydroponics, is cultivation by putting plant roots on circulating

nutrients and containing nutrients according to the needs of vegetables and roots will develop in

nutrient solutions.

2.3. Based on the Hydroponic System Nutrition Movement

Based on the nutritional movement, there are two hydroponic systems, namely: Active /

Dynamic Hydroponics is a mobile solution circulating using a sample pump: NFT (Nutrient Film

Technique) and Aeroponic, a circulating solution rich in dissolved oxygen, the initial investment

are relatively expensive, installation is more complicated. Passive / Static Hydroponics, this

system depends on the capillary force of the growing media. Example: Wick System (axis

system) and Floating Hydroponic System (floating raft system). The excess is a nutrient-rich

solution, absorbed by the medium and passed on to plant roots. Good for leafy vegetables, but

not recommended for large fruit plants. Inability to provide sufficient oxygen through the roots

to support plant growth. For optimal results, it can be helped by aerating air bubbles using

Aerator / Bubbler like in an aquarium. Based on nutritional movements, there are two types of

hydroponic planting methods:

1. Hydroponic Substrate (open system)

The hydroponic substrate is a way of planting almost the same as conventional farming.

a. Using pots and solid media that can absorb/provide water nutrition, oxygen, and a little

organic matter.

b. The planting media used are made from artificial planting media such as husk charcoal,

rock wool, cocopeat, hydro towns and sawdust.

c. Nutrition in this technique is done by drip irrigation which is wetting a part of the area

around the plant.


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2. Non-Substrate Hydroponic System (closed system)

a. Passive / Static Hydroponics

Example: Wick System (axis system) and Floating Hydroponic System (floating raft system).

b. Active / Dynamic Hydroponics

Example: Hydroponic NFT (Nutrient Film Technique) and Aeroponics

Hydroponic Wick System (Wick System) is the simplest hydroponic method because it only

utilizes the principle of water capillarity. The nutrient solution has flowed from the reservoir to

the root of the plant above with the intermediate axis, "Similar to the Way of the Oil Stove".

Figure 1: Hydroponics of the Wick System (Wick System)

Hydroponics NFT (Nutrient Film Technique) is a model of cropping cultivation by placing plant

roots on circulating nutrients and containing nutrients according to vegetable requirements of

3mm so that the water (nutrition) and oxygen needs can be fulfilled. Good and balanced nutrition

will help get maximum yields. This underlies the existence of a simple or automatic control

system in a nutrient solution.


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Figure 2: Hydroponic NFT (Nutrient Film Technique)

2.4. Hydroponic Media Requirements

The media used in growing hydroponic systems should meet the following requirements:

a. It contains lime or calcium and must be mild,

b. Has acidity from neutral to alkaline pH 6-7,

c. Free from pest and disease organisms,

d. Can store enough water for plant growth,

e. Easy to dispose of excessive and porous water,

f. It contains low salinity levels.

2.5. Nutrient and Oxygen Solution

Nutrient solutions that must be considered are the appropriate amount and element of pH. The

pH element ranges from 5.5 -7.5. Nutrient solutions contain large concentrations of N, P, K, Ca,

Mg and S, while the elements Fe, Mn, Zn, Cu, B, Mo and Cl in small amounts. The nutrient

solution is made by dissolving fertilizer salts in water.

Oxygen plays an important role in growing hydroponic systems, lack of oxygen will make it

difficult to penetrate cell walls, so plants will lack water and wither quickly because the solution

does not contain oxygen. Giving oxygen into the solution can be through air bubbles like bubble

water pumps used for aquariums.


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3. EXCESS AND LACK OF HYDROPONIC SYSTEMS

3.1. Hydroponic Advantages or Strengths

In choosing a hydroponic farming system, the advantages/disadvantages and disadvantages need

to be considered so that the results can be in accordance with the desired needs.

a. Flexible Can be applied in various conditions, in urban areas with narrow land,

hydroponics can be done on the porch of the house, garden in space, home yard.

b. Nutrition control is easy to do. The nutrient solution used is guaranteed to be balanced,

because it is easier to add or reduce nutrients so that it is easy to control.

c. Higher production. Hydroponics produces two to four times higher production than

conventional systems because essential nutrients are always available.

d. The resulting crop products are uniform because the plant media used are more stable and

the irrigation system and nutrient circulation are standard.

e. Product quality is guaranteed in terms of product cleanliness and safety. Hydroponics

uses sterile media and complete nutrient solutions so that the product is clean.

f. Save labor, because there is no-tillage and weeding.

g. While the hydroponic, fertilizing and irrigation systems are carried out with an electric

pump, which is equipped with a timer.

h. It is easy to start a new planting because there is no-tillage, so you just need to replant it.

i. Saving water and fertilizer, the use of water and fertilizer in a hydroponic system is very

efficient, because the amount of water and nutrient concentrations are given according to

plant needs.

j. There are almost no weeds because the media used is not soil and sterile conditions, so as

to reduce the growth of weeds.

k. Transplanting is easy to do

l. Replacing dead or damaged plants is easy.

m. Continuity of production maintained.

3.2. Hydroponic Weakness or Weakness


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a. The operation of a hydroponic system needs continuous monitoring, especially electricity

and control of nutrient solutions.

b. If the planting area is attacked by disease, it will easily spread.

c. Requires workers who have special skills to run the hydroponic system.

d. If a failure occurs, it will cause a substantial loss.

e. Not all plants can be planted with the hydroponic method.

f. The initial investment is expensive.

g. Requires special skills to weigh and mix chemicals.

4. HYDROPONIC SYSTEMS HAVE NOT DEVELOPED IN THE COMMUNITY

4.1. Introduction Stage

The introduction of hydroponic farming systems in the community is needed, the first stage can

be done by finding information from anywhere. Counseling and training can also be carried out

to the expert level and advanced level, so as to obtain maximum results. It is hoped that after

finishing receiving training, the community can utilize their skills well so that they can be

applied to improve their standard of living. The main community is farmers, usually doing

farming activities using land media. The introduction of planting with a hydroponic system needs

to be introduced so that the community can provide an assessment of the hydroponic farming

system compared to the method of planting using land so as to maximize yields.

A. Information from the Book Reference

Reference books on hydroponic planting and ways of making installations on hydroponics have

been widely circulated in bookstores, even on social media, but the public is still not moved to

learn them and practice them. This is due to the fact that planting hydroponic techniques is still

difficult to do, so the community still needs assistance in doing so.

B. Counseling

Counseling methods are generally associated with a device or system to be used. The explanation

will be explained briefly about various counseling methods that will be carried out. A good

extension method really needs to be applied to provide an introduction and in-depth

understanding of landless cultivation (hydroponic systems), how it works, the benefits and


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benefits obtained when using a hydroponic system, weaknesses of the hydroponic system, which

method should be used, adjusted with plants to be planted.

C. Hydroponic Systems Training

Hydroponic technical training for the community should be carried out, to provide skills to an

advanced level, to the community, in order to be able to create a joint entrepreneurial business in

their environment. For this reason, cooperation from government agencies and private parties is

expected to provide training as well as assistance, so that the hydroponic system cultivation

technique is more developed and popular. With an instructor who has sufficient expertise, will

minimize the risk of mistakes and failures in the implementation of planting.

4.2. The Development of Hydroponics in Indonesia

The development of hydroponic cultivation system cultivation in Indonesia is still not much in

demand by the community, so it is necessary to conduct counseling and training so that the

hydroponic farming system is growing. Hydroponic techniques require a fairly high investment

and require special expertise. It is this factor that inhibits hydroponic plantations that have not

been widely carried out in Indonesia. But now there are already Indonesian hydroponic

entrepreneurs who have succeeded in exporting produce harvest the garden. Regions in

Indonesia that have planted hydroponic techniques are in Jabodetabek. Currently, in West Java,

simple hydroponic cultivation can be witnessed in Lembang, Purwakarta, and Garut while in

East Java can be found in Nangkojajar (Pasuruan), Bedali Lawang and Batu (Malang).

4.3. Introducing Hydroponic Farming in Rumha Ladders

In hydroponic cultivation techniques require expertise to an advanced level in the field. Starting

from the introduction and understanding of planting a true hydroponic system, to conducting

training and assistance, so that the community can apply and utilize it for their own environment.

The community, especially housewives, needs to know the hydroponic system deeply because

there are many benefits and benefits that can be derived from growing a hydroponic system. In

general, housewives (who do not work) only depend on the salaries of their husbands. The work

of a mother in the household itself is very dense, so with entrepreneurial business at home, the

mothers do not need to find a side job outside the home. By attending counseling, training in

hydroponic system farming to an advanced level, mothers are expected to apply it in their own

plots. Besides the beginning as a hobby, a farming hydroponics system can be maximized to get

profit, so it can be used to increase family income. Thus this hydroponic system can be cultivated

commercially, with good and maximum management and maintenance that will produce quality

crops, which in the long run will have an impact on improving their welfare.


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5. CONCLUSION

Hydroponic cultivation is planting without soil media, can use nutritious solutions, husk

charcoal, sand, coal, coconut coir, gravel and so on. Many benefits can be taken from hydroponic

cultivation. Communities need knowledge in order to know and understand these methods in-

depth. Besides that, it can find out the benefits obtained if planting in hydroponics when

compared to planting using soil media. The byproduct of hydroponic farming can get more

additional income if managed properly because planting a hydroponic system does not require

large tracts of land so that it can maximize yields. Hydroponics is a farming system without soil

media that can produce good quality crops. The government should provide counseling and

training to the community, then provide capital loans, so that the community can try to plant with

hydroponic techniques. If counseling, training and pilot planting provided during the training

goes well, then the community (PKK mothers) can apply the knowledge they have to grow

medium-scale hydroponics in their environment.

6. ACKNOWLEDGMENTS

Thank you to the Faculty of Agriculture, the University of Wisnuwardhana Malang for the

support so that this article can be published and the editor and reviewer of the International

Journal of Agriculture and Environmental Research.

REFERENCES

Anonim, 2009 Mengenal Hidroponik. Diakses di http://ficusbenyamina.blogspot.com/2009/09/

mengenal –hidroponik.html pada 20 Oktober 2013.

Anonim, 2012. Kelebihan dan KekuranganHidroponik. Diakses di

http://apandi2.blogspot.com/2012/05/kelebihan-dan-kelemahan-hidroponik.htmlpada

tanggal 20 Oktober 2013.

Anonim, 2012. Berbagai Keunggulan Hidroponik. Diakses di shyro-group.blogspot.com/

2012/06/berbagai-keunggulan-hidroponik.htmlpada tanggal 20 Oktober 2013.

Anonim, 2013. Mengenal Hidroponik. Diakses di http://heejo.com/blog/artikelmengenal-

hidroponikpada tanggal 20 Oktober 2013.

Anonim, 2013. Teknik Hidroponik untuk Budidaya Tanaman. Diakses di

http://www.anneahira.com/teknik-hidroponik.htmlpada tanggal 20 Oktober 2013.

http://ficusbenyamina.blogspot.com/2009/09/

http://apandi2.blogspot.com/2012/05/kelebihan-dan-kelemahan-hidroponik.htmlpada%20tanggal%2020%20Oktober%202013

http://apandi2.blogspot.com/2012/05/kelebihan-dan-kelemahan-hidroponik.htmlpada%20tanggal%2020%20Oktober%202013

http://heejo.com/blog/artikelmengenal-hidroponikpada%20tanggal%2020%20Oktober%202013

http://heejo.com/blog/artikelmengenal-hidroponikpada%20tanggal%2020%20Oktober%202013

http://www.anneahira.com/teknik-hidroponik.htmlpada


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Lingga, P.2011. Hidroponik: Bercocok Tanam Tanpa Tanah. Jakarta: Penebar Swadaya.

Primantoro, H., dan H.I. Yovita, 2001. Selada Hidroponik dan Non Hidroponik. Jakarta:

Penebar Swadaya.

Susanto, S. 2002. Budidaya Tanaman Hidroponik. Modul Pelatihan Aplikasi Teknologi

Hidroponik untuk Pengembangan Agribisnis Perkotaan 28 Mei-7 Juni 2002. Bogor:

Kerjasama CREATA-IPB dan Depdiknas.

Untung, O, 2000. Hidroponik Sayuran Sistem NFT (Nutrient Film Technique). Jakarta Penebar

Swadaya.


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THE EFFECTS OF GOOD GOVERNANCE ON THE

AGRICULTURAL SECTOR

Marzieh Ronaghi1, Sayed Saghaian2, Mohammadreza Kohansal3,*,

M. Reed4, Mohammad Ghorbani5

1PhD student, Department of Agricultural Economics, Mashhad Ferdowsi University, and Researcher at the

University of Kentucky, 326 C.E. Barnhart Building, Lexington, Kentucky 40546-0276.

2Professor, Department of Agricultural Economics, University of Kentucky,

314 C.E. Barnhart Building, Lexington, Kentucky 40546-0276.

3Professor, Department of Agricultural Economics, Ferdowsi University of Mashhad.

4Professor, Department of Agricultural Economics, University of Kentucky,

308 C.E. Barnhart Building, Lexington, Kentucky 40546-0276.

5Professor, Department of Agricultural Economics, Ferdowsi University of Mashhad.

*Director of Marzieh Ronaghi's Ph.D. dissertation

ABSTRACT

In this research, we use the Meta-synthesis method to find important factors of good governance

to increase food security, using the Shanon Entropy model and weighing variables by a Fuzzy

method. The Meta-synthesis results show international policy, group participation, and

observance standards have the highest importance and rank. Fuzzy analysis shows agricultural

employment, group participation and cooperative companies have the highest weight in political,

social and environmental areas, and increased production, and financial and capital markets have

the highest weight in the economic area. The policy recommendation is an implementation of

agricultural governance to improve employment, financial markets, and group participation.

Keywords: Agricultural governance, Fuzzy Method, Meta Synthesis.

1. INTRODUCTION

The role of governance is garnering much attention in the development literature and has often

touted as a major reason why some countries have experienced faster economic growth than

others (Samarasinghe, 2018). Governance is the process of making and implementing decisions

that improve economic, political and social institutions (UNESCAP, 2014). Good governance


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affects the quality of life and welfare of people. Good governance involves many actors such as

companies, political parties, military, non-government organizations and even influential

individuals (Pere, 2015). While all these institutions have an influence on how decisions are

made within a country, government sets the rules and norms that strengthen the ability of the

public and private sectors to play a meaningful role. Without good governance, economic growth

creates gaps within society’s social and economic sectors (Pere, 2015).

In general, benefits of good governance are reduced corruption (due to transparency and

accountability), realization of democracy, and increasing international cooperation (through trust

and conformation with international law). Improving each of good-governance components, for

example, increased civil liberties, can make a difference in the population’s well-being (Stead,

2015). Good governance has three attributes (Janssen and Van der Voort, 2016): 1) supports

good relations and cooperation between the government, civil society and the private sector; 2)

upholds principles of partnership, decision clarity, accountability, justice, predictability,

democracy, civil liberties and free access to information; and 3) establishes a set of norms and

values desirable for institutions, and governmental and international organizations.

Meanwhile, the agricultural sector plays a major role in human welfare. To improve agricultural

productivity in the developing countries, efforts have involved improvements in technology and

input availability. Such efforts emphasize on providing tangible products, such as capital and

modern agricultural technology for modernization (the focus of most international assistance

programs), irrigation equipment, and chemical fertilizers. Yet researchers are realizing that these

noticeable achievements are limited unless they are supported by good governance (Lio et al.,

2008).

Good agricultural governance could be the key to food security and development, improving

management of domestic resources and eliminating the internal and external barriers to

development. This research investigates the role of the government, private sector and civil

society in implementing good agricultural governance in order to increase food security in Iran, a

country that faces challenges relative to good governance such as transparency and

accountability, effectiveness, regulatory quality, rule of law, and corruption. Iran has an oil-

dependent economy with technology adoption lags and little organizational innovation. Despite

Iran’s high agricultural potential, growth in agriculture’s share of GDP is lower than other

sectors. An improved agricultural sector could help reduce the economy’s dependence on oil and

increase food security.

The World Bank calculates a governance indicator by country, which varies from -2.5 (the

weakest) to +2.5 (the strongest). All of the governance indicators for Iran are negative (Figure 1).

This research investigates the role of governance in improving agricultural performance and food


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security in Iran. It builds on arguments that poor governance constrain agricultural productivity,

and more emphasis is needed on the governance infrastructure to enhance agricultural

performance to positively influence a country’s agricultural productivity (Liu et al. 2008).

Figure 1: Governance Indicators in Iran in 2010- 2015.

2. LITERATURE REVIEW

Analyzing the role of improved governance infrastructure on economic performance is important

for development and food security (Globerman and Shapiro, 2002). Good governance is a

prerequisite for economic growth as other stakeholders become partners in the development

process. Given the importance of agricultural, several studies have focused on agricultural

governance. Lio et al. (2008) investigated the effects of governance infrastructure on agriculture

and tested the hypothesis that good governance improves agricultural productivity. They showed

a country with good governance can produce more agricultural output with the same amounts of

inputs. They also investigated whether good governance can indirectly enhance agricultural labor

productivity by driving agricultural capital accumulation. The results revealed that given the

same amounts of agricultural capital stock and land, workers in a country with good governance

produce more.

Bitzer et al., (2016) studied the governance of agricultural extension systems, concluding that

demand-driven services in the agricultural sector have led to improvements in the efficiency of

agricultural governance. Mohammadzadeh et al., (2017) studied the impact of government size

on good governance and economic performance. Their results show that the size of government

has a negative effect on economic growth, but good governance has a positive impact, and

employment and education factors have positive effects on governance indicators.

-2.00

-1.50

-1.00

-0.50

0.00Voice and

Political

Governmen

Regulatory

Rule of Law

Control of


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Despite many studies on agricultural governance, there is no comprehensive study that

determines how it affects economic, political, social and environmental aspects of agriculture

(Elmenofi et al., 2014; Hayami et al., 1985; Liu et al., 2008). Due to economic sanctions and

numerous challenges in investment and employment, Iran's agricultural conditions require

improved governance. Iran is an oil-dependent economy, and an improved agricultural sector

could help reduce the economy’s dependence on oil and increase development and food security.

In this research, we study the effects of improved agricultural governance on the economic,

political, social and environmental aspects of the agricultural sector in Iran. Good governance

could result in adopting appropriate agricultural policies, increasing agricultural productivity,

production, and food security, reducing investment risk, and increasing farmers’ participation in

improving the agricultural sector.

3. METHODOLOGICAL APPROACH AND MODEL DEVELOPMENT

To determine the factors of good agricultural governance to improve Iran’s agricultural sector,

we use a meta-synthesis of a systematically retrieved sample of academic agricultural

governance literature. This method identifies, evaluates, and synthesizes the articles produced by

researchers, scholars and practitioners (Fink, 2010). This systematic review adheres to a set of

principles that limits biases in the sample of studies (Booth et al., 2012; Moher et al., 2009;

Petticrew and Roberts 2006). We collected academic articles from seven different academic

databases: Web of Knowledge, Scopus, Science Direct, IEEE, Alps, Acs, Jstor, and Rsc sciences.

They cover the 2000-2018 period, which is the focus of this review. A well-defined research

based on the research questions in Table 1 is used to ensure sensitivity and specificity of the

literature searches (Petticrew et al., 2006).

Table 1: Research Questions

Research questions

What are the variables which explain agricultural governance?

What importance and weight does each variable have in agricultural governance?

Who are experts to determine the variables that explain agricultural governance?

How does agricultural governance improve the country?

We use the Meta-synthesis method to find the important factors/variables used in the literature,

and then present these variables to faculty members in the field of agriculture (as members of the

expert group), to rank them using the Shannon Entropy method. The variables are also presented

to a selected group of ten experts in the field of agriculture to identify the important agricultural-

governance variables in Iran by modifying/adding/ deleting variables from the meta-synthesis.


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They weigh the variables by a Binary Comparison Matrix. Experts are selected by Snowball

sampling, who complete their ranking through the Delphi method. Finally, we compare the

global agricultural governance variables from the Shannon Entropy method with those from a

pairwise comparison matrix using the Fuzzy method.

3.1 Meta-synthesis Model

In this method, the researcher performs a complete study and synthesizes the findings of related

studies (Dekker and Bekkers, 2015), creating an interpretive combination of the findings. The

seven-step method of Yahyapour et al., (2016) is used in this study:

Step 1: Formulating the review questions

The research questions in the meta-synthesis are: What are the variables which explain

agricultural governance? What importance and weight does each variable have in agricultural

governance? Who are experts to determine the variables that explain agricultural governance?

How does agricultural governance improve the county’s economy?

Step 2: Conducting a systematic literature search

It takes considerable effort to develop an exhaustive list of studies that might be included in the

qualitative meta-analysis. Keywords are identified and used with all available databases within

the study period. In this study, various databases, journals, key words and search engines were

studied for the years 2000-2018. This process included 252 articles.

