Factors affecfting improved wheat seed technology adoption: A double hurdle approach

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Presentation by Dr. Hassen Beshir (Wollo University, Ethiopia) at Wheat for Food Security in Africa conference, Oct 9, 2012, Addis Ababa, Ethiopia.

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Factors affecfting improved wheat seed technology adoption: A double hurdle approach

  1. 1. FACTORS AFFECTING IMPROVED WHEAT SEED TECHNOLOGY ADOPTION: A DOUBLE HURDLE APPROACH BY HASSEN BESHIR1, BEZABIH EMANA2, BELAY KASSA3 AND JEMA HAJI31 Department of agricultural Economics, Wollo University, Ethiopia2 HEDBES Consult, Ethiopia3 Haramaya University, School of Agricultural Economics and Agribusiness, Ethiopia October 9, 2012
  2. 2. Outline of the presentation• Introduction• Methodology• Results and discussion• Conclusion and policy implication
  3. 3. 1. INTRODUCTION• The economic development of Ethiopiahighly dependent on the performance of its agricultural sector.• The average growth rate of agriculture•  1.68%, Imperial period (1960-1974)•  3.75% socialist period (1974-1991)•  5.54% Current period (1991-2011)
  4. 4. • The growth rate of agriculture and GDP  low for several decades mainly due to  severe weather fluctuation,  inappropriate economic policies and  low adoption of improved agricultural technologies• Due to this reason Ethiopia received significant food aid and become highly dependent on food import (FAO, 2007).• The country received 674000 metric tons of cereal mainly wheat in the form of food aid in 2006 alone and more than 1.2 million tone in 2012
  5. 5. INTRODUCTION (CONTINUED) • Farm productivity as indicated by the yield of major crops and livestock  remained low due to mainly to limited adoption of improved agricultural technologies by smallholder farmers. • For example the yield of wheat in south Wollo •  1.36 ton per hectare in 2008/09 •  >2.5 ton per hectare by using improved variety. • Various empirical studies conducted to identify determinants of adoption of improved agricultural technologies whether the farmers adopt or not the agricultural technologies in Ethiopia, for example (Asfaw et al., 1997; Tesfaye and Alemu, 2001; Tesfaye et al., 2001; Mergia, 2002; Kiflu and Berhanu, 2004) but nothing has been done in the study area. • Therefore, this study assessed the determinant of the rate and intensity of improved wheat adoption by smallholder farmers in the study area in order to fill the information gap.
  6. 6. 2. METHODOLOGY OF THE STUDY• 2.1. Description of the Study Area• This study  carried out in South Wollo.• South Wollo  located in the North East part of Ethiopia.• South Wollo  one of the eleven administrative zones of the Amhara Region.• Among the twenty rural districts, Dessie Zuria and Kutaber  selected•
  7. 7. METHODOLOGY ( CONTINUED) • 2.2. Sampling Procedure • Multistage random sampling • Firstly, Dessie Zuria and Kutaber selected • At the second stage of sampling  a total of 6 FAs were selected. • Then 252 sample farmers  selected using simple random sampling technique and distributed proportionately over the 6 farmers associations. • 2.3. Data Collection and Sources • Primary data  collected by interviewing the selected respondents. • A structured questionnaire  designed  pre-tested and refined. • Data collected from July to November 2009.
  8. 8. METHODOLOGY (CONTINUED) • 3.4. Analytical Models • 3.4.1. Econometric specification of agricultural technology adoption model • The double-hurdle model •  a parametric generalization of the Tobit model, •  two separate stochastic processes determine the decision to adopt and the level of adoption of technology.
  9. 9. The double-hurdle model has an adoption (D) decision with an equation: if Di*>0 and zero if Di*<0The level of adoption (Y) decision has an equation: If Yi*>0 and Di*>0 Otherwise
  10. 10. METHODOLOGY (CONTINUED) • 3.4.2 Definitions of variables for adoption • 1. The Dependent variables of Probit and truncated regression models • The dependent variable of Probit model  a dichotomous value. • The truncated regression model Dep. Var  a continuous value which should be the intensity, the use and application of the technology. • 2. The Independent variables and their definitions used in double hurdle model • Adoption literatures provide a long list of factors that may influence the adoption of agricultural technologies.
