A STUDY ON PRODUCTIVE EFFICIENCY OF SUGARCANE IN BANGLADESH: STATUS AND POTENTIALITY Dissertation Submitted in accordance with the requirements of the Bangladesh Agricultural University, Mymensingh For the Degree of DOCTOR OF PHILOSOPHY BY SAYEEDA KHATUN Roll No. 4 Session 2003-04 Registration no. 12204 (1983-84) Department of Agricultural Economics Bangladesh Agricultural University Mymensingh June 2011
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A STUDY ON PRODUCTIVE EFFICIENCY OF SUGARCANE
IN BANGLADESH: STATUS AND POTENTIALITY
Dissertation
Submitted in accordance with the requirements of the Bangladesh Agricultural University, Mymensingh
For the Degree of
DOCTOR OF PHILOSOPHY
BY
SAYEEDA KHATUN
Roll No. 4 Session 2003-04
Registration no. 12204 (1983-84)
Department of Agricultural Economics Bangladesh Agricultural University
Mymensingh
June 2011
A STUDY ON PRODUCTIVE EFFICIENCY OF SUGARCANE IN BANGLADESH: STATUS AND POTENTIALITY
Dissertation
Submitted in accordance with the requirements of the
Bangladesh Agricultural University, Mymensingh
for the Degree of
DOCTOR OF PHILOSOPHY
BY
SAYEEDA KHATUN
Roll No. 4 Session -2003-04
Registration no. 12204 (1983-84)
Approved as to style and contents by:
Professor Dr. Md. Habibur Rahman Supervisor
Professor Dr. M. Harun-Ar Rashid Co- Supervisor
Professor Tofazzal Hossain Miah Chairman,
Examination Committee Department of Agricultural Economics
June, 2011
Dedicated To My
Parents and Beloved Husband
Author’s Declaration
I declared that, except where otherwise stated, this dissertation is entirely my own work and has not been submitted in any form to any other university for any degree.
..........................
Sayeeda Khatun
Date:........................
ACKNOWLEDGEMENT At the inception, I wish to acknowledge the immeasurable grace and profound kindness of the “Almighty Allah” without whose desire I could not have materialized my dream to conclude this thesis. I would like to express my sincere gratitude to my respected teacher and supervisor Dr. Md. Habibur Rahman, Professor Department of Agricultural Economics, Bangladesh Agricultural University, Mymensingh, for his intense interest in this study and also his scholastic guidance, prompt comprehensive feedback, encouragement and patience throughout the entire duration of the research undertaken for this dissertation. I deem it a proud privilege to express my deep sense of gratitude and indebtedness for the kind cooperation of my co-supervisor Dr. M. Harun-Ar Rashid, Professor, Department of Agricultural Economics, Bangladesh Agricultural University, Mymensingh, whose insightful hints, stimulating suggestions and encouragement have helped me on numerous occasions throughout the study period. I would like to extend my sincere thankfulness to my respected and honorable teachers Professor Dr. M. A. Sattar Mandal, Professor, Department of Agricultural Economics and Vice- Chancellor, Bangladesh Agricultural University, Mymensingh, Professor Md. Tofazzal Hossain Miah, Professor Dr. W M H Jaim, Professor Dr. Rezaul Karim Talukder, Dr. Shamsul Alam, for their teaching and encouragement throughout my years as a student. My heartfelt thanks and gratitude are due to Dr.Taj Uddin, Professor and Head Department of Agricultural Economics, Professor Dr. Akteruzzaman, Associate Professor Md. Moniruzzaman, Assistant Professor Ismail Hossain Department of Agribusiness and Marketing, Bangladesh Agricultural University, Mymensingh whose support, cooperation, advice and inspiration were instrumental towards the completion of this work. I extend my grateful thanks and gratitude to the authority of Bangladesh Sugarcane Research Institute (BSRI) and Bangladesh Agricultural Research Council (BARC) for providing me scholarship to undertake this research I wish to thank Director General of Bangladesh Sugarcane Research Institute Dr. Gopal Chandra Paul, former Director Generals Dr. A. B. M. Mafizur Rahman and Dr. M.A. Mannan, Md. Nasir Uddin Khan, Ex-station in charge RSRS, Thakurgaon, Md. Mahmudul Alam, Head Agricultural Economics Division, Dr. Ibrahim Talukdar, Head, Pathology Division, Dr. Khalilur Rahman, Head, Agronomy and Farming Systems Research Division and my other colleagues at Bangladesh Sugarcane Research Institute for their immense interest, valuable advice and moral support to pursue my study. I also express my deepest gratitude to Julfikar Ali, Junior Officer, Rajshahi substation, Anwar Hossain, Junior Officer and Md. Tarajul Islam RSRS, Thakurgaon for their cooperation and inspiration for collecting data in my study period. My cordial thanks and appreciation are due to the farmers in the study area who supplied relevant information. I express my supreme gratitude to my parents, brothers, sisters and brother in laws for providing me affection, love, encouragement and support to make me capable educated and to get out in the world. In the day of my success, I would like to extend my gratitude and deep appreciation to all of them.
Last but not the least I extend my heartfelt thanks and appreciation to my beloved husband Dr. Md. Shamsur Rahman, Senior Scientific Officer, BSRI for his moral support, advice, sacrifice and inspiration to complete this thesis. The Author
69
BIOGRAPHICAL SKETCH
The author was born in 16th February, 1966 at Ghoshpara in Thakurgaon district of Bangladesh. She was the daughter of Md. Mansur Alam Mojibur Rahman and Mrs. Magfera Khatun and 3rd position among six brothers and sisters.
She completed her secondary school education from Thakurgaon Govt. Girls’ High School under Thakurgaon district and passed Secondary School Certificate (SSC) in 1981. She passed Higher Secondary Certificate (HSC) examination in 1983 from Thakurgaon Govt. College under Thakurgaon district. The author obtained both of her Bachelor of Science (Honors) in Agricultural Economics and Master of Science in Agricultural Economics degrees from Bangladesh Agricultural University (BAU), Mymensingh in 1987 and 1999 respectively.
The author started her professional career on 23rd December 1989as Scientific Officer (Agricultural Economics) at Farming System Research and Development Project, Bangladesh Sugarcane Research Institute (BSRI). After that, in 17th January, 1993 she joined as Scientific Officer (Agricultural Economics) in the main setup of Bangladesh Sugarcane Research Institute (BSRI). She was promoted as Senior Scientific Officer (SSO) on 5th August, 2007 and posted in Agricultural Economics Division, Bangladesh Sugarcane Research Institute, Ishurdi. Presently, she is working as Head of Agricultural Economics Division, BSRI. She has published 25 scientific articles in different national and international journals. She attended and successfully completed some in country professional training courses.
The author was awarded ARMP (Agricultural Research Management Project) scholarship for in country M.S. study in 1998 and successfully completed the degree. Moreover,she was also awarded a scholarship in 2004 funded by Bangladesh Agricultural Research Council (BARC) to pursue Ph. D. in Agricultural Economics at Bangladesh Agricultural University, Mymensingh. As part of Ph.D. study she carried out a piece of research work entitled “A study on productive efficiency of sugarcane in Bangladesh: Present status and potentiality”.
The author is happily married to Dr. Md. Shamsur Rahman, Senior Scientific Officer, Pathology Division, Bangladesh Sugarcane Research Institute.
A STUDY ON PRODUCTIVE EFFICIENCY OF SUGARCANE IN BANGLADESH: STATUS AND POTENTIALITY
S. Khatun
ABSTRACT Sugarcane is an important cash cum industrial crop in Bangladesh. It is the main raw material of
sugar and gur. Meeting the increasing demand for sugar and gur, a parallel sugar/gur production is required. This can be achieved by increasing the utilization of inputs and effectively organizing the management of production. The present study was undertaken in the selected areas viz., Rajshahi, Thakurgaon and Panchagar as well as Bangladesh to analyze the possibilities for improving productivity and reducing yield gap of sugarcane by increasing the farmers’ productive efficiency. The study employed farm level cross sectional data collected from 300 sample farmers during the period 2007/08. For growth rate and area response log linear and Nerlovian Partial Adjustment model were used. Time series data for the period 1975/76 to 2007/08 were also employed in this study. It was observed that the average yield was found to be 58.53 t/ha with the highest average at Rajshahi (62.30 t /ha) followed by Panchagar (57.80 t/ha) and Thakurgaon (55.80 t/ha). Among the farm categories, the large farmers produced the highest yield (59.83 t/ha) followed by medium (59.09 t/ha) and small (56.67 t/ha) farmers. The estimated stochastic frontier production function model showed that the human labour, animal labour, seed, urea, furadan 5 G 5 G and irrigation cost had a positive and significant impact on sugarcane production. The mean technical efficiency of sugarcane growers was 0.76 suggesting the existence of technical inefficiency by 24 percent. So, there was an ample scope to increase the farmers’ income and sugarcane yield by adopting the technologies by the farmers. Likewise, the mean allocative efficiency of 0.82 implied that there was an average level of 18 percent allocative inefficiency. The estimated cost frontier showed positively significant value of human labour and Muruate of Potash (MP) price positive and significant, which implied that increase of human labour and MP price resulted in the increase of sugarcane production cost. The average economic efficiency was found to be 0.62. Thus farmers’ economic efficiency could be enhanced by 38 percent through the improvement of both technical and allocative efficiency. The coefficients of sugarcane farming experience, plot visit by the field workers and training on sugarcane production were negatively significant in the inefficiency model implying that inefficiency decreases with the increases in farmers’ experience, visit by the field workers and training on sugarcane production. In the comparison of sugarcane and major crops the highest and positive growth rate of area and production were obtained by potato followed by wheat, lentil and rice. The growth rate of sugarcane area was positive but not significant although the production was stagnant. The growth rate of real price of all crops was negative but non significant, while that of sugarcane was negative and significant. This indicated that the sugarcane farmers received decreased real price. The instability of potato real price was the highest followed by that of lentil and wheat. Within the three sugar mills the sugarcane area instability of Thakurgaon was the highest followed by Rajshahi and Panchagar. For the country as a whole the lag one year area, price, relative price and relative price risk played a dominant role in determining current year allocation under sugarcane. The farmers did not take into consideration the relative yield risk and irrigated area for making a decision about the allocation of sugarcane area. Though irrigation had a positive impact on sugarcane production but the lag one year irrigated area had the negative impact on the current year sugarcane land allocation. When irrigated area increased the farmers were interested to allocate their land for cereals and other short growing high value crops. The yield gap-I was estimated at 23.28 t/ha which was the difference between experimental yield and potential farm yield and again the yield gap –II was 25.69 t/ha which was the difference between potential farm yield and actual farm level yield. The respondent reports a large number of technical and socio economic constraints which are the causes of yield gaps. The study suggests the existence of some gaps in sugarcane yield, which may be reduced through increasing efficiency. Government policy can be taken in order to increasing efficiency by farmers’ training, increased extension activities, subsidized input supply, price support and price declaration before planting season.
CONTENTS
71
Title Page
Declaration iv
Acknowledgement v
Biographical Sketch vii
Abstracts viii
Contents ix
List of Tables xiii
List of Figures xv
List of Appendix Tables xvii
Glossary xviii
Chapter 1 : INTRODUCTION 1-30
1.1 Agriculture in the Economy of Bangladesh 1
1.2 Sugarcane in Bangladesh 2
1. 3 Statement of the Study 7
1. 4 Objectives of the Study 10
1.5 Review of Literature of the Study 10
1.5.1 Productive Efficiency 10
1.5.2 Estimation of Growth Rate Related to Area, Production and Yields
18
1.5.3 Acreage Response/Supply Response 24
1.6 Organization of the Dissertation 29
Chapter 2 : METHODOLOGY
31-64
2.1 Theoretical and Conceptual Frameworks 31
2.1.1 Technical, Allocative and Economic Efficiency 32
2.1.2 Non-frontier Approaches 34
2.1.3 Frontier Approaches 35
2.1.3.1 Data Envelopment Analysis (DEA) 35
2.1.3.2 Stochastic Frontiers 36
2.1.4 Non- Parametric Frontier 37
2.1.5 Parametric Frontier 37
2.1.5.1 The Deterministic Frontier Approach 38
2.1.5.2 Stochastic Frontier
41
2.2 Sampling and Data Collection 46
2.2.1. Selection of The Samples and Sampling Techniques 46
72
2.2.2. Period of Data Collection 47
2.2.3. Data Collection Procedure and Collected Data 47
2.3 Measurement of Farmers’ Profitability 48
2.3.1. Analytical Technique of Profit Estimation 48
2.3.1.1. Estimation of Gross Returns 48
2.3.1.2. Estimation of Total Cost 49
2.3.1.3. Estimation of Profits 49
2.4 Determination of Productive Efficiency 50
2. 4.1 Analytical Techniques for Productive Efficiency 50
2.4.1 .1 Stochastic Frontier Production Function 50
2.4.1.2. Stochastic Frontier Cost Function 53
2.4.1.3 Technical Inefficiency Model 54
2.5 Growth Rates and Instability Analysis 55
2.5.1. Selection of the Study Area 56
2.5.2. Data Collection Procedure and Collected Data 56
2.5.3. Analytical Techniques of Growth Rate 56
2.6 Supply Response Analysis 58
2. 6.1. Analytical Techniques of Supply Response Analysis 59
2.7 Yield Gap Analysis 62
Chapter 3 : RESULTS AND DISCUSSION
3.1 COST, RETURN AND PROFITABILITY OF SUGARCANE PRODUCTION
63-79
3.1.1 Introduction 63 3.1.2 Variable Cost of Production 63 3.1.2.1 Human Labour Cost 63 3.1.2.2 Animal Labour Cost 66 3.1.2.3 Seed Cost 66 3.1.2.4 Organic Manure Cost 69 3.1.2.5 Fertilizer and Insecticide Cost 69 3.1.2.6 Irrigation Cost 70 3.1.2.7 Carrying Cost 70
3.1.3 Fixed Cost of Sugarcane Production 70
3.13.1. Land Use Cost 70
3.1.3.2 Interest on Operating Capital 71
3.1.4 Total Cost of Production 71
73
3.1.5 Yield and Gross Returns of Sugarcane Production 74
3.1.6 Net Returns of Sugarcane Production 75
3.1.7 Benefit Cost Ratio (BCR) 75
3.1.8 Summary of the Findings 78
3.2
EFFICIENCY AND DETERMINANTS OF EFFICIENCY IN SUGARCANE PRODUCTION
80-106
3.2.1 Introduction 80
3.2.2 Maximum Likelihood Estimates of Farm-specific Stochastic Frontier Production Function and Inefficiency Model
81
3.2.3 Maximum Likelihood Estimates of Location-specific Stochastic Frontier Production Function and Inefficiency Model
84
3.2.4 Maximum Likelihood Estimates of Farm-size Specific Stochastic Frontier Production Function and Inefficiency Model
87
3.2.5 Technical Efficiency and Its Distribution 91
3.2.6 Maximum Likelihood Estimates of Farm-specific Stochastic Frontier Cost Function and Economic Inefficiency Model
94
3.2.7 Maximum Likelihood Estimates of Location-specific Stochastic Frontier Cost Function and Economic Inefficiency Model
96
3.2.8 Maximum Likelihood Estimates of Farm-size Specific Stochastic Frontier Cost Function and Economic Inefficiency Model
