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Scaling up the Adoption of Improved Technologies in Staple Food Production: The Case of Row Planting of Teff
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Scaling up the Adoption of Improved Technologies in Staple Food Production: The Case of Row Planting of Teff

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Scaling up the Adoption of Improved Technologies in Staple Food Production: The Case of Row Planting of Teff

Scaling up the Adoption of Improved Technologies in Staple Food Production: The Case of Row Planting of Teff

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  • agricultural growth is a key driver for economic development b/c Growth in the agricultural sector contributes to rural income growth and sustained food security.

    Evidence shows that the adoption of agricultural technologies as well as the provision of agricultural extension programs can be important stimuli for agricultural productivity increases (Duflo et al. 2008, Winters et al. 2011) and lead as well to positive impacts on welfare, food security and povert.
    There are therefore potentially large benefits from the scaling-up of the adoption of yield-increasing technological innovations.
    An important determinant of the success of new agricultural technologies in boosting agricultural productivity is not only the superiority of the technology itself but also the way that these new technologies are promoted to farmers. Improved technologies are usually developed in controlled and experimental settings but the appropriate knowledge of such improved agricultural technologies has to be successfully transmitted to farmers.
  • However, there is a lack of evidence on how being exposed to a promotion campaign of improved technologies increases land productivity in farm settings and the process of successful transmission of extension messages is often not well understood (e.g. Krishnan and Patnam 2014).
    This article contributes to the literature that tries to understand the often large yield gaps that exist for food staple crops in developing countries between research setting and farmer fields
    We believe that our analysis is the first to provide rigorous evidence on the on-farm productivity effects of a promotion campaign for an improved technology of a staple crop using an experimental design
  • The impact of implementing row planting on teff productivity is defined as the expected difference in teff productivity between the two sowing technologies:
  • Negative interaction effect of ‘bad adoption’ mechanism: the larger the difference between the seed rate and the scientific recommendation for row planting, the lower the yield for a row planter
    Negative direct effect of ‘input supply’ mechanism: is the same for row planters and broadcasters as inputs were the same and distributed at village level
    Positive interaction effect of ‘DA quality’: the larger the yield benefit achieved by a DA, the larger the yield benefit of an individual row planting farmer
    Controlling for this effects: positive and significant yield effect of row planting!
  • Transcript

