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					        Financial and Economic
        Profitability of Selected
    Agricultural Crops in Bangladesh
                Institution:
  Department of Management and Finance
 Sher-e-Bangla Agricultural University (SAU)


          Principal Investigator:
    Dr Mohammad Mizanul Haque Kazal
Department of Development & Poverty Studies
                   SAU
                 Research Team
Dr. Sanzidur Rahman               Mr. Ripon Kumar Mondal, SAU
University of Plymouth, UK


Dr. Mohammad Jahangir Alam         Mr . Shah Johir Rayhan, SAU
Bangladesh Agricultural University



Dr. Shaikh Tanveer Hossain        Mr. Sajeeb Saha , SAU
FIVDB
       Background of the Study
• Crop sector is the source of staple food for 150
  million people
• major source of livelihood for 16 million farm
  households.
• The crop and horticulture sector jointly
  contributed US$9,643 million (11.3% of the
  GDP)
• Financial profitability differs from economic
  (social) profitability because of distortions in
  the factor and product markets
            Introduction

•   Trade and price policy
•   stability in food prices
•   input subsidy and output support
•   food security of the poor
•   strategic element for poverty
    alleviation
                      Objectives
 To examine the financial and economic profitability of the
  various crops including an assessment of the comparative
  advantage for import substitution and/or export (i.e. conduct
  a standard PAM analysis).

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

 To explain changing patterns of agricultural land use since
  2000 based on different levels of financial profitability for
  different crops.
              Research Design
The study was designed to conduct into three phases
Phase-I deals with farm level survey, financial and
  economic cost and return analysis and assessment of
  comparative advantages of crops;
Phase-II deals with assessing the impact of fertilizer
  subsidies on profitability using experimental data as
  well as farm-survey data for rice only;
Phase-III measure the changing patterns of agricultural
  land use and identifies its socio-economic
  determinants using through the secondary time-
  series data.
Figure: Survey District
             Table: Distribution of sample according to farm type
                                                                 Farm Type
SL No.         District           Upazilla                            Medium /   Total surveyed
                                              Marginal   Small
                                                                       Large         Farms
  1      Tangail               Mirzapur          35       35            35             105
  2      Mymensingh            Phulpur           34       36             35           105

  3      Kishoreganj           Karimganj         35       35             35           105

  4      Netrokona             Khaliajuri        21       38             46           105
  5      Faridpur              Bhanga            35       35             35           105
  6      Faridpur              Boalmari          20       20             20           60
  7      Rajshahi              Charghat          35       35             35           105
  8      Natore                Lalpur            34       35             36           105
  9      Sirajganj             Ullapara          35       35             35           105
 10      Bogra                 Sherpur           31       34             33           98
 11      Bogra                 Sariakandi        35       35             35           105
 12      Jaipurhat             Kalai             35       35             35           105
 13      Dinajpur              Chirirbander      36       30             39           105
 14      Dinajpur              Birganj           70       35             35           140
 15      Thakurgaon            Balia Dangi       35       35             35           105

 16      Lalmonirhat           Hatibandha        34       34             37           105
 17      Barisal               Bakerganj         35       35             35           105
 18      Kushtia               Sader             35       35             35           105
 19      Sunamganj             Derai             35       35             35           105

 20      Habiganj              Baniachang        31       38             36           105
                       Total                    696      685             702         2083
          Table : Study area based on land elevation and technology


Crops               Regions                          Region wise survey district
                Specified       North-western      Central       Southern   South-     Haor
                Character                                                   central
Boro            High land       Dinajpur,
                                Rajshahi
                Medium land                        Mymensing Kushtia
                                                   h
                Low land                           Kishoreganj,                        Sunamganj,
                                                                                       Habiganj,
                                                                                       Netrokona
Aman            Rainfed         Dinajpur,                        Barisal
                                Rajshahi
                Supplementary   Bogra, Joypurhat
                irrigation
Wheat           Irrigated       Dinajpur,
                                Thakurgaon
                Supplementary       Rajshahi                                Faridpur
                irrigation    /
                rainfed
Maize           Winter          Dinajpur,
                                Lalmonirhat
Jute                                               Kishoreganj              Faridpur

Lentils                         Natore, Bogra
Mustard                                            Tangail,
                                                   Sirajganj
            Analytical Techniques