99
3.2.9 Economic Efficiency and Its Distribution 101
3.2.1 Allocative Efficiency and Its Distribution 104 3.3
YIELD GAP AND CONSTRAINTS IN SUGARCANE PRODUCTION 107-117
3.3.1 Introduction 107
3.3.2 Yield Gap 107
3.3.3 Yield Gap due to Technical Inefficiency 111
3.3.4 Yield Constraints 111
3.3.5 3.3.4.1 Technical Constraints 112
3.3.4.2 Socio- Economic Constraints 113
3.3.5 Summary of the Findings 116
3.4 GROWTH AND INSTABILITY ANALYSIS OF AREA, PRODUCTION AND YIELD OF SUGARCANE
118-140
3.4.1 Introduction 118
3.4.2 Growth Rate Analysis 119
3.4.2.1 Compound Growth Rate in Area, Production, Yield and Price of Sugarcane and Other Major Agricultural Crops
127
3.4.2.2 Growth Rate in Sugarcane Area Among Different Locations 128
3.4.2.3 Growth Rate in Sugarcane Production Among Different 129
74
Locations
3.4.2.4 Growth Rate in Sugarcane Yield Among Different Locations
131
3.4.3 Instability of Sugarcane Area, Production Yields 132
3.4.3.1 Instability of Area, Production, Yield And Prices of Sugarcane And Other Crops
133
3.43.2 Instability of Sugarcane Area in Different Locations 134
3.4.3.3 Instability of Sugarcane Production in Different Locations 137
3.4.3.4 Instability of Sugarcane Yields in Different Locations 137
3.5.4 Summary of the Findings 138
3.5
SUPPLY RESPONSE ANALYSIS OF SUGARCANE PRODUCTION 141-147
3.5.1 Introduction 141
3.5.2 Supply Response Models of Sugarcane in Bangladesh 142
3.5.3 Short and Long –Run Elasticity and Coefficient of Adjustment 145
3.5.4 The test of Multicollinearity and Autocorrelation among the Explanatory Variables
147
3.55 Summary of the Findings 147
Chapter 4 : SUMMARY CONCLUSION AND POLICY IMPLICATION 139-148
4.1 Summary and Findings 139
4.2 Conclusions and Recommendation 145
4.3 Limitation of the Study 148
REFERENCES 149
List of Tables
Table Title Page
1.1 Agricultural sector and sub-sector share of GDP of Bangladesh at constant prices (Base: 1995-96).
2
1.2 Demand and supply of sugar and gur in Bangladesh 5
2.1 No. of sample in different locations and farm sizes 47
3.1.1 Per hectare production cost of sugarcane 64
3.1.2 Per hectare production cost of sugarcane at Rajshahi zone 65
3.1.3 Per hectare production cost of sugarcane at Thakurgaon zone 67
75
3.1.4 Per hectare production cost of sugarcane at Panchagar zone 68
3.1.5 Sugarcane production cost by different farm size groups (Tk./ha) 73
3.1.6 Per hectare cost, gross return and net return at different locations 73
3.1.7 Per hectare cost, gross return and net return at different locations 76
3.1.8 Per hectare cost, gross return and net return of different farm categories 76
3.2.1 Maximum likelihood estimates of the stochastic Cobb-Douglas frontier production and technical inefficiency model for sugarcane
82
3.2.2 Maximum likelihood estimates for parameters of location-specific Cobb-Douglas stochastic frontier production and technical inefficiency model for sugarcane
85
3.2.3 Maximum likelihood estimates for parameters of farm size-specific Cobb-Douglas stochastic frontier production and technical inefficiency model for sugarcane
89
3.2.4 Farm specific technical efficiency of sugarcane production 92
3.2.5 Frequency distribution of technical efficiency of sugarcane production 93
3.2.6 Maximum likelihood estimates for parameters of Cobb-Douglas stochastic normalized cost frontier and economic inefficiency model for sugarcane
95
3.2.7 Maximum likelihood estimates for parameters of l0cation-specific stochastic normalized cost frontier and economic inefficiency model for sugarcane
98
3.2.8 Maximum likelihood estimates for parameters of farm size-specific Cobb-Douglas stochastic normalized cost frontier and economic inefficiency effect model
100
3.2.9 Farm specific economic efficiency of sugarcane production 102
3.2.10 Frequency distribution of economic efficiency of sugarcane farmers 103
3.2.11 Farm specific allocative efficiency of sugarcane production 105
3.2.12 Frequency distribution of allocative efficiency of sugarcane farmers 106
3.3.1 Sugarcane yield realized and the estimated yield gap under different field situations
109
3.3.2 Estimated indices of yield gaps in sugarcane under different field situations 110
3.3.3 Yield gap of sugarcane due to technical inefficiency 111
3.3.4 Constraints and problems of sugarcane production as mentioned by the farmers 115
3.4.1 Compound growth rate of area, production, yield and price of major crops during the period of 1975/76 to 2007/08(in percent)
120
3.4.2 Compound growth rate of area, production and of sugarcane in three districts, sugar mill zone and overall Bangladesh for the period of 1975/76 to 2007/08.
123
3.4.3 Instabilities of area, production, yield and real prices of sugarcane and other crops
126
3.4.4 Instability index of area, production and yield in Bangladesh, mill zones and some selected districts during the period of 1975/76 to 2007/08.
127
3.4.5 Instability index of area, production and yield of sugarcane in Bangladesh in different period
128
76
3.4.6 Instability index of area, production and yield of sugarcane in Mill zone in different period
128
3.4.7 Instability index of area, production and yield of sugarcane in Panchagar in different period
128
3.4.8 Instability index of area, production and yield in Thakurgaon district in different period
130
3.4.9 Instability index of area, production and yield in Rajshahi district in different period.
130
3.5.1 Estimated parameters of Nerlovian Partial Adjustment Model of sugarcane in Bangladesh for the period from 1975/76 to 2007/08.
135
3.5.2 Estimated short-run and long-run elasticity 137
List of Figures
Figure Title Page
1.1 Different sugarcane production areas in Bangladesh 3
1.2 Utilization of sugarcane during 2007-08 4
2.1 Technical, allocative and economic efficiency 34
2.2 Technical efficiency of farms in relative input –output 40
2.3 Technical efficiency of stochastic frontier production. 45
3.1 Human labour cost for different locations for sugarcane cultivation 65
3.2 Human labour cost for different farm categories for sugarcane cultivation 65
3.3 Animal labour cost in different locations for sugarcane cultivation 67
3.4 Animal labour cost of different farm categories for sugarcane cultivation 67
3.5 Seed cost for different locations for sugarcane cultivation 68
3.6 Seed cost for different farm categories for sugarcane cultivation 68
3.7 Organic manure cost for different locations for sugarcane cultivation 72
3.8 Organic manure cost for different farm categories for sugarcane cultivation 72
3.9 Fertilizers and insecticides cost for sugarcane cultivation in different 72
77
locations
3.10 Fertilizers and insecticides cost for sugarcane cultivation for different farm categories
72
3.11 Irrigation cost for sugarcane cultivation in different locations 72
3.12 Irrigation cost for different farm categories for sugarcane cultivation 72
3.13 Percent shares of different input cost in production cost 74
3.14 Total production cost at different locations for sugarcane cultivation 74
3.15 Total production cost of different farm categories for sugarcane cultivation 74
3.16 Gross return, total cost and net returns at different locations 77
3.17 Gross return, total cost and net returns of different farm categories 77
3.18 Comparative benefit cost ratio of sugarcane cultivation at different locations 77
3.19 Comparative benefit cost ratio of sugarcane cultivation in different farm categories
78
3.20 Technical efficiency level of sugarcane producers in different locations 93
3.21 Technical efficiency level of sugarcane producers by different farm categories
93
3.22 Economic efficiency level of sugarcane producers in different locations 103
3.23 Economic efficiency level of sugarcane producers by different farm categories 103
3.24 Allocative efficiency level of sugarcane producers in different locations 106
3.25 Allocative efficiency level of sugarcane producers by different farm categories
106
3.26 Experimental station, potential farm and actual farm yield in sugarcane cultivation in Bangladesh
108
3.27 Growth rate of sugarcane area in different locations of Bangladesh 124
3.28 Growth rate of sugarcane production in different locations of Bangladesh 124
3.29 Growth rate of sugarcane yield in different locations of Bangladesh 124
3.30 Growth rate of sugarcane area, production and yield of sugarcane in Bangladesh
124
78
List of Appendix Tables
Table Title
Page
1 Area, production, yield and price of sugarcane in different years (1975/76 to 2007/08)
157
2 Sugar production, recovery and sugar price in different years (1975/76 to 2007/08)
158
3 Area, production and yield of sugarcane in Rajshahi Sugar mills in different years (1975/76 to 2007/08)
159
4 Area, production and yield of sugarcane in Thakurgaon Sugar mills in different years (1975/76 to 2007/08)
160
5 Area, production and yield of sugarcane in Panchagar Sugar mills in different years (1975/76 to 2007/08)
161
6 Area, production, yield and price of rice in different years (1975/76 to 2007/08)
162
7 Area, production, yield and price of wheat in different years (1975/76 to 2007/08)
163
8 Area, production, yield and price of potato in different years (1975/76 to 2007/08)
164
9 Area, production, yield and price of lentil in different years (1975/76 to 2007/08)
165
10 Per hectare cost and returns of sugarcane and sugarcane with intercrops 166
11 Cane yield, intercrop yield and adjusted cane yield under pared row system of planting
166
12 Zero order correlation matrix among the explanatory variables in Supply Response Model.
166
79
GLOSSARY
AE Allocative Efficiency
BARI Bangladesh Agricultural Research Institute
BBS Bangladesh Bureau of Statistics
BCR Benefit Cost Ratio
BSFIC Bangladesh Sugar and Food Industries Corporation
BSRI Bangladesh Sugarcane Research Institute
C-D Cobb-Douglas
CV Coefficient of Variation
DAE Department of Agricultural Extension
DAM Department of Agricultural Marketing
DEA Data Envelopment Analysis
EE Economic Efficiency
ha Hectare
HYV High Yielding Variety
I Instability Index
IRRI International Rice Research Institute
Kg Kilogram
Ln Natural Logarithm
LR Likelihood Ratio
MLE Maximum Likelihood Ratio
MP Muriate of Potash
OLS Ordinary Least Squares
80
R2 Coefficient of Determination
SD Standard Deviation
t Tonne
TE Technical Efficiency
Tk Taka (Bangladeshi Currency, 1 dollar = 70 Taka)
TSP Triple Super Phosphate
USDA United States Department of Agriculture
Chapter 1
INTRODUCTION
1.1 Agriculture in the Economy of Bangladesh
Bangladesh is a developing country in the world with high density of population
and unfavorable land-man ratio. Most of the people depend on agriculture. Agriculture being
a crucial sector of the economy, it is indispensable to develop this sector for attaining
economic growth and poverty alleviation. Since provision of food security, improving the
living standard and generation of employment opportunities of the huge population of the
country are directly linked to the development of agriculture, there has been continuous effort
by the government for the overall development of this sector. Agriculture plays a vital role in
the economy. It employs around 62 percent of the labour force of which 57 percent is in the
crop sector (Karim, 2005). This sector not only employs most of the national labour force but
also supplies food for human and animal consumption, raw materials for industrial
production and some value added commodities for export. In 2008-09, it contributed around
20.60 percent of the Gross Domestic Product (GDP). Among them, only crop sector
contributed around 11.55 percent (Table 1.1). The total cropped area is 12,141.70 thousand
hectares with 180 percent average cropping intensity (BBS, 2008).
Rice is the main food crop of Bangladesh which occupies 75 percent of total
cropped area and the remaining 25 percent is devoted to other crops which include wheat,
jute, sugarcane, oilseeds, pulses, vegetables, spices and condiments etc. In fact, the entire
growth in crop production can be explained by the growth in food grain production,
81
particularly rice. However, production of other crops such as sugarcane, vegetables, pulses,
oilseeds and fruits are rather disappointing. Currently, Bangladesh has been producing only
around 7.37 million tons of sugarcane (BSFIC, 2009), 0.28 million tons of pulses, 0.58
million tons of edible oilseeds (BBS, 2008) which are far less than the requirements of total
consumption in the country. It indicates unadjusted food plan which causes not only
imbalanced food supply but also malnutrition problem. In addition, the country is compelled
to import sugars, oils, pulses, etc. from abroad. Therefore, crop diversification is essential in
order to achieve the goal of overall nutritional self-sufficiency, balanced food supply,
production of industrial raw materials and so on. Furthermore, it is also needed to encourage
the production of cash crop. .
Table 1.1 Agricultural sector and sub-sector share of GDP of Bangladesh at constant prices (Base: 1995-96).
z3i = Education level of the ith operator (year of schooling)
z4i = Experience in sugarcane farming of the ith operator (years)
z5i = Household size of the ith operator (persons/household)
z6i = Dummy for extension linkage of the ith operator (1 = yes, 0 = otherwise)
z7i = Dummy for sugarcane training of the ith operator (1 = yes, 0 = otherwise)
Wi = Unobservable random variables or classical disturbance term, which were
assumed to be independently distributed, obtained by truncation of the normal distribution
with mean zero and unknown variance σ2, such that ui , is non negative.
2. 5. Growth Rates and Instability Analysis
In this section, the methodology of growth rates and instability of sugarcane with
respect to area, production and yield, factors influencing yield growth rate was discussed.
Time series data on sugarcane production, area, yield, prices of sugarcane were collected
from secondary sources like Bangladesh Bureau of Statistics (BBS) in different years,
Bangladesh Sugarcane Research Institute (BSRI), Bangladesh Sugar and Food Industry
Corporation (BSFIC), Department of Marketing (DAM), Metrological Department and other
related agencies in Bangladesh. The detailed methodologies for estimation of growth rates
were as follows:
2.5.1. Selection of the Study Area
Three districts in the Northwest of Bangladesh namely, Rajshahi, Thakurgaon and
Panchagar districts and whole Bangladesh were selected for growth rate study of sugarcane.
136
2.5.2. Data Collection Procedure and Collected Data
Time series data on sugarcane area, production, yield, price, temperature, rainfall, and
data of competing crop in the respective year were collected from secondary sources like
Bangladesh Bureau of Statistics (BBS), Bangladesh Sugarcane Research Institute (BSRI),
Bangladesh Sugar and Food Industries Corporation (BSFIC), Department of Agricultural
Extension (DAE), Department of Agricultural Marketing (DAM), Metrological Department
and other related agencies in Bangladesh. Secondary data for growth study covered the period
from 1975/76 to 2007/08 and data was collected manually from BBS census and yearbooks
of 2004 to 2008 and annual report of BFIC (2004 to 2009).
2.5.3. Analytical Techniques of Growth Rate
Any increase or decrease in the production of a crop depends basically on the changes
in area under the crop and its average yield. Measuring agricultural growth had been one of
the most extensively performed research areas. Although there were alternative methods by
which the growth rates can be calculated for a specified data series, in this study the growth
rates of area, production and yield of sugarcane was estimated by log linear function. A
simple growth model represented by the following exponential function:
btaeY = ...................... (2.44)
Where, ‘a’ and ‘b’ are parameters to be estimated, and ‘e’ is the natural exponential.