  11. 11. TABLE 1 SUMMARY OF DEFINITIONS AND MEASUREMENTS OF PROBIT ANDTRUNCATED MODEL VARIABLESNo definitions and measurements of variables Expected sign1 Dependent Variable(s)1.1 Have you used improved wheat? 1. Yes 0. No How much Kg of improved wheat variety you used?1.22 Independent variables2.1 Respondents sex 1. Male 0. female +2.2 Respondents age in years -2.3 Highest Level of schooling of the head in years -2.4 Number of adult equivalent in the family +2.5 Number of man equivalent in the family -2.6 Were you engaged in off-farm activities? 1. Yes 0. No +2.7 Total cultivated area in hectare +2.8 Number of plots -2.9 Total Tropical Livestock Unit +2.10 Distance from distribution centre for improved wheat variety in minutes -2.11 Distance from home to nearest market (walking minutes) +2.12 Distance from home to nearest all weather road (walking minutes) +2.13 Did extension agent visit you? 1. Yes 0. No +2.14 Do you participate in credit for production? 1. Yes 0. No +
  12. 12. 3. RESULTS AND DISCUSSION• Improved technologies such as improved seed and breed, fertilizers and herbicides  a significant role in enabling farmers to increase the production and hence improve the standard of living of smallholder farmers.• The majority of smallholder farmers in Ethiopia  producing both crops and livestock.• Yield of these activities  very low due to low adoption and application of improved agricultural technologies• The study depicted low utilization of improved wheat which was 3.4% of total cultivated wheat land in Ethiopia which was 2.6% in south Wollo (CSA, 2009).
  13. 13. TABLE 2 DESCRIPTIVE STATISTICS OF EXPLANATORY VARIABLES AFFECTING ADOPTION AND INTENSITY OF ADOPTION OF IMPROVED WHEAT VARIETY (MEANS) Non- Adopters Total adopters (57) (252)Variables (195) F Sig.Distance from home to nearest market 84.12 81.40 83.50 0.117 0.733Distance from home to nearest all weather road 41.24 14.96 35.30 23.979 0.000Respondents age 52.82 54.74 53.25 0.741 0.390Highest Level of years of schooling of the head 2.09 2.67 2.22 1.311 0.253Number of man equivalent in the family 3.64 4.68 3.87 14.509 0.000Number of adult equivalent in the family 4.55 5.66 4.80 13.111 0.000Total cultivated area in hectare 0.60 0.93 0.68 22.504 0.000Number of plots 3.58 4.65 3.82 11.361 0.001Total Tropical Livestock Unit 3.43 4.37 3.64 8.388 0.004Distance from distribution centre 104.90 38.60 89.90 117.10 0.000
  14. 14. TABLE 3 DUMMY VARIABLES CHARACTERISTICS OF FARM HOUSEHOLD Variable Non- Characte adopters r (195) Adopters (57) Total (252) χ2 P-Value Sex female 47 3 50 9.843 0.002 male 148 54 202 Off-farm no 58 11 69 2.420 0.120 Income yes 137 46 183 DA visit no 105 12 117 19.071 0.000 yes 90 45 135 Credit no 156 48 204 0.507 0.476 Participation yes 39 9 48
  15. 15. RESULT AND DISCUSSION(CONTINUED)• There are farmers who are adopter and non-adopter of improved wheat technologies.• Adopters are farmers who use improved wheat seed (for example HAR 1685).• Non-adopters are farmers who use none of this technology during the survey year (2008/2009 production year).• the likelihood (rate) of adoption of improved wheat was modest; an average farmer had 22.62% predicted probability of adopting the technology• An average farmer had used improved wheat seed of 30.77kg with an average cultivated area of 0.31 hectare for adopters.