Step 3: Screening and selecting appropriate research articles

This step involves developing a means for determining the similarities among studies by using

comparison parameters such as title, abstract and content.

Step 4: Extracting information from the articles

Information from papers is categorized by paper title and author, year of publication, and other

important factors mentioned in the article. This step determines the range of factors for the Meta-

Synthesis. The results are shown in “Table 2”.


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Table 2: Factors and References on Agricultural Governance

Factors/Variables References

Improving the quality of

agricultural production and foods

Corsi et al (2014)

Herman et al (2015)

Hughes et al (2013)

Benson and Jafry (2013)

Papadopoulos (2003)

Keulartz (2007)

Paulino (2014)

Mandemaker et al (2014)

Lio and Liu (2008)

Thirtle et al (2007)

Herman et al (2015)

Wang et al (2016)

von Braun et al (2016)

Corsi et al (2012)

The size and scale of agricultural

land cultivation

Gehan et al (2014)

Moguesand Owusu-Baah (2014)

Paulino (2014)

Biermann (2007)

Cash et al (2006)

Deininger et al (2014)

Dryzek et al (2011)

Paulino (2014)

Hornidge et al (2015)

Dinnie et al (2015)

Mazzocchi et al (2014)

Operating expenses and taxes

Beckmann et al ( 2015)

Yu (2015)

Group participation

Gera (2016)

Zhou (2016)

Soma et al (2016)

Lemos and Agrawal (2006)

Ford (2003)

Bitzer et al (2016)

Water management and social

justice

Bijman et al (2014)

Huo et al (2016)

Neal et al (2016)

Barnard (2007)

Huitema et al (2009)

Fish et al (2010)

Conrad et al (2016)

Thirtle et al (2013)


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Settre1and Wheeler (2016)

Investing in agricultural research Thirtle et al (2013)

Saunier& Meganck (2009)

Observance of standards

Schouten and Bitzer (2015)

Hospes (2014)

Glasbergen and Schouten (2015)

Hatanaka (2014)

Environment and natural

resources

Pirani et al (2014)

Stål et al (2015)

Ay rikyan et al (2012)

Bronen and Chapin (2013)

Brunner and Lynch (2010)

Paavola (2007)

Primmer et al (2015)

Zhou (2016)

Gera (2016)

Toddi (2014)

Soma et al (2016)

Business

Av ram (2014)

Biewald et al (2016)

Birner et al (2016)

Agricultural employment Tomiasi Paulino (2014)

Agricultural share of GDP Gallego-Álvarez et al (2016)

Mechanization and technology

Greiber and Schiele (2011)

Bernard and Rollin (2014)

Hartley et al (2016)

Social justice and poverty

Ravnborg et al (2014)

Fuchs and Glaab (2011)

Elmenofi et al (2014)

Agricultural sustainability

Sanwal (2004)

Fuchs and Glaab (2011)

Hart at al (2016)

Gaviglio et al (2014)

Fielke and Wilson (2016)

Governments policies

Giessen et al (2016)

Bitzer et al (2016)

Keulartz (2007)

Lawrence et al (2008)


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Hanisch et al (2014)

Step 5: Analyzing and synthesizing qualitative findings

In this step we use the factors for the Meta-Synthesis analysis and aggregate them into concepts

and then into basic categories so that we have summary data for using the Shannon Entropy

method. The experts determined there were four categories, eleven concepts, and 22 factors from

the 80 articles selected on agricultural governance. “Table 2” shows that a systematic study of

agricultural governance has not been performed because all previous studies have focused on

only one aspect of agricultural governance. Multiple dimensions of agricultural governance have

not been considered in a coherent and systematic framework. The factors with each category and

concept are shown in “Table 3”.

Table 3: Categorization of Findings from the Meta-Synthesis

Factors Concepts Categories


Agricultural employment

Operating expense

Taxes

Marketing efficiency systems

Efficiency and production scale

Agricultural land

Development of institutions, capital markets and

financial markets

Infrastructure

Agricultural share of GDP

Production and management

Marketing

Natural resources

Development

Economic

Government policy

The role of market players in policy making

Monetary and credit system

International policy

Domestic

International

Political

Reduce poverty and observance of justice

Public participation

Use of cooperative companies and groups in the

agricultural sector

Creation and expansion of consulting companies,

public extension, education

General

Governmental

Social

Sustainability

Water management

Protection of environment and resources


Determine the permitsand

Governmental laws

International laws

Managerial

Environment


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Step 6: Control the extracted factors

We used the experts to classify the factors for agricultural governance. Cohen's Kappa Index

(Barnett 2009) is used to test whether there is a consensus among the experts in their

classification of factors.

The Meta-Synthesis analysis seeks to measure the importance of the various factors. The

Shannon Entropy method provides a quantitative measure of importance, which incorporates the

probability that a factor is important (Artstein et al., 2004; Hillborn, 1974; Shannon, 1948). The

probability stems from the likelihood that a particular factor is mentioned in the literature. A

system with higher Shannon entropy has more transitive information and, therefore, greater

uncertainty. Events with higher probability contribute less transitive information to the system

than events with lower probability (Rongbao, 2017).

In the Shannon Entropy method the rate that factors appear in the literature is counted toward its

degree of importance. Equations 1 and 2 are used to calculate uncertainty and importance

coefficients, respectively.

𝐸 ≈ 𝑆 {𝑃1, 𝑃2, … , 𝑃𝑛} = −𝑘 ∑ [𝑝𝑖𝑗𝑙𝑛𝑝𝑖𝑗]𝑚𝑖=1 , (j=1,2,…., n) (1)

here Ej is an uncertainty coefficient, which is expressed by the probability distribution for each

factor j. Pij is the probability that factor j is used in concept i.

𝑃𝑖𝑗 =𝑓𝑖𝑗

∑ 𝑓𝑖𝑗𝑚𝑖=1

(𝑖 = 1, 2, . . , 𝑚 ; 𝑗 = 1, 2, … , 𝑛), 𝑘 =1

ln 𝑚

𝑚 = 𝑡ℎ𝑒 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑡𝑢𝑑𝑖𝑒𝑠

∑ 𝑓𝑖𝑗𝑚𝑖=1 = 𝐹𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦 𝑜𝑓 𝑠𝑡𝑢𝑑𝑖𝑒𝑠 𝑓𝑜𝑟 𝑒𝑎𝑐ℎ 𝑐𝑜𝑛𝑐𝑒𝑝𝑡

𝑤𝑗 =𝐸𝑗

∑ 𝐸𝑗𝑛𝑗=1

(2)

Wj is the importance of each study from the Shannon entropy method.

3.2 Fuzzy hierarchical analysis method

The literature where responses to questions involving imprecise judgements are compared is

extensive (Leung ve Chao, 2000). When human impressions are imprecise they are not

successful in making quantitative predictions, but they are more efficient in qualitative

forecasting (Kulak ve Kahraman, 2005). The uncertainty in the preference judgments gives rise


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to uncertainty in the ranking of alternatives as well as difficulty in determining consistency of

preferences (Leung ve Chao, 2000). This is where fuzzy methods come into play.

In this application survey respondents were asked to rank factors of agricultural governance

based on their relative importance using a scale with six rankings, from extremely important to

weakly important. Those rankings were given weightings that reflect uncertainty on the part of

the respondent that are consistent with other fuzzy analytical applications. With the fuzzy

method, a pairwise comparison matrix is formed among evaluators and a weighted score, Sk, is

calculated for each respondent as follows (Ping Wan et al., 2017).

Sk= ∑ 𝑀𝑘𝑗𝑛𝑗=1 *[∑ ∑ 𝑀𝑖𝑗

𝑛𝑗=1

𝑚𝑖=1 ]-1 (1)

where Mkj is the ranking given for row k and factor j. In the fuzzy analysis method, we calculate

the sk's order of magnitude, where the order of magnitude for the two fuzzy numbers that

constitute the upper (u) and lower (l) bounds for the ranking, M1 and M2, respectively, is shown

as V (M1> M2) in equation (2).

(2)

M2 = (l2, m2, u2), M1= (l1, m1, u1)

{𝑉(𝑀1 ≥ 𝑀2) = 1 𝑖𝑓 𝑚1 ≥ 𝑚2

𝑉(𝑀1 ≥ 𝑀2) = 𝐻𝑔𝑡(𝑀1 ∩ 𝑀2) 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

Hgt (𝑀1 ∩ 𝑀2) =𝑢1−𝑙2

(𝑢1−𝑙2)+(𝑚2−𝑚1)

The weight vector (T) of the factors is the same as the abnormal vector of the fuzzy analysis

process, which is as W’(x):

(3)

𝑊(𝑥𝑖)́ = 𝑀𝑖𝑛{𝑉(𝑆𝑖 ≥ 𝑆𝑘)} → 𝑊(𝑥𝑖)́ = [𝑊ˊ(𝑐1), 𝑊ˊ(𝑐2), … , 𝑊ˊ(𝑐𝑛)]𝑇

, 𝑘 = 1,2, … , 𝑛

𝑘 ≠ 𝑖

𝑊𝑖 =𝑊𝑖

ˊ

∑ 𝑊𝑖ˊ (4)


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4. EMPIRICAL RESULTS

The Cohen's Kappa Index is used to test the classification of factors. This calculated statistic is

0.68 and is significant at the 0.001 probability level; we reject the null hypothesis that the factors

are independent and conclude that the factors and concepts are appropriate. The results of the

Shannon entropy method and the final ranking are shown in “Table 4”. The coefficients

international organization policies, group participation, and observance of standards have the

highest importance and rank coefficient, the important variables of agricultural governance

worldwide. Finally, we compare the global agricultural governance factors from the Shannon

Entropy method with those from the Fuzzy hierarchical analysis method.

Table 4: Shannon Entropy Results and the Ranking Agricultural Governance Factors

Total

rank

Importance

jcoefficient W

jUncertainty E

Frequently Factors Concepts

9

11

3

2

11

0.0323

0.0169

0.0566

0.0580

0.0169

-0. 044

-0.023

0.077

-0.079

-0.023

-0.203

-0.105

-0.35

-0.36

-0.105

3

1

14

11

1


Agricultural employment

Efficiency and production scale

Agricultural land

Operating expense

Production

and

Management

Natural

resources

4

8

0.0551

0.0338

-0.075

-0.046

-0.34

-0.21

1

3

Taxes

Market efficiency systems

Marketing

4

4

0.0551

0.0551

-0.075

-0.075

-0.345

-0.345

2

2

Institutional development, capital

market and financial market, the

infrastructures

Agriculture share of GDP

Development

2

5

0.0580

0.0492

-0.079

-0.067

-0.364

-0.306

2

3

Government policy and the role

of market players in policy

making

Monetary and credit system

Domestic

policy

1

7

0.0588

0.0433

-0.080

-0.059

-0.36

-0.27

1

2

International organizations policy

Climate policy

International

policy

5

2

0.0492

0.0558

-0.067

-0.080

-0.306

-0.364

3

2

Reduce poverty and observance

of justice.

Creation and expansion of

consulting companies, public

extention, education

Government


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1

10

0.0588

0.0198

-0.080

-0.059

-0.36

-0.27

1

2

Proper use of group and

cooperative companies in

agricultural policies.

Use group participation in

agricultural sector and

technology transfer.

General

2

6

0.0580

0.0455

-0.079

-0.062

-0.364

-0.285

5

9

Sustainability

Water management,

permitstand

Governmental

laws

7

1

0.0419

0.0588

-0.057

-0.080

-0.262

-0.363

15

7

Environmental protection and

resources


International

laws

Managerial

After determining the important variables of agricultural governance globally, we make the

pairwise comparison matrix based on the responses to the questionnaires. “Table 5” shows the

result for one responder in the economics area and “Table 6” shows the result for one responder

in the political, social, and environmental area. Total fuzzy numbers in the pairwise comparison

matrix are calculated based on equation 1.

[∑ ∑ 𝑀𝑖𝑗

𝑛

𝑗=1

𝑚

𝑖=1

]

−1

= (0.009 ,0.01, 0.013)

The abnormal weight vector (T) of the factors in the economic area (calculated from equation 3)

are:

W’(Xi) = [0, 0.5, 0.2, 0.6, 0.1, 0.09, 0.09, 0.6, 0.7] T

The normalized factor weights (calculated from equation 4) are:

Wi= (0, 0.17, 0.06, 0.20, 0.03, 0.31, 0.31, 0.20, 0.24)

The abnormal and normalized factor weights of factors for the political, social and environmental

areas are calculated from the same formulas and are:

W’(Xi) = [0.17, 0.02, 0.12, 0.20, 0.40, 0.08, 0.12, 0.13, 0.34] T

Wi = (0.10, 0.012, 0.07, 0.12, 0.25, 0.05, 0.07, 0.08, 0.21)

The Meta-synthesis method shows that international policy, group participation, cooperative

companies, and observance standards have the highest importance and rank. For the fuzzy


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analysis the factors of agricultural employment, group participation and cooperative companies

have the highest weight in the political, social and environmental areas and the factors of

increase production, and financial and capital markets have the highest weight in the economic

area.

Table 5: Pairwise Comparison Matrix for One Respondent in the Economic Area

Mechanization

and

technology

Poverty

Reduction

Marketing

efficiency

systems

Operating

expense

and taxes

Agricultural

land

Efficiency

and

production

Development

of

institutions,

capital

markets and

financial

markets

Infrastructure

Agricultural

Share of

GDP

Mechanization

and technology

(1,1,1) (2/3,1/2,2/5) (1,2/3,1/2) (1,1,1) (2,1,2/3) (2,1,2/3) (2/3,1/2,2/5) (1,1,1) (2/3,1/2,2/5)

Poverty

Reduction

(3/2,2,5/2) (1,1,1) (1/2,2/5,1/3) (1,1,1) (2,1,2/3) (2,1,2/3) (1,2/3,1/2) (2/3,1/2,2/5) (1,1,1)

Marketing

efficiency

systems

(1,3/2,2) (2,5/2,3) (1,1,1) (1,1,1) (1,1,1) (2,1,2/3) (1/2,2/5,1/3) (1,2/3,1/2) (1/2,2/5,1/3)

Operating

expense and

taxes

(1,1,1) (1,1,1) (1,1,1) (1,1,1) (2,1,2/3) (2,1,2/3) (1/2,2/5,1/3) (1,2/3,1/2) (2/3,1/2,2/5)

Agricultural

land

(1/2,1,3/2) (1/2,1,3/2) (1,1,1) (1/2,1,3/2) (1,1,1) (1,2/3,1/2) (2/3,1/2,2/5) (1/2,2/5,1/3) (2/5,1/3,2/7)

Efficiency and

production

(1/2,1,3/2) (1/2,1,3/2) (1/2,1,3/2) (1/2,1,3/2) (1,3/2,2) (1,1,1) (1,1,1) (2/3,1/2,2/5) (1/2,2/5,1/3)

Development

of institutions,

capital

markets and

financial

markets

(3/2,2,5/2) (1,3/2,2) (2,5/2,3) (1,1,1) (3/2,2,5/2) (1,1,1 (1,1,1) (2,1,2/3) (2/5,1/3,2/7)

Infrastructures (1,1,1) (3/2,2,5/2) (1,3/2,2) (1,1,1) (2,5/2,3) (3/2,2,5/2) (1/2,1,3/2) (1,1,1) (2/5,1/3,2/7)

Agricultural

Share of GDP

(3/2,2,5/2) (1,1,1) (2,5/2,3) (3/2,2,5/2) (5/2,3,7/2) (2,5/2,3) (5/2,3,7/2) (5/2,3,7/2) (1,1,1)


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Table 6: Pairwise Comparison Matrix in the Political, Social, and Environmental Are

Government

policy

The role of

market

players in

policy

making

Monetary

and credit

system

International

Policy

Agricultural

employment

Observance

standards

and

permitstand

Sustainability Water

management

group

participation

and

consulting

companies

Government

policy

(1,1,1) (2/5,1/3,2/7) (1,2/3,1/2) (1,1,1) (1,2/3,1/2) (2/3,1/2,2/5) (2/3,1/2,2/5) (2,1,2/3) (2/5,1/3,2/7)

The role of

market

players in

policy

making

(5/2,3,7/2) (1,1,1) (1/2,2/5,1/3) (1,2/3,1/2) (2,1,2/3) (2,1,2/3) (1,2/3,1/2) (2/3,1/2,2/5) (1,1,1)

Monetary

and credit

system

(1,3/2,2) (2,5/2,3) (1,1,1) (2,1,2/3) (2/3,1/2,2/5) (2,1,2/3) (1/2,2/5,1/3) (1,2/3,1/2) (2/3,1/2,2/5)

International

Policy

(1,1,1) (1,3/2,2) (1/2,1,3/2) (1,1,1) (2,1,2/3) (2,1,2/3) (1/2,2/5,1/3) (2/3,1/2,2/5)

(2/3,1/2,2/5)

Agricultural

employment

(1,3/2,2) (1/2,1,3/2) (3/2,2,5/2) (1/2,1,3/2) (1,1,1) (2/5,1/3,2/7) (2/3,1/2,2/5) (1/2,2/5,1/3) (2/5,1/3,2/7)

Observance

of standards

and

permitstand

(3/2,2,5/2) (1/2,1,3/2) (1/2,1,3/2) (1/2,1,3/2) (5/2,3,7/2) (1,1,1) (1,1,1) (2/3,1/2,2/5) (1,2/3,1/2))

Sustainability (1/2,1,3/2) (1,3/2,2) (2,5/2,3) (1,1,1) (3/2,2,5/2) (1,1,1 (1,1,1) (2,1,2/3) (2/5,1/3,2/7)

Water

management

(1,1,1) (3/2,2,5/2) (1,3/2,2) (3/2,2,5/2) (2,5/2,3) (3/2,2,5/2) (1/2,1,3/2) (1,1,1) (1/2,2/5,1/3)

group

participation

and consulting

companies

(3/2,2,5/2) (5/2,3,7/2) (3/2,2,5/2) (3/2,2,5/2) (5/2,3,7/2) (1,3/2,2) (5/2,3,7/2) (2,5/2,3) (1,1,1)

5. CONCLUSIONS AND DISCUSSION

Agricultural development and food security are important issues facing developing countries.

While a great deal of effort has been devoted to improve agricultural technologies, physical

infrastructure and education, researchers and policy-makers have recently attached more

importance to the impact of governance on agricultural performance. In this research, we used

the Meta-synthesis and Fuzzy analysis methods to idenify important governance variables for

Iran’s agricultural. Those are agricultural employment, group participation, and cooperative

companies in the political, social and environmental areas, and increased production as well as

financial and capital markets in the economic area. The policy recommendation is the

implementation of agricultural governance to include a broad approach that encompasses the

whole agriculture sector to improve employment, financial markets, and group participation in

Iran’s agriculture sector.


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The group participation and cooperative companies are identified in both methods. Group

participation and cooperative companies provide a connection between people and government

and makes sense that these factors are important in agricultural governance because they

complement and enhance public accountability mechanisms. Rural production cooperatives help

small farmers increase their production and marketing capacities (Khosravipour et al., 2014), and

increase the bargaining power of the operators, reducing costs of transportation and marketing.

Government’s role is to provide information, as well as financial and technical support for the

formation and development of production cooperatives in Iran’s agricultural sector.

Easy access to financial resources is a requirement for investment and development of the

agricultural sector. However, due the lack of developed agricultural financial markets in Iran,

this sector faces investment constraints. This paper shows that one of the important variables in

agricultural governance is increased efficiency in agricultural financial markets so that credit

constraints are loosened. To achieve this goal, the government needs to adopt policies that

strengthen financial structures, which allow farmers to access financial markets easily.

Agricultural production is vitally important as the main source of livelihood for 2.5 billion

people in the world, yet the growth of agricultural productivity has stalled. Yields for major

grains grow by about 1 percent per year, which is lower than the population growth rate. Given

that expanding the cultivated area is not possibility to meet future needs, increasing agricultural

productivity is the only solution to feeding the growing (urbanized) population (who has higher

food demand). The use of modern communications methods in extension services can foster

adoption of new technologies and promote profitable cultivation among farmers. Increasing

productivity among smallholders in developing countries is a crucial instrument to guarantee

food security in the long-run (Dethier et al., 2011).

Balancing world agricultural production and prices in an ever-changing global environment is

notoriously difficult. The world population will reach 9.3 billion by 2050 (UN). World demand

is estimated to require a 60% increase in agricultural production globally compared with 2009

levels. Natural resources across the globe, notably soil and water on which farming depends, are

under unprecedented strain from productivity demands and climate change. These events focus

attention on the pivotal role of agricultural policies as crucial variables of agricultural

governance for food security and rural prosperity.