For simplicity, the error term was excluded. Because equation (2.44) is nonlinear in the
parameters, it is necessary to linearize this equation in order to apply the classical regression
model. This may be accomplished by taking the log of both sides (David, 1982);
bta lnY += ........................ (2.45)
Where,
Y = Dependent variable (production, area and yield of sugarcane, rice, wheat, potato and lentil).
137
t = Time (1975/76 to 1984/85, 1985/86 to 1994/95, 1995/96 to 2007/08 and 1975/76 to 2007/08) referred as the first, second, third and the overall period.
a = Intercept
b = Trend growth rate parameter for the period, to be estimated.
ln = natural log of the variables.
In the exponential equation b is the growth rate in ratio scale and when multiplied by
100 it represents annual percentage growth i.e. annual growth rate.
To check the significance of results t-test will also be employed. To test growth rate
following equation was followed:
Se(b)
bt = with (n-2) df. ................... (2.46)
Where, b is the growth rate, n is the number of observation and Se(b) is the standard error of
the growth.
The equation (2.44) is generally used on the consideration that the change in
agricultural output in a given year would depend upon the output in the preceding year
(Dandekar, 1980; Minhas and Vaidyanathan, 1965). It had limitation in that it assumes a
uniform rate of growth over the entire period under consideration, which may not be true in
reality. To study changes in the rate of growth, the time period is often divided into two or
more sub- periods based on some external information or arbitrarily fixed criteria (Reddy et.
al., 1998). During the whole study period, sugarcane area and production was spitted into
four sub- period e.g., period I= 1975/76 to 1984/85, period II =1985/86 to 1994/95, period III
= 1995/96 to 2007/08 and the overall period IV =1975/76 to 2007/08.
The simplest measure of instability consists of measuring percentage changes in
production (yield, acreage) from one year to another, summing these and dividing by number
of observation. Alternatively, the sum of the deviations of each observation from the mean of
the observations could be used to compute the ‘coefficient of variation’ (CV). However, both
these indices tend to overstress instability at a time of rapid growth being unable to
138
distinguish between growth and instability. This problem was overcome by computing the
C.V. from the deviations from an estimated trend line, rather than from the mean.
100XSEC.V. ×= ........................ (2.47)
Where, SE is the standered error of the estimate and X refers to the mean of observation.
Test of stability of the growth parameter:
Instability:
Instability was measured by using instability index as follows:
Land use cost 12000.00(14.19) 12000(15.05) 12000.00(15.48) Interest in operating capital
3455.67(4.09) 3224.77(4.05) 3121.07(4.02)
C. Total cost 84569.35(100) 79720.11(100) 77542.47(100) Sources: Compiled from Table no. 3.1.2, 3.1.3 and 3.1.4
cliv
Table 3.1.6 Per hectare sugarcane production cost by different farm size groups (Tk)
Particulars Large Medium Small All Human Labour 26195.90(33.07) 26470.23(33.54) 30133.82(36.20) 27599.98(34.30) Animal labour 4803.89(6.06) 4290.20(5.44) 4258.54(5.12) 4407.63(5.48) Seed 7715.66(9.74) 7277.09(9.22) 6631.10(7.97) 7272.06(9.04) Organic manure 3201.61(4.04) 2007.16(2.54) 1770.45(2.13) 2326.41(2.89) Fertilizers/Insecticides: 13925.40(17.58) 13793.04(17.48) 12905.24(15.50) 13541.23(16.83)
Figure 3.16 Gross return, total cost and net returns at different locations
99576
79222
20354
99113
78916
20198
96787
83247
13539
0
20000
40000
60000
80000
100000
Cost
and
retu
rns
(Tk/
ha)
Large Medium Small
Farm Categories
Gross return Total cost Net returns
Figure 3.17 Gross return, total cost and net returns at different farm categories
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1.24
1.22
1.20
1.18
1.19
1.20
1.21
1.22
1.23
1.24
BCR
(%)
Rajshahi Panchagar Thakurgaon
Locations
Figure 3.18 Comparative benefit cost ratio at different locations
1.26 1.25
1.16
1.10
1.12
1.14
1.16
1.18
1.20
1.22
1.24
1.26
BC
R (%
)
Large Medium Small
Farm Categories
Figure 3.19 Comparative benefit cost ratio at different farm categories
3.1.8 Summery of the Findings
Sugarcane is an important cash cum industrial crop in Bangladesh. In this section, the
profitability of sugarcane production has been considered. Average yield of sugarcane was 58.53
tonne per hectare. On an average, per hectare production cost and net return of sugarcane was Tk
80,461.69 and Tk 18,030.38 respectively.
Considering the locations, the farmers of Rajshashi incurred the highest production cost
and achieved the highest amount of yield. The highest benefit cost ratio (1.24) of sugarcane
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production achieved by the farmers of Rajshahi. According to farm categories the highest
production cost incurred by small farmers (Tk 83,247.7/ha) followed by large (Tk 79,222.02/ha)
and medium farmers (Tk 78,95.88/ha). The small farmers used themselves as agricultural labour
in production but they could not use required amount of other inputs for their financial condition.
Thus the productivity was declined. On the other hand, the large farmers used hired labour
according to requirements and used higher input costs. As a result, the large farmers got the
highest yield (59.83 t/ha) and thereby the highest net return (Tk 20,354.20/ha) followed by
medium and small farmers.
Sugarcane is a long duration crop and requires high cost to grow. Every farmer wants to
get more return from their investment within short period. So, especially for the poor farmers it is
difficult to wait for such a long time. Moreover, due to the increasing demand of cereal and
vegetables crops, now the cultivation of sugarcane is decreased gradually. Therefore, the
sustainability of long duration sugarcane is now at threat. However, to combat the declining of
sugarcane area, intercropping is one of the ways for increasing productivity and economic
profitability from per unit area. Systematic, intercropping in sugarcane with various short
duration crops like potato, cabbage, onion, lentil, mungbean etc. has been proven profitable in
comparison to growing sugarcane as sole crop (Yadva and Verma, 1984, Alam et al., 2007;
Kabir et al. 2004(Appendix-B). It is reported that in Mauritius, about 70 - 77 percent cane areas
are intercropped mainly with potato and tomato (Imam, 1990). However, in our country the
adoption rate of intercropping with sugarcane is still low and it ranges 20-30 percent so far
(Anon. 2009). Therefore, there is an ample scope to increase productivity by introducing
intercrop systematically.
To increase productivity it is essential to estimate productive efficiency. Efficiency is an
important issue of productivity growth in the agriculture based economy of developing countries.
Therefore, after estimating profitability it is essential to determine the productive efficiency of
sugarcane production.
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3.2 EFFICIENCY AND DETERMINANTS OF EFFICIENCY IN SUGARCANE PRODUCTION
3.2.1. Introduction
The concept of efficiency is at the core of economic theory. The theory of production
economics is concerned with optimization and optimization implies efficiency. Efficiency is an
important issue of productivity growth in the agriculture based economy of developing countries.
The crucial role of efficiency in increasing agricultural output has been widely recognized by
researchers and policy makers alike. The estimation of efficiency with the help of production
function has been a popular area of applied econometrics. Recent works in duality theory, which
has linked production and cost function, has made this topic more attractive. However, definition
of a technical efficiency reflects the ability of a farm to obtain the maximum possible output
from a given level of inputs and production technology. It is a relative concept, since each farm’s
production performance is compared to a best- practice input-output relationship or production
frontier. A farm is technically inefficient in the sense that if it fails to produce maximum output
from a given level of input. Technical inefficiency is then measured as the deviation of an
individual farm from the best-practice frontier. Economic efficiency reflects the minimum cost
of producing a given level of output at the same set of input prices. Estimates on the extent of
efficiency may help improve productivity through input reallocation or cost minimization. The
main objective of this chapter is to estimate the technical, allocative and economic efficiencies as
well as frequency distribution of different level of sugarcane farmers.
The technical efficiency in production was estimated by using the stochastic frontier
production. The primary advantage of a stochastic frontier production function is that it enables
one to estimate Ui (non-negative random variable which is under the control of the farm) and
therefore also to estimate farm specific technical efficiencies. The measure of technical
efficiency is equivalent to the ratio of the production of the ith farm to the corresponding
production value if the farm effect Ui were zero.
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3.2.2 Maximum Likelihood Estimates of Farm-specific Stochastic Frontier Production Function and Inefficiency Model
The maximum likelihood estimates (MLE) methods of the parameters of the stochastic
production frontier were obtained using the programme, computer software, FRONTIER 4.1
(Coelli, 1996). Asides from estimates of coefficients in the model, the output also provides other
variance parameters such as sigma squared (σ2), gama (γ) and log likelihood function. To
estimate the farm specific technical efficiency for sugarcane production in the study area, yield
as dependent variable and other independent variables were standardized on the basis of per
hectare of land. The maximum likelihood estimates for the parameters of the Cobb-Douglas
stochastic frontier production function for the sugarcane farmers are presented Table 3.2.1.
In this study to estimate farm specific technical efficiency for sugarcane production, the
stochastic frontier production function was used. The empirical results indicated that the
coefficients of human labour, animal labour, seed, urea, Furadan 5G and irrigation cost were
positive and significant, while that of organic manure, TSP and MP were positive but not
significant.
At 1% level of significance, the coefficients of human labour, seed, urea, Furadan 5G and
irrigation costs were positive. In other words the elasticity of human labour, seed, urea, Furadan
5G and irrigation cost were 0.264, 0.139, 0.168, 0.023 and 0.013 respectively. It implied that
human labour, seed, urea, Furadan 5G and irrigation cost had significant and positive impact on
sugarcane production. The yield of sugarcane would increase by 0.264, 0.139, 0.168, 0.023 and
0.013 percent if the farmers apply 1 percent additional human labour, seed, urea, Furadan 5G and
irrigation cost respectively. Moreover, the coefficient of dummy variables D2 (Rajshahi =1 and
others =0) was found positive and significant at 1 % level. This implied that sugarcane
production is higher in Rajshahi than in other locations. This is because the farmers of Rajshahi
location are more efficient and advanced in sugarcane production than farmers of other locations
and got highest BCR (Table 3.1.6). On the other hand the dummy variable D1 of Thakurgaon
location was negative and significant. This implied that the farmers of Thakurgaon were not
clxiii
efficient than farmers of other locations. At 5% level of significance the coefficients of animal
labour was positive and significant. It
Table 3.2.1 Maximum likelihood estimates of the stochastic Cobb-Douglas frontier production and technical inefficiency model for sugarcane
[ [[ Independent variables Parame-
ters Coefficients Standard error t-ratio
Constant β0 6.472 0.469 11.932
Ln Human labour β1 0.264* 0.028 9.507
Ln Animal labour β2 0.075** 0.031 2.380
Ln Seed β3 0.139* 0.0317 4.371
Ln Organic manure β4 0.003 0.002 1.427
Ln Urea β5 0.168* 0.065 2.576
Ln TSP β6 0.036 0.032 1.133
Ln MP β7 0.0531 0.030 1.794
Ln Furadan 5G β8 0.023* 0.007 3.109
Ln Irrgation β9 0.013* 0.002 5.499
Dummy for location (Thakurgaon=1, others=0)D1
β10 -0.059** 0.024 -2.460
Dummy for location (Rajshahi=1, others=0) D2
β11 0.081* 0.024 3.358
Technical inefficiency model:
Constant δ0 0.329 0.267 1.232
Experience δ1 -0.0013** 0.0008 -1.606
Age δ2 -0.0006 0.0007 -0.837
Education (year) δ3 -0.0008 0.0017 -0.487
Visit by field worker δ4 -0.030* 0.0071 -4.233
Farm size δ5 -0.0256* 0.0100 -2.549
Dummy for sugarcane training (1=Yes, 0= otherwise)
δ6 -0.026** 0.013 -1.925
Variance parameters :
Sigma- squared σ2 0.097* 0.008 12.12
Gamma γ 0.233 0.649 0.359
Log likelihood function 269.85 * and ** indicate significant at 1% and 5% level of probability Source: Field survey (2007-08)
clxiv
indicates that the elasticity of animal labour was 0.075, which was playing a significant positive
role on sugarcane production. It further implied that holding other things remaining same, the
yield of sugarcane would increase by 0.075 percent as farmers would apply 1 percent additional
animal labour.
The coefficients of the explanatory variable in the model for the inefficiency effects,
defined by equation 2.43, were of particular interest to this study. The coefficient for the
experience was negative and significant, which implied that the inefficiency effects decrease
with the increase of the experiences of farm operators of sugarcane. In other words, technical
efficiency increased with the increase of experiences of the farmers. The farmers, who had
greater experience about sugarcane production, were technically more efficient than less
experienced ones in managing and allocating productive resources. This result conforms to result
of similar studies (Sumi et al. 2004). The coefficients for the age variable was 0.006 and
negative too, but it is non- significant so it can not be said with emphasis that older farmers are
technically less inefficient than the younger farmers. It can be said, however, that with the
increase of age of farmers the efficiency of farmers tends to increases. This result is in line with
those of Rao et al. (2003), Hussain (1999) and Coelli (1996). According to them the negative
coefficients suggest that as the age, education increases, the inefficiency decreases. The
coefficient for the education variable was negative, which indicates that the farmers with greater
years of formal schooling tend to be more technically efficient. This indicates that the farmers
with more education respond more readily in using the new
technology and produce output closer to the frontier. This result is similar to that of Seyoum et
al. 1998; Dey, et al. 2000; Pagan, 2001. The coefficient for the visit by the field worker was 0.03
and negative at one percent level of significance. This implied that the regular visit by field
workers to the farmers’ sugarcane plot tends to decrease inefficiency or increase efficiency. This
is similar to that of Rahman et al. (2000), Chaudhry (2001), and Islam (2003). The estimated
coefficient of the farm size variable is 0 .026 and negative at five percent level of significance,
which indicated that the increase of sugarcane farm size tends to decrease inefficiency or
increase efficiency. This is similar to that of Rahman et al. (2000), Ahmed et al. (2002). Training
improves skills and the result supports that professional training reduces inefficiency. The
clxv
coefficient of sugarcane training (Dt) was negative and significant at 5% level. It indicated that
the training on sugarcane production reduced the production inefficiency and increased the
technical efficiencies of sugarcane production. If a farmer gets more training programmes, his
level of inefficiencies would decrease. The estimated values of the variance parameters (σ and γ )
were large and significantly different from zero, which indicated a good fit and correctness of the
specified distributional assumption.
3.2.3 Maximum Likelihood Estimates of Location-specific Stochastic Frontier Production Function and Inefficiency Model
Estimation of location specific technical efficiency for sugarcane is essential for drawing
appropriate policy for the location. If we find out the significance of efficiency differences
between locations then we will be able to identify the factors which are responsible for those
differences. The maximum likelihood estimates of the coefficients of location specific stochastic
Cobb- Douglas production frontier and technical inefficiency model is shown in Table 3.2.2.
The empirical result indicated that at Rajshahi, the coefficients of human labour, organic
manure, urea, MP, Furadan 5G and irrigation costs were positive and significant, while that of
animal labour, seed, TSP cost was positive but not significant. It indicated that human labour,
organic manure, urea, MP, Furadan 5G and irrigation costs had significant and positive impacts
on sugarcane production at Rajshahi.