    • 1. ETHIOPIAN DEVELOPMENT RESEARCH INSTITUTE Scaling up the Adoption of Improved Technologies in Staple Food Production: The Case of Row Planting of Teff Joachim Vandercasteelen (LICOS, KU Leuven); Mekdim Dereje (EDRI, ESSP); Bart Minten (IFPRI, ESSP); and Alemayehu Seyoum Taffesse (IFPRI, ESSP) Ethiopian Economics Association (EEA) and the Econometric Society 19th Annual Conference of the African Region Chapter of the Econometric Society 12th International Conference on the Ethiopian Economy July 16-19, 2014 Addis Ababa 1
    • 2. Introduction • Agricultural growth matters – Economic development and food security – Low in Sub Saharan Africa • Improved technology adoption crucial – Agricultural productivity to be increased – Welfare, food security and poverty implications – However, often not understood how to scale up adoption of technological innovations • We study effect of promotion campaigns – Transfer of knowledge and information – Awareness, teaching, training
    • 3. Introduction • Lack of evidence on potential and impact of such promotion campaigns • Evaluation of on-farm productivity effects – Yield benefit at farm level using an experimental survey – Yield gaps • Specific study of row planting in teff production in Ethiopia – Low teff yield – Large scale promotion campaign
    • 4. BACKGROUND
    • 5. Teff in Ethiopia • Teff – Major staple food • 2 out of 3 Ethiopians consume teff daily • Produced by 6 million farmers • In value of production/area, most important crop – Low agricultural productivity • Limited knowledge • Constraints inherent to the crop (small seeds…) – Row planting • High agronomic yields • Promotion campaign – Package (fertilizer, improved seeds) – From 1,400 to 1,600,000 farmers targeted Broadcasting Row planting
    • 6. Experimental Survey • Rolled out in line with “pre-scale-up” phase (2013) – Scientific recommendations and definitions intensively promoted during training days – Input provision (seed and fertilizer for free) – Selection and extension done by Development Agents (DAs) • 2 stage randomization approach – 4 Farmer Training Centers (FTC) in 9 Woreda’s of Oromia – 10 farmers row planting/ traditional broadcasting • Experiment – Farm and village level – Experimental plot of 300 m² – Free improved seed and fertilizer
    • 7. Survey outcome Sample Random (19) n=537 Non-Random (17) n=341 Compliers n= 506 Non-compliers n= 31 878 Farmers 36 Villages 187 village demonstration plots
    • 8. METHODOLOGY
    • 9. Treatment effect • The impact of promoting row planting – Yield from crop-cut – Reported yield after harvest • Average Treatment effect on the Treated (ATT) • 𝑌𝑖 = 𝛼 + 𝛽 ∗ 𝑇𝑖 + 𝜀𝑖 • Identification of program effect – Confounding factors – Control group (counterfactual) – Sample selection bias
    • 10. Randomized Control Trial • Expected mean yield difference = ATT – Assignment of treatment is independent of Yi and Ti – Broadcasting and row planting farmers are statistically identical • Balancedness of random sample – Household characteristics • Demographics, education, assets – Experimental plot characteristics • Plot quality, input use, production practices • But plot size – Unobserved heterogeneity • 𝑌𝑖𝑡 = 𝑐𝑖 + 𝛽 ∗ 𝑋𝑖𝑡 + 𝜀𝑖𝑡 • Management, intellectual capacities, networks
    • 11. Randomized Control Trial Variable Traditional Broadcasting (n=173) Row Planting (n=333) Mean Difference t-value Household head characteristics Age (years) 43.8 -0.80 -0.72 Gender (male=1) 99.4 -2.73 -2.39** Literacy (yes=1) 68.9 4.79 -1.12 Primary education (yes=1) 65.9 3.17 0.72 Household characteristics Distance to FTC (minutes) 34.2 -0.60 -0.26 Household size (members) 7.1 -0.18 -0.86 Total agricultural assets value (ln of ETB) 6.8 0.00 0.03 Total assets value (ln of ETB) 7.2 0.14 0.72 Income from other activities (yes=1) 76.3 -7.83 -0.96 Experimental plot Area (m²) 599.5 -161.80 -4.29*** Number of plows (number) 4.9 0.04 0.29 Number of weedings (number) 1.9 0.07 0.66 Organic input used (yes=1) 11.6 -2.25 -0.77 Inorganic fertilizer used (yes=1) 99.4 -0.32 -0.42 Manure used (yes=1) 10.0 -3.20 -1.12 Rate of Urea used (g/m²) 9.2 1.91 1.06 Rate of DAP used (g/m²) 11.7 0.19 1.04 Rate of herbicides used (100 ETB/m²) 2.0 -0.11 -0.37 Teff characteristics in Meher 2011/2012 Teff cultivated in both seasons (yes=1) 0.7 0.01 0.21 Average teff area (ha) † 0.6 0.01 0.24 Average teff yield (ton/ha) † 0.9 0.08 1.61 Farmers’ unobserved heterogeneity (.) † 1.6 0.04 1.05