An important variable of agricultural governance worldwide is international policy. The

international aspects of agriculture policy have an important role in pursuing the fundamental

objectives of governments. For instance, the Common Agricultural Policy of the European Union

emphasizes agricultural productivity, a fair standard of living for farmers, ensuring reasonable

prices for consumers, and promoting stability in markets (in particular stabilizing imports and


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exports) as well as food security (Ciolos, 2012). Other key elements have a direct bearing on the

international aspects of agricultural policy, including commercial policy that sets negotiations

and conclusions for tariff and trade agreements; policies that are coherent with improving food

security and rural prosperity in developing countries; policies that contribute toward global

sustainability of the farming sector (encompassing the challenges of climate change and

conservation of biodiversity); policies that support the rules-based global trading system in a way

that takes account of the fundamental role of agriculture in ensuring food security.

Another significant variable in agricultural governance worldwide is observance standards.

Standards and technical regulations have attracted increasing attention in ongoing regional and

global trade policy dialogue as tariff and quota issues seem to assume a declining dimension.

With the reduction in the applicability of tariff barriers, the adoption rate of standards as a trade

restrictive strategy has increased significantly. This growing emphasis on non-tariff barriers, in

the face of increased globalization and rapid agricultural trade liberalization, has attracted

considerable public debate on the impact of standards on regional and international market

access for agricultural commodities.

In addition to hindering access to markets for agricultural commodities produced by smallholder

farmers, standards also raise the cost of agricultural exports. Thus serving as disincentives to

smallholder farmers (Odularu et al., 2011). In fact, Sanitary and Phyto-Sanitary (SPS) measures,

which apply to domestically, sub-regionally, and regionally produced/traded agricultural

products, take many forms, such as requiring products to come from a disease-free area,

inspection of products, specific treatment or processing of products, or permitted use of only

certain additives in food products. Ultimately, the measures help to ensure that agricultural

commodities are safe for consumers, and prevent the spread of pests or diseases among animals

and plants.

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systems. kit working papers.

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Booth, A., Papaoianno, D., & Sutton, A. (2012). Systematic approaches to a successful literature

review. London: Sage.

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Liu, M., and Lio, M. (2008). Governance and agricultural productivity: A cross-national

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http://dx.doi.org/10.1108/JKM-05-2015-0166


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IMPACT OF BIOFERTILIZERS AND CHEMICAL

FERTILIZERS ON NODULATION, N UPTAKE AND

GROWTH OF SOYBEAN (Glycine max L.)

1Betty Natalie Fitriatin, 2Rahadian Nur Prathama, 1Reginawanti Hindersah

1Soil Science Department, Faculty of Agriculture, Universitas Padjadjaran, Indonesia

2Graduated from Agrotechnology, Faculty of Agriculture, Universitas Padjadjaran, Indonesia

ABSTRACT

Soybean is one of the important food crops as a source of protein. Nowadays, soybean

production should be increased due to higher demand in certain region in Indonesia.

Biofertilizer inoculation combine with chemical fertilizer is suggested to increase the soil fertility

to support soybean cultivation and decrease the use of chemical fertilizer. The pot experiment

was conducted to get the information concerning nodulation, nitrogen uptake and growth of

soybeans (Glycine max L.) after biofertilizer and Nitrogen (N), Phosphorous (P) as well as

potassium (K) single fertilizer application. The experiment was set up in a randomized block

design with seven treatment and four replications. The treatments consisted of two doses of

biofertilizer (3 L ha-1 and 5 L ha-1) combined with three doses of N, P, K fertilizer (50%, 75%

and 100% recommended dosage). The pot control received no biofertilizer. Consortium

biofertilizers contained N-fixing bacteria (Azotobacter chroococcum, A. vinelandii, Azospirillum

sp. and endophytic Acinetobacter sp.) and phosphate solubilizing microbes (Pseudomonas

cepaceae and Penicillium sp.). The results of experiment showed that the application of

biofertilizer 5 L ha-1 combined with 75% chemical fertilizer increased the nodules number,

nitrogen uptake and dry weight of plant at the end of vegetative stage. This study suggested that

biofertilizer might be used to increase growth of soybean and chemical fertilizer efficiency.

Keywords: Biofertilizer, Inceptisols, N-uptake, Nodule, Soybean (Glycine max L.).

INTRODUCTION

Soybean is known as a plant that requires large amounts of nitrogen. If these needs by inorganic

N fertilizer, it requires a large cost and long-term use has a negative impact on the environment.

Soybean plants to get N through symbiosis with N-fixing bacteria called rhizobia. Therefore it

can be overcome with the application of biological fertilizers that contain bacteria that can fix N2


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from the air (Htwe et al., 2019). Biofertilizers are inoculants made from active living organisms

in liquid or solid forms that have the ability to mobilize, facilitate and increase the availability of

nutrients not available into available forms through biological processes.

One way to increase soybean production is to improve the root area. Rooting areas are important

to note because roots have a major role in transporting water and nutrients to the leaves which

are related to plant survival. In addition, good root development will also support the process of

nitrogenase, absorption of other nutrients and adaptation and acclimatization of plants more

quickly (Kleinert et al. 2018). Soybean roots will symbiosis with Rhizobium bacteria to form

root nodules. Nodule development in legumes directly affects nitrogen fixation. The effects of

exogenous factors affecting nodulation in soybeans as well as in other larger legumes

(Choudhury et al., 2019). N-fixing bacteria increased significantly plant growth, nodulation,

nitrogen fixation, NPK uptake, and yield of mung beans and soybeans (Htwe et al., 2019).

Phosphate solubilizing bacteria increase soil P-available due to organic acids and phosphatases

produced by PSB that can release fixed P and P mineralization (Kalayu, 2019).

The use of a biofertilizer can help the growth of soybean plants and increase crop yields because

it can fixed free nitrogen from the air, help provide phosphate for plants (Fitriatin et al. 2014)

and can produce growth-stimulating hormones such as IAA, cytokinins, gibberellins, auxins

(Olanrewaju et al., 2017) and exopolysaccharide (Hindersah et al., 2017). The added consortium

of biofertilizers containing phosphate solubilizing bacteria (PSB) such as Pseudomonas

cepaceae; phosphate solubiizing fungi (PSF) such as Penicillium sp; and nitrogen-fixing bacteria

such as Azospirillum, Azotobacter vinelandii, and Azotobacter chroococcum; and endopytic

bacteria (Acinetobacter). These microbes are used for the consortium of biofertilizers because it

can provide benefits to plants. Some research results showed that consortium isolates are better

than single isolates. According to Olumwambe and Kofoworola (2016) that tomato seed treated

with the consortium of several effective strains for growth enhancement performed better than

their individual culture.

Utilization of the consortium of biofertilizers on soybean is expected to improve plant growth

and productivity and reduce chemical residues caused by inorganic fertilization. Therefore,

research is needed the application of biofertilizers to find out treatment gives the best response

to nodulation, nitrogen uptake and growth of soybean.

MATERIALS AND METHODS

The pot experiment was conducted in field station, Faculty of Agriculture, Universitas

Padjadjaran, West Java, Indonesia. The experimental site was located in the tropics at 756 m

above the sea lavel. The soybean cv Anjasmoro were sown in soil of Inceptisols order collected


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from Jatinangor District, West Java, Indonesia (clay texture; pH 5,58; C-org 1,89%; N 0,24%;

P2O5 Bray 30,25 ppm; CEC 20,76 cmol.kg-1). Consortium biofertilizer which contained N-fixing

bacteria Azotobacter chroococcum, A. vinelandii,Azospirillum sp, and Acinetobacter sp., as well

as Phosphate solubilizing microbes Pseudomonas cepaceae and Penicillium sp. has been

developed by Laboratory of Soil Biology in said Institution. The density of bacteria and fungi in

liquid biofertilizers were 107 CFU mL-1 and 105 CFU mL-1 respectively. Urea, TSP and KCl

single Chemical fertilizers were produced by national fertilizer industry. The recommended

dosage of said fertilizer for soybean cultivation in Indonesia were Urea (46% N) 50 kg ha-1,

triple super phosphate (36% P2O5) 100 kg ha-1, KCl (60% K2O)100 kg ha-1 and cow manure 2 t

ha-1. The compost of cow manure was prepared by Faculty of Husbandry Universitas

Padjadjaran; manure was neutral in acidity 7.50 , N 0.94%, P2O5 0,37% and K2O 0.29% and

contained 25.38% water.

The experiment arranged in a randomized block design with seven combinations and four

replications. The treatments consisted of two doses of biofertilizer/BF (0, 3 L ha-1 and 5 L ha-1),

combined with three recommended doses of chemical fertilizer/CF (50%, 75% and 100%). All

the treatments were replicated three times. N uptake, nodulation and growth of Soybean (plant

height, shoot and root dry weight) were measured at the end of vegetative period.

Soil was collected from top soil prior to separating plant debris from the bulk soil. A total of 15

kg soil was placed in polybag, mixed with 15 g organic matter (equal to 2 t ha-1) and incubated

for 5 days in field without shade.

Soybean seeds were inoculated with liquid Bradyrhizobium inoculant mixed with gum arabic;

two soybean seeds were placed in seperated hole, and covered with soil. Biofertilizer was diluted

5% by using ground water and inoculated 20 mL per polybag by pouring around 5-days old

transplant. Chemical fertilizers were applied twice at 7 days and 14 days after planting. The

experimental units were maintain for 42 days after planting when the plants were at the end of

vegetative period.

At the end of experiment, shoots were separated from roots and heated in the oven of 70o C prior

to nitrogen content analysis and dry weight measurement. Roots were washing gently using tap

water; all the nodule were collected and dried using filter paper and weighed. Nitrogen uptake

were calculted based on Nitrogen content which determined by Kjeldahl Method. All the data

were subjected to variance analysis using F test (p 0.05%). If the effect of treatment on said plant

parameter was significant then Duncan’s Multiple Range Test (p 0.05%) was conducted.

RESULTS AND DISCUSSIONS

Nodule number


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The effect of biofertilizers and chemical fertilizers increase nodule number. Figure 1 shows that

application of 5 L ha-1 biofertilizers + 75% dosage of chemical fertilizers are better treatment to

increase the nodule number. This is supported that Azotobacter in the biofertilizers able to

produce exopolysaccharides needed for Rhizobium sp. in inducing the formation of nodules

(Gauri et al. 2012). This exopolysaccharide is used as a signal by the bacterium Rhizobium so

that it can stick to the ends of the roots of soybean hair and then infect the soybean plant root

cells (Ibanez et al. 2017).

Fig 1. Nodule number of soybean grown with different biofertilizer and chemical fertilizer

treatments at the end of vegetative period; BF biofertilizer, CF chemical fertilizer

Treatment of 100% dosage of chemical fertilizer (control) had roots with the lowest nodule

number. This is caused by the treatment was not combined with biofertilizers so that the

infection of Rhizobium sp. into the root is not as effective as other treatments. Bekere et al.

(2013) reported that higher amount of inorganic fertilizer inhibits the nitrogen fixation in early

stage of plant.

Phosphate solubilizing microorganisms in the consortium of biofertilizers affect the formation of

nodules because it can increase phosphate availability (Zaidi et al. 2010). This is because

phosphate is one of the energy sources by plants to compile adenosine triphosphate (ATP) where

ATP is used as an energy source by Rhizobium sp. (Malhotra et al. 2018).

The addition of sufficient chemical fertilizer also plays a role in increasing the effectiveness of

Rhizobium sp. in the formation of nodules. Application of sufficient amount of N fertilizer

144 a164 ab 164 ab

246 bc

174 abc

253 c

213 abc

0

50

100

150

200

250

300

Control BF 3L ha-1

CF 50%

BF 3L ha-1

CF 75%

BF 3L ha-1

CF 100%

BF 5L ha-1

CF 50%

BF 5L ha-1

CF 75%

BF 5L ha-1

CF 100%

NO

DU

LE

NU

MB

ER

FERTILIZER TREATMENTS


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stimulate growth of root hair more quickly and cause N inhibition by Rhizobium sp. to the

maximum. P fertilizer plays an important role in the formation of root nodules because it helps

the synthesis of ATP and nicotinamide adenine dinucleotide phosphate (NADPH) as a source of

energy for microbes (Malhotra et al. 2018). K Fertilizer plays a role in increasing the

translocation of photosynthesis to the roots used by Rhizobium sp.

Plant N uptake

Table 1 shows that the combination of biofertilizers and chemical fertilizers increase the N

uptake of soybean significantly. This shows that N-fixing bacteria contained in biofertilizers

increased soil N so that N absorbed by plants increases. The treatment of 5 L ha-1 biofertilizer

and 75% dosage of chemical fertilizer are able to absorb N higher than other treatments.

Increased N uptake of soybean plants is thought to be due to an increase in the nodules number

caused by N fixation activity by Azotobacter sp., Acinetobacter sp., and Azospirillum sp.

contained in the consortium's biological fertilizer. According to Ohyama et al. (2017) which

states that soybean plants that have enough N due to N2 fixation by effective nodules can

increase nutrient uptake and reduce the dose of chemical fertilizer so that plants will grow better.

With the increase in the number of root nodules, the absorption of nitrogen by plants will also

increase.

Table 1: Plant N-uptake at the end of vegetative period

Treatments N uptake g plant-1

Control (CF 100%) 0.17 a*

BF 3L ha-1 + CF 50% 0.27 ab

BF 3L ha-1 + CF 75% 0.28 ab

BF 3L ha-1 + CF 100% 0.37 bc

BF 5L ha-1 + CF 50% 0.30 b

BF 5L ha-1 + CF 75% 0.42 c

BF 5L ha-1 + CF 100% 0.31 bc

*Note: Numerics followed by the same letters were non-significant on 95 % Duncan’s

New Multiple Range Test.

Increasing of chemical fertilizers did not improve N-uptake with application biofertilizers 5 L ha-

1. However, application 100% dosage of chemical fertilizers were too high, and biofertilizers

could have partially replaced the NPK fertilizer inputs. According to Solanki et al. (2018) that

nutrient uptake decreases with increasing NPK fertilizer dosage.


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Growth of soybean

The application of biofertilizers and chemical fertilizers did not affect significantly on plant

height at the end of vegetative period. This was thought to be due to humidity reaching 92%

during the planting period making soybean growth less than optimal. According to An et al.

(2001), that humidity influence to growth of soybean. The optimal air humidity for soybean

growth is 75-90%.

Table 2: Growth of soybean (plant height, shoot dry weight and root

dry weight) at the end of vegetative period.

Treatments Plant height (cm) Shoot dry weight

(g)

Root dry weight

(g)

Control (CF 100%) 28,6 a 5,31 a 1,69 a

BF 3L ha-1 + CF50% 31,1 a 7,68 ab 2,52 ab

BF 3L ha-1 + CF 75% 32,9 a 8,69 bcd 2,39 ab

BF 3L ha-1 + CF 100% 34,7 a 11,10 cd 3,47 cd

BF 5L ha-1 + CF 50% 31,6 a 8,31 bc 2,54 ab

BF 5L ha-1 + CF 75% 35,3 a 11,38 d 3,61 d

BF 5L ha-1 + CF 100% 34,5 a 8,95 bcd 2,72 bc

*Note: Numerics followed by the same letters were non-significant on 95 % Duncan’s New Multiple Range

Test

Table 2 shows that the plant height in the treatment of 100% dosage of chemical fertilizers were

28.6 cm while 5 L ha-1 of biofertizer and 75% dosage of chemical fertilizers was 35.31 cm. This

indicates that the application of biofertilizers has the potential to increase plant height of

soybean. Azotobacter sp., Azospirillum sp., and Acinetobacter sp. in biofertilizers can produce

growth regulators such as IAA, cytokinins and gibberellins which promote cell elongation and

division (Olanrewaju et al. 2017). Pseudomonas cepaceae and Penicillium sp. which is also

present in biofertilizers dissolve P into available so that it can stimulate cell division and cell

differentiation which can increase plant height. According to Fitriatin et al. (2018), that

biofertilizers contain Pseudomonas cepaceae and Penicillium sp. as phosphate solubilizing

microorganisms increase plant height.

The results of experiment showed that the combination of biofertilizers and chemical fertilizers

significantly affected on the plant dry weight. Table 2 shows that the application of biofertilizers

and chemical fertilizers increased the shoot and root dry weight. The application 5 L ha-1 of

biofertilizer and 75% dosage of chemical fertilizers increased shoot dry weight up to 114,31%

compare with control (Fig.2). This increase is higher than other treatments. The increase in plant

growth due to this treatment is in line with the data on plant N uptake as has been shown in Table


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1. The same effect of this treatment on the increase to root dry weight reached 81.18% was

higher than other treatments.

Control Biofert 5L ha-1 + chemical fertilizers 75%

Fig. 2: Growth of root at the end of vegetative period (comparison

of control with BF 5L ha-1 + CF 75%)

CONCLUSIONS

This study indicate that biofertilizers consortium of N-fixing bacteria (Azotobacter chroococcum,

Azotobacter vinelandii, Azospirillum), phosphate solubilizing microbes (Pseudomonas cepaceae,

Penicillium sp.) and endopytic bacteria (Acinetobacter sp.) increased nodulation, plant N uptake

and growth of soybean. The application of biofertilizers 5 L ha-1 and 75% N, P, K fertilizer

increased the nitrogen uptake, nodules number, dry weight of plant. This study implied that

biofertilizer might be used to increase growth of soybean and NPK fertilizer efficiency.

ACKNOWLEDGMENTS

We are grateful to assistant of Soil Biology Laboratory Faculty of Agriculture, Universitas

Padjadjaran for supporting us during experiment. We are thankful National Fertilizer Company,

PT. Pupuk Kujang for collaborating on the development of biofertilizers.

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Ohyama, T., K. Tewari, S. Ishikawa, K.Tanaka, S. Kamiyama, Y. Ono, S. Hatano, N. Ohtake,

K. Sueyoshi, H. Hasegawa, T. Sato, S. Tanabata, Y. Nagumo, Y.Fujita and Y. Takahashi.

2017. Role of Nitrogen on Growth and Seed Yield of Soybean and a New Fertilization

Technique to Promote Nitrogen Fixation and Seed Yield. Chapter 9 In Book : Soybean -

The Basis of Yield, Biomass and Productivity. http://dx.doi.org/10.5772/66743. pp 154-

185

Olanrewaju, O.S., B.R. Glick, and O.O Babalola. 2017. Mechanisms of action of plant growth

promoting bacteria. World J Microbiol Biotechnol 33:197. DOI 10.1007/s11274-017-

2364-9.

Olumwambe, T.M. and A.A. Kofoworola,. 2016. Comparison of single culture and the

consortium of growth-promoting rhizobacteria from three tomato (Lycopersicon

esculentum Mill) varieties. Advances in Plants & Agriculture Research. 5 (1):448‒455

Solanki, A.C., M.K. Solanki, A.Nagwanshi, A.K. Dwivedi1 and B.S. Dwived. 2018. Nutrient

Uptake and Grain Yield Enhancement of Soybean by Integrated Application of Farmyard

Manure and NPK. International Journal of Current Microbiology and Applied Sciences.

7 (09): 1039-1102

Zaidi A., M. Ahemad, M. Oves, E. Ahmad,and M.S. Khan. 2010. Role of Phosphate-Solubilizing

Bacteria in Legume Improvement. Chapter 11 In book: Microbes for Legume

Improvement. DOI. 10.1007/978-3-211-99753-6_11. Pp. 273-292

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https://www.researchgate.net/deref/http%3A%2F%2Fdx.doi.org%2F10.1007%2F978-3-211-99753-6_11?_sg%5B0%5D=wklNnGgWDghgjLGVswoy_ClvLlsbhnpr2xFHeJ6MOkjPX_KWdiB5PeyC4DXEFhDqDZwchmOV-ac0dJlYp7qvb2jCjw.YWowvmY7gVH-LhRrwbb_9ali-ph4td70nTHcHPYoq0nrvMeJ-5DzrAKJfjXjm4HqJ5-Nez9DwiTF_9zryVrktw


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EVALUATING THE EFFICIENCY AND RESISTANCE TOWARDS THE

ABIOTIC FACTORS IN THE NEWLY BRED VARIETIES OF THE

CEREAL CROPS

1Hamlet Martirosyan, 2Marine Hovhannisyan, 3Mariam Abovyan

1Associate Professor, Department of Plant Growing and Soil Science,

Candidate of agricultural sciences, Armenian National Agrarian University

2Researcher, Research Center of Armenian National Agrarian University

3Master, Armenian National Agrarian University

ABSTRACT

New varietal samples of cereals (wheat-rye, barley, emmer) obtained at the Research Center of

the Plant Gene pool and selection of the Armenian National Agrarian University were tested in

arid and rainfed zones of the Republic of Armenia. Wild forms of wheat and barley served as the

paternal form for obtaining the varietal samples.

As a result of research, it was found that the yield of the obtained varietal samples under arid

conditions exceeded the traditionally cultivated in these zones varieties of wheat-rye, barley and

emmer, by 8.1; 9.2; 7.7 kg/ha, respectively. Their introduction into production can be extremely

important on the way to solving the problem of climate change in the context of global warming,

and provide high profits for farmers.