At 1% level of significance human labour had the largest positive coefficient compared to
other inputs. In other words, the elasticity of human labour (0.42) was the biggest amount than
all other inputs at Rajshahi. Holding other things constant, the yield of sugarcane would increase
by 0.415% as farmers used 1% additional human labour in different types of management
practices like, weeding, mulching, properly fertilizer application, insect and pest control
(mechanical control by hand), tying, etc. At 1% level of significance, the coefficients of organic
manure (0.012), MP (0.102), Furadan 5G (0.022) and irrigation cost (0.011) were positive
implying that holding other things constant, the yield of sugarcane at Rajshahi would increase by
0.0120, 0.102, 0.022 and 0.01 percent as farmers increase 1% additional organic manure, MP,
clxvi
Furadan 5G and irrigation cost respectively. The coefficient of urea (0.167) was significant at 5%
level, implying that holding other things
Table 3.2.2 Maximum likelihood estimates for parameters of location-specific Cobb-Douglas stochastic frontier production and technical inefficiency model for sugarcane
Log likelihood function 114.199 89.86 104.00 Source: Field survey (2007-08) * and ** indicate significant at 1% and 5% level of probability. Figures in the parentheses indicate standard error
clxvii
constant, the yield of sugarcane would increase by 0.17 percent as farmers would apply 1%
additional urea. In technical inefficiency effect the coefficient for the experience (0.007), age
(0.013), education (0.020) and farm size (0.035) had negative effect but not significant. This
indicated that the farmers, who had greater experience about sugarcane production, older,
educated and large farmers were technically more efficient than the less experienced, younger,
less educated and small farmers but not significant. The coefficient of the field visit by the field
worker was negative (0.049) and significant at 1% level which indicated that the field visit could
increase the technical efficiency. The farmers who are in touch with the extension workers in
order to seek advice are more efficient in sugarcane production. The coefficient of dummy
training was negative (0.04) and significant at 1% level significant, which indicated that the
training on sugarcane production increased the technical efficiency of the sugarcane farmers.
At Thakurgaon, the coefficients of human labour, seed urea, MP and irrigation cost were
positive and significant, while that of animal labour, organic manure and TSP were positive but
not significant. It indicated that human labour, seed urea, MP and irrigation costs had positive
and significant impact on sugarcane production at Thakurgaon. On the other hand the coefficient
of Furadan 5G was negative but non significant. At 1% level of significance the coefficients of
human labour (0.22), seed (0.17), urea (0.14) and MP (0.15) were positive implying that holding
other things constant, the yield of sugarcane at Thakurgaon would increase by 0.22, 0.17, 0.14
and 0.15 percent as farmers increased 1% additional human labour, seed urea and MP. The
coefficient of sugarcane irrigation cost at Thakurgaon was (0.027) positive and significant at 5%
level, which indicated that other things remaining the same; the yield of sugarcane would
increase by 0.02 percent as farmers would increase 1% irrigation cost in sugarcane plot. In the
technical inefficiency effect the coefficient of dummy Dt was (0.06) negative and significant at
1% level, which indicated that the trained farmers could increase technical efficiency.
At Panchagar in sugarcane production, the coefficients of human labour, seed, organic
manure and irrigation cost were positive and significant, while that of animal labour, urea, TSP,
MP and Furadan 5G were positive but not significant. It indicated that human labour, seed,
organic manure and irrigation cost had positive and significant impacts on sugarcane production
at Panchagar. At 1% level of significance human labour had the largest positive coefficient
clxviii
compared to other inputs. In other words, the elasticity of human labour (0.29) was the biggest
amount than all other inputs at Panchagar. Holding other things constant, the yield of sugarcane
would increase by 0.29% as farmers used 1% additional human labour in different types of
management practices like, weeding, mulching, properly fertilizer application, insect and pest
control (mechanical control by hand), tying, etc. At 1% level of significance the coefficients of
seed (0.15), organic manure (0.08) and irrigation cost (0.03) were positive indicating that holding
other things constant, the yield of sugarcane would increase by 0.1548, 0.08 and 0.03 percent as
farmers would apply 1% additional seed, organic manure and irrigation cost respectively (Table
3.2.2).
In technical inefficiency model the coefficients of experience of Rajshahi and Panchagar
was negative and significant, at Thakurgaon it was negative also which implied that the
experience of sugarcane farmers tends decrease the sugarcane production inefficiencies or
increase efficiencies. The coefficients of education and farm size at Rajshahi was negative and
Panchagar it was negative and significant which indicates that an increase of education and farm
size decreases the inefficiencies of sugarcane production and increases the efficiencies. The
coefficients of field visit and dummy for sugarcane training of three locations was negative and
significant which indicated that the regular visit by field workers to the farmers’ sugarcane plot
and sugarcane training tends to decrease inefficiency or increase efficiency. When a farmer got
training on sugarcane production, then his knowledge increased and he can follow the
appropriate measure on sugarcane production and ultimate his technical efficiency increased. At
the same way, the field worker visited the farmers plot and advised them properly, as a result
technical efficiency of that farmer increased. This is similar to that of Rahman et al. (2000),
Chaudhry (2001), and Islam (2003). The variance parameters sigma-squared was positive and
significant at 1% level of significance which indicated a good fit and correctness of the specified
distributional assumption.
3.2.4 Maximum Likelihood Estimates of Farm-size Specific Stochastic Frontier Production Function and Inefficiency Model
The maximum likelihood estimates o f the coefficients of farm-size specific stochastic
Cobb-Douglas production frontier and technical inefficiency model are presented in Table 3.2.3
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The empirical result indicated that in case of large farmers the coefficients of human labour,
animal labour, seed, organic manure, MP and irrigation cost were positive and significant
impacts on sugarcane production for the large farmers. On the other hand, the coefficient of
Furadan 5G was negative but not significant At 1% level of significance, the coefficients of
human labour (0.12) was positive and significance for large sugarcane farmers. It indicated that
other things remaining constant, the yield of large farmers would increase by 0.12 percent as
farmers used 1% additional human labour. At 1% level of significance, the coefficients of animal
labour (0.19), seed (0.08), organic manure (0.03), MP (0.17) and irrigation cost (0.04) were
positive (Table 3.2.3) implying that holding other things constant, the yield of sugarcane would
increase by 0.19, 0.08, 0.03, 0.179 and 0.045 percent as large farmers would apply 1 percent
In medium farm categories, the coefficients of human labour, animal labour, seed,
organic manure and irrigation cost were positive and significant, while that of urea, TSP, MP and
Furadan 5G were positive but not significant. It indicated that human labour, animal labour, seed,
organic manure and irrigation cost had significant and positive impacts on sugarcane production
for medium farmers. At 1% level of significance, human labour had the largest positive
coefficient compared to other inputs. In other words, the elasticity of human labour (0.24) was
the biggest among all inputs, implying that human labour had positively the greatest impacts on
sugarcane production for medium farmers. Holding other things constant, the yield of sugarcane
would increase by 0.24 percent as farmers would apply 1% additional human labour. At 1% level
of significance, the coefficients of animal labour (0.073), seed (0.14), organic manure (0.006)
and irrigation cost (0.01) were positive implying that holding other things constant, the yield of
sugarcane would increase by 0.07, 0.14, 0.006 and 0.01 percent as farmers would apply 1%
additional animal labour, seed, organic manure and irrigation cost respectively.
The empirical results of the small farmers indicated that the coefficients of human labour,
animal labour, organic manure, MP, Furadan 5G and irrigation cost were positive and
significant, while that of urea and TSP were positive but not significant. It indicated that human
labour, animal labour, organic manure, MP, Furadan 5G and irrigation cost had significant and
clxx
positive impacts on sugarcane production for small farmers. At 1% level of significance human
labour had the largest and positive coefficients compared to other inputs.
Table 3.2.3 Maximum likelihood estimates for parameters of farm size-specific Cobb-Douglas stochastic frontier production and technical inefficiency model for sugarcane
Independent variables Parameters Farm Size Group
Large Medium Small
Constant β0 7.628(0.455) 6.991(0.495) 9.11(0.826)
Ln Human labour β1 0.118*(0.033) 0.235*(0.039) 0.452*(0.049)
Ln Animal labour β2 0.196*(0.055) 0.073**(0.039) 0.129*(0.049)
* and ** indicate significant at 1% and 5% level of probability. Figures in the parentheses indicate standard error
clxxi
In other words, the elasticity of human labour (0.45) for small farmers was the biggest of all
other groups. Other things being constant the yield of sugarcane would increase by 0.45% as
farmers used 1% additional human labour in different types of management practices like, seed
cutting, land preparation, planting, weeding, mulching, application of fertilizers, tying, earthling
up, harvesting and carrying. At 1% level of significance, the coefficients of animal labour (0.13),
organic manure (0.008), MP (0.10), Furadan 5G (0.01) and irrigation cost (0.007) were positive
(Table 3.2.3) implying that holding other things constant, the yield of sugarcane would increase
by 0.139, 0.008, 0.10, 0.01 and 0.007 percent for small farmers would apply 1 percent additional
human labour, animal labour, organic manure, MP, Furadan 5G and irrigation cost respectively.
In the technical inefficiency model the coefficients of experience of large, medium and
small farmers were negative and significant. The negative and significant coefficient of
experience measured in years indicated that the farmers with more experience tend to be less
inefficient or more efficient. So, farming experience (the farmers who cultivate sugarcane for
longer period can increase the technical efficiency. This result is in line with those of Ahmad
(2002), Kamruzzaman et al. (2008), Coelli (1996), Bettese et al. (1992) and Hassan et al.
(2005). At 5% level of significance the negative coefficient of age of large farmers indicated that
the older farmers are more efficient than the younger ones. The negative and significant
coefficient of education of small farmers indicated that the farmers with greater years of formal
schooling tend to decrease inefficiency or increase technical efficiency. This indicates that the
farmers with more education respond more readily in using the new technology and produce
output closer to the frontier. This result is similar to that of Seyoum et al. (1998); Dey, et al.
2000; Pagan, (2001). The positive but non- significant coefficient of education of large farmers
indicated that the educated large farmers were less efficient than the less educated large farmers.
Usually, the high educated large farmers do not operated by themselves. They are engaged on
other services and business and they depend on others for sugarcane cultivation. On the other
hands, the less educated large farmers efficiently operate their lands by themselves. The
estimated coefficient of the farm size variable was negative and significant of large and medium
farmers, which indicated that the increase of sugarcane farm size tends to decrease inefficiency
or increase efficiency. This is similar to that of Rahman et al. (2000), Ahmed et al. (2002). The
clxxii
coefficient of visit by the field workers and sugarcane training were negative and significant of
three categories of farmers. This implied that the regular visit by field workers to the farmers’
sugarcane plot tends to decrease inefficiency or increase efficiency. This is similar to that of
Rahman et al. (2000), Chaudhry (2001), and Islam (2003). Training improves skills and the
result supports that professional training reduces inefficiency. The coefficient of sugarcane
training (Dt) was negative and significant. It indicated that the training on sugarcane production
reduced the production inefficiency and increased the technical efficiencies of sugarcane
production. If a farmer gets more training programmes, his level of inefficiencies would
decrease. The estimated values of variance parameters were large and significantly different
from zero which indicated a good fit and correctness of the specified distributional assumption.
The significant value of γ also indicated that there were significant technical inefficiency effects
in the production of sugarcane.
3.2.5 Technical Efficiency and its Distribution
The estimated location specific and farm size specific technical efficiencies are presented
in the Table 3.2.4. It was observed that the mean value of technical efficiency was 0.76 with a
range from 0.53 to 0.89. This implied that, on average, the sugarcane production in the study
areas were producing sugarcane to about 76 percent of the potential (stochastic) frontier
production level, given the levels of their inputs and the technology currently being used. This
also indicated that there existed an average level of technical inefficiency of 24 percent. The
technical efficiency of large, medium and small farmers was 84%, 77% and 68% respectively.
The variation in technical efficiency was observed higher with large farmers (ranged 60-89%)
than medium (ranged 60-88%) and small (ranged 53-79%) farmers. On the other hand, the mean
technical efficiency was higher at Rajshahi (80%) as compared to Thakurgaon (73%) and
Panchagar (76%). The variation of technical efficiency was higher at Thakurgaon ranged 54-
88% whereas, it was 60-89% at Panchagar and 63-88% at Rajshahi. The higher variation in
technical efficiency implied that technical efficiency was fluctuating to some extent for small
farmers as well as for the farmers at Thakurgaon in sugarcane production.
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Considering different locations, the farmers at Rajshahi were found to be more
technically efficient in sugarcane production compared to the farmers of other locations. Seventy
nine percent of the farmers at Rajshahi achieved technical efficiency level of more
Table 3.2.4 Farm specific technical efficiency of sugarcane production
Location Farm category
No. of farms
Technical efficiency
Mean Maximum Minimum Standard deviation
Rajshahi Large 10 0.88 0.88 0.82 0.02
Medium 51 0.78 0.82 0.66 0.02
Small 39 0.74 0.79 0.63 0.01
All 100 0.80 0.88 0.63 0.02
Thakurgaon Large 14 0.79 0.86 0.60 0.04
Medium 62 0.79 0.88 0.73 0.04
Small 24 0.62 0.76 0.54 0.03
All 100 0.73 0.88 0.54 0.04
Panchagar Large 34 0.85 0.89 0.65 0.02
Medium 54 0.75 0.79 0.60 0.03
Small 12 0.70 0.78 0.65 0.04
All 100 0.76 0.89 0.60 0.04
All Large 58 0.84 0.89 0.60 0.03
Medium 167 0.77 0.88 0.60 0.04
Small 75 0.68 0.79 0.53 0.04
All 300 0.76 0.89 0.53 0.04
Source: Field survey (2007-08) than 70 percent (Table 3.2.5). On the other hand, 78 percent farmers at Thakurgaon and 62
percent of the farmers at Panchagar achieved technical efficiency level more than 70 percent. On
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the contrary, more number of farmers at Panchagar (38%) achieved technical efficiency level of
less than 70% followed by Thakurgaon (22%) and Rajshahi (21%).When considering the farm
categories, it was observed that 79 percent large farmers obtained more than 70 percent technical
efficiency level. On the other hand, 80 percent of the medium and 51 percent of the small
farmers achieved more than 70 percent technical efficiency level (Figure 3.21). On the contrary,
49 percent of the small, 20 percent of the medium and 21 percent of
2122
38
59
43
36
20
35
26
0 0 00
10
20
30
40
50
60
Farm
ers
(%)
<70 71-80 81-90 91-100
Technical efficiency (%)
Rajshahi Thakurgaon Panchag
2119
49454646
34 34
5
0 0 00
5
10
15
20
25
30
35
40
45
50
Farm
ers
(%)
<70 71-80 81-90 91-100
Technical efficiency (%)
Large Medium Small
Figure 3.20 Technical efficiency level of sugarcane producers in different locations
Figure 3.21 Technical efficiency level of sugarcane producers by different farm categories
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Table 3.2.5 Frequency distribution of technical efficiency of sugarcane production
Region/ Location
Farm category
Number of farmer under different efficiency level (%) < 70 71-80 81-90 91-100 All
Rajshahi Large - - 10(100) - 10(100) Medium 1(2) 40(78) 10(20) - 51(100) Small 20(51) 19(49) - - 39(100) All 21(21) 59(59) 20(20) - 100(100) Thakurgaon Large 7(50) 7(50) - - 14(100) Medium - 31(50) 31(50) - 62(100) Small 15(62) 5(21) 4(17) 24(100) All 22(22) 43(43) 35(35) - 100(100) Panchagar Large 5(15) 19(57) 10(28) - 34(100) Medium 31(58) 7(12) 16(30) - 54(100) Small 2(17) 10(83) - - 12(100) All 38(38) 36(36) 26(26) - 100(100) All Large 12(21) 26(45) 20(34) - 58(100) Medium 32(19) 78(46) 57(34) - 167(100) Small 37(49) 34(46) 4(5) - 75(100) All 81(27) 138(46) 81(27) - 300(100) Figures in the parentheses indicate percent of total Source: Field survey (2007-08)
the large farmers achieved less than 70 percent technical efficiency level. Technical efficiency
level of different farm categories indicated that that there was no farmer above the level of 90
percent denoting that technical efficiency was somewhat satisfactory in sugarcane production
(Table 3.2.5).
