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Agricultural Production Economics Review Assignment

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MARCHUS POWERPOINT.pptxokaforhenry

Kathirvelchelliah paramasivan

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- 1. Department of Agricultural Economics (MSc) Course: Agricultural Production Economics (AgEc 521) Article Review on “Analysis of the Impact of the Agricultural Productivity Enhancement Program on the Technical and Allocative Efficiency of Maize Farmers in Masindi District Uganda” By: Yishak Shitaye GPAgEcR/0009/14 Kutoya Kusse GPAgEcR/0005/14 Instructor: Negussie Zeray (PhD) Hawassa, Ethiopia January 06/2022 Hawassa University College of Agriculture
- 2. Introduction • Problem Statement • The major objective of the article • The theories discussed in the paper • Theories and the author? Methodology • Sampling procedure and Sample size • Data used (data collection and type of data) • Methods of data analysis • The production function used in the paper Findings, Conclusions, and Policy recommendations Criticisms on the review article at overall The overall topic of the course Presentation Outlines 2
- 3. Problem Statement Most under developed and developing countries are still facing the problem of high poverty levels. ↑ growth rate is 3.4%, limited agricultural resources, e.g. arable land. Improving yields of major staples (maize) better food security & livelihoods of rural households by using resources the most efficiently. Improved efficiency is expected to improve food security by cutting hunger halfway in 2015 (Amos, 2007). Most farmers practice subsistence farming with low productivity. This may be attributed to high inefficiencies (technical and allocative) because farmers lack access or less information on efficiency, and low literacy levels. 3
- 4. Problem Statement…. In order to realize increased production and efficiency, small-scale farmers need to efficiently utilize the limited resources (Amos, 2007). APEP provided technical services to the farmers to boost productivity. APEP (2005) indicated a positive response amongst its maize farmers; with higher yields compared to non-APEP farmers. It is not clear whether they were relatively more efficient both allocatively and technically compared with those who did not use the APEP advisory services. No known studies that have been done to determine the technical and allocative efficiency of APEP maize farmers in study area. So, this study was carried out to establish technical and allocative efficiency and factors affecting technical efficiency of APEP in comparison with non-APEP farmers. 4
- 5. Objectives of the Study Main Objective The overall objective of this study was to establish the impact of APEP on the efficiency of maize farmers in Masindi District, Uganda. Specific Objectives To characterize APEP and Non-APEP maize farmers To determine and compare the level of farm input allocative and technical efficiency in maize production between APEP and non-APEP farmers. To determine the factors affecting technical efficiency among maize farmers. 5
- 6. The theories discussed in the paper In economic theory, a production function is described in terms of maximum output that can be produced from a specified set of inputs, given the existing technology available to the farm (Battese, 1992). When the farm produces at the best production frontier, it is considered efficient, the most common assumption is that the goal of the producers is profit maximization (Debertin, 1992). Technical efficiency is achieved when a high level of output is realized given a similar level of inputs. It is therefore concerned with the efficiency of the input to output transformation. 6
- 7. Theories and the author? The author supported the theories due to the following reasons:- This study assumes that producers aim at maximizing output subject to existing constraints. Also the main function of the technical efficiency research is to understand factors that shift production function upwards. 7
- 8. Methodology Sampling procedure and Sample size Masindi district (purposively) Multi-stage 29 farmers with in 2 parish 3 Maize growing sub- counties APEP farmers Randomly 29 non-APEP farmers Sub total of 58 farmers interviewed When the 3 sub-counties were combined, they made an overall number of 175 farmers but 170 questionnaires were used in analysis of which 81 farmers were APEP and 89 non-APEP maize farmers. 8
- 9. Data Collection and type of data • Primary data were collected from farmers (both APEP and non-APEP) using a survey method. The socio-economic data:- Gender, Age, Marital Status Formal education levels Production and market information 9 Figure 1