Keywords: Wheat-rye, Barley, Emmer, Variety, Abiotic factors, Cereal crops, Irrigated and

unirrigated lands, Climate change, High efficiency.

INTRODUCTION

Breeding of the new and highly efficient agricultural crop varieties which are most resistant

towards the stressful situations caused by the global climate changes is of paramount importance

for meeting the increasing demand of the population for food, providing feed for the livestock

sector and for solving the issues in agri-food system on the whole.

Growing under the conditions of the changed climate the crops become stricken with various

water and thermal stresses, which deprive the crops of the opportunity to manifest their potential


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yielding capacities. In this respect the role of selective varieties, which have been bred through

the participation of the parental forms and are endowed with high resistance rate towards the

pests and diseases and to various stressful situations [2,3], is extremely important.

Similar valuable properties and features are observed in some cereal crop varieties bred at the

research center of Plants Selection and Genofond, which have inherited the mentioned properties

from their wild parental forms. It is worth mentioning that Armenia is rich in the wild cereal crop

varieties, which come forth as a valuable selection source material distinguished by such

characteristic traits as high rate of frost-, drought-resistance, high protein content in a grain, etc.

From this perspective the crop varieties, which have been bred in the result of the crossbreeding

with the wild crop varieties, are highly evaluated. These are the two emmer varieties (Garni,

Zvartnots) and a barley variety (Araratyan), which is a multiple-row crop and is mostly peculiar

to the winter barley species, for which the wild emmer (Triticum. dicoccoides Koern) and

bulbous barley (Hordeum bulbosum L.)[1] have served as wild parental forms. The wheat-rye

variety “Tchyughavor” bred through analytic selection method at the abovementioned research

center is of no less importance. The latter can be of great significance as the best source for grain

and green fodder.

Scientific investigations have been conducted on the mentioned varieties to disclose whether

those valuable properties are fixed and sustainable in the plants᾿ genotypes. The mentioned

researches went on throughout the vegetation year of 2018-2019 in 2 zones, namely in the

irrigated lands of the Edjmiatsin province at the Armavir region and in the pre-mountainous dry

lands of the Abovyan province at the Kotayk region. The mentioned investigations were aimed at

the disclosure of the resistance rate of those variety samples to the water stress. In the extreme

situation of the irrigation water shortage the relocation of the cereal plantations from the irrigated

lowlands to the dry areas of pre-mountainous zones and the provision of high efficiency of

stress-resistant varieties can be a relevant solution for such urgent and actual issue.

Sowing of the wheat-rye variety “Tchyughavor” was carried out in the second ten days of

September and October (in the Kotayk and Armavir regions), while the barley variety

“Araratyan” and emmer varieties “Garni” and “Zvartnots” were sown in the third and first ten

days of March respectively. Before planting 25 t/ha manure and phosphoric-potash fertilizers per

P90K60 active agent were introduced under the deep plowing as the main fertilizers. In spring

during the bush formation period the plants were provided with N70 nitrogen. Planting was

implemented with the same dose of 5,0 mln/germ grain for all variety samples. The planting

dose has been chosen with relatively lower indices, so as the plants could have the best

conditions for air and soil nutrition, which would promote the increase of potential yield

capacity.


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Similar conditions for all varieties have been created and the treatment activities have been

implemented at the same time upon the same principle. When following the plant growing

process it becomes vivid that in the dry pre-mountainous conditions the maturation phase in the

barley and emmer varieties has been delayed for almost a month (10.08-17.08.2019) as

compared to the sowings at Armavir region (14.07- 19.07.2019), while this discrepancy in the

wheat-rye has made only 9 days (table 1).

Table 1: Results of phenological observations in the studied regions

Crop

Growth and development phases

Dura

tion o

f th

e

veg

etat

ion p

erio

d,

day

sow

ing

ger

min

atio

n

till

leri

ng

booti

ng

spik

eng

flow

erin

g

ripen

ing

Armavir marz

Wheat-rye

“Armenian

Tchyughavor” 18.1

0.1

8

30.1

0.1

8

17.1

1.1

8

27.0

4.1

9

19.0

5.1

9

01.0

6.1

9

21.0

7.1

9

268

Barley “Araratyan”

05.0

3.1

9

14.0

3.1

9

30.0

3.1

9

27.0

4.1

9

18.0

5.1

9

-

14.0

7.1

9

122

Emmer

“Zvartnots”

05.0

3.1

9

17.0

3.1

9

04.0

4.1

9

08.0

5.1

9

31.0

5.1

9

10.0

6.1

9

19.0

7.1

9

129

Emmer “Garni”

05.0

3.1

9

17.0

3.1

9

05.0

4.1

9

07.0

5.1

9

01.0

6.1

9

11.0

6.1

9

19.0

7.1

9

129

Kotayq marz (unirrigated)


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Wheat-rye

“Armenian

Tchyughavor” 26.0

9.1

8

11.1

0.1

8

27.1

0.1

8

09.0

5.1

9

30.0

5.1

9

12.0

6.1

9

29.0

7.1

9

292

Barley “Araratyan” 22.0

3.1

9

30.0

3.1

9

16.0

4.1

9

31.0

5.1

9

17.0

6.1

9

-

10.0

8.1

9

134

Emmer

“Zvartnots”

22.0

3.1

9

02.0

4.1

9

21.0

4.1

9

05.0

6.1

9

28.0

6.1

9

05.0

7.1

9

17.0

7.1

9

138

Emmer “Garni”

22.0

3.1

9

02.0

4.1

9

21.0

4.1

9

07.0

6.1

9

29.0

619

07.0

7.1

9

22.0

8.1

9

139

Based on those indicators the duration of vegetation period of the mentioned variety samples has

been calculated and it has been found out that in Armavir it makes 122-129 days, while in

Kotayk it is prolonged making 134-139 days and for wheat-rye it has fluctuated within 268-292

days.

When describing the advantages of tested variety samples it is very important to evaluate their

efficiency, resistance indices to the environmental factors, morphological properties, as well as

their resistance to diseases [5]. The results of the corresponding investigations are summed up in

table 2.


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Table 2: Indicators of efficacy and tolerance to the abiotic factors

of the tested varieties in the studied regions

Crop

Pla

nt

hei

ght,

cm

Tillering

Num

ber

of

pla

nts

per

1m

2, pcs

Num

ber

of

pla

nts

per

1 m

2 o

n

the

eve

of

har

ves

t

Pla

nt

conse

rvat

ion, %

Lyin

g r

esis

tance

, p

oin

t

Dis

ease

res

ista

nce

, poin

t

gen

eral

effe

ctiv

e

Armavir marz

Wheat-rye

“Armenian

Tchyughavor”

197.8 2.6 1.1 453 409.1 90.3 5.0 5.0

Barley

“Araratyan” 79.5 2.4 1.1 462 426.1 92.4 5.0 5.0

Emmer

“Zvartnots” 69.4 2.0 1.0 457 428.0 93.7 4.0 4.0

Emmer “Garni” 73.2 2.1 1.0 447 413.4 92.5 5.0 5.0


Wheat-rye

“Armenian

Tchyughavor”

181.5 2.2 1.03 441.2 369.8 83.7 5.0 5.0

Barley

“Araratyan” 71.3 2.0 1.02 444.1 402.8 90.7 5.0 4.5

Emmer

“Zvartnots” 61.4 1.7 1.07 439.2 392.9 89.5 4.5 4.5

Emmer “Garni” 68.7 1.8 1.05 434.4 394.8 90.9 5.0 5.0

Regarding the plants’ height the wheat-rye records 198 cm height and in dry conditions it makes

181.5 cm, which is a rather high indicator for such conditions. In case of its cultivation for green

mass it can provide up to 450 c/ha yield, while the barley and emmer have been marked out with

their short stalks in both zones with the height of 61.4-79.0 cm.


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The overall bush formation in wheat-rye has made 2.6-2.2 in the irrigated and dry conditions,

besides in irrigated conditions the bushing size has exceeded that of observed in the dry

condition only by 0.4. It is a very vital qualitative index particularly for the forage crops used for

green fodder, like, for instance, wheat-rye, which is the primary guarantee for the expected high

yield of the green mass.

By following the plants survival process during the vegetation period it has become clear that it

is rather high and has ensured reliable indices. In this view the winter wheat-rye considerably

lags behind the spring cereals (barley, emmer) in both zones, which is surely predictable and

real. In order to determine the winter-hardiness of the wheat-rye, monoliths method has been

applied, since there was little snow in the winter of 2018 and in this regard more stressful

conditions were established for the plants [4]. As a result, it has been found out that this wheat-

rye variety has provided 94.1 % winter-hardiness in irrigated conditions and in dry conditions it

is lower by 3.1 % (91.0 %).

On the whole, when evaluating the variety samples a great stress is put on the plants behavior in

the resulted undesired, stressful conditions for the plants growth. In this regard the plants

resistance, especially in the dry conditions, has been determined through the creation of artificial

covers and, as a result, it has been disclosed, that in the dry conditions of pre-mountainous zones,

when the air temperature exceeds 30օC and only 385 mm precipitation is fallen during the

vegetation period (according to the data of meteorological station of the Kotayk region) the

regular turgor state in plants (particularly in barley and emmer) is preserved. This extremely vital

property has been inherited by the plants from their wild parental forms. The same interpretation

can be true about the high resistance of the variety samples towards the diseases. During the

trials, in the experimental plots provided for all variety samples the principle of artificial

infestation of the plants with various fungal diseases has been applied, and in the result it has

been found out, that the plants have shown a rather high resistance; only in the emmer variety

“Zvartnots” at Armavir region and in the emmer variety “Zvartnots and barley variety

“Araratyan” at Kotayk region small traces of leaf rust have been observed. For these varieties the

resistance to the diseases has been assessed with 4 and for the other varieties with 5 points.

The bending phenomenon in the cereals is also undesired, which makes the grain filling process

significantly difficult and the grain quality falls down. As a result of the investigations it

becomes clear that in Armavir region except from the emmer variety “Zvartnots” which has been

assessed with 4 points, the other variety samples are rather resistant and have been assessed with

5 points; in Kotayk region the emmer variety has been assessed with 4 points and the wheat-rye

variety, despite the significant height (185-200 m) of its stalk, is rather resistant towards bending

and has been assessed with 4.7 points (in some plants this phenomenon was slightly observed).


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Analysis of the yield structural elements has been conducted on the samples taken from the

experimental plots (Edjmiatsin and Kotayk) on the eve of the harvesting day. The results of the

mentioned analysis are introduced in table 3.

Table 3: Results of structural element analysis

Crop

Spik

e le

ngth

, cm

of 1 spike on 1 m2

Wei

ght

of

1000

gra

ins,

g

Fac

tual

yie

ld,

c/ha

Num

ber

of

spik

elet

s, p

cs

Num

ber

of

gra

ins,

pcs

Wei

ght

of

gra

ins,

g

Wei

ght

of

pla

nts

, g

Wei

ght

of

gra

ins,

g

Armavir marz

Wheat-rye

“Armenian

Tchyughavor”

11.5 22.3 58.1 2.9 1390.0 1130.0 50.1 52.9

Barley

“Araratyan” 8.3 58.2 42.0 1.6 880.0 716.3 38.5 32.1

Emmer

“Zvartnots” 7.2 12.7 26.7 0.9 462.2 385.2 33.7 22.1

Emmer

“Garni” 5.9 10.9 24.5 0.8 396.8 330.7 32.6 20.7


Wheat-rye

“Armenian

Tchyughavor”

10.1 20.1 49.8 2.3 1021.3 851.4 46.2 38.1

Barley

“Araratyan” 7.8 51.2 39.7 1.4 691.5 578.4 35.8 27.1

Emmer

“Zvartnots” 6.4 10.9 21.5 0.7 345.5 295.1 32.7 18.8

Emmer

“Garni” 5.0 8.9 22.6 0.7 354.8 288.2 30.9 16.3

As a result of the investigations it has been found out that the length of wheat-rye spike/ear

fluctuates within 10.1-14.5 cm; in the irrigated conditions (Edjmiatsin) the barley variety has

recorded a spike length (9.3 cm) peculiar to the genotype of its maternal form, which, anyhow, in

the dry conditions (Kotayk) has decreased by 0.5 cm falling down to 8.8 cm, while the emmer

varieties have developed the shortest spikes -5.0-7.2 cm- keeping the decreasing tendency of the

spike/ear length in the dry conditions, like in the previous cases. The spike/ear length has had its

significant impact on the spikelets formed in the ear, as well as on the number and weight of the


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grains developed in an ear. In the ear/spike of the wheat-rye 58.1-49.8 grains have averagely

developed, besides, in the dry conditions the number of grains has decreased by 8.3 items in the

relatively shorter spikes. This regularity held true both in case of barley and emmer varieties,

anyhow, in the first case the discrepancy has made only 2.3 grains, while in the emmer varieties

it fluctuates within the range of 4.2-1.9 grains. Here it is worth mentioning that in the ear/spike

of the barley variety, unlike its maternal form, a significant increase in the grain number has been

observed, which is related to the functions taking place throughout the crossbreeding and to the

correct selection of the paternal form. The weight of the grains in an ear has been also

determined, which is the main indicator of the biological yield and it is greatly related to both the

ear length and the number of grains in an ear. Depending the weather and irrigation conditions of

the experimental year, this indicator fluctuates strongly as well, which makes respectively 0.6,

0.2 for the wheat-rye, while for the barley and emmer it is 0.1 and 0.2 g.

The weight of 1000 grains is also of paramount importance, which is related to the grain size and

has a great impact on the resulted yield amount. This indicator has been greatly influenced by the

cultivation conditions as well, since in case of cultivating the wheat-rye in dry conditions the

mentioned indicator has decreased by 3.9 g swinging down from the 50.1 to 46.2 g. The same

regularity is observed in the other cereal crop varieties as well. For example, in the barley variety

of “Araratyan” this indicator has decreased by 2.7 g (from 38.5g to 35.8g) in the dry conditions,

while in the emmer varieties it fluctuates within the range of 1.0-1.7 g. Despite the

aforementioned the experimental variety samples have provided sufficient yield (table 3), which

makes 20.1-22.1 c/ha (irrigated) and 16.3-18.8 c/ha (dry) in the emmer varieties, while in the

barley and wheat-rye varieties it has made 32.1, 27.1 c/ha and 52.9, 38.1 c/ha respectively.

It is noteworthy that the common varieties of the experimented crops demonstrate 18-21 % lower

grain yield both in the irrigated and dry conditions as compared to that of the investigated variety

samples. The yield amount of the wheat-rye, barley and emmer varieties exceeds the cultivated

common varieties (dry conditions) by 8.1, 9.2 and 7.7 c/ha respectively.

After passing through the State Variety Trials the new cereal crop varieties bred at the research

centre can further come forth as rather perspective, well-established and valuable varieties,

which can be best adapted to the stressful situations caused by the climate change and provide

extra income to the farm households.

REFERENCES

1. Harutyunyan M.G., Martirosyan H.S., Hovhannisyan M.Ts. – The role of wild relatives

in cereal crop breeding. Bulletin of AAA, Materials of International Conference,

Yerevan, 2003, 3-4.


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2. Guzhov Yu.L. – Selection and seed breeding of cultivated plants. M. Kolos, 2003, 536 p.

3. Zhuchenko A.A. Opportunities of creating plant varieties and hybrids taking into account

climate change // Strategy for adaptive selection of field crops with regard to global

climate changes. Saratov, 2004, 2, pp. 10-16.

4. Ashraf M., Ozturk M. & Athar H.R. Salinity and Water Stress. Improving Crop

Efficiency. Springer-Verlag, Berlin, 2009.

5. Murphy D. Plant breeding and biotechnology. United Kingdom. Cambridge University

Press, 2007.


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AFLATOXINS CONTAMINATION IN MAIZE- BASED FOOD AND

HUMAN HEALTH IMPLICATION IN BAFIA (CENTRE-CAMEROON)

Evelyne Nguegwouo1,4*, Emmanuel Ediage Njumbe2, Patrick Berka Njobeh3, Gabriel Nama

Medoua4, Zachée Ngoko5, Martin Fotso4, Sarah De Saeger2, Elie Fokou1and François-Xavier

Etoa6

1University of Yaoundé I, Faculty of Sciences, Laboratory of Food Sciences

and Metabolism, Yaounde, Yaounde 237, Cameroon

2Ghent University, Faculty of Pharmaceutical Sciences, Centre of Excellence

in Mycotoxicology and Public Health, Ghent 32, Belgium

3University of Johannesburg, Faculty of Science, Laboratory of Biotechnology and

Food Technology, Johannesburg, Johannesburg 27, South Africa

4Institute of Medical Research and Medicinal Plants Studies, Centre for Food and Nutrition Research,

Laboratory of Food and Quality Study IMPM, Yaounde, Yaounde 237, Cameroon

5Catholic University of Cameroon, Department of Science, Bamenda, Bamenda 237, Cameroon

6University of Yaoundé I, Faculty of Sciences, Laboratory of Microbiology, Yaounde. Yaounde 237, Cameroon

*Corresponding Author

ABSTRACT

Background: In sub-Saharan Africa and particularly in Cameroon, several research has shown

the presence of aflatoxins (AFs) in food intended to human consumption. The evaluation of the

health risk associated with consumption of contaminated foods is needed to know the sanitary

statute of the population.

Objective: This study was conducted from January to December 2014 in Bafia in the Centre

Region of Cameroon with the objectives to determine the levels of AFT (AFB1, AFB2, AFG1 and

AFG2) in dishes where maize is the staple food and to estimate the health risk (Body Mass Index,

Estimate Daily Intake, Risk Exposure, Risk of Liver Cancer Incidence) among the rural

population of Bafia.

Method: A validated Enzyme Linked Immuno Sorbent Assay was performed to estimate AFT

contamination levels in a total of 109 samples of maize-based foods. A food survey was carried


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out using standard method involving 102 children [5-8 years], 106 adolescents [9-15 years] and

156 adults [>15 years]) and was permit to estimate the average amount of maize -based food

Results: AFT were detected in 100% of samples and the levels ranged from 0.8 µg/kg (roasted

maize or maize fritters) to 18.6 µg/kg (dry or fresh flat maize cake with groundnuts). Dietary

exposure was age-depending. Children were more vulnerable to AFT (43.77c ± 0.56 ng/kg

bw/day) followed by adolescents (31.88b ± 0.32 ng/kg bw/day) and finally adults (27.38a ± 0.49

ng/kg bw/day). The same tendency were also obtained concerning the risk of liver cancer

incidence/100 000/year attributable to dietary AFT among all subgroups under study ( Children:

0.6c; Adolescents: 0.4b; Adults: 0.3a).

Conclusion: This highlights the need for continuous monitoring of maize-based food for AFT

and to implement strategies for their control in Cameroon.

Keywords: Maize-based food, AFT, dietary intake, health risk, rural population.

ABBREVIATIONS:

ALARA: As Low As Reasonably Achievable

AFB1: Aflatoxin B1

AFB2: Aflatoxin B2

AFG1: Aflatoxin G1

AFG2: Aflatoxin G2

AFT: total aflatoxins (AFB1, AFB2, AFG1 and AFG2).

AFs: Aflatoxins

BMI: Body Mass Index

DON: Deoxynivalenol

EDI: Estimate of Daily Intakes

ELISA: Enzyme Linked Immunosorbent Assay

FBs: Fumonisins

OTA: Ochratoxin A


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RE: Risk of Exposure

RLCI: Risk of liver cancer Incidence/year/100.000 populations

TDI: Tolerable of Daily Intakes

ZEN: Zearalenone

INTRODUCTION

Maize (Zea mays L.) is the first cereal cultivated in Cameroon and constitutes the very important

staple food in the diet of the rural Cameroonian population (Nguegwouo et al., 2011). It is

consumed in the form of pap, paste, pancake or grains. It is also used for livestock feeding or for

the making of traditional beers, starch for batteries and packaging industries, and for supply to

local food processing industries (Breweries, Food industries). Maize grain is susceptible to

contamination and degradation by fungi including Aspergillus, Fusarium and Penicillium spp

(Gamanya and Sibanda, 2001). Contamination effect the quality of grain through discoloration,

reduction in nutritional value and production of mycotoxins. Mycotoxins are toxic fungal

secondary metabolites that include aflatoxins (AFs), Fumonisins (FBs), Deoxynivalenol (DON),

Ochratoxin A (OTA) and Zearalenone (ZEN) (Marin et al., 2013). Globally, it is estimated that

aflatoxins are the most important chronic dietary risk fact. Each year, 550,000-600,000 new

cases of liver cancer are registered worldwide, and about 25,200-155,000 cases are attributable to

aflatoxin exposure, representing about 4.6 -28.2% (WHO, 2008). The retrospective

epidemiological survey in hospitals of Bafia (Centre Region, Cameroon) has shown an incidence

of 0.8-0.9 deaths from liver cancer for 55 700 inhabitants per year (Anonyme, 2007). The

resurgence of cancer in this locality may be due to their geographic position, situated in the

humid forest with bimodal rainfall, which enable fungi growth. AFs (figure 1) are naturally

occurring toxins produced by certain fungi, most importantly Aspergillus flavus and Aspergillus

parasiticus and are classified as group1 that means human carcinogen (IARC, 2002; Hove et

al., 2016). They may cause liver cancer, suppressed immune systems, and retarded growth and

development by contributing to malnutrition Children are more sensitive to the effects of

aflatoxin- contaminated food (Tchana et al., 2010). The occurrence of many fungi (Aspergillus,

Fusarium) and mycotoxins (aflatoxin, fumonisin, zearalenone and so one) in Cameroonian food

commodities such as maize, peanuts, beans, soybeans etc. has been reported by many authors.