3.2.6 Maximum Likelihood Estimates of Farm-specific Stochastic Frontier Cost Function and Economic Inefficiency Model
The economic efficiencies were estimated for all and different locations and different
farm – size groups with the help of derived normalized cost frontier by maximum livelihood
estimate (MLE) method using a computer software, Frontier 4.1 (Coelli, 1996). Asides from
estimates of coefficients the model, provides sigma squared (σ2 ), gama (γ) and log livelihood
function. To estimate economic efficiency indices for sugarcane production in the study areas,
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the stochastic frontier cost function with production cost as dependent variable was estimated in
which all input prices were normalized by price of seed. The maximum likelihood Estimates
(MLE) of the coefficients of stochastic Cobb- Douglas cost performances are presented in Table
3.2.6.
The empirical results indicated that the coefficients of output, human labour price, MP
price and dummy for location (D2) were positive and significant implying that an increase in the
magnitudes of these variables would result in the corresponding increase of cost of producing
sugarcane for the farmers. The coefficients of animal labour price, organic manure price,
Furadan 5G price, irrigation cost, and dummy of location (D1) were positive but not significant.
They had positive effect on production cost but not significant. At 1% level of significance the
coefficients of output (0.80) was positive implying that holding other things constant, 1 percent
increase in output will lead the production cost to increase by 0.80 percent. At 1% level of
dummy variable (D2), Rajshahi was positive and significant which implies that the production
cost of sugarcane at Rajshahi was higher than that of other locations. At 5% level of
significance, the coefficient of human labour price (0.07) and of MP price (1.32) were positive,
which implies that holdings other things constant the production cost would increase by 0.07
and 1.32 percent as 1% additional increase in human labour cost and MP cost. On the other hand
the coefficients of urea price and TSP price were negative but not significant
Table 3.2.6 Maximum likelihood estimates for parameters of Cobb-Douglas stochastic normalized cost frontier and economic inefficiency model for sugarcane
Independent variables Parameters Coefficients Standard
error t-ratio
Constant β0 -0.773 0.463 -1.667
Ln Output (Tk/ha) β1 0.801* 0.033 23.637
Ln Human labour (Tk /man days) β2 0.068** 0.038 1.796
Ln Animal labour (Tk/pair-days) β3 0.004 0.028 0.144
Ln Organic manure (Tk/kg) β4 0.051 0.043 1.175
Ln Urea (Tk/kg) β5 -0.041 0.039 -1.047
Ln TSP (Tk/kg) β6 -0.454 0.609 -0.745
Ln MP (Tk/kg) β7 1.323** 0.600 2.204
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Ln Furadan 5G (Tk/kg) β8 0.003 0.003 0.832
Ln Irrgation (Tk/ha) β9 0.0003 0.002 0.156
Dummy for location (Thakurgaon=1, others=0) D1
β10 0.029 0.017 1.67
Dummy for location (Rajshahi=1, others=0) D2
β11 0.101* 0.014 7.015
Technical inefficiency model:
Constant δ0 0.017 0.090 0.141
Experience (years) δ1 -0.002** 0.001 -1.204
Age (years) δ2 0.0008 0.0016 0.489
Education (year of schooling) δ3 -0.007** 0.003 -2.330
Visit by field worker (no.) δ4 -0.026* 0.009 -2.88
Farm size (ha) δ5 -0.018 0.024 -0.754
Dummy for sugarcane training (1=Yes, 0= otherwise) Dt
δ6 -0.089* 0.030 -2.96
Variance parameters :
Sigma- squared σ2 0.005* 0.0005 10.392
Gamma γ 0.016 0.005 0.170
Log likelihood function 364.09
* and ** indicate significant at 1% and 5% level of probability. Dependent variable = Production cost (Tk/ha)
In inefficiency model, the coefficient of experience was negative and significant at 5%
level. This indicated that the farmers, who had greater experience about sugarcane production,
are economically more efficient than the less experienced. Better performance among the
experienced farmers is attributable to significantly lower labour use for per unit of sugarcane
production, lower wage rate, lower input price, followed the agronomical practices (plantation,
weeding, fertilizer application, insects and diseases control, irrigation, tying, etc) in proper way
and proper time than the less experienced farmers. The coefficient of farmers’ education was
negative and significant. This indicates that the farmers with more education become more
efficient. The coefficient for the visit by the field worker was negative and significant. This
implied that the regular visit by field workers to the farmers’ sugarcane plot tends to decrease the
economic inefficiency or increase efficiency. Dummy for sugarcane training were negative and
significant which indicated that the sugarcane training decrease the economic inefficiency. The
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estimated value of the variance parameter σ2 was large and significantly different from zero,
which indicated a good fit and correctness of the specified distributional assumption.
3.2.7 Maximum Likelihood Estimates of Location-specific Stochastic Frontier Cost Function and Economic Inefficiency Model
The maximum likelihood estimates of the coefficients of location specific stochastic
frontier cost function and inefficiency effect are presented in table 3.2.7. The empirical results
indicated that at Rajshahi, the coefficients of output (0.92), human labour (0.24) and irrigation
cost (0.004) were positive and significant implying that an increase in the magnitudes of these
variables would result in corresponding increase of cost of producing sugarcane. The coefficient
of organic manure cost was negative and significant 1% level which meant that an increase in the
magnitudes of organic manure cost would result in the corresponding decrease of cost of
producing sugarcane. The coefficients of urea, MP, Furadan 5G price were also positive but not
significant which implies that urea, MP, Furadan 5G price had positive impact on production
cost but not significant. On the other hand the coefficients of animal labour and TSP price were
negative but not significant.
At Thakurgaon the coefficients of output (0.74), organic manure price (0.29), MP pricet
(0.06) and Furadan 5G price were positive and significant implying that an increase in the
magnitude of these variables would result in the corresponding increase of cost of producing
sugarcane for the farmers. The coefficient of human labour price (0.11) was positive but not
significant. On the other hand the coefficients of animal labour price (0.007), urea price (0.19),
TSP price (0.34) and irrigation costs (0.001) were negative but not significant implying that the
coefficients of animal labour price, urea price, TSP price and irrigation cost had decreased
sugarcane production cost but this was not significant.
At Panchagar, the coefficients of output, human labour price, MP price and irrigation cost
were positive and significant implying that an increase in the magnitude of these variables would
result in the corresponding increase of cost of producing sugarcane for the farmers. The
coefficients of animal labour, organic manure and Furadan 5G price were found to be positive
but not significant. On the other hand the coefficient of irrigation cost was negative and
clxxix
significant which implies that an increase in the magnitude of this variable would result in the
decrease of sugarcane production cost for the farmers. The coefficients of urea and TSP cost
were found to be negative but not significant.
In the inefficiency model the coefficients of experience of Rajshahi and Panchagar was negative
and significant. This indicates that the economic inefficiency of the sugarcane farmer of Rajshahi
and Panchagar decrease with the increase of the experiences of the farm operator. The
experienced farmers are more efficient than less experienced ones in economic efficiency by
using lower input cost. The coefficient of education was negative and significant at 5% percent
level, which implied that the sugarcane farmers of Panchagar with greater year of schooling tend
to be less economic inefficient. The coefficient of visit by the field workers of Rajshahi and
Thakurgaon was negative and significant. This implied that the regular visit by field workers to
the farmers’ sugarcane plot tends to decrease the economic inefficiency or increase efficiency.
The coefficient of sugarcane training (Dt) was negative and significant. It indicated that the
training on sugarcane production reduced the economic inefficiency and increased the technical
efficiencies of sugarcane production. If a farmer gets more training programmes, his level of
inefficiencies would decrease. The estimated values of variance parameters were large and
significantly different from zero which indicated a good fit and correctness of the specified
distributional assumption. The significant value of γ also indicated that there were significant
technical inefficiency effects in the production of sugarcane.
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Table 3.2.7 Maximum likelihood estimates for parameters of location–specific Cobb-Douglas stochastic normalized cost frontier and economic inefficiency effect model
* and ** indicate significant at 1% and 5% level of probability. Figures in the parentheses indicate standard error Dependent variable = Production cost (Tk/ha)
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3.2.8 Maximum Likelihood Estimates of Farm-size Specific Stochastic Frontier Cost Function and Economic Inefficiency Model
The empirical results from farm specific estimates showed that for the large farms, the
coefficients of human labour and MP prices were positive and significant implying that an
increase in the magnitudes of these variables would result in the corresponding increase of cost
of producing sugarcane (Table 3.2.8). The coefficients of output, organic manure price and
dummy location for Thakurgaon (D1) were found to be positive but not significant. On the other
hand, the coefficients of animal labour, urea, TSP, Furadan 5G price, irrigation costs and dummy
location for Rajshahi (D2) were negative but not significant implying that an increase in the use
of animal labour, urea, TSP, Furadan 5G and irrigation costs would result in the decrease of cost
of producing sugarcane for the large farmers but not significantly. In case of medium farmers the
coefficient of output (0.78), organic manure price (0.14), MP price (1.26), D1 and D2 were
positive and significant (Table 3.2.8) implying that an increase in the magnitude of these
variables would result in the corresponding increase of cost of producing sugarcane for the
farmers. The coefficients of human labour, animal labour, Furadan 5G cost and irrigation cost
were positive but not significant.
On the other hand, the coefficients of urea and TSP price were found to be negative but also not
significant, which implied that an increase in the magnitude of these variables would result in
the corresponding decrease of cost of producing sugarcane for the farmers but not significant. In
case of small farmers the coefficient of output and human labour price were positive and
significant which implied that an increase in the magnitude of these variables would result in the
corresponding increase of economic efficiency. On the other hand the coefficient of organic
manure price was negative and significant which implied that an increase in the magnitudes of
this variable would result in the decrease of cost of producing sugarcane for the small farmers.
In inefficiency effects model the coefficients of experience of large and small farmers
were negative and significant which indicated that the experience of the farmers decreases
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economic inefficiencies and increases economic efficiencies. The coefficient of large farmers’
education was negative and significant, which indicates that the education of large farmers
decreases economic inefficiency and increases efficiency. For medium farmers the
Table 3.2.8 Maximum likelihood estimates for parameters of farm size –specific Cobb-Douglas stochastic normalized cost frontier and economic inefficiency effect model
Log likelihood function 68.819 215.400 99.777 * and ** indicate significant at 1% and 5% level of probability. Figures in the parentheses indicate standard error Dependent variable= Production cost (Tk/ha).
farm size had negative and significant effect on economic inefficiency. This implied that the
large farms are relatively more economically efficient than the smaller ones.
The large size farmers operate large size of sugarcane area, as a result per unit production
cost decreases and ultimately increased the economic efficiency. Better performance among
larger farms is attributable to significantly lower labour use per unit of output produced on large
farms than an smaller ones (Sharma et al., 1997). The coefficients of visit by field worker of
large and small farmers were negative and significant. This implied that the regular visit by field
workers to the farmers’ sugarcane plot tends to decrease the economic inefficiency or increase
efficiency. The coefficient of sugarcane training (Dt) of large farmers was negative and
significant. It indicated that the training on sugarcane production of large farmers reduced the
economic inefficiency and increased the technical efficiencies of sugarcane production. It also
indicated that the large farmers received the training on sugarcane production and applies it
properly. The estimated values of variance parameters were large and significantly different from
zero which indicated a good fit and correctness of the specified distributional assumption. The
significant value of γ also indicated that there were significant technical inefficiency effects in
the production of sugarcane.
3.2.9 Economic Efficiency and its Distribution
Locations specific and farm size specific economic efficiency was estimated by using
cost function and normalized by seed price (Table 3.2.9). It was observed that the mean value of
economic efficiency was 0.62 with a range from 0.30 to 0.78. This implied that, on average, the
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sugarcane producers in the study area were producing sugarcane to about 62 percent of the
potential (stochastic) frontier levels, given the levels of their inputs and the technology currently
being used. This also indicated that there existed an average level of economic inefficiency of 38
percent. Considering the farm categories, the mean economic efficiencies of large, medium and
small farms were 63%, 61% and 62% respectively. The variation in economic efficiency was
observed higher with the medium (ranged from 30-78%) and small (ranged from 30-73%)
farmers than the large (ranged from 40-74%) farmers. It was found that 69 percent of the large
farmers obtained economic efficiency level of more than 60 percent in comparison to small
farmers (67) and medium (61) percent, indicating better performance was observed in the large
farmers (Table 3.2.10 and Figure 3.23). On the contrary, 6 percent of the medium farmers and 4
percent of the small and 3 percent of the large farmers achieved economic efficiency level of less
than 50 percent which indicated that large farmers are more economically efficient than small
and medium farmers. It also indicated that economic efficiency was somewhat unstable for small
and medium farmers due probably to their poorest resources base and resources constraints.