- 10. Data Collection and type of data…. Production information • Size of farmland owned • Land tenure system • Size of land under maize production • Type of labour used • Varieties and amount of seed planted • Amount of fertilizer application used, and seasonal yields • Amount of credit, access to extension services (number of visits) Market information was also collected (prices of seeds, seasonal quantities produced, incomes earned from maize farm sales) 10
- 11. Analytical Methods The first objective is attained by using descriptive statistics Frequencies Percentages Means, and standard deviation. The 2nd objective is attained by using a Cobb Douglas production function and a stochastic production frontier function. The 3rd objective is attained by estimating a robust linear regression. 11
- 12. The production function used in the paper The Stochastic Frontier Production Model (reasons to apply): The stochastic parametric method decomposes random errors into error of farmer’s uncontrollable factors, dependent variable as well as farm specific inefficiencies. Deterministic and non-parametric methods have drawbacks, i.e., forces all outputs to a frontier, it distorts efficiency measurements (Ogundele et al., 2006). Both methods of estimating technical efficiency using stochastic frontier production function and price efficiency to measure allocative efficiency have been widely used and yielded results. A robust Ordinary Least Squares (OLS) was used instead of Tobit Model in second stage of stochastic frontier production efficiency estimation because it’s unbiased, and consistent estimator. 12
- 13. Model(s) and production function(s) Estimation of Allocative Efficiency (A.E) • The maize production is dependent on human labour, fertilizers applied, amount of seed planted, size of land allocated, animal draught power and capital invested (expenses incurred purchasing Variable inputs). • The allocative efficiency is estimated by the Cobb – Douglas production function of Equation (1). • Thus, the specific model estimated is given by From (1) the linear production function can be re-written as 13
- 14. Cont’d • Allocative efficiency analysis is done by estimating a Cobb-Douglas function using OLS. • It is followed by computing the value of marginal product (VMPi) for each factor of production, which then is compared with the marginal input cost (MICi). • Results from (2) give Beta ( bi) 14
- 15. Cont’d • Using the coefficient estimates from (3), the marginal product MPi of the ith factor X is calculated as Where Y is the geometrical mean of maize output (mean of natural logarithm); Xi is the geometrical mean of input i ; βi is the OLS estimated coefficient of input i . The value of marginal product of input i ( VMPi ) can be obtained by multiplying marginal physical product ( MPi ) by the price of output ( Py ). 15
- 16. Cont’d Hence, if VMPi > Pxi , the input is underused and farm profit can be raised by increasing the use of this input. If VMPi < Pxi , the input is overused and to raise farm profits its use should be reduced. The point of allocative efficiency (maximum profit) is reached when VMPi = Pxi (Chavas et al., 2005). 16
- 17. Estimation of Technical Efficiency (T.E) Technical efficiency of maize production is estimated using a stochastic production frontier, which is specified as Y, Xi and βi are vectors of output, input levels and estimated parameters, respectively. The error term is “composite and Where v is a two-sided (−∞ < v < ∞) normally distributed random error that captures the stochastic effects outside the farmer’s control. 17
- 18. Estimation of Factors Affecting Technical Efficiency To estimate these factors, a linear model is used with estimates. A Robust regression is performed to solve the heteroskedasticity problem and the linear model is estimated as shown below for each farmer. Where TE = level of technical efficiency; X is a vector of explanatory variables 18
- 19. Key Findings (Result and discussions) Table 1: Socio-economic Characteristics of the Farmers Characteristics Description APEP Members (n =81) Non APEP members (n = 89) Total (n= 170) Chi-Square Test Sex of farmer Female 16% 11% 13% 0.947 Male 84% 89% 87% Marital status of farmer Married 96% 88% 92% 4.374 Single 1% 7% 4% Widowed 3% 5% 4% Type of Seeds used Recycled 29% 42% 35% 3.286* Improved 71% 58% 65% Output Market Place Farm gate 87% 89% 88% 0.283 Elsewhere 13% 11% 12% Mean-value Mean-value Average Mean value T-Test Age of farmer (Years) 43 38 41. 5 28. 12*** Household size 7 7 7 1.53 Education level of farmer (yrs) 6 7 6.5 -1. 433 Size of land owned (acre) 6 6.2 6.1 -0.1 Source: Survey Data 2007 *, **, *** = significance levels at 10%, 5% and 1% respectively. 19