1. Financial and economic costs and returns from crops


2. Assessment of comparative advantage of crops

    Policy Analysis matrix (PAM) framework
    applied to measure economic efficiency and
    competitiveness under different production
    systems
Financial profitability of major crops
Cost and return analysis is the most common method of
determining and comparing the profitability of different farm
enterprises. In estimating the level of profitability in crop
production the following formula was used:

               ∏ = P1Q1 + P2Q2 - ∑PiXi – TFC

Where,
∏ = Profit per hectare for producing the crop;
 P1 = Per unit price of the output; Q1 = Quantity of output obtained
(per hectare);
P2 = Per unit price of by-product; Q2 = Quantity of by –product
obtained (per hectare);
 Pi = Per unit price of the ith input used for producing the crop; Xi =
Quantity of the ith input used for producing the crop; and
TFC = Total fixed cost.
This analyses was done by using two different approaches
such as

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

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


 Approach 1: Using experimental data from BRRI

First step is to find the yield / profit maximizing level of N
fertilizer use.
Approach 2: Using farm-survey data for rice crops only
    A profit function approach will be adopted to examine the
    impact of fertilizer subsidies on profitability of rice farming.
    The general form of the translog profit function, dropping the
    subscript for the farm, is defined as:
                       4                      4    4                            4    4
      ln  '   0    i ln P'i  1   ih ln P'i ln P'h   ik ln P'i ln Z k
                                    2
                      i 1                   i 1 h 1                         i 1 k 1
                                 4                        4    4
                                 k ln Z k  1   kj ln Z k ln Z j  
                                               2                                                  (1)
                                k 1                     k 1 j 1

 The corresponding factor share equations are expressed as,
                    Pi X i    ln  '          4                4
             Si                      i    ih ln P' h    ik ln Z k               ( 2)
                     '       ln P'i         h 1             k 1


                     Py X y ln  '        4      4 4               4 4
              Sy     1           1   i     ih ln P' h  ik ln Z k             (3)
                   '       ln Py       i 1   i 1 h 1         i 1 k 1
Phase 3: Socio-Economic and Environmental Determinants of
Crop Diversity in Regions of Bangladesh (1990-2008)

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

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

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

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

                    ln Ait   0   Tit   it             (1)
Policy Analysis Matrix for rainfed Aman rice in southern region of Bangladesh
(Average of 2010 and 2011):

                                      Costs
     Items       Revenue Tradable inputs    Domestic             Profit
                                             factors
 Private          62150       2216            25621              34313
 prices
 Social prices   72835           4530             21665          46640
 Divergences     -10685         -2314             3956           -12327

    Item                                              Value
    Nominal Protection Coefficient on              0.853 (<1)
    Output (NPCO)
    Nominal Protection Coefficient on Input        0.489 (<1)
    (NPCI)
    Effective Protection Coefficient (EPC)         0.877 (<1)
    Domestic Resource Cost (DRC)                   0.317 (<1)
    Private Cost Ratio (PCR)                       0.427 (<1)
Table. Actual and economic optimum levels of urea fertilizer per hectare


     Variables             HYV               HYV                 HYV Aus
                          AMAN                Boro                 model
                           model             model
                           Mean    Standard Mean     Standard      Mean    Standard
                                   deviation         deviation             deviation
     Experimental P        11.18        2.18 20.71        6.04     13.43        5.05
     Experimental K        40.71        6.56 51.88      12.31      44.93      10.60
     Experimental N        75.42      17.79 125.71      16.97      66.08      20.76
     Optimum N            128.26     102.97 232.38      17.42      47.93      42.49
     Optimum urea (10%    127.22     102.96 232.12      17.42      47.96      42.48
     rise in urea price
     Optimum urea (20%    126.17     102.95 231.86      17.41      47.98      42.48
     rise in urea price
     Optimum urea (30%    125.13     102.95 231.60      17.41      48.01      42.48
     rise in urea price
     Optimum urea (40%    124.09     102.94 231.34      17.41      48.04      42.48
     rise in urea price
     Optimum urea (50%    123.04     102.94 231.07      17.41      48.07       5.05
     rise in urea price
Table. Yield response function of rice using economically optimum dose of urea fertilizer
  Variables            Parameter   HYV AMAN                   HYV Boro model                  HYV Aus model
                                        model