Table 3.2.9 Farm specific economic efficiency of sugarcane production
Location Farm category
No. of farms
Economic efficiency
Mean Maximum Minimum Standard deviation
Rajshahi Large 10 0.69 0.69 0.59 0.02 Medium 51 0.51 0.74 0.46 0.06 Small 39 0.60 0.73 0.30 0.08 All 100 0.63 0.74 0.30 0.06 Thakurgaon Large 14 0.58 0.70 0.40 0.08 Medium 62 0.59 0.71 0.30 0.08 Small 24 0.62 0.70 0.41 0.06 All 100 0.60 0.71 0.30 0.08 Panchagar Large 34 0.64 0.74 0.46 0.06 Medium 54 0.63 0.78 0.30 0.07 Small 12 0.63 0.71 0.54 0.05 All 100 0.63 0.78 0.30 0.07 All Large 59 0.63 0.74 0.40 0.07
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Medium 167 0.61 0.78 0.30 0.07 Small 75 0.62 0.73 0.30 0.07 All 300 0.62 0.78 0.30 0.07
Source: Field survey (2007-08)
49
2
27
38
28
5151
62
18
2
8
00 00
10
20
30
40
50
60
70
Farm
ers
(%)
<50 50-60 61-70 71-80 81-100
Economic efficiency (%)
Rajshahi Thakurgaon Panchagar
36 4
28
3329
43
56
62
26
5 50 0 0
0
10
20
30
40
50
60
70
Farm
ers
(%)
<50 50-60 61-70 71-80 81-100
Economic efficiency (%)
Large Medium Small
Figure 3.22 Economic efficiency level of
sugarcane producers in different locations
Figure 3.23 Economic efficiency level of sugarcane producers by different farm categories
Table 3.2.10 Frequency distribution of economic efficiency of sugarcane farmers Region/ Location
Farm category
Number of farmer under different efficiency level (%)
≤ 50 51-60 61-70 71-80 80-100 All
Rajshahi Large - - - 10(100) - 10(100)
Medium 2(4) 16(31) 28(55) 5(10) - 51(100)
Small 2(5) 11(29) 23(59) 3(7) - 39(100)
All 4(4) 27(27) 51(51) 18(18) - 100(100)
Thakurgaon Large 1(7) 8(58) 5(35) - - 14(100)
Medium 7(11) 23(37) 30(49) 2(3) - 62(100)
Small 1(4) 7(29) 16(67) - - 24(100)
All 9(9) 38(38) 51(51) 2(2) - 100(100)
Panchagar Large 1(3) 8(23) 20(59) 5(15) - 34(100)
Medium 1(2) 16(30) 35(64) 2(4) - 54(100)
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Small - 4(33) 7(58) 1(9)) - 12(100)
All 2(2) 28(28) 62(62) 8(8) - 100(100)
All Large 2(3) 16(28) 25(43) 15(26) - 58(100)
Medium 10(6) 55(33) 93(56) 9(5) - 167(100)
Small 3(4) 22(29) 46(62) 4(5) - 75(100)
All 15(5) 93(31) 164(55) 28(9) - 300(100)
Source: Field survey (2007-08) Considering different locations, the mean economic efficiency was highest at Panchagar and
Rajshahi (63%) compared to Thakurgaon (60%). The variation in economic efficiency was
observed higher at Panchagar (ranged 30-78%) whereas it was 30-74 percent at Rajshahi. and
30-71 percent at Thakurgaon. The farmers at Panchagar were found to be economically more
efficient in sugarcane production compared to farmers of other two locations. Seventy percent of
the farmers of Panchagar achieved economic efficiency level of more than 60 -80 percent (Figure
3.22 and Table 3.2.10). There were no farmers who achieved more than 80 percent economic
efficiency. On the contrary, more of the farmers at Thakurgaon (47%) achieved economic
efficiency level of less than 60 percent followed by Rajshahi (31%) and Panchagar (30%). Nine
percent of the farmers at Thakurgaon, 4 percent at Rajshahi and 2 percent at Panchagar had
economic efficiency below 50 percent.
3.2.10 Allocative Efficiency and its Distribution
Allocative efficiency is the ability of a farm to use the inputs in optimal proportions.
Given their respective prices and technical efficiency, it is the ability of a farm to obtain
maximum output from a given set of inputs. These two measures combined provide the measure
which is called economic efficiency and can be estimated by the expression, EE = TE ×AE or
AE = EE/TE.
The estimated and location specific and farm size specific allocative efficiencies are
presented in Table 3.2.11. It was observed that mean value of allocative efficiency was 0.82
percent with a range from 0.41 to 0.99. This implied that, on average, the sugarcane producers of
the study areas were allocating their resources to about 82 percent of the potential (stochastic)
clxxxvii
frontier levels for sugarcane production. This also indicated that there existed an average level of
allocative inefficiency of 18 percent. Considering the different locations, the highest variation
were obtained of the farmers (Table 3.2.11) at Thkurgaon (ranged from 41- 92%) followed by
Panchagar (ranged from 50- 99%) and Rajshahi (ranged from 48- 92%). The farmers at
Panchagar were found to be more allocatively efficient in sugarcane production compared to
other two locations. Fifty four percent of the farmers at Panchagar achieved allocative efficiency
level of more than 90 percent (Figure 3.24 and Table 3.2.12). On the other hand, 48 percent of
farmers at Thakurgaon and 33 percent of the farmers at Rajshahi achieved allocative efficiency
level more than 90 percent. On the contrary, more number of farmers at Rajshahi (22%) achieved
allocative efficiency level less than 80 percent followed by Thakurgaon (18%) and Panchagar
(3%). Forty three percent of the farmers at Rajshahi and Panchagar achieved within 81-90
percent allocative efficiency level followed by Thakurgaon 34 %. When consideration of
different farm categories were taken, it was observed that 87 percent of the larger farmers
obtained allocative efficiency level more than 80 percent in comparison with medium and small
farmers 86 and 81 percent respectively (Table 3.2.12 and Figure 3.25) indicating better
performance of large farmers.
Table 3.2.11 Farm specific allocative efficiency of sugarcane production
Location Farm category
No. of farms
Allocative efficiency
Mean Maximum Minimum Standard deviation
Rajshahi Large 10 0.78 0.78 0.71 0.04-
Medium 51 0.78 0.90 0.70 0.08
Small 39 0.81 0.92 0.48 0.11
All 100 0.79 0.92 0.48 0.08
Thakurgaon Large 14 0.73 0.81 0.67 0.10
Medium 62 0.75 0.81 0.41 0.11
Small 24 0.99 0.92 0.76 0.07
All 100 0.82 0.92 0.41 0.10
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Panchagar Large 34 0.75 0.83 0.71 0.06
Medium 54 0.84 0.99 0.50 0.08
Small 12 0.90 0.91 0.83 0.06
All 100 0.83 0.99 0.50 0.07
All Large 58 0.75 0.83 0.67 0.08
Medium 167 0.79 0.99 0.41 0.09
Small 75 0.91 0.92 0.48 0.09
All 300 0.82 0.99 0.41 0.09 Source: Field survey (2007-08)
1 1 05 5 1
1812
2
4334
43
35
48
54
0
10
20
30
40
50
60
Farm
ers
(%)
<60 61-70 71-80 81-90 91-100
Allocative efficiency (%)
Rajshahi Thakurgaon Panchagar
0 1 13 3 5
10 1012
30
4341
7
4340
05
1015
2025
30
3540
45
Farm
ers
(%)
<60 61-70 71-80 81-90 91-100
Allocative efficiency (%)
Large Medium Small
Figure 3.24. Allocative efficiency level of
sugarcane producers in different locations
Figure 3.25. Allocative efficiency level of sugarcane producers by different farm categories
Table 3.2.12 Frequency distribution of allocative efficiency of sugarcane farmers
Region/ Location
Farm category
Number of farmer under different efficiency level (%)
≤ 60 61-70 71-80 81-90 91-100 All Rajshahi Large - - 2(20) 3(30) 5(50) 10(100) Medium - 2(4) 10(20) 26(51) 13(25) 51(100) Small 1(3) 3(8) 6(15) 14(36) 15(38) 39(100)
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All 1(1) 5(5) 18(18) 43(43) 35(35) 100(100) Thakurgaon Large - 1(7) 3(21) 5(36) 5(36) 14(100) Medium 1(2) 3(5) 7(11) 19(31) 32(51) 62(100) Small - 1(4) 2(8) 10(42) 11(46) 24(100) All 1(1) 5(5) 12(12) 34(34) 48(48) 100(100) Panchagar Large - 1(3) 1(3) 9(26) 23(68) 34(100) Medium - - - 27(50) 27(50) 54(100) Small - - 1(8) 7(59) 4(33) 12(100) All - 1(1) 2(2) 43(43) 54(54) 100(100) All Large - 2(3) 6(10) 17(30) 33(7) 58(100) Medium 1(1) 5(3) 17(10) 72(43) 72(43) 167(100) Small 1(1) 4(5) 9(12) 31(41) 30(40) 75(100) All 2(1) 11(4) 32(11) 120(40) 135(43) 300(100) Source: Field survey (2007-08)
3.3 YIELD GAP AND CONSTRAINTS IN SUGARCANE PRODUCTION
3.3.1 Introduction
Farm level yield of sugarcane is much lower than the yield obtained in on-station
experiment and farmers’ field demonstration. This difference is called the yield gap and it
resulted due to the variation in input use and poor management at farm level. Besides these, there
are many causes of constrains for yield gap. This chapter is devoted to the presentation and
discussion of yield gap and constraints in sugarcane production in Bangladesh. The specific
objectives of estimating the magnitude of yield gaps, sources contributing to the yield gaps, the
constraints responsible for yield gaps and to suggest appropriate measures to bridge the yield
gaps in sugarcane production in Bangladesh. The concept of yield gap came from the constraints
studies carried out by the International Rice Research Institute (IRRI) which makes a
quantitative difference between experiment station yield and the actual; farm yield. In this
chapter yield level of sugarcane in different situation is identified and then yield level is
quantified in relation to technical inefficiency.
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3.3.2 Yield Gap
To estimate the yield gap in sugarcane production it is required to determine the yield
levels at different phases. According to IRRI methodology, the total yield gap (TYG) is the
difference between the potential yields (Yp – Experiment station yield) and the actual yield (Ya –
yield of sample farmers’ fields). The total yield gap (TYG) comprised Yield Gap-I (difference
between the potential yield and the potential farm yield (Yd –yield released on the demonstration
plots) and Yield Gap-II [difference between the potential farm yield (demonstration plot yield)
and the actual yield].
Experiment station Yield (Potential yield): It is the highest level of yield obtained by the
researchers in the experiment station under favorable environment and proper management
practices. BSRI (Bangladesh Sugarcane Research Institute) released varieties which presently
covers 95 percent of the total sugarcane area, experimental yield was estimated at 107.50 t/ha
(Rahman, et al. 2008,) (Figure 3.26).
Potential farm yield: Before releasing any variety to the farmers for adoption, it is sufficiently
tested under different agro-climatic conditions at research stations through trials and
demonstrations. It may not be always possible for the farmers to raise the crop productivity on
their farms to the level of research station. However, it would be realistic to aim at demonstration
plot yield (potential farm yield) level and achieved by on-trials. The potential farm yield was
considered 84.22 t/ha (BSRI, 2007-08).
Actual farm yield: Actual farm yield is the observed yield of any variety in the field. When a
variety of a crop is cultivated under farmers’ condition i.e. in farmers’ environment and
management with available technology and in the presence of constraints and stresses the yield
obtained is the actual farm yield. The observed yield of sugarcane varied with locations and farm
categories with a mean yield of 58.53 tonne /ha.
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With the advent of new technology in agriculture, significant improvement in the crop
productivity was noticed. However, proper resource mix and appropriate cultural practices
become a pre-requisite for the adoption and success of new farm technology, which are often
beyond the reach of a majority of the farmers. It could be observed from Table 3.3.1 that there
existed a wide gap in the sugarcane productivity between the research station, the potential farm
(demonstration plots) and the sample farmers’ field.
115.94
84.22
58.53
0
20
40
60
80
100
120
Yiel
d (t/
ha)
Experimental yield Potential farm yield Actual farm yield
Figure 3.26 Experimental, potential and actual yield of sugarcane in Bangladesh
Table 3.3.1 Sugarcane yield realized and the estimated yield gap under different field situations
Sl. No. Particulars Yield (t/ha) 1. Experiment station (Potential) Yield 107.50
2. Potential farm yield 84.22
3. Actual farm yield
(a) Large farms 59.83
(b) Medium farms 59.09
(c) Small farms 56.67
(d) Overall farms 58.53
4. Yield Gap- I 23.28
5. Yield Gap- II
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(a) Large farms 24.39
(b) Medium farms 25.13
(c) Small farms 27.55
(d) Overall farms 25.69
6. Total yield gap
(a) Large farms 47.67
(b) Medium farms 48.41
(c) Small farms 50. 83
(d) Overall farms 48.97
Sources: BARC 2008, BSRI 2007-08 and survey 2007-08.
The magnitude of total yield gap worked out to be 48.97 t/ha, which comprised relatively higher
size of Yield Gap- II (25.69 t/ha) than Yield Gap- I (23.28) in the overall study area. Yield Gap-
I is calculated to understand to what extent the potential yield of research station possible is
achieved at the field demonstration. Similarly, the Yield Gap- II, between demonstration and
actual yield realized by the farmers, helps to know to what extent the farmers by all categories,
on an average, could have achieved at their field conditions. Yield Gap- I implied that greater
amount of potential yield was left untapped on the demonstration plots. This was attributable to
the significant environment differences and parity to the non-transferable component of
technology like cultural practices. Hence, the technology developed at research station could not
fully replicate on the demonstration plots. Farm size-group wise analysis of the total yield gap
over the districts showed the highest (50.83 t/ha) magnitude recoded on the small farms while the
lowest (46.67 t/ha) magnitude was noticed on the large farms.
The estimated index of yield gap worked out to be 45.55 percent (Table 3.3.2). So, there
existed a tremendous scope to improve the sugarcane production in the study area. The index of
potential yield worked out to be 54.44 percent in the overall category of sample farms. It may not
always be possible for the farmers to adopt certain aspects of new technology developed in
research stations due to differences in the environmental factors and other constraints operating
at the farm level. The sample farmers realized 69.50 percent (index of realized potential farm
yield) of the farm potential in the study area (Table 3.3.2). Thus, if all the recommended
packages and production technology used on the demonstration plots are adopted, the sample
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farmers could obtain 35 percent more sugarcane output. Farm size-group analysis showed that
the large cultivators obtained relatively better yield levels than their small counterparts.
Sugarcane being more capital intensive, it requires more of costly inputs. Due to better economic
conditions, large farmers probably made timely application of fertilizers and insecticides and
realized higher level of yield.
Table 3.3.2 Estimated indices of yield gaps in sugarcane under different field situation
Sl no. Particulars (%) 1. Index of yield gap:
(a) Large farms 44.34
(b) Medium farms 45.03
(c) Small farms 47.28
(d) Overall 45.55
2. Index of realized potential yield:
(a) Large farms 55.65
(b) Medium farms 54.96
(c) Small farms 52.72
(d) Overall 54.44
3. Index of realized potential farm yield:
(a) Large farms 71.04
(b) Medium farms 70.16
(c) Small farms 67.29
(d) Overall 69.50
Sources: BSRI, 2006-07; BARC, 2008 and field survey, 2007-08. 3.3.3 Yield Gap Due to Technical Inefficiency
The yield gap that occurred due to technical inefficiency was 24 percent (Table 3.2.4)
which caused 25.69 t/ha yield gap (difference between potential farm yield and actual farm
yield) of sugarcane (Table 3.3.3). Within the three locations the highest yield gap of sugarcane
due to technical inefficiency was recorded with the farmers at Thakurgaon (27.46 t/ha) followed
by Panchagar (26.60 t/ha) and Rajshahi (22.70 t/ha) implying that the farmers of Thakurgaon had
more potentiality to increase yield than the Rajshahi farmers of with their existing technology.
Considering the farm categories the highest yield gap was noted in small farmers (27.19 t/ha)
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followed by medium (25.11 t/ha) and large farmers (24.77 t/ha). It existed because of technical
and socio economic constraints.
Table 3.3.3 Yield gap of sugarcane due to technical inefficiency
Location/farm categories
No. of farms
Mean technical inefficiency (%)
Observed yield
(tonne/ha)
Potential yield
(tonne/ha)
Yield gap (tonne/ha)
Locations: Rajshahi 100 0.20 62.30 85.00 22.70
Panchagar 100 0.24 57.80 84.40 26.60
Thakurgaon 100 0.27 55.80 83.26 27.46
Farm categories:
Large 49 0.16 59.83 84.60 24.77
Medium 176 0.23 59.09 84.20 25.11
Small 75 0.32 56.67 83.86 27.19
All farms 300 0.24 58.53 84.22 25.69 Sources: Field survey 2007-08, BSRI, Table: 3.1.6, 3.1.7, 3.2.4
3.3.4 Yield Constraints
Yield gap is a great problem in agricultural sector. There are many constraints which pervert to
attain the potential level of yield of sugarcane in Bangladesh. Many of the farmers still follow
the traditional practice and they do not follow the modern technology. According to the opinions
given by the sugarcane growers the constraints of sugarcane farmers are divided into two groups
– technical and socio- economic constraints.