Ngoko et al. (2008, 2001) reported Aspergillus sp., Fusarium sp. and Penicillium sp. as part of

the main fungal contaminants of crops in Cameroon. AFs has been detected by Njobeh et

al.(2010) in 55% of their samples including maize at concentrations between 0.1-15 µg/Kg. In

2015, the analysis of Kutukutu, a fermented maize-based dough largely consumed in the

Northern part of the country reveals an aflatoxin B1 content which in some cases exceeded 4 ppb,


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the European Union standard fixed for such products. Abia et al.(2017)also detected low levels

of aflatoxin in Maize-fufu (also known as fufu-corn), a boiled maize-dough dish that is consumed

especially in the western highland of Cameroon.To the best of our knowledge, there are no

studies establishing the link between AFT exposure via consumption of maize-based food and

the risk of liver cancer in the rural Cameroonian population. this study was carried out with the

aims 1) to quantify the levels of AFT i.e. AFB1, AFB2, AFG1, AFG2 in a total of 109 maize-

based food sampled from some villages of Bafia in the Centre of Cameroon and 2) to establish a

link between dietary AFT exposure and the risk of human liver cancer.

Figure 1: Chemical structure of principal aflatoxins (B1, B2, G1 and G2)

MATERIAL AND METHODS

Site description and study design

This study was conducted in Bafia, one of the administrative subdivision of the Mbam and

Ibounou Division in the Centre Region of Cameroon between January and December 2014. It is

crossed by the national route No4 linking Bafoussam to Yaounde. It is located at 120 km North

of Yaounde at latitude 4o 45´00´´ North and longitude 11o14´00´´ East. Their total population is

about 55700 inhabitants (MINAGRI, 2002).

Bafia was chosen based on the maize consumption and climatic conditions (annual rainfall

ranges between 3500 and 4000 mm, average temperature between 27°C and 36.7°C, humidity

ranged between 60 and 70% which are favorable to fungi growth and chances of AFs

contamination are high. Four villages of Bafia (Donenkeng, Binya, Goufan I, Goufan II and

Thekané) were purposively selected

Food survey

A food survey was carried out using the method described by Kroes et al.(2002) in year 2014

based on the collection in the study site of maize-based food consumed by 366 apparently

healthy and not fasting individuals divided into 108 children [5-8 years], 102 adolescents [9-15


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years],and 156 adults [>15 years] for 7 consecutive days. The objective of this survey was to

determine the average amount of maize -based food consumed by the subgroup of Bafia’s

population, their average body weight and height. Electronic scale was used to estimate the

consumption of food. The individual body weight and height of each participant were taken

using fathom and human scale respectively.

Collection of samples

A total of 109 samples of maize-based food, each weighing approximately 2 kg (consisted of

four 500 g subsamples from households) was collected according to the European Commission

(EC) No 401/2006 (EC, 2006)from the villages of study site between September and November

of the year 2014.Samples included maize beer (n=9); dry or fresh maize cake with vegetables

(n=10); dry or fresh maize cake with groundnuts (n=15); maize porridge (n=10); maize fufu or

couscous (n=13); maize milk (n=9); vegetables with maize (n=8); roasted maize (n=8); boiled

maize (n=9); fried maize with groundnuts(n=10) and maize fritters (n=8) were placed in

polyethylene bags and taken immediately to the laboratory. Upon arrival, all food samples were

homogenized, finely milled for solid samples and lyophilized for liquid samples before frozen at

−20 °C until analysis for AFT.

Analysis of AFT using quantitative ELISA Test

AFT (AFB1, AFB2, AFG1 and AFG2) content in each maize-based food was determined using a

quantitative competitive direct ELISA (Enzyme Linked Immunosorbent Assay) kit for AFT in

cereals and derivatives purchased from Reneekabio (CAT. N° AF012714 Qual, HELICA

Biosystems, Inc., Santa Ana, CA, USA). The AFT extraction was conducted according to

manufacturer’s protocol. For each sample, 20 g of the powder was added to 100 mL of methanol

70%. The mixture was then homogenized for 5 min using a magnetic stirrer, filtered through

whatman paper N° 4 and the supernatant used for the ELISA test. The AFT content was

inversely proportional to the color intensity established using an automated microplate reader

(EL × 800, BIOTEK, Instruments Inc., Winooski, VT, United States) at 450 nm. A calibration

curve was plotted using AFT standard at different concentrations (0.2, 2.5, 5, 10 and 20 µg/kg or

ppb). Limit of detection (LOD) and limit of quantification (LOQ) for AFT were < 0.01 and 0.01

µg/kg respectively. Samples with AFT levels lower than 0.01 µg/kg were considered as

aflatoxin-free The recovery percentage was determined using reference material (FAPAS test

material specification sheet, TO4138) from manufacturer.

Estimate of Body Mass Index, Daily Intake


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The Body Mass Index (BMI) is defined as the body mass (weight) over the square of the body

height, and is universally expressed in units of kg/m2. For this study, we used the average BMI of

the subgroups (children, adolescents and adults)

𝐵𝑀𝐼 = 𝑊

𝐻2 (1)

With BMI: Body Mass Index (kg/m2);

W: Weight (kg) of each subgroup studied;

H: Height (m) of each subgroup studied.

The Estimate of Daily Intakes (EDI) of AFT contaminated maize-based food were obtained by

multiplying individual (Average)intake of maize-based food by the average concentration of

AFT in each contaminated food consumed and then summing the contributions of all the maize-

based food. These contributions, initially expressed in ng/day (AFT) were divided by mean body

weight (b.w) measured for each population subgroup so that they were expressed in ngkg -1 body

weight/day (AFT) thus facilitating comparison with the toxicological reference values (Tolerable

Daily Intake or TDI) proposed by different international bodies. For this study we used 1ng kg-1

(b.w.) day-1(JECFA, 2001)

𝐸𝐷𝐼 = ∑𝐶 × 𝑄

𝑏. 𝑤 (2)

With:

EDI: Estimate of Daily Intakes of AFT in ng kg-1 of body weight (b.w.) day-1;

C: The AFT concentration found in each maize-based food (µg/kg or ng/g);

Q: The daily intake of each maize-based dish (g/day);

bw: The average body weight of the subgroup of the population

Risk Exposure and Cancer Risk Incidence

The characterization of the Risk of Exposure (RE) to AFT of each subgroup of population is

calculated using the following formula (ASTEE, 2003):

https://en.wikipedia.org/wiki/Human_body_weight

https://en.wikipedia.org/wiki/Square_(algebra)

https://en.wikipedia.org/wiki/Human_height

https://en.wikipedia.org/wiki/Human_height

https://en.wikipedia.org/wiki/Units_of_measurement


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𝑅𝐸 = 𝐸𝐷𝐼

𝑇𝐷𝐼 (3)

With RE: Risk of Exposure

EDI: Estimate of Daily Intakes of AFT in ng kg-1 of body weight (b.w.) day-1

TDI: Tolerable of Daily Intakes of AFT in ng kg-1 of body weight (b.w.) day-1

If: RE <1 means that the exposed population is theoretically out of danger, that is, this exposed

population is not likely to develop the health effects studied.

- RE> 1 means that the toxic effect can occur without being possible to predict the probability of

the occurrence of this event.

According to epidemiological data of JECFA (1999), it is considered that ingestion of 1 ng AFT

/ kg b.w./day would increase the incidence of liver cancer by 0.013 cancer cases per year per

100.000 populations. This suggests that the annual incidence of liver cancer in the rural

population of Bafia: 0.013 x EDI (for maize) per 100,000 for each population subgroup studied.

Hence, the Risk of liver cancer Incidence (RLCI) can be calculated as follow (JECFA, 1999):

(RLCI) = EDI × 0.013(4)

With RLCI: Risk of liver cancer Incidence/year/100.000 populations

EDI: Estimate of Daily Intakes of AFT in ng kg-1 of body weight (b.w.) day-1

0.013: Constance of liver cancer per year per 100. 000 populations

Statistical analysis

Tree replicates of the data were performed and the data was expressed as means and standard

deviation (SD). The homogeneity of the mean concentration of AFT was assessed by an analysis

of variance by the Fischer test using SPSS version 10.0 for windows and a “p” < 0.05 was

considered as statistically significant.

RESULTS

Anthropometric data of population subgroup studied.

The Body Mass Index (BMI) is the anthropometric data calculated for this study and results were

summarize in Table 1. BMI was normal for all children and adolescents surveyed. In contrast,

some of the adults (19%) were overweight (BMI ≥ 25 kg/m2).


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Table 1: BMI data of subgroup of rural population in Bafia (Centre - Cameroon)

Subgroup of rural population in

Bafia

Weight (kg) Height

(m)

Body Mass Index

BMI (kg/m2)

% of individual’s

BMI ≥25

Children 23.2±0.3 1.3±0.1 13.7±0.9 0.0

Adolescents 34.0±0.3 1.4±0.1 16.6±0.2 0.0

Adults 56.4±0.8 1.7±0.3 20.0±0.3 19.0

Quality control

The concentration of AFT in quality control reference material determined by the manufacturer

and the concentration we obtained using the ELISA method generated a standard deviation of

8.02 and relative standard deviation was 9.20%. The average recovery rate for AFT at the three

spiking concentrations was 105%, standard deviation was 2.30 and relative standard deviation

was 2.2%. The calibration curve used for quantification of AFT gave correlation coefficient (r2)

of 0.998 and 0.981 for standard solution and matrix-matched calibration, respectively. No cross

reactivity was observed, thus, the method was considered specific for this analysis.

Level of AFT in maize-based food and data on food consumption by subgroup of rural

population in Bafia.

Table 2 provides data on AFT levels in maize–based foods and the average amount consumed

per day by rural population in Bafia. AFT were detected in 100% of samples analyzed and varied

significantly (P<0.05) between dry or fresh flat maize cake with vegetable, dry or fresh maize

cake or also fried maize with groundnuts and other maize-based foods. The levels were ranging

from 0.8 µg/kg in roasted maize or maize fritters to 18.3 µg/kg in dry or fresh flat maize cake

with groundnuts. A high frequency of occurrence was noted in maize-based food consumed by

the rural population. The quantities of average maize-based food consumed on dry weight (g

DW) by 108 children (4-8 yrs) and 102 adolescents (9-14yrs) showed significant difference

(P<0.05) between dry or fresh flat maize cake with vegetable, dry or fresh maize cake with

groundnuts, maize beer, maize porridge and other maize -based food. Foods such as roasted

maize (192.9 g DW/day) are mostly consumed by children whereas maize porridge was least

consumed (5.5 g DW/day). The average maize-based food consumption among 102 adolescents

shown the same things. Average consumption of maize-based foods in g DW/ day of 156 adults

showed a significant difference (P<0.05) between dry or fresh flat maize cake with vegetable,

dry or fresh maize cake with groundnuts, maize beer, maize porridge, maize milk and other food-

based foods. Foods such as roasted maize (219.7 g DW/day), boiled maize (218.4 g DW/day)


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and maize fufu (149.6 g DW/day) were highly consumed than other maize products, while maize

porridge was the least consumed food (5.5 g DW/day).

Table 2: AFT levels in maize-based foods and average food

consumption per day by rural population in Bafia.

Food type

AFT (µg/kg)

n= 109

Average food consumption (g DW/day)

Children

(4-8 yrs)

N= 108

Adolescents

(9-14 yrs)

N=102

Adults

(>15 yrs)

N= 156

Maize beer 1.5±0.2a 0 ±0.0a 0±0.0a 2.5± 0.1a

Dry or fresh flat maize cake

with vegetable

16.8±0.1b 12.1±0.1a 13.4±0.0a 20.7±0.1a

Dry or fresh flat maize cake

with groundnuts

18.3±0.8b 8.3±0.0a 7.4±0.1a 10.3±0.1a

Maize- porridge 0.9±0.1a 5.5±0.1a 5.5±0.0a 5.5±0.0a

Maize fufu (couscous) 1.2±0.1a 104.3±0.2b 110±0.1b 149.6±0.1b

Maize milk 1.1±0.1a 34.8±0.1b 34.8±0.1b 13.6±0.1a

Maize vegetable 1.6±0.3a 63.2±0.3b 63.2±0.0b 70.3±0.0b

Roasted maize 0.8±0.1a 192.9±0.2b 192.9±0.0b 219.7±0.11b

Boiled maize 1.0±0.1a 149.4±0.2b 149.4±0.1b 218.4±0.1b

Maize fritters 0.8±0.1a 29.1±0.1b 29.1±0.1b 29.6±0.0b

Fried maize with groundnuts 16.8±1.5b

13.3±0.0a 13.3±0.0a 13.2±0.0a

Values for average food consumption are mean ± SD; N: number of individuals; n: number of maize-based

samples analysed; Numbers in a column with different superscripted letters are significantly different (P <

0.05); DWB: Dry weight basis.

Table 3: Daily intake of AFT, risk exposure and risk of liver cancer from subgroup of

rural’s population in Bafia (Centre-Cameroon).

Subgroup of Bafia

rural population

Estimate Daily

Intake (EDI) (ng/kg

bw/day)

Tolerable Daily

Intake (TDI) JECFA

(2001)

(ng/kg bw/day)

Risk of

Exposure

(RE)

Risk of liver cancer

Incidence

attributable to dietary

aflatoxin

Children 43.8±0.6c 43.8c 0.6c

Adolescents 31.9±0.3b 1 31.9b 0.4b

Adults 27.4±0.5a 27.4a 0.3a

Numbers in a column with different superscripted letters are significantly different (P<0.05). Mean body weight of rural’s population subgroup [children: 23. 2 kg; Adolescents: 34.0 kg; Adults: 56.4 kg].


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Estimate Daily intake of AFT from the rural’s population of Bafia, risk of exposure and risk

of liver cancer.

The results of the mean EDI of the different subgroups of the Bafia population surveyed (Table

3) showed the TDI were varied significantly (P <0.05) from one group to another. The were

higher in children (43.8 ng / kg bw/day) compared to adolescents (31.9 ng / kg bw / day) and

adults (27.4 ng / kg bw / day). The risk of exposure and the risk of liver cancer incidence showed

the same tendency that were higher in children (43.8 and 0.6) compared to adolescents (31.9 and

0.4) and adults (27.4 and 0.3) (P<0.05) respectively.). There was a high risk of increased liver

cancer incidence associated with high AFT exposure among all subgroups under study.

DISCUSSION

In this study, the population studied had normal BMI in infants, adolescents and majority of

adults, that means this population would not a particularly obesity problem. However, all

samples of maize – based food analyzed were contaminated by AFT at different levels. The

majority of samples (75%) presented the level of AFT above the regulated maximum limit by the

commission European (4 µg/kg). The most contaminated samples were dry or fresh flat maize

cake with vegetable, dry or fresh flat maize cake with groundnuts (18.3 µg/kg) and fried maize

with groundnuts (16.8 µg/kg). The highest AFT contents of these samples could be explained by

poor conditions of drying and storage of maize that is the major ingredient. The maize harvesting

time is generally in the rainy season, drying in wet weather is relatively slow, resulting in the

proliferation of Aspergillus. Similar observations were noticed by (Yogendrarajah et al. 2014).

They found high level of AFT in agronomical crops and justified such contamination by

temperature, humidity, storage conditions and duration which are key factors in the development

of fungi that produce aflatoxins.

Giving that, adverse effect of mycotoxins in the body are directly link to the quantity of

contaminated food ingested, a correlation between the amount of food consumed by the

population of Bafia and their AFT content was assessed. The results obtained showed that, the

amount of maize-based food consumed by the population in Bafia varied with the age of the

population and the type of maize-based food. Maize beer was consumed only by adults. This

could be explained by the fact that alcoholic beverages were not allowed to children and

adolescent consumption. The low level of AFT presented in maize beer and the low quantity of

maize beer consumed demonstrate that, the consumption of this maize-based food could be

associated with lower adverse AFT effects in the Bafia population.

The most consumed maize-based foods in the three groups were maize fufu (couscous) followed

by roasted maize and boiled maize. This could be explained by the food habit of the population


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of the region, maize constitute the staple food of population (Nguegwouo et al, 2017). Fufu is

consumed associated various sauces. During harvest period, fresh maize is mostly sold and

consumed in boiled form or in roasted. These highly consumed maize base foods presented AFT

content of 1.2, 0.8 and 1.0 µg/kg (respectively for maize fufu, roasted maize and boiled maize)

which are very lower than the maximum recommended value of 4µg/kg. Regarding the highly

AFT contaminated samples, althought their consumption amount per type of age were low, the

population could be exposed to adverse effect of AFT present in these samples.

In order to verify this hypothesis, the level of exposition of population to the amount of food

consumed was assessed. In general, exposure to mycotoxins depends on the degree of exposure

and the amount consumed (Marin et al., 2013).

According to literature, the estimate daily intake of AFT by a person is one of the most important

parameter used to assess his risk of exposure to the adverse effect.

The consumption of the same maize-based foods was not significantly different in different

group of population nevertheless they have different body weights. Children have significantly

lower body weight than adolescents and adults. Since EDI depended on body weight, children's

was more exposed than adults and adolescents. There was a correlation between EDI and the risk

of exposure and the risk of liver cancer incidence (Table 3). Thus, children more were exposed

with high risk of liver cancer incidence. Some adults have been shown to be obese and would be

more likely to be exposed to acute toxicity due to AFT in the long term (Liu and Wu, 2010). Our

target populations were more exposed to AFT than the French and Balkane (Morrocco)

population (Soubra, 2008). The estimated exposure of children and adolescents of the Balkane

population showed that for AFT, the average level exceeded 1 ng/kg bw/day and children were

also more exposed. Children, a specific vulnerable population group, are routinely exposed to

many mycotoxins through food in many parts of the world (Etzel, 2014). Data from the risk

assessment of AFB1 (Ediage et al., 2014) the most toxic of all types of AFT showed that the

calculated exposure for infants as well as adults exceeded the TDI through maize, peanut, and

cassava consumption sampling in the three agro-ecological regions of Cameroon (the western

highland, the humid forest with monomodal rainfall and the humid forest with bimodal rainfall).

These authors recommended that particular attention should be paid to AFB1, especially in

populations with a very high prevalence of Hepatite B virus (10%). Vulnerable groups and/or

individuals (such as elderly or immune-compromised people and pregnant women) living in

these study zones should be alerted to the potential danger arising from the consumption of

mycotoxin-contaminated foodstuffs. For aflatoxins, where carcinogenity is the basis of concern,

TDIs are not applicable. Exposure of as little as <1 ng/kg bw/day to AFs can lead to a risk of

liver cancer and because of this, a numerical TDI for aflatoxins could not be established.


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Therefore, it is recommended that levels of AFT should be as low as technologically feasible or

as low as reasonably achievable (ALARA). Nevertheless, TDIs of 1 ng/kg bw/day have been

used in other risk assessment (Etzel, 2014; Matumba, 2014).

The ratio of (EDI) to (TDI) was determined to assess the risk of exposure of rural’s consumers. It

has been noted that the EDI value of AFT ingested per maize-based food exceeded nearly 44

times the TDI in children, 32 times in adolescents and 27 times in adults. In the three subgroups

of the population, all subjects (100%) exceeded the TDI. This means that the toxic effects can

occur without enabling prediction of the probability of the occurrence of this event.

Our study demonstrated for the first time in Cameroon the risk of liver cancer Incidence/100

000/year attributable to dietary AFT. The risk of liver cancer incidence was associated with the

high AFT exposure. This parameter was high in children compared to other subgroups of the

study population. Aflatoxins in foods are converted to the aflatoxin-8,9-epoxide metabolite in the

liver which seems to be responsible of many of the toxic effects in the body (Groopman et al.,

2008). Epidemiological studies in Bafia on the pathologies caused by AFT are needed to

reinforce the results obtained.

CONCLUSION

This study clearly demonstrated that AFT were present in all maize- based products analyzed

with children being most at risk. Presently, there are no regulations for mycotoxins in maize-

based food intended to human consumption in Cameroon, however the urgency is reported for

the routine monitoring of these foods. We recommend that for staple foods, the maximum level

should be reconsidered in specific case of Cameroon with more restriction than for other foods.