- 20. Table 2: T-test for Mean Difference in Input Use and Output among APEP and Non-APEP APEP Members (n=81) Mean Non-APEP Members (n=89) Mean Overall Mean t-value Land under maize production (Ha) 1.12 (5.62) 0.96 (3.18) 1.04 -0.58 Years farmer has been growing maize 18.36 (10.19) 14.50 (10.13) 16.43 -2.47** Total number of person days worked (days/season) 37.25(35.55) 51.41(48.53) 44.33 2.15** Cost spent on seeds and fertilizers purchase (UGX/hectare) 37,752.26 (71,168.93) 19, 649.82 (32,967.97) 28,701.04 -1. 79* Quantity harvested (kg) 2,615. 30 (6,508. 52) 1,255.54 (2,009.53) 1,935.42 -1. 88* Yields (Kg/hectare) 367.52 (467.60) 230.08 (353.95) 298.80 -5.37*** Output prices UGX/kg 206.73 (47.85) 200.02 (55.07) 203.375 -0. 85 Gross profits (UGX/hectare ) 20,581.70 (148,601.85) -23,695.19 (83,855.84) -3,113.49 -2.18** Off-farm incomes (UGX) 93,703.70 (216,270.6) 195,842.70 (518,716.86) 191,625.05 -1.70* Source: Survey Data 2007, Figures in parentheses/brackets are standard deviations *, **, *** = significance levels at 10%, 5% and 1% respectively. SD is standard deviation. UGX= Ugandan Shillings, Kg =Kilogram 20
- 21. Estimation of Allocative and Technical Efficiency of Farmers Input Elasticities Dependent = maize output APEP Non-APEP members members Variable Elasticity Elasticity Labour 0.10 0.0002 Animal Draught power 0.057 -0.064 Seed planted 0.047 0.2730* Fertilizers -0.022 -0.037 Land 1.009*** 0.8030*** Capital 0.187* 0.073 Source: Survey Data 2007 *, *** = significance levels at 10% and 1% respectively. 21
- 22. Allocative Efficiency Estimation VMP should be equal to the respective unit factor price. Both groups were not quite efficient in allocating the inputs with exception of improved seed used by APEP members (0.92). Estimation of Allocative Efficiency Variable Coefficients APP MPP Output VMPi Pi Allocative ( bi ) unit (UGX) efficiency prices (UGX) (VMPi / Pi ) (Py) Scores (UGX) APEP Labour (person days) 0.10 98..28 9.83 206 2,024.98 3,000 0.68 Seed (Kg) 0.05 148.10 7.41 206 1,526.46 1,666 0.92 Animal Draught power 0.06 537.00 32.22 206 6,637.32 30,000 0.22 (person days) Non-APEP Labour (person days) 0.0002 40.28 0.0081 200 1.62 3,000 0.001 Seed (Kg) 0.27 74. 69 20.17 200 4,034 1,666 2.42 Animal Draught power 0.06 310.89 18.65 200 3,730 30,000 0.12 (person days) 22
- 23. The Stochastic Production Frontier Maize Output 2nd season (Y) = P-value Dependent Variable Independent Variables Coefficient S.E Z Labour used (Person days) 0.07 0.05 1.58 0.113 Animal draught power used (day/acre) 0.07 0.05 1.47 0.140 Amount of seeds used (Kg) 0.17*** 0.05 3.26 0.001 Amount of land under maize (ha) 0.75*** 0.15 5.11 0.000 Amount of money (Capital) invested 0.24*** 0.05 4.54 0.000 (UGX) Cons 3.19*** 0.54 5.89 0.000 sigma_v 0.28 0.04 sigma_u 0.73 0.08 sigma2 0.61 0.10 Lambda 2.63 0.10 Log likelihood = -170.74 Wald chi2(5) = 427.35 *** = significance levels at a1%. 23
- 24. Difference in efficiency levels of APEP and Non-APEP Members Membership to APEP Sample Mean Standard Standard size efficiency Error Deviation Members (y) 81 0.67 0.022 0.20 Non members (x) 89 0.49 0.026 0.25 Combined 170 0.58 0.20 0.24 Satterthwaite's degrees of freedom = 168 t = - 5.2064 Ho: mean(x) - mean(y) = 0 (no mean difference) Ha: diff < 0 Ha: diff ≠ 0 Ha: diff > 0 Prob (T < t) = 0.0000 Prob (T > t) = 0.0000 Prob (T > t) = 1.0000 The table results shows APEP farmers had a relatively higher level of mean technical efficiency (67%) than the non-APEP farmers (49%). 24
- 25. Factors Affecting the Level of Technical Efficiency Variable Coefficient Robust S.E T-value P-value Dummy (APEP Member = 1) 0.18*** 0.03 5.52 0.00 Household size 0.01** 0.01 2.06 0.04 Dummy (use improved seeds =1) 0.10*** 0.04 2.54 0.01 Dummy (Sell maize at farm gate =1) -0.09** 0.04 -2.08 0.04 years farmer has spent growing maize -0.003* 0.002 -1.71 0.09 Years farmer spent in school 0.002 0.01 0.29 0.77 Constant 0.46*** 0.08 6.00 0.00 Number of Observation = 170 F-value = 9.08*** Adjusted R2 = 0.22 ***, **, * denote significance at 1%, 5% and 10% levels. 25
- 26. Conclusions The t-test results revealed that there was a significant difference of some input use among them. APEP farmers spent more on purchase of fertilizers and improved seeds, compared with non-APEP farmers. Non-APEP members were using more of personal labour and earned more off farm incomes. A Cobb Douglas function in form of a log linear function used (Allocative efficiency and the elasticities of production). Land was positively related to maize output and significant at 0.01 levels. Maize seed (β = 0.273) was found significant at a 0.1 level used by non- APEP members. 26
- 27. Conclusions…. • Seed input was the most efficiently allocated for APEP farmers (0.92) is closer to 1. • The stochastic frontier production model results indicated that there was a significant difference between APEP and Non-APEP mean technical efficiency at 1% level. • Use of new technology in most cases positively affects technical efficiency and these technologies may include improved maize seeds among others (Wambui, 2005). • Results further showed that APEP farmers were more technically efficient than non APEP. • Selling at home maize and number of years farmer has spent negatively and significantly affected technical efficiency. 27