                                      Coefficient   t-ratio         Coefficient   t-ratio         Coefficient        t-ratio
  Constant                         3536.1430***      14.85           -968.9645     -0.28       3326.3390***            6.57
  X1 (N)               β1               1.2377**      2.39            51.4000*      1.80              5.1078           0.53
  X1 x X1 (N x N)      γ11                0.0001      0.15             -0.0852     -1.45             -0.0650          -0.66
  2002                                529.1220*       1.73           -219.2515     -0.82           -493.4921          -1.05
  2003                                   25.9761      0.11           -232.3814     -0.90         -885.7895*           -1.74
  2004                               652.8753**       2.16           -234.2008     -0.74 --                     --
  2005                                  395.1257      1.36       -797.7428***      -2.69 --                     --
  2006                                   60.0974      0.26        -468.5997**      -2.34           -298.6252          -0.55
  2007                               561.9247**       2.28           -325.8483     -1.24
  2008                                   90.5782      0.37            -84.5475     -0.39          -235.4953           -0.62
  2009                                  120.2893      0.45        -555.7791**      -2.43           137.4528            0.27
  2010                                  327.9542      1.38       -878.4116***      -4.05        1087.6020**            2.54
  2011                                  362.7055      1.52         -366.4178*      -1.68         828.0839**            2.44
  Gazipur                          -413.2901***      -3.26            -55.2649     -0.36           300.3102            0.59
  Sylhet                                284.0253      0.86        797.4019***       3.80            44.0407            0.08
  Kushtia                                48.9909      0.25            512.1128      1.93          -156.4561           -0.33
  Rajshahi                              249.6528      1.58            103.9682      0.46          -100.1389           -0.20
  Khula                              313.4603**       2.25        -381.4991**      -2.25
  Barisal                                23.7443      0.16           -106.0647     -0.58
  Dhaka                                  49.4871      0.16           -195.5642     -0.38
  Dinajpur                              161.1312      0.50           -392.8672     -0.83
  Rangpur                                80.1611      0.40           -324.2421     -1.44
  Noakhali                              -14.8516     -0.08      -1197.7840***      -3.06
  Faridpur                          939.0849***       3.83       1606.6420***       7.24
  Mymensingh                           -150.1463     -0.53           -309.2255     -0.86
  Jessore                              -131.6670     -0.32            409.4536      0.99
  Bogra                                -302.4152     -0.65                   --        --
  Model diagnostics
  Adjusted R-squared                        0.14                          0.17                          0.53
  F – value                              6.68***                       8.74***                       7.22***
  Sample size                                884                           918                            72
Table. Production elasticity of optimum dose of urea fertilizer.




Variables       Parameter   HYV AMAN             HYV        HYV
                                 model          BORO         AUS
                                                model       model
Production       η                   0.04         0.48       -0.10
elasticity of N*
                                   (0.03)       (0.17)      (0.17)
Results indicate that the experimental level of urea
fertilizer use is far lower than the economically
optimum level of urea fertilizer for Aman and Boro
seasons but higher for Aus season. The discrepancy
is highest for HYV Boro rice where the profit
maximizing level of N fertilizer dose is 232.4 kg/ha as
compared to only 125.7 kg/ha. Also, production
elasticity of HYV Boro rice is highest at 0.48, implying
that a one percent increase in the optimum dose of N
fertilizer will increase rice yield by 0.48% which is
substantial. Changes in price of urea will exert some
reduction in the optimum doses of urea fertilizer only
in Aman season with no noticeable effect on Boro and
Aus season.
          Table. Trends in cultivated area under different crop groups in Bangladesh


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



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

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

Chittagong                 -0.075***     0.021***     0.103***       -0.066***     -0.020***     0.031***           NG        0.012***      0.049***
                           -0.040***     0.050***      -0.013        -0.054***     -0.098***     0.074***      -0.226***      0.017***      0.065***
Chittagong Hill Tracts

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

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

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

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

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

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

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

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

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

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

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

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

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

Bangladesh                 -0.063***     0.038***     -0.016***      -0.068***     -0.033***     0.049***      -0.038***      -0.017***     0.051***
                Table. Shannon index crop diversity in Bangladesh
Regions             Mean index   1990 level   2008 level   % change     Diversity   Average non-cereal share
                                                                                    in GCA (%)