Furthermore, awareness/educational interventions are required to enhance caregiver adherence to

consumption advice for specific foods while adopting good hygiene and preparation practices.

ACKNOWLEDGEMENTS

Authors are grateful to the Bafia’s population in Cameroon, for their avaibility. The authors wish

to thank Dr. Alex Tchuenchieu and Dr. Hippolyte Mouafo Tene for proofreading of the article.

Funding: This study was supported in part by VLIR-UOS in Belgium via a travel grant to the

Centre of Excellence in Mycotoxicology and Public Health, Ghent University.

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IMPACT OF CARBON DIOXIDE EMISSIONS ON ECONOMIC GROWTH

AMONG DIFFERENT REGIONS OF WORLD

1Sana Iftikhar, 2Muhammad Abdul Quddus

1PhD Scholar, Department of Economics.,

National College of Business Administration & Economics, Lahore

2Head of Department, Department of Economics,

National College of Business Administration & Economics, Lahore

ABSTRACT

Global climate change is a change in the long-term weather patterns that characterize the regions

of the world. In the long run, the climatic change could affect agriculture in several ways such as

quantity and quality of crops in terms of productivity and growth rate. This study investigates the

impact of climate change, cereal production and economic growth in East Asia & Pacific, Latin

America & Caribbean, Europe & Central Asia and Sub-Saharan Africa. The study employed the

variables are carbon dioxide emissions, cereal production and GDP growth rate. The results

show that climate change and economic growth is positively related East Asia & Pacific and

Europe & Central Asia, while economic growth and climate change are negatively related in case

of Latin America & Caribbean and Sub-Saharan Africa. There is need to overcome the problem

of climate change in the form of carbon dioxide emissions both in Latin America & Caribbean

and Sub-Saharan Africa.

Keywords: climate change, agricultural production, economic growth, East Asia & Pacific,

Latin America & Caribbean, Europe & Central Asia, Sub-Saharan Africa

1. INTRODUCTION

Today it is believed that the accumulation of carbon dioxide and other greenhouse gases will

lead to global warming and other significant climatic changes over the next century and beyond.

The last 100 years have shown an increase in global mean surface temperature of 0.4 to 0.8°C.

Simulations for the coming 100 years show an increase in global mean surface temperature

ranging from 1.4 to 5.8°C, while the atmospheric CO2 concentration is more than twice the pre

industrial level (IPCC, 2001). Global warming is widely seen as one of the most serious

environmental problem. It affects not only ecosystems by altering the composition of the

vegetation as well as plant, animal diversity and human health, but also economies through a


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variety of channels, such as water resources, agriculture, energy and tourism. Tackling the

problem of future climate change is one of the most challenging issues of this century and has

major implications for policies of development and environmental management.

Figure 1: Situation of Carbon Dioxide Emission (kt) in East Asia and Pacific Region.

Source: Author’s Estimation

Figure-1 shows the situation of carbon dioxide emission of East Asia and Pacific, China, Japan,

Australia, Indonesia, Korea and Malaysia are found in the higher rank with highest values.

Timor-Leste, Palay, Tonga, Nauru and Tuvalu are found in the lower rank with low values of

carbon dioxide emissions. Figure-2 shows the situation of carbon dioxide emission of Latin

America & Caribbean, Brazil, Mexico and Argentina are found in the higher rank with highest

values. Bahamas, Barbados and Antigua & Barbudas are found in the lower rank with low values

of carbon dioxide emissions.

Figure-3 shows the situation of carbon dioxide emission of Europe and Central Asia, Russian

Federation, Germany, France, Italy, Kazakhistan, Poland, Spain, Turky, United Kindom and

Ukrain are found in the higher rank with highest values. Cyprus, Georgia, Lithuania, Stovenia,

Moldova, Montenegro and Tajikistan are found in the lower rank with low values of carbon


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dioxide emissions. Figure-4 shows the situation of carbon dioxide emission of Europe and

Central Asia, Russian Federation, Germany, France, Italy, Kazakhistan, Poland, Spain, Turky,

United Kindom and Ukrain are found in the higher rank with highest values. Cyprus, Georgia,

Lithuania, Stovenia, Moldova, Montenegro and Tajikistan are found in the lower rank with low

values of carbon dioxide emissions.

Figure 2: Situation of Carbon Dioxide Emission (kt) in Latin America and Caribbean.



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Figure 3: Situation of Carbon Dioxide Emission (kt) in Europe and Central Asia Region.






dioxide emissions.

The main purpose of this study is to empirically investigate the relationship between CO2

emissions, cereal production and economic growth of East Asia & Pacific, Latin America &

Caribbean, Europe & Central Asia and Sub-Saharan Africa. World Bank has divided the world

into these groups based upon the regions and climatic situations. Among them we select the four

groups of countries. To the best of our knowledge, there has been no study that tried to estimate

these variables for East Asia & Pacific, Latin America & Caribbean, Europe & Central Asia and

Sub-Saharan Africa through linear equation modeling for the year 2014. The paper is organized

in the following manners;

Section-2: Literature review containing predictions and findings.


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Section-3: Material and Methodology.

Section-4: Econometric Analysis.

Section-5: Conclusion along with Policy recommendations.

Figure 4: Situation of Carbon Dioxide Emission (kt) in Sub-Saharan Africa Region.






dioxide emissions.

The main purpose of this study is to empirically investigate the relationship between CO2

emissions, cereal production and economic growth of East Asia & Pacific, Latin America &

Caribbean, Europe & Central Asia and Sub-Saharan Africa. World Bank has divided the world

into these groups based upon the regions and climatic situations. Among them we select the four

groups of countries. To the best of our knowledge, there has been no study that tried to estimate

these variables for East Asia & Pacific, Latin America & Caribbean, Europe & Central Asia and


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Sub-Saharan Africa through linear equation modeling for the year 2014. The paper is organized

in the following manners;

Section-2: Literature review containing predictions and findings.

Section-3: Material and Methodology.

Section-4: Econometric Analysis.

Section-5: Conclusion along with Policy recommendations.

1.1 Objectives

Following are the proposed objectives:

To investigate the effect of climate change and agricultural production on economic

growth in East Asia and Pacific.


growth in Europe and Central Asia.


growth in Latin America and Caribbean.


growth in Sub-Saharan Africa.

To provide policy implications.

2. LITERATURE REVIEW

Following are some research work which has done to see the issue of climate change and its

affected dimensions. By examining the literature in perspective to the studied topic help to

displaying the consequences of unsure climate shocks on agricultural efficiency.

Table 1: Predictions and findings in existing literature.

STUDY PREDICTION AND FINDING

Dell, et al. (2008) Increase in temperature leads to increase in economic growth in case of poor

countries.

Janjua, et al. (2010) Climate change has negative effect on wheat production.

Shakoor, et al.

(2011)

Temperature has negative impact on agriculture production.

Siddiqui, et al.

2012

Both in short and in long term the impact of climate change on wheat

productivity is non-negative, while the impact of climate change is negative


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for Rice, Cotton and Sugarcane.

Akram, (2012) Economic growth is negatively affected by changes in temperature,

precipitation and population growth whereas urbanization and human

development stimulates economic growth.

Tariq, et al. 2014 In the irrigated region, rising maximum temperature during January and

November has negative effect, whereas variables such as wheat area,

minimum temperature during November and March are positively related

with wheat production.

Tebaldi and

Beaudin (2015)

Real GDP growth rate decreased by 0.92% in the direct result of spring

droughts in Northeast region

Jammazi & Aloui,

(2015)

The results pointed out the existence of bilateral causal effects between EC

and EG while only a unidirectional relationship was found from EC to CO2

emissions.

Afzal, Ahmed &

Ahmed (2016)

Temperature effects the wheat production negatively at flowering stage

while rainfall effects the wheat production negatively at every stage.

Qureshi, et al.

(2016)

CO2 emissions is positively and energy sources is negatively related wit

agricultural value added. Greenhouse gas emission severely affected the

agricultural production which includes cotton production, rice production

and wheat production.

Arshad, et al.

(2016)

Comparing variation in observed climatic parameters in the year of study to

medium-term patterns, rice, and wheat yields were both negatively affected,

indicative of production risk and of farmers’ limited capacity for within-

season adaptation (South Asia)

Bayramoglu and

Yildirim (2017)

They concluded that energy saving policies such as technological progress

and organizational rearrangements may have the booster effect for impact of

the positive component of energy consumption.

2.1. Conceptual Framework

Conceptual model presented in below figure depicts the linkage between climate change,

agricultural production and economic growth.


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Figure 1: Conceptual framework showing the relationship between variables in the study.

3. METHODOLOGY

Methodology has a very important role in accomplishing the desired objectives of the study

through using various tools & techniques. As the situation of climate change is worsen day by

day all around the world. The selected target research areas in this study are East Asia & Pacific,

Latin America & Caribbean, Europe & Central Asia and Sub-Saharan Africa.

3.1 Study area

World is mainly categorized into seven groups by region (World Bank, 2018)1. The groups are

East Asia and Pacific, Europe and central Asia, Latin America and Caribbean, Middle East and

North Africa, North America, South Asia and Sub-Saharan Africa consist of 38, 58, 42, 21, 3, 8

and 48 countries respectively. In this study we used four groups for analysis, whose countries are

greater than 30 observations because our analysis is basically cross-sectional analysis. In cross-

sectional analysis, required minimum number of observations should be 30.

The categories which are used in the analysis are given below in table with countries name.

East Asia and Pacific

American Samoa Hong Kong SAR,

China Marshall Islands

Palau Papua New Guinea

Australia Indonesia Micronesia, Fed. Sts. Philippines Tonga

Brunei Darussalam Japan Mongolia Samoa Tuvalu

Cambodia Kiribati Myanmar Singapore Vanuatu

China Korea, Rep. Nauru Solomon Islands Vietnam

1 https://data.worldbank.org/country

https://data.worldbank.org/country


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Fiji Lao PDR New Caledonia Taiwan, China

French Polynesia Macao SAR, China New Zealand Thailand

Guam Malaysia Northern Mariana

Islands Timor-Leste

Europe and Central Asia

Albania Czech Republic Iceland Moldova Slovenia

Andorra Denmark Ireland Monaco Spain

Armenia Estonia Isle of Man Montenegro Sweden

Austria Faroe Islands Italy Netherlands Switzerland

Azerbaijan Finland Kazakhstan Norway Tajikistan

Belarus France Kosovo Poland Turkey

Belgium Georgia Kyrgyz Republic Portugal Turkmenistan

Bosnia and

Herzegovina Germany Latvia Romania Ukraine

Bulgaria Gibraltar Liechtenstein Russian Federation United Kingdom

Channel Islands Greece Lithuania San Marino Uzbekistan

Croatia Greenland Luxembourg Serbia

Cyprus Hungary Macedonia, FYR Slovak Republic

Latin America & the Caribbean

Antigua and

Barbuda Cayman Islands El Salvador Panama Suriname

Argentina Chile Grenada Paraguay Trinidad and Tobago

Aruba Colombia Guatemala Peru Turks and Caicos

Islands

Bahamas, The Costa Rica Guyana Puerto Rico Uruguay

Barbados Cuba Haiti Sint Maarten (Dutch

part) Venezuela, RB

Belize Curacao Honduras St. Kitts and Nevis Virgin Islands (U.S.)

Bolivia Dominica Jamaica St. Lucia


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Brazil Dominican

Republic Mexico

St. Martin (French

part)

British Virgin

Islands Ecuador Nicaragua

St. Vincent and the

Grenadines

Sub-Saharan Africa

Angola Congo, Dem. Rep. Guinea-Bissau Namibia South Sudan

Benin Congo, Rep Kenya Niger Sudan

Botswana Côte d'Ivoire Lesotho Nigeria Swaziland

Burkina Faso Equatorial Guinea Liberia Rwanda Tanzania

Burundi Eritrea Madagascar São Tomé and

Principe Togo

Cabo Verde Ethiopia Malawi Senegal Uganda

Cameroon Gabon Mali Seychelles Zambia

Central African

Republic Gambia, The Mauritania Sierra Leone Zimbabwe

Chad Ghana Mauritius Somalia

Comoros Guinea Mozambique South Africa

3.2 Data and Variables

In the study we used secondary data which is collected from World Development Indicators

(WDI), 2014. Variables which are utilized to fulfill the desired objectives of the study are carbon

dioxide emissions, agricultural land, cereal production, infant mortality rate and economic

growth. We use carbon dioxide emissions as a proxy variable for climate change because among

total greenhouse gases, the concentration of carbon dioxide is higher as compared to other gases.

Concentration of CO2, out of overall GHG is greater as compared to other gases, concentration of

CO2 is 0.48% in Pakistan, 6.8% in India, 15% in USA and 30% in China.


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4. ECONOMETRIC TECHNIQUES

4.1 Descriptive Analysis

Table 2: Descriptive Statistics of East Asia and Pacific.

GDP Growth

Annual (%)

Cereal Production

(Metric tons)

CO2 Emissions

(kt)

Mean 4.72 36284787 466510.4

Median 3.40 3555033 14974.19

Maximum 36.52 5.57E+08 10291927

Minimum -2.34 0.000000 11.00100

Std. Dev. 6.73 1.13E+08 1873284.

Skewness 3.715 4.322650 5.051813

Kurtosis 18.49 20.42103 27.00054

Probability 0.000 0.000000 0.000000

Sum 136.98 8.71E+08 13995311

Sum Sq. Dev. 1270.19 2.94E+17 1.02E+14

Table-2 shows the descriptive statistics of the main studied variables used for East Asia and

Pacific’s countries. In case of GDP growth rate, mean value is 4.72, median is 3.40, minimum

value of GDP growth rate is -2.34 while the maximum value is 36.52 and Standard deviation of

GDP growth rate is 6.73. It is shown in the table that means value cereal production in case of

East Asia and Pacific countries is 36284787, median is 3555033 and standard deviation is

1.13E+08. Mean CO2 emission is 466510.4 while median, maximum, minimum and standard

deviation are 14974.19, 10291927, 11.001 and 1873284 respectively.

Table 3: Descriptive Statistics of Latin America and Caribbean.

GDP Growth

Annual (%)

Cereal Production

(Metric tons)

CO2 Emissions

(kt)

Mean 2.56 7994532. 64801.71

Median 3.05 838643.0 8800.800

Maximum 7.60 1.01E+08 529808.2

Minimum -3.89 74.00000 209.0190

Std. Dev. 2.766068 21557643 132307.9


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Skewness -0.304603 3.408223 2.711883

Kurtosis 2.730748 14.06245 9.339381

Probability 0.764888 0.000000 0.000000

Sum 74.26448 2.32E+08 1879249.

Sum Sq. Dev. 214.2316 1.30E+16 4.90E+11

Table-3 shows the descriptive statistics of the main studied variables used for Latin America and

Caribbean countries. In case of GDP growth rate, mean value is 2.56, median is 3.05, minimum

value of GDP growth rate is -3.89 while the maximum value is 7.60 and Standard deviation of

GDP growth rate is 2.76. It is shown in the table that mean value cereal production in case of

East Asia and Pacific countries is 7994532, median is 838643 and standard deviation is

21557643. Mean value of CO2 emission is 64801.71 while median, maximum, minimum and

standard deviation are 8800.80, 529808.2, 529808.2 and 209.01 respectively.

Table-4 shows the descriptive statistics of the main studied variables used for Europe and Central

Asian countries. In case of GDP growth rate, mean value is 2.31, median is 8.32, minimum value

of GDP growth rate is -6.55 while the maximum value is 8.32 and Standard deviation of GDP

growth rate is 2.51. It is shown in the table that mean value cereal production in case of Europe

and Central Asian countries is 12850125, median is 4197476 and standard deviation is

21245541. Mean value of CO2 emission is 134025.1 while median, maximum, minimum and

standard deviation are 42251.17, 1705346, 2211.201 and 276315.2 respectively.

Table 4: Descriptive Statistics of Europe and Central Asia

GDP Growth

Annual (%)

Cereal Production

(Metric tons)

CO2 Emissions

(kt)

Mean 2.319026 12850125 134025.1

Median 1.987559 4197476. 42251.17

Maximum 8.328387 1.03E+08 1705346.

Minimum -6.552619 7362.000 2211.201

Std. Dev. 2.515469 21245541 276315.2

Skewness -0.452273 2.621019 4.355030

Kurtosis 5.541074 9.901719 24.27941

Probability 0.000938 0.000000 0.000000

Sum 106.6752 5.91E+08 6165155.

Sum Sq. Dev. 284.7413 2.03E+16 3.44E+12


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Table 5: Descriptive Statistics of Sub-Saharan Africa

GDP Growth

Annual (%)

Cereal Production

(Metric tons)

CO2 Emissions

(kt)

Mean 4.912276 3493771. 18937.03

Median 4.901067 1427435. 3085.781

Maximum 10.25749 24495794 489771.9

Minimum 0.611213 762.0000 113.6770

Std. Dev. 2.454265 5769444. 77828.56

Skewness -0.043155 2.629374 5.767173

Kurtosis 2.443642 9.330749 35.25217

Probability 0.767856 0.000000 0.000000

Sum 196.4910 1.40E+08 757481.2

Sum Sq. Dev. 234.9132 1.30E+15 2.36E+11

Table-5 shows the descriptive statistics of the main studied variables used for Sub-Saharan

Africa. In case of GDP growth rate, mean value is 4.91, median is 4.90, minimum value of GDP

growth rate is 0.61 while the maximum value is 10.25 and Standard deviation of GDP growth

rate is 2.45. It is shown in the table that mean value cereal production in case of Europe and

Central Asian countries is 3493771, median is 1427435 and standard deviation is 5769444. Mean

value of CO2 emission is 18937.03 while median, maximum, minimum and standard deviation

are 3085.78, 489771.9, 113.677 and 77828.56 respectively.

4.2 Regression Analysis

The relationship between carbon dioxide emissions, agricultural production and economic

growth can be expressed in a linear relationship as shown:

(GDP)i = α0 + β1 (CO2)i + β2 (CP)i + ε ……………(1)

Here; GDP= GDP growth rate (%)

CO2= Carbon Dioxide Emission (kt)

CP = Cereal Production (metric tons)


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Table 6: Impact of Carbon Dioxide Emissions on Cereal Production

and Economic Growth in East Asia and Pacific.

Dependent Variable = GDP Growth Annual (%)

Independent

Variables

Co-efficient Standard Error t-statistics p-value

Agriculture Land 0.077744 0.035460 2.192433 0.0445

Cereal Yield 0.000562 0.000316 1.778158 0.0956

CO2 emission 1.20E-07 2.90E-07 0.415779 0.6835

Infant Mortality Rate 0.124707 0.043584 2.861296 0.0119

Constant -2.879425 2.042039 -1.410074 0.1789

R-square = 0.48

F-statistics = 3.461

Prob = 0.0341

The regression model in Table 6 shows the relationship between carbon dioxide emissions,

cereal yield and economic growth of East Asia and Pacific countries. The results show that

carbon dioxide emissions and cereal yield have positive relationship with economic growth, with

the increase in carbon dioxide emissions and cereal yield, economic growth tends to increase.

Here in the model agricultural land and infant mortality rate act as a control variable. The

coefficient values indicate that when there will be 1% increase in carbon dioxide emissions and

cereal yield, economic growth will tend to increase by 0.0001% and 0.0007% respectively. As

per values of F-statistics and concerning p-values, it is evident that the econometric model is fit.

The value of R-square is 48%. Agricultural land, cereal yield and infant mortality rate are

statistically significant while the CO2 emission shows the insignificant result in case of East Asia

and Pacific countries.


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Table 7: Impact of Carbon Dioxide Emissions on Cereal Production and

Economic Growth in Latin America and Caribbean.