3.3.4.1 Technical Constraints:
Technical constraints are related to production techniques and technologies. The farmers in the
study areas mentioned a number of technical constraint which affected sugarcane production.
The summary of the sugarcane production constraints (Table 3.3.4) are discussed below:
cxcv
Lack of clean/ certified seed: Seed/ is a main factor of any production system. Although all the
farmers were found to produce high yielding varieties of sugarcane, 98 percent of them
mentioned that they had lacking of clean seed/good quality of seed and this constraint ranked 1st
among the constraints. Farmers are usually used sugarcane seed from their own plots or from
neighbors, which were not good quality seeds for their poor germination and were not free from
diseases. A few numbers of farmers used certified seed from sugar mills, these are not sufficient.
Irrespective of locations 94, 98 and 99 percent of the sugarcane farmers from Rajshahi,
Thakurgaon and Panchagar mentioned about lack of clean seeds/good quality.
Pest and diseases: Pest and diseases is one of the important constraints of sugarcane
production. Pest and diseases can damage the whole plot of sugarcane. It is essential to control it.
In the study area 96.67 percent farmers faced pest and diseases as a problem and it ranks 2nd
among the constraints. Considering the locations the farmers of Rajshahi, Thakurgaon and
Panchagar faced 90, 100 and 100 percent of pest and diseases as problems respectively.
Irregular supply of fertilizers and insecticides: Sugarcane is a long duration crop and it needs
large amount of inputs. In the mill zone farmers get some fertilizers and insecticides from sugar
mills on loan. In the study area on average 67.67 percent farmers responded that supply of
fertilizers and insecticides from the mills were irregular and inadequate.
Non –availability of tractors: For a good production, deep plough is of immense need and the
use of tractor and power tiller is needed for this purpose. It is found that 64.67 percent of the
farmers responded that the availability of tractors is a constraint (Table 3.3.4).
Lack of irrigation facilities: Irrigation is an important factor of sugarcane production. Lack of
irrigation facilities was another constraint for sugarcane production mentioned by 69.67 percent
of the responded. These constraints arise mainly due to ownership of irrigation equipment,
excessive irrigation charge during peak periods and mechanical trouble of irrigation equipment.
This constraint was severe for the farmers at Thakurgaon (72%), Panchagar (72%) and Rajshahi
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also(65%) because they had very limited access to irrigation equipment for the irrigation during
the early stage of sugarcane production.
Long duration: Sugarcane is a long duration crop. Most of the farmers can earn their livelihood
by growing food crops on their small piece of land. But when their lands were engaged for
sugarcane cultivation, they faced many problems like, want of food and money in between the
time of planting to harvesting. About 92 percent of the farmers mentioned the above problems.
3.3.4.2 Socio-economic Constraints:
Farmers in the study areas mentioned a number of socio- economic constraints which
affected sugarcane production. In order to get a gross picture of socio-economic constraints the
responses are presented irrespective of farm size in Table 3.3.4 and discussed below:
Lack of proper knowledge: The sugarcane growing farmers in the study areas mentioned that
they lacked proper knowledge regarding modern technology of sugarcane production. The
knowledge gap prevails in every stage of sugarcane production especially for the adoption of
modern sugarcane production technology. Most of the farmers had knowledge gap about new
variety, seed treatment, time of planting, spacing, recommended fertilizer management, time of
irrigation, measurement of pest and diseases control which were essential for yield increment.
About 56 percent of the farmers mentioned that they lacked proper knowledge about sugarcane
production and it ranked 12th position among the constraints. Considering the locations 55, 65
and 48 percent sugarcane farmers of Rajshahi, Thakurgaon and Panchagar respectively
mentioned that they had lack of proper knowledge about modern sugarcane cultivation.
Lack of adequate operating capital: Sugarcane is a high cost involved crop. Capital is a
common problem of the subsistence farming in Bangladesh. Especially for the small farmers it is
very difficult to bear the investment cost of sugarcane production. On the other hand, agricultural
credit from formal sources is very limited and farmers often can not afford it for various reasons.
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It is found from Table 3.4.4 that on average 82.67 percent farmers in the study area mentioned
that adequate capital was a problem and it ranks 6th among the constraints.
High price of input: High price of inputs was a socio-economic constraint of sugarcane
production. Forty four percent of sugarcane farmers in the study area mentioned that high input
price was a problem of sugarcane production and it ranks 14th position among the constraints.
Considering the locations 52, 45 and 35 percent farmers of Panchagar, Thakurgaon and Rajshahi
respectively faced constraints of high price of inputs.
Low product price: The problem of low price of sugarcane was mentioned by 69.33 percent of
the respondents in the study areas. The more number of farmers at Thakurgaon (85%) mentioned
that the price of sugarcane was low. The farmers of Rajshahi (56%) and Panchagar (67%)
reported about low price of sugarcane. They said that the price of sugarcane was not sufficient
and it should be increased.
Labour scarcity in the peak period: Shortage of human labour is a seasonal problem and
generally occurs in the peak period of sugarcane cultivation. Shortage of human labour hampered
different intercultural management and delayed harvesting which ultimately reduced yield. On
average, about 44.67 percent of the farmers faced the problems of labour scarcity in the peak
period. A larger number of farmers at Thakurgaon (52%) faced this problems followed by those
at Rajshahi (42%) and Panchagar (40%).
Scarcity of purzi : Purzi is the supply order of sugarcane to the sugar mills. Farmers claimed
that they did not get purzi in time and in the sufficient numbers even when the sugarcane was
fully matured and was about to become dry. Sugar mills have a limited capacity to crush cane
within a period. They have no capacity to crush all sugarcane at a time. Therefore, there was a
scarcity about purzi. . On average, 91.33 percent of the farmers reported that scarcity of purzi is a
constraint and it ranked 4th position among the constraints.
Table 3.3.4 Constraints and problems of sugarcane production as mentioned by the farmers
Constraints of sugarcane production Farmers responded (%) Rank
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Rajshahi Thakurgaon Panchagar All
Technical :
Lack of certified seed 94 98 99 97.00 1
Pest and diseases 90 100 100 96.67 2
Irregular supply of fertilizers and insecticides
75 68 60 67.67 9
Non –availability of tractors 60 62 72 64.67 11
Lack of irrigation facilities 65 72 72 69.67 7
Long duration 90 98 88 92.00 3
Socio- economic:
Lack of proper knowledge 55 65 48 56.00 12
Lack of adequate operating capital
82 78 88 82.67 6
High price of input 35 45 52 44.00 14
Low product price 56 85 67 69.33 8
Labour scarcity in the peak period
42 52 40 44.67 13
Scarcity of purzi 80 98 96 91.33 4
Corruption of purzi distribution 90 85 88 87.67 5
Delay payment of Taka 56 70 72 66.00 10
Theft of sugarcane 45 30 42 39.00 15
Top cutting 40 25 28 31.00 16
Note : Same farmers mentioned more than one constraint as they faced at different time period of sugarcane production. As a result adding of all responses exceeded hundred.
Corruption of purzi distribution: The respondents claimed that the purzi was available to the
prominent and influential farmers. Fictitious cane growers collect purzi from officials and
receive the value of sugarcane with the help of cashier. About 88 percent of the farmers claimed
that there was a corruption in purzi distribution.
Delay payment of Taka: Taka payment from sugar mills is a problem. After delivery cane to
the sugar mills the farmers did not get money in time. On average 66 percent of the farmers
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reported that delay in payment of Taka after delivery of sugarcane to the sugar mills is a
constraint.
Theft of sugarcane: Fifteen percent of the farmers reported that theft of sugarcane is a social
problem.
Top cutting : Top cutting is another social problem. People cut the top cane for cattle feeder.
Thirty one percent of them reported that sugarcane top cutting is another social problem.
3.3.5. Summary of the Findings
This analysis indicates overall resource use efficiency in sugarcane production by the
sample farmers. Productive efficiency constitutes three parts- technical efficiency, economic
efficiency and allocative efficiency. Average technical efficiency was 76 percent, this implied
that, on average, the sugarcane farmers in the study areas were producing sugarcane to about 76
percent of the potential (stochastic) frontier production level, given the levels of their inputs and
the technology currently being used. This also indicated that there existed an average level of
technical inefficiency of 24 percent. The technical efficiency of large, medium and small farmers
was 84%, 77% and 68% respectively. On the other hand, the mean technical efficiency was
higher at Rajshahi (80%) as compared to Thakurgaon (73%) and Panchagar (76%). Considering
different locations, the farmers at Rajshahi were found to be more technically efficient in
sugarcane production compared to the farmers of other locations. Average economic efficiency
level was 62 percent which indicated that there existed an average level of economic inefficiency
of 28 percent. Among three locations the farmers of Rajshahi and Panchagar achieved the
highest level of economic efficiency. On the other hand the large farmers achieved the highest
level of economic efficiency. The variance parameters estimated through MLE confirm the
results of the productivity analysis in such a way that the farm specific variability in farmers’
socioeconomic factors
(e.g., age, experience, education, farm size, field visit by field worker, sugarcane training) and
farmers infrastructure attributes are the most important factors that contributed more to the
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variation in yield and/or productivity among farmers growing sugarcane. Improvement of
farmers’ knowledge through education, training, field demonstration and extension contact (field
visit by field worker) could help minimize the productivity and/or yield gap among farmers. The
yield gap observed between the frontier and actual farmers’ yield of sugarcane was 45.55
percent. This yield gap between the frontier and actual yields indicates that a massive increase in
total production. However, to increase total national production, there should be a shift in the
frontier production level which may be possible through development of new varieties. This is
possible through advanced research techniques as biogenetic engineering technique rather than
by using traditional and conventional breeding research methods.
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3.4 GROWTH AND INSTABILITY ANALYSIS OF AREA, PRODUCTION AND YIELD OF SUGARCANE
3.4.1 Introduction
Achievement of rapid agricultural growth, particularly self-sufficiency in food has been
the basic objective of development planning since independence. To achieve this goal, the
dissemination of modern technologies has been placed more emphasis as a strategy for
agricultural development. The increase yield of a crop is considered as an indicator of progress
and achievement. Increased in output may be achieved through area allocation from alternative
uses and/ or through yield increases. Analysis of yield growth pattern, effects of area fluctuation
on total production and instability of output have important policy relevance in designing
strategies for stable supply of sugarcane for smooth running of sugar mills to meet shortage of
sugar in the country.
An analysis of fluctuation in crop-output, apart from growth, is important for
understanding the nature of food security and income stability. A wide fluctuation in crop output
brings sharp fluctuation of total production and result in wide variations in disposable income of
the farmers. The magnitude of fluctuations depends on the nature of crop production technology,
its sensitivity to weather, economic environment, availability of materials, inputs and many other
factors (Kaushik, 1993, p. 337). Understanding the area fluctuations and sources of variability it
is essential to reduce the instabilities.
The average yield of sugarcane in Bangladesh is quite low (46 t/ha) compared with
China (66.3 t/ha), Thailand (73.3 t/ha) and India (65 t/ha) (USDA, 2009). Bangladesh entered
into an important phase of development in sugar industry. Time has come to evaluate the
progress made in sugar industry. The growth rate and fluctuations of area and production of
sugarcane in Bangladesh will help facilitate compilation, interpretation and forecasting on the
future development of sugarcane. Keeping this in view, in this chapter, the following are
discussed separately:
(a) Growth rate of area, production and yield of sugarcane and other comparable crops.
(b) Instability of area, production and yield of sugarcane and other crops.
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(c) Area response of sugarcane in Bangladesh.
3.4.2 Growth Rate Analysis
The progress of agriculture during recent years is quite impressive. But the growth is not
same for all crops. Production of some crops is increasing at a very faster rate whereas a few
crops are showing the decreasing trend. In this section, compound growth rate has been
estimated by fitting exponential function of semi-log type (Ln Y1 = a + bt) to the data to analyze
the overall growth rate during the study period. Growth rate in area, production and yield of
sugarcane in different districts, mill zones and overall Bangladesh were computed to have a
comparative measure to analyze the relative growth and their relationship during the period from
1975/76 to 2007/08.
In the recent years, the superior crops like HYV rice, wheat, potato had shown an
increasing trend in area of Bangladesh. Due to the introduction of modern irrigation facilities and
development of modern varieties of different crops, the farmers are expanding the area of
different cereal crops and other short growing crops and giving less emphasis on the minor crops.
Presently, the sugar sector is experiencing a great crisis. The sugar production is very low
compared to our national requirements (Table1.1). To enrich our production it is important to
explore the past performance and present status for the planners and policy makers. Therefore, it
is essential to examine the growth rate of area, production, yield and price of sugarcane and
different short growing crops like rice, wheat, potato and lentil. Moreover, it is also needed to
estimate the growth rate of area, production and yield of sugarcane over different time periods.
For this purpose, in this chapter, growth rate of sugarcane in different selected growing districts
(study area), where sugarcane has been grown intensively, mill zone and all over Bangladesh
were computed to have comparative measure to analyze the relative growth and their relationship
during the period 1975/76 to 2007/08 in three segments (I=1975/76 to1984/85, II=1985/86
to1994/95, III= 1995/96 to 2007/08 and all = 1975/76 to 2007/08). Although sugarcane is
produced all over the country, its production is concentrated in mill zones.
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3.4.2.1 Compound Growth Rate in Area, Production, Yield and Real Prices of Sugarcane and Other Major Agricultural Crops in Bangladesh
The growth rates of area, production and yield of sugarcane and some other agricultural
crops (rice, wheat, potato and lentil) for the period of 1975/76 to 2007/08 are presented in Table
3.4.1. Significant and positive compound growth rate of area of rice, wheat, potato and lentil
were 0.30, 3.10, 5.20 and 2.00 percent per annum respectively during the study period, where it
was positive but insignificant in sugarcane (0.30). The yield and production of rice and lentil
increased significantly during the entire period in spite of declining growth rate of real price. But
wheat and sugarcane witnessed negative growth rate in yield as against the area and production
growth rate 3.10, 0.30 and 2.90, 0.001 percent respectively. The negative and insignificant
growth rate of real price was found in rice, wheat, potato and lentil but in sugarcane it was
negative and significant during the study period. The growth rates of sugarcane area and
production were positive but non significant in spite of negative and significant trend of real
price. However, the increase in production owes much to the favorable prices, the introduction of
minimum support prices and the market intervention scheme.
Table 3.4.1 Compound growth rate of area, production, yield and price of major crops during the period of 1975/76 to 2007/08(in percent)
Crops Area Production Yield Real Price Rice 0.30*
(4.53) 2.80*
(22.82) 2.60*
(30.20) -1.10
(-1.50) Wheat 3.10*
(4.14) 2.90* (3.73)
-0.20 (-0.49)
-0.60 (1.40)
Sugarcane 0.30 (1.82))
0.001 (0.92)
-0.30* (-3.60)
-1.60* (-7.38))
Potato 5.20* (10.91)
5.60* (14.79)
0.04 (1.55)
-1.10 (-1.37)
Lentil 2.00* (2.89)
4.50* (6.89)
2.40* (2.77)
-0.80 (-1.08)
Sources: BBS (1976-2008), DAM, BSFIC (1976-2008) * and ** indicate significant level of at 1% and 5% error respectively; Figures in parentheses indicate t- values.