- 28. Policy recommendations • Farmer training on the use of inputs is important. • For improved technical and allocative efficiency, NGO and government should start devising means of improving on farm-gate maize prices by provision of the necessary infrastructure, market information, and promoting value addition among maize farmers. • Furthermore, amount and type of seed planted were found to be vital for improved farmers’ maize output. Therefore, the government of Uganda, APEP program and other NGOs should help in empowering farmers at subsidized prices for increased maize output. 28
- 29. Policy recommendations…. • Stakeholders in agricultural sector need to design programs that attract more youth in agricultural production. Since increase in number of years farm spend in maize production is negatively affecting efficiency, thus they grow old they loose energy needed in accomplishing hard tasks in farming. • This may result into reduced technical and allocative efficiency and hence low agricultural productivity further result into food insecurity both in rural and urban population. 29
- 30. Criticisms on the review article at overall Title ⁘ Academic research papers have several characteristics one is that the title should be limited to substantive words. ⁘ The title or topic of the research is too long and not precise to catch the readers to understand and grasp the purpose of the research paper immediately. Abbreviations * The author does not list the abbreviations he used in his paper. Abstract * There are no keywords under it. 30
- 31. Criticisms…. The abstract of the paper should be written in one paragraph that touch every part of the paper (250-300 words). It is precise and catches the reader’s attention before proceeds to read the whole document. The author wrote an abstract in three paragraphs. This also makes the readers be bored (421 words). Introduction Related with relevant literature but mostly cited by old references. There is no clearly stated about to what extent maize occupies (only the maize is cultivated) in terms of hectares of the total Maize production land at the national level (Uganda). 31
- 32. Criticisms…. In the background of the study, no clearly stated about in Uganda what agriculture looks like, its contribution (GDP, export earning, create job opportunities, food security, etc). Problem statement • At the Problem Statement, “most farmers in Uganda practice subsistence farming with low productivity”, this may be different factors beyond the above mentioned. • The possible one is institutional, governmental, and farmers-related factors. • Only farmers-related factors were mentioned. 32
- 33. Criticisms…. Objective of the study – Not clearly stated, one objective that related to the impact should be needed. Limitation of the study not mentioned The research does not substantially revise or extend the existing knowledge. There is no in deep evidence from others to support this research paper. From the author, the research gap is not well articulated and indicated for the reader. 33
- 34. Criticisms…. Methodology Sample size calculation Only the desired sample size was mentioned (170) but hadn’t talked about how it was calculated as well as isn’t based on sampling techniques. A multi-stage sampling technique was used but the way of the procedure isn’t mentioned (in the first stage, in the second stage, in the third stage….). 34
- 35. Criticisms…. Data collection Sources of data were mentioned, but types were not stated. Data processing and analysis The author doesn’t listed appropriate (SPSS and STATA) software used in his data analysis. Results • There is no pre-estimation test to check whether there were problems of multicollinearity or not among continuous and categorical explanatory variables. • In the technical efficiency analysis the variables should be transformed in to logarithm, but the author, didn’t transformed the analysis variables in to logarithm/ln form. 35
- 36. Criticisms…. • Descriptive as well as appropriate inferential statistics were used. Discussion:- not supported with the current findings. Conclusion:- stated, but not aligned with the findings of the study. Policy recommendations:- mentioned • The policy recommendations aren’t inline with the key findings of the paper. Referencing:- Old references cited • A few of the references were older than 10 years (1957, 1971, 1977, 1981, 1989, etc). Suggestions for further studies (directions for future research):- • Mentioned. 36
- 37. The overall topic of the course • Basically this course equipped us with how we apply production economics theory and methods (different production economics-related models) in the future for our agricultural research. • Besides this, the course helped us differentiate the efficiency factor (all input used) and the inefficiency factor (socio-economic factors) related to production. 37