Independent Variables Co-

efficient

Standard

Error

t-statistics p-value


Cereal Production 7.69E-09 4.38E-08 0.175370 0.8623

CO2 emission -9.37E-06 7.27E-06 -1.288859 0.2103

Crude Death Rate -0.704586 0.313758 -2.245638 0.0346


Constant 4.314324 2.558981 1.685954 0.1053

R-square = 0.38

F-statistics = 2.8

Prob = 0.036


cereal production and economic growth of Latin America and Caribbean countries. The results

show that carbon dioxide emissions have negative relationship with economic growth, with the

decrease in carbon dioxide emissions, economic growth tends to reduce. Cereal production has

positive relationship with economic growth, with the increase in cereal production economic

growth will increase. Here in the model agricultural land, crude death rate and infant mortality

rate act as a control variable. The coefficient values indicate that when there will be 1% increase

in carbon dioxide emissions and cereal yield, economic growth will tend to decrease by 0.0006%

and increase by 0.0009% respectively. As per values of F-statistics and concerning p-values, it is

evident that the econometric model is fit. The value of R-square is 38%.


cereal production and economic growth of Europe and Central Asian countries. The results show

that carbon dioxide emissions have positive relationship with economic growth, with the increase

in carbon dioxide emissions, economic growth tends to enhance. Cereal production has negative

relationship with economic growth. Here in the model agricultural land, crude death rates and


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infant mortality rate act as a control variable. The coefficient values indicate that when there will

be 1% increase in carbon dioxide emissions and cereal yield, economic growth will tend to

increase by 0.00004% and decrease by 0.0007% respectively. As per values of F-statistics and

concerning p-values, it is evident that the econometric model is fit. The value of R-square is

40%. CO2 emission, Agricultural land, cereal yield and crude death rate are statistically

significant while infant mortality rate shows the insignificant result in case of Europe and Central

Asian countries.


and Economic Growth in Europe and Central Asia.



efficient

Standard

Error



Cereal Production -7.73E-08 2.99E-08 -2.588517 0.0134

CO2 emission 4.44E-06 2.24E-06 1.982981 0.05443

Crude Death Rate -0.259368 0.131193 -1.976995 0.0550


Constant 3.137977 1.629912 1.925243 0.0613

R-square = 0.40

F-statistics = 5.268

Prob = 0.0008


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and Economic Growth in Sub-Saharan Africa.



efficient

Standard

Error


Agriculture Land -0.002275 0.020386 -0.111598 0.9118

Cereal Production 2.19E-07 7.42E-08 2.948191 0.0057

CO2 emission -1.22E-05 6.11E-06 -1.991851 0.0545

Crude Death Rate -0.150000 0.368195 -0.407394 0.6863


Constant 4.689068 1.773033 2.644659 0.0123

R-square = 0.25

F-Statistics = 2.293

Prob = 0.0671


cereal production and economic growth of Sub-Saharan African countries. The results show that

carbon dioxide emissions have negative relationship with economic growth, with the decrease in

carbon dioxide emissions, economic growth tends to reduce. Cereal production has positive

relationship with economic growth, with the increase in cereal production economic growth will

increase. Here in the model agricultural land, crude death rates and infant mortality rate act as a

control variable. The coefficient values indicate that when there will be 1% increase in carbon

dioxide emissions and cereal yield, economic growth will tend to decrease by 0.0001% and

increase by 0.0002% respectively. As per values of F-statistics and concerning p-values, it is

evident that the econometric model is fit. The value of R-square is 25%. CO2 emission,

Agricultural land, cereal yield and crude death rate are statistically significant while infant

mortality rate shows the insignificant result in case of Sub-Saharan African countries.

5. CONCLUSION

The study attempts to investigate the interrelationship between carbon dioxide emissions, cereal

production/yield and economic growth of different groups of countries worldwide. These groups


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are categorized on the bases of regions, developed by World Bank. Different regions of world

have different situation of climatic change. According to results, climate change and economic

growth is positively related East Asia & Pacific and Europe & Central Asia, while economic

growth and climate change are negatively related in case of Latin America & Caribbean and

Sub-Saharan Africa. The reason behind this scenario might be that East Asia & Pacific and

Europe & Central Asia are the groups of those countries which control the carbon dioxide

emissions, adopted those technology which are produce low carbon emissions. While the Latin

America & Caribbean and Sub-Saharan Africa are the group who do not opt the latest

technologies to reduce the carbon dioxide production. There is need to overcome the problem of

climate change in the form of carbon dioxide emissions both in Latin America & Caribbean and

Sub-Saharan Africa. Government of these economies should focus on controlling CO2 to

improve the economic growth of particular economies.

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and An Application to Employment Equations. Rev. Econ. Stud. 58, 277– 297.

Calzadilla, A., Zhu, T., Rehdanz, K., Tol, SJR., & Ringler, C. (2008). Economic-wide Impacts of

Climate Change on Agriculture in Sub-Saharan Africa. University of Hamburge Working

Paper FNU-170, Hamburg, Germany.

Dell, Melissa, Benjamin F. Jones, and Benjamin A. Olken. (2008). Climate Shocks and

Economic Growth: Evidence from the Last Half Century, NBER Working Paper 14132.

Government of Pakistan (2016). Economic Survey of Pakistan, Federal Bureau of Statistics,

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Climate change 2007. Synthesis report summary for policy makers. Geneva: IPCC.

Janjua, P. Z., Samad, G., & Khan, N. U. (2010). Impact of Climate Change on Wheat

Production: A Case Study of Pakistan. The Pakistan Development Review, 799-822.

Qureshi, M. I., Awan, U., Arshad, Z., Rasli, A. M., Zaman, K., & Khan, F. (2016). Dynamic

Linkages among Energy Consumption, Air Pollution, Greenhouse Gas Emissions and


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Agricultural Production in Pakistan: Sustainable Agriculture Key to Policy success.

Natural Hazards, 367-381.

Shakoor, U., Saboor, A., Ali, I., & Mohsin, A. Q. (2011). Impact of Climate Change on

Agriculture: Empirical Evidence from Arid Region. Pak. J. Agri. Sci, 327-333.

Tebaldi, Edinaldo, and Laura Beaudin. 2016. Climate Change and Economic Growth in Brazil.

Applied Economics Letters 23 (4–6): 377–81

Yang, D. T., & Zhu, X. (2013). Modernization of Agriculture and Long-Term Growth. Journal

of Monetary Economics, 367-382.

Ziervogel, G., Bharwani, S., Downing, T.E. (2006). Adapting to Climate Variability: Pumpkins,

People and Policy. Natural Resources Forum 30:294–305.


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EFFECT OF DIFFERENT MULCHES ON GROWTH

AND YIELD OF TOMATO

M. R. Islam1, M. G. Kibria2, A. K. Das1 and S. D. Setu1

1Scientific Officer, Bangladesh Agricultural Research Institutre, RARS, Barishal-8211

2Principal Scientific Officer, Bangladesh Agricultural Research Institutre, RARS, Barishal-8211

ABSTRACT

The study was conducted at the experimental field of horticulture division, RARS, Rahmatpur,

Barishal during the winter season of 2018-19 to determine the effect of various mulches on

growth and yield of tomato. The treatments of the experiment comprised five mulch materials

viz. Sawdust (5cm thick), Cocodust (5cm thick), Rice husk (5cm thick), Water hyacinth

(chopping and 10cm thick), Black Ploythene with no mulch as control and BARI Tomato 15 as a

variety. Mulching significantly increased the total number of fruit/plant of tomato over bare

plants. The highest number of fruit was recorded in chopped Water hyacinth mulch (28.74) and

the lowest was in treatment control i.e. no mulch (22.80). Similar trend was found in single fruit

weight, being the highest in chopped Water hyacinth mulch (66.24g) lowest was in cocodust

mulch (53.26g). The highest yield was recorded in chopped Water hyacinth mulch (94.96t/ha)

followed by rice husk mulch (81.84t/ha) and the lowest was in control i.e. no mulch (62.86 t/ha).

From 1st year experiment, the study reveals that BARI Tomato 15 can be cultivated using

chopping water hyacinth as a mulch material for higher yield.

Keywords: Tomato, Mulch, Growth and Yield

INTRODUCTION

Tomato (Lycopersicon esculentum) is one of the popular vegetables extensively grown during

the winter season in Bangladesh. It is drawing attention of the growers and consumers and made

its position among the cultivated vegetables. It contributes significantly to the nutrition of the

people as a source of vitamins and minerals. In Bangladesh, congenial atmosphere remains for

tomato production during November to March. It is mainly grown in winter season. Among

various factors responsible for higher yield, supply of nutrient and availability of moisture play

vital role in the production and quality of tomato. Its production can be increased by adopting

improved cultural practices. Mulching is the effective means to reduce weed infestation and


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conserve moisture in the root zone. This practice also encourages deeper and denser rooting.

Mulches have been found to decrease soil moisture losses by reducing soil temperature and

evaporation, promoting favorable soil biotic activities, reducing hard soil setting and contributing

plant nutrients (A. R. Pal et al., 1994; H. S. Bhella, 1994; R. C. Chakraborty, 1994, R. S. Hooda,

1999). Mulching has also been identified by many workers as a method to provide a favourable

soil environment by minimizing crusting at the soil surface and keep it stable (A. P. Mehta,

1973). Little information regarding mulching on tomato cultivation in the southern area is

available. Therefore, the present study was conducted to determine the effect of various mulches

on growth and yield of tomato.

MATERIALS AND METHODS

The experiment was conducted during 2018-19 at Regional Agricultural Research Station,

Rahmatpur, Barishal. The treatments of the experiment comprised five mulch materials viz.

Sawdust (5cm thick), Cocodust (5cm thick), Rice husk (5cm thick), Water hyacinth (chopping

and 10cm thick), Black Ploythene with no mulch as control and BARI Tomato 15 as a variety.

The experimental design was randomized complete block design (factorial) with three

replications. Unit plot size was 4.8m×1m. The seedlings were transplanted on the 24th October,

2018 maintaining a spacing 60cm×40cm. The crop was fertilized with cowdung 10 t/ha, Urea

550 kg/ha, TSP 200 kg/ha, MOP (muriate of potash) 200 kg/ha. Total amount of cowdung, TSP

(triple superphosphate) and 1/3 of each urea and MOP were applied during final land

preparation. The rest of urea and MOP were applied in three equal installments at 15, 30 and 45

days after transplanting. Irrigation was done after application of fertilizer. Other intercultural

operations and plant protection measures were taken as deemed needed. Data was collected on

different yield contributing characters and yield. Recorded data were analysed statistically and

means were compared by LSD (Least Significant Difference) (Gomez & Gomez, 1984).

Table 1: Nutrients status of different fertilizers and soil characteristics

of tomato planted study sites.

Parameters Physico-chemical properties of

study site soil

pH 7.9

Organic carbon (%) 0.90

Total kjeldahl nitrogen (%) 0.081

Total phosphorous (%) 34.0

Total potassium (%) 0.12

Total Zinc (%) 2.05

C/N ratio (%) 19.33


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RESULTS AND DISCUSSION

The results on the effect of different mulch material on the yield and yield components are

presented in Table 2. There are not statistically significant among the parameter of Days to 1st

flower initiation, DAS to 50% flowering, Date of 1st harvest but significantly different among

the other parameters. In case of plant height, there are significantly different among the different

mulches. The tallest plant was recorded in water hyacinth mulches (115.64cm) and shortest plant

was in the treatment control i.e. no mulch (89.17cm).

Table 2: Effect of Mulching on the Yield and Yield Components of Tomato Varieties

Treatment Days to 1st flower

initiation

DAS to 50%

flowering

Date of 1st

Harvest

Plant Height at

1st harvest (cm)

T1 (No mulch) 37.33 47.33 62.33 89.17

T2 (Sawdust) 36.00 46.00 61.00 95.28

T3 (Cocodust) 35.00 45.00 60.00 109.98

T4 (Rice husk) 36.67 46.67 61.67 109.07

T5 (Water hyacinth) 37.67 47.67 62.67 115.64

T6 (Black Ploythene) 35.00 45.00 60.00 98.80

CV (%) 4.20 3.29 2.49 3.10

LSD (0.05) NS NS NS 5.80

Mulching significantly increased the total number of fruit/plant of tomato over bare plants. The

highest number of fruit was recorded in chopped Water hyacinth mulch (28.74) and the lowest

was in treatment control i.e. no mulch (22.80). Similar trend was found in single fruit weight,

being the highest in chopped Water hyacinth mulch (66.24g) lowest was in cocodust mulch

(53.26g). Length and diameter of fruit were found to be insignificant. Yield of tomato

significantly increased in mulches over without mulch. The highest yield was recorded in

chopped Water hyacinth mulch (94.96t/ha) followed by rice husk mulch (81.84t/ha) and the

lowest was in control i.e. no mulch (62.86 t/ha).

Table 2. Cont’d

Treatment No. of

fruit/Plant

Individual

fruit wt.

(g)

Fruit

Length

(cm)

Fruit

Diameter

(cm)

Yield/plot

(kg)

Yield

(t/ha)

T1 (No mulch) 22.80 55.18 5.41 4.63 30.17 62.86

T2 (Sawdust) 25.68 55.63 5.96 4.71 34.29 71.43

T3 (Cocodust) 26.96 53.26 6.15 4.61 34.40 71.68

T4 (Rice husk) 26.24 62.39 6.07 4.77 39.28 81.84

T5 (Water hyacinth) 28.74 66.24 6.06 4.75 45.58 94.96


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T6 (Black Ploythene) 25.06 63.86 6.12 4.56 38.39 79.97

CV (%) 5.45 3.54 5.00 5.48 3.67 3.66

LSD (0.05) 2.57 3.82 NS NS 2.47 5.14

CONCLUSION

This was the 1st year experiment. From 1st year, the study reveals that BARI Tomato 15 can be

cultivated using chopping water hyacinth as a mulch material for higher yield.

REFERENCES

Pal A. R., Baghel S. S., Rathore, et al. Response Management for Rainfed Rice and Ricebased

Cropping Systems. A paper presented at the 29th All India Annual Rice Group Meeting,

Indira Gandhi Agricultural University Raipur (MP), India, 20–22 Mar 1994.

Bhella H. S. Tomato Response of Trickle Irrigation and Black Polyethylene Mulch. J. Amer.

Soc. Hort. Sci. 1988; 113(4): 543–546p.

Chakraborty R. C., Sadhu M. K. Effect of Mulch Type and Colour on Growth and Yield of

Tomato (Lycopersicon esculentum Miller). Indian J. Agric. Sci.1994; 64: 608–612p.

Hooda R. S., Singh J., Malik, V. S., et al. Influence of Direct Seeding, Transplanting Time and

Mulching on Tomato Yield. Veg. Sci. 1999; 26(2): 140–142p.

Mehta A. P., Prihar S. S. Seedling Emergence in Soybean and Cotton as Affected by

Seedbed Characteristics and Surface Mulches. Indian J. Agric. Sci. 1973; 43(1): 45–49p.

Gomez K. A., Gomez A. A. Statistical Procedure for Agricultural Research. 2nd Ed. John

Wiley and Sons, New York. 1984; 67-215p.


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ANALYSIS OF THE EFFECT OF CREDIT ON PER CAPITA ANNUAL

FARM INCOME OF RICE FARMERS; BENEFICIARIES OF SACCO

CREDIT IN BENUE STATE NIGERIA

Okolo Samson Ayegba, Olotu Olafemi Ayopo

Department of Agribusiness, Federal University of Agriculture, Makurdi.

ABSTRACT

The analysis of the effect of credit on per capita farm income of beneficiaries of Savings and

Credit Cooperative in Benue state, Nigeria is the focus of this work. Random and stratified

sampling method was used to select 236 respondents in the study area. Out of the selected

respondents only 208 responded and submitted the administered well structured questionnaires

correctly. Therefore, the study was based on 208 primary data collected from registered SACCO

members in the three Local Government Areas of Benue State. In this study the year 2011 was

used for before credit was obtained and 2015 for after credit was obtained. Descriptive statistics,

double difference estimator, logistic regression analysis and independent sample t-test were used

to achieve the objectives and hypothesis of the study. The result of the double difference estimate

showed that the SACCO credit had a positive effect on the per annual farm income of the

beneficiaries of the credit with per capita annual farm income of ₦4719.86. Sex, education and

household size were significant factors that influence participation in SACCO. The sex is

significant at 5% level of significance while education and household size were significant at 1%

level of significance as obtained from the logistic regression analysis. About 77.3% and 76.2%

of the beneficiaries and non beneficiaries identified and ranked poor access to credit as the major

constraint faced by SACCO. Other cardinal constraints were illiteracy level and high cost of

farm inputs in the order of severity. SACCO executives and the Government should develop

strategies that will bring in more funding, loans and grants to the cooperative consequently

enhance availability of credit to members. This will help members who are smallholder farmers

to become big estate farm holders. It is also possible that more credit availability to members is a

key to poverty reduction due to its positive effect on the increase in per capita annual farm

income as seen in the study.

Keywords: Analysis, annual farm income; rice farmers; constraints; participation; Benue State


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INTRODUCTION

Benue state is known to be a state engaging more than 70 percent of its population in agriculture;

agriculture is the back bone of the economy of the state (Ajaero, 2007). The performance in

agriculture is relatively average and dwindling due to the poor agricultural finance. The

research on the poverty reduction among rice farmers is very important since rice is a major

staple in the study area.

Rice is consumed by more than 4.8 billion people in 176 countries and is the most important

food crop for over 2.89 billion people in Asia, over 40 million people in Africa and over 150.3

million people in America, (Biyi, 2005). According to Jones, (1995), rice is the second most

important cereal in the world after wheat in terms of production; while Nigeria ranks the highest

as both producer and consumer of rice in the West Africa sub region. Akande and Akpokodje

(2003) opined that, since the mid-1970s, rice consumption in Nigeria has risen tremendously, at

about 10% per annum due to changing preferences while domestic production has never been

able to meet the demand leading to considerable imports which today stands at about 1,000,000

metric tons yearly. The imports are procured on the world market with Nigeria spending

annually over US $300 million on rice imports alone. Similarly, Biyi (2005) observed that the

annual domestic output of rice still hovers around 3 million metric tons, leaving the huge gap of

about 2 million metric tons annually, a situation, which has continued to encourage dependence

on importation. This calls for the need to finance the rice farmers via the umbrella of the savings

and credit cooperatives. With adequate financing of the SACCO it is very possible to meet the

demand for rice in Nigeria and subsequently reduce poverty from rice farm families.

Therefore, the need for farmers to come together and form an autonomous association of

individuals, voluntarily united to meet their common economic, social and cultural needs

through a jointly-owned and democratically controlled enterprise (International cooperative

alliance, 1996). According to the global Multidimensional Poverty Index, International

Monetary Fund (IMF) report by the United Nations (2015) the national average of poverty rate

is 46.0%, the national proportion of those living above the poverty line is 54%. Benue State

ranked 24th amongst the states living above the poverty line with 40.8% above the line and about

59.2% living below the poverty line.

Savings and credit Cooperatives (SACCO) are important in the provision of financial and

banking services to low income households who for economic reasons cannot be covered by the

activities of formal banks and financial institutions (Mwakajuilo, 2011). SACCO performs three

major functions in relations to its members and general economic development of the country.

These functions are collecting savings, giving credit and giving financial and non- financial


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advice to its members in order to facilitate and ensure that SACCO members utilize the micro

credit they have borrowed from SACCO.

In some cases, some government and private institutions may also give financial assistance to

SACCO in order to enable them give micro credit to their members (Mwakajumilo, 2011). He

further posited that the different activities done by households in both urban and rural areas also

mean the existence of different SACCO with the aim of assisting the Government to reduce high

level of poverty and income inequality in the society.

Unemployment breeds a lot of private and social consequences which are negative (Alam,

Khalifa, and Shahjamal, 2009; Alam, 2009). These include poverty, crime, social inequality, loss

of output, family disintegration, among others. Governments all over the world make concerted

efforts to mitigate these problems (Alam, 2009). In Nigeria several efforts have been made to

create jobs for the teaming able bodied people who are available for work but who are yet to find

jobs (Goodluck, 2011). One key source of unemployment in Nigeria is dearth of capital required

to combine with other factors of production, which are land, labor and entrepreneurship

(Nieman, Hough, and Niewenhuizen, 2003). Although growth is critical for poverty reduction,

focus on growth alone is not enough (Almas, 2013). Micro-lending has been considered as the

latest panacea for poverty alleviation (Magbagbeola, Adetoso, and Owolabi, 2010). Cooperative

societies all over the world have been seen as one of the ways of reaching out to the un-banked

and the neglected in the society and not a few have come to see it as an alternative to the regular

banking, since it, in most case provides members of the group with the financial incentives

without the rigors usually experienced in banking halls (Adewakun, 2012). Traditional

cooperatives are common throughout Nigeria, but these groups tend to be small, with a common

bond based on membership of a kinship, societal and low professional group (Adewakun, 2012).

Saving and credit cooperatives Societies are known to provide funding to their members at

reasonable interest rate and without requirement of collateral. They are therefore vital organs for

financing food crop production (Mavimbela, Masuku, and Belete, 2010).

OBJECTIVES

i. To examine the effect of credit on the annual farm income of beneficiaries and non-

beneficiaries of SACCO credit;

ii. To determine the factors influencing the participation and intensity of participation in

SACCO; and

iii. To identify the constraints faced by beneficiaries and non-beneficiaries of SACCO

credit in poverty reduction in the study areas.


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STATEMENT OF HYPOTHESIS

There is no significant difference between the per capita annual farm income of beneficiaries and

non-beneficiaries of SACCO Credit

METHODOLOGY

Study Area

Benue State, the State lies between Latitudes 6025ꞌN and 808ꞌN of the equator and Longitudes

7047ꞌ and 10ᵒE. (Ministry of land and survey, 2016). It has a total land-area of about 33,955

square kilometers with a population of 4,253,641 (NPC, 2006), with an average population

density of 99 persons per square kilometer. The State is blessed with a great loamy soil for

agricultural activities. It is one of the 36 states of Nigeria, It comprises 23 Local Government

Areas (LGAs) grouped into 3 agricultural zones; A, B, C, respectively. The major food crops

produced are yam, rice, cassava, maize, soybean, sesame, cowpea and groundnut at subsistence

level. At the end of 2011, the poverty rate of Benue State was estimated at 31.9% (National

Bureau of Statistics, 2012). Meanwhile at the end of 2015 the poverty rate of Benue State was

estimated to be 59.2% based on data collected between 2004 and 2014 (Multidimensional

Poverty Index, 2015) published by the United Nations.