    • 12. Intention To Treat • Imperfect compliance – 6% are non-compliers • Intention To Treat (ITT) – No spill-over effects – 𝑌𝑖 = 𝛼 + 𝛽 ∗ 𝑆𝑖 + 𝜀𝑖
    • 13. Matching • Full sample of farmers • Possible selection bias because of purposive selection • Propensity Score Matching (PSM) – Estimate Propensity Score – Yield difference between matched farmers
    • 14. RESULTS & DISCUSSION
    • 15. Farm level • RCT – Positive, but non-significant, effect of row planting on yields Treatment effect Yield from crop-cut Reported yield after harvest control row planting Control row planting Coefficient 1.096*** 0.015 1.147*** 0.116 ATT se (0.049) (0.065) (0.051) (0.075) Observations 403 506 Coefficient 1.132*** 0.108 ITT se (0.051) (0.072) Observations 531
    • 16. Heterogeneous effect VARIABLES Yield from crop-cut harvest Reported yield after harvest Level Interaction with treatment Level Interaction with treatment Row planting (yes=1) 0.653 0.120 (0.555) (0.543) Age of household head (years) 0.002 -0.002 0.004 0.011 (0.004) (0.006) (0.004) (0.008) Primary education household head (yes=1) 0.077 -0.175 0.234** -0.178 (0.094) (0.138) (0.096) (0.169) Gender of household head (male=1) -0.774*** 0.332 -0.805*** 0.341 (0.010) (0.368) (0.084) (0.313) Farm size (ln of ha) 0.047 -0.048 0.045 -0.051 (0.038) (0.043) (0.060) (0.079) Size of the household (number of persons) 0.052 -0.042 0.048 -0.046 (0.037) (0.043) (0.032) (0.042) Experimental plot size in line with guidelines (yes=1) -0.048 -0.208 0.204* -0.173 (0.043) (0.156) (0.109) (0.157) Constant 0.955*** 0.957*** (0.336) (0.321) Observations 403 0.045 506 0.045R-squared
    • 17. Farm level • Matching – First stage: Probit model of selection – Second stage: ATT; again positive effects but not significant Matching algorithm Yield from crop-cut Reported yield after harvest Observations Row-planters 456 541 Controls 293 371 Nearest Neighbor Matching (NNM) ATT 0.027 0.060 Standard error (0.070) (0.076) Kernel Matching (KM) ATT 0.056 0.078 Standard error (0.058) (0.065) Radius Matching (RM) ATT 0.028 0.057 Standard error (0.058) (0.065)
    • 18. Village level • Village level – Plot is managed by an extension agent directly – Stronger and significant effect Yield from crop-cut ATT 0.307** Standard error (0.117) Control 1.016*** Standard error (0.108) observations 187
    • 19. Mechanisms • Yield gap at farm level – “Bad adoption”: plot management – Roll out of promotion campaign • “Problematic input supply” • “Extension quality” • Tested by interaction effects: – Seed rate different w.r.t. scientific recommendations – Receiving inputs too late – Yield benefit achieved by DA at village level
    • 20. Mechanisms Mechanisms Yield from crop cut reported yield after harvest row planting 0.427*** 0.558*** (0.071) (0.149) Bad adoption° 0.001 0.000 (0.004) (0.001) Bad adoption * row planting -0.005*** -0.005*** (0.001) (0.001) Problematic input supply -0.254*** -0.201* (0.060) (0.101) Problematic input supply * row planting -0.144 -0.636** (0.123) (0.235) Extension quality° 0.020*** -0.001 (0.004) (0.010) Extension quality * row planting 0.022** 0.040*** (0.008) (0.011) Constant 1.064*** 1.125*** (0.052) (0.084) °=normalized with Kebele average difference
    • 21. Farmers plans for next year • Questions asked on plans after the experiment: - Farmers overall positive and still planning to continue but on limited areas, possibly because of labor demand issues Percentage 1. “Will you allocate some part of your teff area to row planting?” 73% 2. “Share of the total teff production land allocated to row planting next year” 19% 3. “The major reasons for not doing row planting next year” (top three): - “Too much additional labor” 96% - “Difficulty of doing row planting after rain” 25% - “It does not give higher yields” 16%
    • 22. CONCLUSION
    • 23. Conclusion • Large scale promotion campaign – Low yields ask for adoption of new technologies – Promising on-station results – No empirical evidence • Promotion of row planting to increase yield in the first year – Moderate farm level effect on yields, seemingly partly explained by some issues with respect to implementation • Implications: – Higher effects found in recent ATA study on effect of TIRR package (improved seed + row planting): 44% increase in yield – Might be explained by learning-by-doing? Improved implementation afterwards? Package approach? External validity issues? Further monitoring and evaluation required that could help improve adoption processes of improved technologies – Mechanization potential to reduce labor issues?