  16. 16. RESULT AND DISCUSSION (CONTINUED) • Therefore, the determinant of the rate of adoption was estimated using Probit model • whereas the determinant of intensity of use of the improved wheat was estimated using truncated regression model. • Hence double hurdle model was used to estimate the determinant of the rate and intensity of adoption of improved wheat variety. • Accordingly explanatory variables were checked for problems of multicollinearity, endogeneity and heteroscedasticity. • Test statistics of double-hurdle model vs Tobit model was conducted • The result suggest the selection of the double hurdle model • The result revealed that the calculated statistical value of likelihood ratio for improved wheat was 42 which was greater than the tabulated or critical value of χ2(14) = 32 at 1% level of significance.
  17. 17. TABLE 5 IMPROVED WHEAT SEED ADOPTION ECONOMETRIC RESULT Probit Truncated Robust Std. Marginal Robust Std. MarginalVariables Coefficient Err. effect Coefficient Err. effectDistance to nearest market -0.005** 0.002 -0.0005 -0.71*** 0.181 -0.209Distance to nearest all weather road -0.020*** 0.005 -0.002 -1.60*** 0.548 -0.471Sex of the household 1.000*** 0.342 0.067 55.060* 30.817 16.163Age of the household head 0.005 0.010 0.0005 2.172*** 0.815 0.637Education of the household head -0.050 0.040 -0.005 2.327 2.020 0.683House hold size in adult equivalent 0.021 0.175 0.002 -1.391 9.582 -0.408Labour force in man equivalent -0.056 0.166 -0.006 9.684 9.634 2.843Farm size 0.290 0.305 0.030 43.96*** 13.274 12.903Fragmentation 0.025 0.058 0.003 3.653 3.735 1.072Livestock owned 0.034 0.059 0.004 -6.202 4.419 -1.821Access to off/non-farm income -0.027 0.316 -0.003 4.142 14.833 1.216Distance to input supply institution -0.029*** 0.006 -0.003 0.113 0.168 0.033Access to extension 0.509* 0.300 0.052 62.918** 24.957 18.470Access to credit 0.104 0.292 0.011 43.26** 21.522 12.699Constant -0.248 0.919 -247*** 77.631 Wald χ2* (14) = 84 Wald χ2* (14)= 30 Log-L =-291 -Test statistics No of observation=252 No of observation=57
  18. 18. DISCUSSION OF RESULTS• The gender + and sig. result is consistent with the finding of Abay and Assefa (2004) and Teklewold et al. (2006).• The access to extension + and sig. result is consistent with the finding of Teklewold et al. (2006).• The credit + and sig. result is consistent with the finding of Abay and Assefa (2004) and Teklewold et al. (2006).• The distance of market and road result is consistent with the finding of Berhanu and Swinton (2003).
  19. 19. 4. SUMMARY AND CONCLUSION• The objective  to identify the major determinant of the rate and intensity of adoption of improved wheat in two districts of south Wollo.• The study employed cross-section data•  to analyse the effect of farmers socioeconomic and institutional setting and physical attributes•  on the determinant of the likelihood and intensity of improved wheat varieties adoption.• Double hurdle model were employed•
  20. 20. SUMMARY AND CONCLUSION (CONT.)• Selected farmers were interviewed about the 2008/09 cropping production season.• The study found access and availability of extension service to be more powerful than other factors in explaining adoption and intensity of adoption improved wheat technologies.• The age of the farmer was significant on intensity of adoption of improved wheat seed technologies.• Accumulated knowledge gained through experience enables older farmers to intensify improved wheat technologies.• The resource endowment of the farm household like farm land size was also significant in affecting the intensity of improved wheat adoption.
  21. 21. SUMMARY AND CONCLUSION (CONT.)• Physical characteristics like distance from farmers’ home to markets, roads, and input supply played a critical role in the adoption of improved agricultural technologies as proximity to information, sources of input supply and credit and markets save time and reduce transportation costs.• Given the critical role of proximity to such centers and better roads for promoting adoption and productivity gains, the effort of investment in improved roads infrastructure should be improved to achieve increased production.
  22. 22. SUMMARY AND CONCLUSION (CONT.) • Therefore the results of the study suggest that the adoption of improved wheat variety should be increased by raising farm household asset formation, providing extension and credit service. • Such actions may, in turn, reduce food shortage problem and fasten economic growth by enhancing productivity.

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