There are areas of low population density such as Guma, Gwer East, Ohimini, Katsina-ala, Apa,

Logo, and Agatu, each with less than seventy persons per square kilometers, while Vandeikya,

Okpokwu, Ogbadibo, Obi, and Gboko have density ranging from 140 persons to 200 persons per

square kilometer. Makurdi LGA has over 380 persons per square kilometers. The study used

zones (A, B, C) in the State to ease sample design and research instrument distribution. Zone A

had the following Local Government Areas: Katsina- ala, Konshisha, Kwande, Logo, Ukum,

Ushongo,Vandeikya. Zone B comprises Buruku, Gboko, Guma, Gwer- West, Gwer and Makurdi

LGAs. Lastly Zone C comprises Agatu, Apa, Obi, Oju, Ogbadibo, Okpokwu, Otukpo LGAs.

Population and Sampling Procedures

Three Local Government Areas where rice cultivation was considerably high were selected, each

from an agricultural zone in the State. The questionnaire was distributed to few active rice

cooperatives whose major focus was solely on rice farming. The active rice cooperatives are

distributed in the three LGAs viz; 9 in Katsina Ala, 82 in Makurdi and 10 in Agatu respectively.

From these we have the following population for each LGA who are active with members

participation measured by their contributions; Katsina Ala- 423 cooperators, Makurdi- 621

cooperators and Agatu- 167 cooperators Desk officer rice cooperative societies BSMANR,

(2017). From the cooperatives actively participating in rice farming, few cooperatives that were


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accessible filled the questionnaire distributed, the following sample frame were taken: Katsina

Ala with 80 registered member rice farmers, Makurdi with 123 registered member rice farmers

and Agatu with 63 registered member rice farmers all with beneficiaries and non beneficiaries

inclusive respectively. A random sampling technique was used to select respondents for this

study. The first stage was done by the selection of these three (3) local government areas because

of the availability of more members of Savings and Credit Cooperative (SACCO) with

documented records among the three agricultural zones of the state. At the end of the

questionnaire administration, out of the 236 questionnaire administered, 208 were correctly filled

and returned. Therefore the analysis was based on 208 completed rice farmers data collected. 128

of the beneficiaries and 80 of the non beneficiaries of SACCO credit made up the 208 completed

questionnaires.

Data Collection and Analysis

Primary data was used for this study. These were collected with the aid of structured

questionnaire. Data was collected from 236 rice farmers using a structured questionnaire. Out of

the 236 questionnaire 208 were retrieved correctly completed. Information collected include: the

demographic details of beneficiaries and non-beneficiaries of SACCO credit. Double difference

estimator was used to analyze the effect of credit on the poverty level or income of beneficiaries

and non – beneficiaries of SACCO Credit. Logistic regression analysis was adopted to determine

the factors that influence the level of participation of members in saving and credit cooperatives.

Amount of contribution by members of savings and credit cooperative societies was used here as

proxy for the level of participation of members in saving and credit cooperatives. Descriptive

statistics was used to describe the level of constraint of beneficiaries of the SACCO credit in the

study area.

Double difference estimate

This was used to achieve objective (iv) that is to analyze the effect of credit on the poverty level

of beneficiaries and non – beneficiaries of SACCO Credit. Per capita annual farm income stood

as a proxy for Poverty Status.

The model is specified as:

DDE = [(1

𝑝∑ (�̅�𝑡𝑖𝑎 − �̅�𝑡𝑖𝑏)𝑝

𝑖 ) − (1

𝑐∑ (�̅�𝑜𝑗𝑎 − �̅�𝑜𝑗𝑏)𝑐

𝑗 )] ………………..( viii)

Where:

�̅�𝑓𝑖𝑎 − �̅�𝑓𝑖𝑏 = difference of per capita annual farm income for beneficiaries after and before

obtaining credit respectively


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�̅�𝑜𝑗𝑎 − �̅�𝑜𝑗𝑏 = difference of per capita annual farm income for non-beneficiaries after and before

obtaining credit respectively

P= number of beneficiaries

C= number of non- beneficiaries

DDE = the difference between the mean changes in per capita annual farm income for

beneficiaries and non- beneficiaries.

Logistic Regression Model

This helped to achieve objective (v) that is to determine the factors that influence the level of

participation of members in saving and credit cooperatives.

Amount of contribution by members of savings and credit cooperative societies was used here as

proxy for the level of participation of members in saving and credit cooperatives.

The regression model specification is:

Y = 𝛽0 +𝛽1X1 +𝛽2X2 +𝛽3X3 +𝛽4X4 +𝛽5X5 +𝛽6X6 +𝛽7X7 ++u…….(xi)

Where

Yi = Amount of contribution by members (Naira)

X1 = Age (years)

X2 = sex (Male=1, Female=0)

X3 = Occupation (farming= 1 non- farming=0).

X4 = Household Size (Number of persons)

X5 = Education (years of schooling)

X6 = Total Farm Income (Naira)

X7 = Total Non -Farm Income (Naira)

𝛽i = the coefficients for the respective variables

Dependent and Independent Variables


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Variable specification

1. Amount of contribution(Y): this served as proxy for the level of participation of

members in savings and credit cooperative societies as the dependent variable. This is the

amount of money contributed by members of savings and credit cooperative societies on

monthly basis.

2. Age(X1): This is the age of the household head in years. An inverse relationship is

expected between the age and the level of participation of members in savings and credit

cooperative societies. It is expected that as farmers grow older they reduce farming

activities due to the energy involve in carrying out farm activities.

3. Sex (X2): The sex of the respondents is included in the model. A direct relationship is

expected between sex and the level of participation of members in savings and credit

cooperative societies.

4. Secondary occupation(X3): this is the economic activities engaged by farmers apart

from farming noted as non farming activities. These include trading, artisans, fishing and

civil servant. This variable is expected to have a positive relationship between income

and participation of members in the cooperative.

5. Household size (X6):thisis the total numbers in the household of the farmer (wives,

children, and other dependents) living in a household at the time of investigation. A direct

relationship is expected between the household size and the level of participation of

members in savings and credit cooperatives.

6. Educational level (X7): This is the number of years that the household head had spent in

formal school, which will be stated as primary education, secondary education and

tertiary education. A direct relationship is expected between the savings and credit

cooperatives.

7. Income (X6 and X8): This is the income (both farm and non-farm) according to the

respondents over a period. A direct relationship is expected between the income (both

farm and non- farm) and the level of participation of members in credit cooperatives.

8. Interest rate (X8): this is the amount of interest charged on the credit, measured in naira.

An inverse relationship is expected between the interest rate charged on credit and the

level of participation of members in savings and credit cooperatives. It is removed since

it is zero for the non beneficiaries.


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Test of difference

Independent sample t-test was used in testing the two hypotheses for this study. That is use of

credit has no significant effect on crop output and there is no significant difference between per

capita annual farm income of beneficiaries and non-beneficiaries of SACCO Credit. The per

capita annual farm income is to be used as proxy for poverty status. It is intended to be used for

before-and-after observations on the same subjects.

The model is specified as follows:

t = �̅�1−�̅�2

√𝑆12

𝑛+

𝑆22

𝑛

…………………………………………………………. (x)

�̅�1 = mean crop output of beneficiaries of SACCO Credit

�̅�2 = mean crop output of non- beneficiaries of SACCO Credit

S12 = variance crop output of beneficiaries of SACCO Credit

S22 = variance crop output of non- beneficiaries of SACCO Credit

n = number of selected members of beneficiaries of SACCO Credit

n = number of selected members of non- beneficiaries of SACCO Credit.

�̅�1 = mean per capita annual farm income of beneficiaries of SACCO Credit

�̅�2 = mean per capita annual farm income of non- beneficiaries of SACCOS Credit

S12 = variance of per capita annual farm income of beneficiaries of SACCO Credit

S22 = variance of per capita annual farm income of non- beneficiaries of SACCO Credit

n = number of selected members of beneficiaries of SACCO Credit

n = number of selected members of non- beneficiaries of SACCO Credit.

RESULT AND DISCUSSION

Effect of credit on the annual farm income of the respondents

Table 1.0 presented the Double Difference estimates of the effect of credit on poverty status of

the respondents in the study area. The average farm income was used as proxy for the poverty

status of the beneficiaries and non beneficiaries of credit. The difference in annual farm income


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of beneficiaries before and after obtaining credit was estimated as ₦29281.74 and ₦51667.31

respectively. The first single difference of the per capita annual farm income after and before

obtaining credit is ₦22385.57 for the beneficiaries of SACCO credit. The non beneficiaries had

₦38991.39 and ₦56657.10 as the per capita annual farm income difference for before and after

credit. The value of the second single difference of the per capita annual farm income for after

and before credit is ₦17665.71 for the non beneficiaries. The difference between the two per

capita annual farm incomes which is the single differences of the beneficiaries and the non

beneficiaries is (₦22385.57-₦17665.71) is ₦4719.86. As stated in the model specification; a

positive double difference estimate of per capita annual farm income value for beneficiaries and

non beneficiaries before and after obtaining SACCO credit indicates a positive effect of credit on

the poverty status of the beneficiaries in the study area. The positive value of the double

difference indicates an increase in the per capita annual farm income of the beneficiaries of the

SACCO credit. The implication is that credit had positive effect on the per capita annual farm

income of beneficiaries of credit Nkonya et al., (2008).

Table 1: Double Difference Estimates of the Effect of SACOO credit on

poverty status of beneficiaries and non beneficiaries

Group per capita annual farm income

Before (₦) After (₦) Difference

Beneficiaries 29281.74 51667.31 22385.57

Non Beneficiaries 38991.39 56657.10 17665.71

Group difference 9709.65 4989.79 4719.86

Source: Field survey 2017

The test of hypothesis carried on the per capita annual farm income of beneficiaries and non

beneficiaries before and after obtaining credit showed that the value of the difference between

the per capita annual farm income of beneficiaries has a positive value 4719.86 therefore

indicates that beneficiaries of credit had more positive increase in per capita annual farm income

than the non beneficiaries of credit. The F-value is 14.413 is positive and significant at one

percent (1%) indicating that all the management variables put together had effect on the per

capita annual farm income of beneficiaries of credit. The use of credit therefore, makes a

difference in the level of per capita annual farm income of beneficiaries as compared to non

beneficiaries. Since there is a significant difference in the per capita annual farm income of

beneficiaries compared to non beneficiaries, the null hypothesis which states that there is no

significant difference between the per capita annual farm income of beneficiaries and non

beneficiaries of SACCO credit was rejected.


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Factors influencing Participation in Savings and Credit Cooperative

Farm credit has been, over the years, recognized as one of the major input for reviving the

agricultural sector in Nigeria (CBN, 2005). This is obvious because it increases the level of

productivity, farm profit, and efficiency, this enhances standard of living in the rural areas (Abu,

Odoemenem & Ocholi, 2011). Therefore, farm credit is one of the crucial inputs considered

fundamental in agricultural production (Omonona et al., 2010). Majorly, it has been perceived

that it is the need for credit and easy access to this credit that motivates participation in the

SACCO by farmers. The factors that influence participation of farmers in the SACCO are

determined using the amount of contribution by members as proxy. This is because the

cooperative believes that all committed and registered active members must be involve in the

monthly contribution otherwise seen as non members. The factors influencing participation is

presented in table 2.0.

The model was statistically significant at 5 percent level of significant, a positive R square value

of 0.513. This implies that a joint effect of most of the variables influences the participation of

members in SACCO. The nine (9) variables were included in the regression model had positive

coefficients. The variables sex, education and household size were found to be statistically

significant at 5 percent and 1 percent level of significance respectively.

The positive significant coefficient on sex indicates that male sex has higher probability of

participating in SACCO programme than being female. As included in the model, the sex from

the a priori expectation which was stated to have a direct relationship with the level of

participation is true with a positive value and significant at 5 percent.

The household size had a positive significant coefficient indicating that higher number of

persons in the household increases the probability of participation in SACCO than lower number

of persons in the household. The pursuit for assistance to take care of the members of the

household makes the farmer seek and take credit from the cooperative as a source of support to

farm income. The positive household size value is statistically significant at 1 percent and

conforms to the a priori expectation that a direct relationship is expected between the household

size and the level of participation of members in savings and credit cooperative. It indicates that

the household size had an influence on the level of participation of members in SACCO NBS

(2007).

According to Elsie (2006); and Sivaram (2000) the level of education plays a significant role in

the participation of members in SACCO. The level of education is statistically significant at 1

percent. This agrees with the finding from the positive value of the educational status and show a

direct relationship with the level of participation of respondents in SACCO as stated in the a


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priori expectation. It shows that the level of education determines the level of awareness on the

importance of participating in cooperatives.

The household income is positive, therefore plays a significant role in farmers participation in

the SACCO. These can be seen in two different ways; from the interaction with cooperators,

majority of them belong to the cooperative for them to save in a form of contribution to the

cooperative by saving proceeds from their rising farm income which is in turn seen as their

shares. Contribution of members is a criterion for commitment to their participation. Why others

participate as a means to improve their per capita annual farm income by benefiting from the

SACCO credit. Thus the result conforms to the a priori expectation of a direct relationship

between the income and level of participation of members in credit cooperatives.

It was found that the coefficient of the non farm income had a positive value and conforms to the

a priori expectation. Farmers who engage in other occupation have other source of income; this

encourages more tendencies to save. The participation level of those with other source of income

order than farm income help them to save the rising income from farming and other secondary

source.

The coefficient of age is positive, indicating a direct relationship between age of respondents and

their participation in the credit cooperatives. This implies that as they grow older, they

participate in the cooperative more due to either the need to get support in terms of credit or to

save rising income generated from farming or other non-farm activities. But the result is contrary

to the a priori expectation which states an inverse relationship between the age of members and

participation in the credit cooperatives. It is rather in conformity to the finding by Idrisa (2007),

Arayesh and Mammi (2010), who reported that educational level, farm and non farm income had

positive relationship and significantly influences the level of participation in cooperative

societies.

Table 2: Logistics Regression Model of Factors influencing

participation of respondents in SACCO

Variables Coefficients Standard Error T-Value

Constant 0.476 1.290 1.610

Sex 0.813* 0.370 2.255

Marital Status -0.360 0.419 0.697

Education 0.138** 0.559 7.148

Household size 0.719** 0.236 5.148

Farming experience 0.059 0.117 1.061

Age 0.293 0.230 1.341

Occupation -0.099 0.461 0.905


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Household income 0.000 0.000 1.000

H. non-farm income 0.000 0.000 1.000

R²

-2log likelihood

Chi-square

0.513

256.194

20.978*

(** Significant level at 1% ⃰significant level at 5%)

Source: field survey 2017

Constraints faced by beneficiaries and non beneficiaries of savings and credit cooperative

In analyzing the constraints of this study, a likert scale was adopted to test the level of the

various limitations caused by the constraints. The result shows that 77.3 percent and 76.2 percent

of the beneficiaries and non beneficiaries are faced with poor access to credit as a major (very

serious factor) constraint to poverty reduction in the study area.

It was found from the result that 57.8 percent and 61.2 percent beneficiaries and non

beneficiaries of the SACCO credit respectively are faced with the constraint of high cost of

inputs. This indicated that high cost of input is one of the major constraints (seriously) affecting

the level of poverty reduction among the rice farmers in the study area.

In the study it was found that 60.9 percent and 78.8 percent of beneficiaries and non beneficiaries

of SACCO credit consider illiteracy level as one of the main constraints (very seriously) to

poverty reduction on rice farmers in the study area. This could be because the level of

educational exposure gained by members of the SACCO is not efficient enough to help them in

innovation adoption. Majority of the members 47 percent and 39 percent of beneficiaries and non

beneficiaries respectively from the field survey have only secondary and primary education.

The study result showed that 43 percent and 53 percent of beneficiaries and non beneficiaries of

SACCO credit see collateral as one of the major constraints (seriously) affecting the level of

poverty reduction among rice farmers in SACCO in the study area. From the survey, it was

found that farmers complain of demand for collateral from financial institutions to access credit.

In most cases, the conditions and categories of collateral demanded from farmers are beyond the

farmers’ asset or available facilities.

The result further disclosed that 52.3 percent and 68.8 percent of the beneficiaries and non

beneficiaries consider government interference from policies and interventions as one of the

constraints (not seriously) affecting the members of the SACCO respectively. This could be

because the government understands that cooperatives are meant to be voluntarily and

democratically run by the members of the organization independent of external leadership and

governance.


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About 35.2 percent and 51.2 percent of the beneficiaries and non beneficiaries of the SACCO

credit consider interest rate as one of the constraints (seriously) affecting the level of poverty

reduction among rice farmers in the study area respectively.

Finally the result showed that 39.1 percent and 61.2 percent of the beneficiaries and non

beneficiaries consider inadequate capital as a constraint yet (not seriously) affecting the level of

poverty eradication of the rice farmers SACCO members in the study area.

The result of the analysis therefore revealed that the constraints faced by the Savings and Credit

Cooperatives are affecting the cooperative in different levels. From the severe to less severe

problems; these can be seen in table 3.0 with poor access to credit (with mean for beneficiaries

and non beneficiaries as 3.711 and 3.750), illiteracy level (3.492 and 3.713), and high cost of

inputs (3.195 and 3.388), collateral(it came as the fourth most server for non beneficiaries and

fifth most severe for beneficiaries of SACCO credit), interest rate (this is the fourth most severe

problem for the beneficiaries and fifth for the non beneficiaries), government interference (2.441

and 1.937 it is also seen as the least of the problems faced by rice farmers cooperators) and

inadequate capital (2.680 and 2.363) respectively as the major constraints faced by the SACCO

members in the study area. This is listed in the order from most severity to less severe problems.

Poor access to credit is ranked as the most server problem faced by both the beneficiaries and

non beneficiaries of credit. This is one of the major functions of the Savings and Credit

Cooperative, making credit available for members to access via collaboration with other

financial outfits or donor agencies.

Table 3: Constraints faced by members of Savings and Credit Cooperative

Constraints

Beneficiaries

Freq % Mean Rank

Non Beneficiaries

Freq % Mean Rank

Poor access to

credit

99 77.3 3.711 1st 61 76.2 3.750 1st

High cost of inputs 74 57.8 3.195 3rd 49 61.2 3.388 3rd

Illiteracy level 78 60.9 3.492 2nd 63 78.8 3.713 2nd

Collateral 55 43.0 2.867 5th 43 53.8 3.012 4th

Government interf 67 52.8 2.441 7th 55 68.8 1.937 7th

High interest rate 45 35.2 2.977 4th 41 51.2 2.650 5th

Inadequate capital 50 39.1 2.680 6th 49 61.2 2.363 6th

Ranking is according to the severity of the constraints 1st to 7th (4= Very seriously, 3= seriously,

2= not seriously and 1= not very seriously)

Source: field survey 2017


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CONCLUSION

The analysis revealed that Savings and Credit Cooperatives have helped the member improve

their livelihood. It has positively changed members’ poverty status by improving per capita

annual farm income of beneficiaries of the SACCO credit. Nevertheless, the beneficiaries and

non beneficiaries had some factors that limited their efforts towards poverty reduction. These

constraints they stated to include poor access to credit, illiteracy level, high cost of inputs and

high interest rate charged on credit. This automatically indicated that the members can achieve

success in fight against poverty but for these constraints. There is room to the reduction of

poverty via the improvement of the per capita annual farm income of the SACCO members in

the study area.

RECOMMENDATION

i. It was revealed that credit had positive effect on out comes by increasing per capita

annual farm income of Savings and Credit Cooperative member who are beneficiaries

of the credit. SACCO executives and the Government should develop strategies that

will bring in more funding; loans and grants to the cooperative consequently enhance

availability of credit to members.

ii. The major constraint identified by members of the Savings and Credit Cooperative

was poor access to credit. Effort should be made to create awareness to the members

of SACCO and other rice farmers in the study area of the availability of formal

agricultural credits such as the current anchor borrowers program, Fadama III project

etc. for rice production.

iii. Farmers need more education by extension services since illiteracy level among rice

famers cooperators is seen as one of the major constraints to high crop output and

increase in farm income. With extension services, even the farmers who have no

formal education will improve their method of farming by adoption of modern

technological innovations.

iv. Farm income and non farm income had positive coefficient and are therefore

significant influence to participation in Savings and Credit Cooperative. SACCO

members therefore need to indulge in some non -farm activities such as trading,

artisan, fishing and even civil service to diversify their income. This will improve

farmers’ per capita annual farm income; hence help in poverty reduction in the study

area.


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