Barisal                  0.95          0.97         0.86                        ↓            13.89
                                                               33.07
Bogra                    1.06          1.27         0.85                        ↓            11.62
                                                               19.35
Chittagong               0.88          0.93         0.75                        ↓            06.83
                                                               20.35
Chittagong   Hill        1.58          1.72         1.37       11.34            ↓            39.77
Tracts
Comilla                  1.33          1.43         1.07                        ↓            14.41
                                                               25.17
Dhaka                    1.59          1.76         1.38                        ↓            25.16
                                                               21.59
Dinajpur                 1.40          1.42         1.05                        ↓            12.79
                                                               26.06
Faridpur                 1.77          1.70         1.78                        ↑            34.20
                                                                -4.71
Jessore                  1.44          1.62         1.13                        ↓            24.09
                                                               30.25
Khulna                   1.07          0.80         1.09                        ↑            09.33
                                                               -36.25
Kushtia                  1.65          1.80         1.34                        ↓            29.83
                                                               25.56
Mymensingh               1.30          1.38         1.05                        ↓            13.23
                                                               23.91
Noakhali                 1.07          1.03         0.99                        ↓            10.08
                                                                3.88
Pabna                    1.65          1.76         1.51                        ↓            22.23
                                                               14.20
Rajshahi                 1.35          1.50         1.13                        ↓            16.63
                                                               24.67
Rangpur                  1.27          1.39         0.96                        ↓            15.53
                                                               30.94
Sylhet                   0.88          0.75         0.80                        ↑            03.69
                                                               -6.67
Bangladesh               1.27          1.32         1.09       17.42            ↓            30.77
Figure. Shannon index of regional crop diversity in Bangladesh
         Table. Determinants of crop diversity in Bangladesh
Variables                                       Random effects GLS model
                                       Coefficients                     z-value
Constant                                 1.5932                          8.78
Prices (Normalized by rice price)
Urea                                   1.4823***                          5.33
TSP                                       0.0580                          0.92
MP                                       -0.0558                         -0.67
Jute                                      0.0438                          0.93
Sugarcane                              -0.9112***                        -4.63
Pulses                                   -0.0345                         -1.31
Vegetables                             0.3123***                          4.35
Spices                                   -0.0042                         -0.25
Oilseeds                                 -0.0100                         -0.65
Socio-economic factors
Extension expenditure per farm           0.0018**                         2.08
Animal power per farm                  -0.0486***                        -3.20
Labour per farm                         0.0245***                         2.75
Share of irrigated area in GCA            -0.1548                        -0.87
Average farm size                          0.0080                         0.22
R&D investment                           0.0267**                         2.31
Average literacy rate                  -0.0169***                        -6.34
Climatic factors
Total rainfall                           -0.0015                         -1.46
Temperature variability                 0.0248**                          2.19
Model diagnostics
R-sq within regression                   0.6098
R-sq between regression                  0.2021
R-sq overall                             0.2569
Sigma_u                                  0.1185
Sigma_e                                  0.0720
Rho (fraction of variance due to ui)     0.7306
Wald Chi-squared (18)                  474.20***
Results demonstrate that other than area under
modern rice, vegetables and spices, all other crop
areas experienced significant decline at variable rates
over time.
The level of crop diversity over time declined for
most regions except Khulna and Sylhet.
In identifying the determinants of crop diversity, the
results clearly reveal that a host of price and non-price
factors influence farmers’ decision to diversify.
Among the prices, an increase in the relative prices
of urea fertilizer and vegetables will significantly
increase crop diversity.
In other words, a rise in urea price and vegetables
relative to other prices will shift farmers to diversify
their cropping portfolio.
Both extension expenditure and R&D investment
significantly positively increases crop diversity which is very
encouraging indeed and the government should seriously
increase investment in these two policy amenable
instruments.
A decline in wealth in terms of livestock induces farmers
to switch to non-cereals that are not heavily dependent on
draft power as these are grown on small scale by individual
farms.
Switching to a diversified cropping system is labour
intensive and our results show that increase in labour stock
per farm allows farms to diversify.
Farmers also seem to respond to climate change as we
see that variation in temperature as well as a reduction in
total annual rainfall induces farmers to diversify their
cropping system.

				
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