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3.4.2.2 Growth Rate in Sugarcane Area among Different Locations
In the mill zone, the highest growth rate of sugarcane area and production were 5.63 and
6.61 percent and significant during the period of 1975/76 to 1984/85 (Table 3.4.2). Among the
three districts the highest growth rate of sugarcane area was found in Thakurgaon district with a
significant rate of 5.14 percent during the period II (1985/86 to 1995/96) followed by Rajshahi
and Panchagar in the period I (1975/76 to 1984/85) with a rate of 4.83 and 3.14 percent
respectively. Negative growth rate was found in Thakurgaon (period I) 1.53, Panchagar (period
III) with a critically significant rate of 3.13 and in Rajshahi (period II and III) with a rate of 0.89
and 0.69 percent respectively (Table 3.4.2). It indicated that the area under sugarcane in these
districts has either decreased or remained the same during this period with year to year
fluctuation mainly due to the highly dependence of sugarcane on weather and competing crops.
The growth rate of sugarcane area in mill zone is higher in all period (I, II, III and all). In
mill zone during the period I, II and all were positive and significant 5.63, 1.83 and 0.66 percent
respectively but in the period III it decreased with insignificant growth rate of 1.06 percent. In
non mills zone average growth rate of area was positive and significant but in period I and II it
was negative and significant. In allover area of Bangladesh (mill zone and non mill zone) during
the period I, II, III and all the growth rate of sugarcane area are significantly 2.04, 1.39, -1.17
and 0.30. The negative growth rate of sugarcane might be due to the fact that a portion of these
crops area was replaced by alternative profitable crops especially wheat, rice, potato and
vegetables. The growers, who put their hard labour and money for sugarcane production, often
do not get adequate remuneration. So, they prefer producing other profitable crops like wheat,
HYV rice, maize and potato. Lastly, it may be concluded that irrigated and fertile lands got
diverted to wheat, rice, vegetables and other rabi crops where both technology based growth in
productivity and increase in prices made these crops more profitable.
3.4.2.3 Growth Rate in Sugarcane Production among Different Locations
In all over Bangladesh the growth rate of sugarcane area and production was positive and
significant although it was negative in yield during the period of I (1975/76 to 1984/85)and II
(1985/86 to 1994/95). During the period III (1995/96 to 2007/08) the growth rate of production
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was negative and significant. Average growth rate of sugarcane production in Bangladesh during
the period all (1975/76 to 2007/08) was positive and non significant. Bangladesh sugarcane area
is composed of mills zone and non mills zone. During the study period (1975/76 to 2007/08) in
mills zones area, due to intervention of mills authority, credit facility, subsidy, available
technology it was positive and significant in area, production and yield. In mills zone the highest
growth rate of area and production was obtained in the period I. But in non mills zone it was
negative and significant. Within the three districts the highest growth rate of sugarcane
production was obtained at Panchagar (11.72) and critically significant during the period -I
followed by Rajshahi (9.84%), Thakurgaon (9.26%) and all mills zone (6.61%) was positive and
significant during the period of I, II and I respectively. During the allover period (1975/76 to
2007/08) the growth rate of sugarcane production was 2.50 at Panchagar and 2.23 at Rajshahi
and it was highly significant. The lowest growth rate was 0.61 in period-III at Rajshahi followed
by at Thakurgaon (-0.68) during the period I (Table 3.4.2). The positive and significant growth
rate in production (average) for Panchagar and Rajshahi districts indicated that the production of
sugarcane has increased due to increase in productivity.
3.4.2.4 Growth Rate in Sugarcane Yield in Different Locations
The highest growth rate of sugarcane yield was in Panchagar district with a critically
significant rate of 8.33 percent in the period -I followed by Thakurgaon with significant rate of
7.97 percent in the period- III and at Rajshahi with a significant rate of 4.77 in period I. Lowest
growth rate was 0.84 percent in Thakurgaon during the period- I followed by Panchagar 1.27
percent and Rajshahi 1.30 percent in the period II and III respectively with a positive and non
significant. During the period all (1975/76 to 2007/08) average growth rate was 1.80, 2.38 and
2.02 in Thakurgaon, Panchagar and Rajshahi respectively with a significant. During the period
all (1975/76 to 2007/08) and period II the growth rate of sugarcane yield was 1.24 and 1.78
percent in mill zone with a significant and positive rate but it was 0.68 and 0.22 with significant
and negative rate in Bangladesh (Table 3.4.2). In overall Bangladesh the growth rate of
sugarcane yield was negative in each period.
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In sugarcane production, Bangladesh constitutes of mill zone and non mill zone area. The
growth rate of sugarcane in mill zone is higher than overall Bangladesh due to sugar mills. A
major portion of sugarcane is produced in mill zone area and sugar mill is the only one customer
of sugarcane. In this area sugarcane production and marketing is directly controlled by sugar
mill. Growth rate in this area was higher due to the availability of better irrigation facilities,
adoption of modern technologies i.e. good seeds, plant protection,
Table 3.4.2 Compound growth rate of area, production and yield of sugarcane in different locations of Bangladesh for the period of 1975/76 to 2007/08.
Zone Period Area Production Yield Rajshahi
I = (1975/76 to 1984/85) 4.83 (1.59)
9.84* (3.38)
4.77** (2.26)
II = (1985/86 to 1994/95) -0.87 (-1.01)
0.68 (0.41)
1.56 (0.82)
III = (1995/96 to 2007/08) -0.69 (-0.26)
0.61 (0.28)
1.30 (1.70)
All = (1975/76 to 2007/08) 0.21 (0.45)
2.23* (4.53)
2.02* (5.57)
Panchagar
I = (1975/76 to 1984/85) 3.14 (0.97)
11.72** (2.11)
8.33* (2.77)
II = (1985/86 to 1994/95) 0.17 (0.14)
1.45 (0.65)
1.27 (0.50)
III = (1995/96 to 2007/08) -3.13** (-1.83)
2.89 (1.45)
6.11* (6.64)
All = (1975/76 to 2007/08) 0.09 (0.21)
2.50* (3.45)
2.38* (4.91)
Thakurgaon
I = (1975/76 to 1984/85) -1.53 (-0.65)
-0.68 (-0.23)
0.84 (0.28)
II = (1985/86 to 1994/95) 5.14* (3.21)
9.26* (4.23)
3.91 (1.47)
III = (1995/96 to 2007/08) -3.11 (-1.44)
4.71** (1.81)
7.97* (3.60)
All = (1975/76 to 2007/08) -1.01** (-2.33)
0.78 (1.32)
1.80* (3.17)
All Mills zones
I = (1975/76 to 1984/85) 5.63* (3.08)
6.61** (2.96)
0.68 (0.99)
II = (1985/86 to 1994/95) 1.83** (2.47)
3.61* (3.32)
1.78* (2.84)
III = (1995/96 to 2007/08)
-1.06 (-0.92)
-0.60 (-0.52)
-0.46 (-0.63)
All = (1975/76 to 2007/08) 0.66** (2.10)
1.91* (4.72)
1.24* (6.35)
Non mills zone
I = (1975/76 to 1984/85) -9.69* (-1.11)
-17.67* (-2.09)
-20.99* (-1.08)
II = (1985/86 to 1994/95) 10.08* (0.90)
-13.15* (-1.73)
-24.84* (-5.42)
ccvii
III = (1995/96 to 2007/08) -14.52* (-1.88)
-4.50* (-1.28)
-3.69* (-5.17)
All = (1975/76 to 2007/08)
6.52 (0.51)
-23.92* (-4.48)
-26.52* (-5.17)
Over all Bangladesh
I = (1975/76 to 1984/85) 2.04* (4.66)
1.36* (2.78)
-0.68* (-3.51)
II = (1985/86 to 1994/95) 1.39* (2.99)
1.18** (2.54)
-0.22 (-0.80)
III = (1995/96 to 2007/08) -1.17* (-7.39)
-1.59* (-5.49)
-0.42 (-1.68)
All = (1975/76 to 2007/08)
0.30** (1.82)
0.001** (1.92)
-0.30* (-3.60)
* and ** indicate significance at 1% and 5% error level respectively. Sources: BBS (1976-2008), DAM, BSFIC (1976-2008) ; Figures in parentheses indicate t- values.
.
Growth Rate Of Sugarcane Area in Different Region of Banglades
-4
-3
-2
-1
0
1
2
3
4
5
6
7
1975-8
4
1985-9
5
1995-0
8
1975-0
8
Year
Gro
wth
Rat
e (%
)
Thakurgaon Panchagar Rajshahi Mill zone Bangladesh
Growth Rate of Sugarcane Production in Different Regions
-10
0
10
20
30
40
50
60
70
1975
-84
1985
-95
1995
-08
1975
-08
Year
Gro
wth
Rat
we
(%)
Thakurgaon Panchagar Rajshahi Mill zone Bangladesh
Figure 3.27 Growth rate of sugarcane area in
different locations of Bangladesh Figure 3.28 Growth rate of sugarcane production
in different locations of Bangladesh
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Growth Rate Of Sugarcane Yield of Different Regions
-25
-20
-15
-10
-5
0
5
10
1975
-84
1985
-95
1995
-08
1975
-08
Year
Gro
wth
Rate
(%
)
Thakurgaon Panchagar Rajshahi Mill zone Bangladesh
Growth Rate of Area, Production and Yield of Sugarcane
-2
-1
0
1
2
3
1975
-84
1985
-95
1995
-08
1975
-08
Year
Gro
wth
Rate
(%
)
Area Production Yield
Figure 3.29 Growth rate of sugarcane yield in different locations in Bangladesh
Figure 3.30 Growth rate of area, production and yield of sugarcane in Bangladesh
adoption of better agronomic practices, ensured market and credit facilities. But in non mill zone
area there is no such facilities. In non mills zone growth rate of sugarcane yield was negative and
significant in all periods. Alam (2001, pp.69) recorded sugarcane area and production increased
with a highly significant growth rate of 1.31 and 1.01 percent and yield decreased 0.28 percent
significantly in Bangladesh during the period of 1971-72 to 1995-96. But in the present study
(1975/76 to 2007/08), the area increased with significantly 0.50 percent growth rate and the
production in lower rate, not significantly 0.19 percent. Yield decreased with a significant 0.30
percent growth rate (Table 3.4.2). Decrease of significant sugarcane yield, indicates the impact
of the aggregate situation.
3.4.3 Instabilities of Area, Production and Yields
Instability is one of the important decision parameters in development dynamics and
more so in the context of agricultural production. Because, the price and yield instability or
uncertainty affects area allocation of farmers to crop production enterprise. Such knowledge of
instability will also help the farmers in making suitable production and investment decisions and
to financing institutions in judging the repayment capacity and risk bearing ability of the farmers
(Gangwar and George, 1971 )
ccix
Instability plays a significant role in agricultural production. Farmers have to decide
which combination of crops they should choose to reduce income instability. Risks and
uncertainties adversely affect the optimization process of investment and production decisions in
agriculture. Yield variability is caused by weather fluctuations and diseases incidence. An
analysis of fluctuations in crop output, apart from growth, is of importance for understanding the
nature of food security, income stability variations in disposable income of the farmers (Kaushik,
1993).
In this section, attempt was made to examine the nature and degree of instability in area,
production and yield of sugarcane in different selected areas during the study period The stability
indices of area, production and yield of sugarcane for selected areas were estimated based on
coefficient of variation and coefficient of determination (R2) obtained from the fitted exponential
functions.
Instability Index (I) = (C.V2) × (1-R2)
Thus instability index captures both explained and unexplained variations of the concerned
variable and should better reflect the true instability situation. Instabilities of area, production,
yield and real price of sugarcane and some major crops were discussed. Moreover, area,
production and yield of sugarcane for different areas during 1975/76 to 2007/08 are discussed
below.
3.4.3.1 Instability of Area, Production, Yield and Prices of Sugarcane and Other Crops
Area instability of rice, wheat, potato, lentil and sugarcane was 7, 841, 1074, 956 and 48
respectively. The highest growth rate and instability index was found in potato area. Lower area
growth rate and instability was found in rice and sugarcane. The highest production instability
was found in wheat (1015), followed by lentil (998), potato (576) rice (53) and sugarcane (36).
Lentil attained the highest yield instability followed by wheat, potato rice and sugarcane. The
real price of potato occupied the highest instabilities followed by lentil, wheat, rice and
sugarcane during the study period (Table 3.4.3). Real price instability was higher than that of
area and yield for all crops. Price instability was caused mainly due to production instability. On
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the other hand, wheat, potato and lentil price instability happened due to combined effect of area
and production instability during the study period ( Table 3.4.3). It is notable that yield instability
of rice and sugarcane was the minimum of all crops studied. So, production instability was
largely influenced by area instability during the entire period and area instability was influenced
by prices.
Table 3.4.3. Instabilities of area, production, yield and real prices of sugarcane and other crops (1975/76 to 2007/08)
Crops Instability index(I)*
Area Production Yield Real price
Rice 7 53 21 616
Wheat 841 1015 897 723
Potato 1074 576 148 9658
Lentil 956 998 1077 923
Sugarcane 48 36 8 149
* I= { (C.V.)2 * (1-R2)
3.4.3.2 Instability of Sugarcane Area in Different Locations
Long duration, unavailability of fertilizers and insecticides in proper time, profitability of
competing crops etc. reduced the land allocation for sugarcane cultivation. During the study
period (1975/76 to 2007/08) the instabilities in sugarcane area for Panchagar, Thakurgaon,
Rajshahi districts, all mills zone and whole Bangladesh were found 240, 395, 323, 173 and 48
percent respectively (Table 3.4.4). The area instability of sugarcane in Thakurgaon occupied the
top level followed by Rajshahi and Panchagar districts. Among the different districts Thakurgaon
was found to be the most risky for sugarcane cultivation. It was risky due to attack of pests and
diseases and less profitability than other locations. In Bangladesh sugarcane area occupied the
lowest instabilities in this study period. Bangladesh consists of mill zone and non mill zone.
There are 15 sugar mills under the mills zone and these mills are located in different districts. In
Bangladesh the highest and lowest area instabilities were 16 and 13 percent found in the period -
II and period - III respectively (Table 3.4.5). But in mill zone area the highest instabilities was
found 207 in period - I and the lowest 37 percent in period - II (Table 3.4.6). Within three
districts in different periods the highest sugarcane area instabilities was noted in Rajshahi 410
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percent in the period -I followed by 316 and 313 percent in Thakugaon during the period of I
and III respectively ( Table 3.4.7; 3.4.8 and 3.4.9). But the overall highest area instability was at
Thakurgaon (Table 3.4.1).
It is concluded that, in Bangladesh sugarcane area was more instable in period –III.
According to the opinions of the farmers, sugarcane cultivation was risky due to high initial
investment cost, long duration, high pest and diseases infestation, lower profitability than other
competing crops, closed market facilities, lower price and cane payment policies. It is also
concluded that in overall Bangladesh area, production and yield of sugarcane are less instable
than in any single district.
Table 3.4.4 Instability index of area, production and yield of sugarcane in different location during the period of 1975/76 to 2007/08.
Particulars Regions
Panchagar Thakurgaon Rajshahi Mill zone Bangladesh