The Economics of Sustainable Land Management Practices in the Ethipian Highlands
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The Economics of Sustainable Land Management Practices in the Ethipian Highlands

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This study was presented during the conference “Production and Carbon Dynamics in Sustainable Agricultural and Forest Systems in Africa” held in September, 2010.

This study was presented during the conference “Production and Carbon Dynamics in Sustainable Agricultural and Forest Systems in Africa” held in September, 2010.

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The Economics of Sustainable Land Management Practices in the Ethipian Highlands The Economics of Sustainable Land Management Practices in the Ethipian Highlands Presentation Transcript

  • The economics of sustainable land management practices in the Ethiopian highlands Menale Kassie, University of Gothenburg; Precious Zikhali, Centre for World Food Studies (SOW-VU); John Pender, United States Department of Agriculture (USDA); Gunnar Köhlin, University of Gothenburg ABSTRACT: This paper uses data from household and plot-level surveys conducted in the highlands of Ethiopia. We examine the contribution of sustainable land management practices to net value of agricultural production in areas with low versus high agricultural potential. A combination of parametric and non-parametric estimation techniques is used to check result robustness. Both techniques consistently predict that minimum tillage is superior to commercial fertilisers, as are farmers’ traditional practices without commercial fertilisers, in enhancing crop productivity in the low agricultural potential areas. In the high agricultural potential areas, by contrast, use of commercial fertilisers is superior to both minimum tillage and farmers’ traditional practices without commercial fertilisers. The results are found to be insensitive to hidden bias. Our findings imply a need for careful agro-ecological targeting when developing, promoting, and scaling up sustainable land management practices. *** DISCUSSION AFTER THE PRESENTATION: The presentation was followed by a question regarding strategies on how to use the study to fill the gap between research and policy processes. It was replied that the study had been presented to the Ethiopian Ministry of Agriculture and that workshops have been organised over the three years at the regional level to discuss the results together with local and international researchers and policy makers. Discussions have also taken place with the World Bank on how to bring these kinds of studies together and synthesise the results in order to develop a tool to guide the promotion of land management strategies in various agro-ecological areas. There was also another comment suggesting that it is not only relevant to carry out research on which land management strategies work where, but also to look at the approaches in order to promote local participation and farmers’ own research and innovations. A final question concerned the Ethiopian extension system, which is highly politically driven.
  • Rationale • Ethiopian economy highly dependent on agriculture • Severe land degradation • Low agricultural productivity • High dependency on food aid • Response from Government, NGOs and donors: – massive programs of natural resource management to reduce environmental degradation, reduce poverty, and increase agricultural productivity and food security
  • However… • …Success has been limited! • Low adoption, dis-adoption or reduced use of technologies – e.g., 16 kg of nutrients per hectare (EEA/EEPRI 2006) • Continued low productivity! View slide
  • Why limited success? • Blanket recommendation: Technology packages are not site or household specific and are disseminated through a ‘quota’ system, eg: -Commercial fertilizer: 100 kg of Di-Ammonium Phosphate (DAP) and 100 kg of urea per hectare is promoted all over Ethiopia -Uniform SWC technologies released and promoted disregarding local agro-ecological and socio-economic variations View slide
  • Realize! • Economic returns to different farm technologies vary by agro-ecology: – e.g. physical soil and water conservation investments (e.g. stone terrace) impacts on productivity are greater in low moisture and low agricultural potential areas than in high moisture and high agricultural potential areas (Gebremedhin et al. 1999; Benin, 2006; Kassie et al. 2008) • Need rigorous empirical research on where particular SLM interventions are likely to be successful, to ensure sustainable adoption of technologies and beneficial impacts on productivity and other outcomes
  • Three comparisons: Impacts on net value of production in high and low rainfall areas: 1. Commercial Fertilizer (CF) versus Farmers’ Traditional Practices (FTP) (i.e. traditional tillage without CF) 2. Minimum Tillage without commercial fertilizer (MTWOCF) versus FTP and, 3. Minimum Tillage (MTWOCF) versus Commercial Fertilizer (CF)
  • Data-1 • Household-and plot-level data conducted in 1998 and 2001 in the highlands (above an altitude of 1,500 m.a.s.l) of the Tigray and Amhara regions of Ethiopia. • A stratified random sample of 99 Peasant Associations was selected from highland areas of the two regions. • The Tigray region is typically low moisture and generally low agricultural potential region (Benin, 2006). • The Amhara region has greater variation in agro-ecological zones that have been classified in ”high potential” and ”low potential” areas, primarily based on rainfall patterns.
  • Descriptive statistics Variables Amhara region Tigray region Sampled household 396 357 Sampled villages 98 100 Sampled plots 1365 1113 Rainfall 1981 mm 641 mm Population density 144 person/km2 141 person/km2 Minimum Tillage plots 15% 13% Fertilized plots 30% 35% Extension system Same Same Rural credit service Same Same Seed and fertilizer markets and distribution systems Same Same Net value of production 2140 ETB 1730 ETB
  • Estimation methods • Semi-parametric method: – Propensity score matching (PSM) method: construction of the counterfactual and reduce problems arising from selection biases. Find a group of non-adopters plots similar to the adopters • Parametric method: – Switching regression framework: to differentiate each coefficient for adopters and non-adopters • The parametric analysis is based on matched samples of adopters and non-adopters obtained from the propensity score matching (PSM) process.
  • PSM matching quality Common support/overlap region for comparisons Effect of CF compared to FTP in high potential areas of Amhara region Effect of CF compared to FTP in low potential areas of Amhara region Effect of MT compared to FTP in high potential areas of Amhara region Effect of MT compared to FTP in low potential areas of Amhara region 0 .2 .4 .6 .8 1 Propensity Score Untreated Treated: On support Treated: Off support 0 .2 .4 .6 .8 1 Propensity Score Untreated Treated: On support Treated: Off support 0 .2 .4 .6 .8 1 Propensity Score Untreated Treated: On support Treated: Off support 0 .2 .4 .6 .8 1 Propensity Score Untreated Treated: On support Treated: Off support
  • Common support/ overlap region Effect of CF compared to FTP in Tigray region Effect of MT compared to FTP in Tigray region Effect of MT compared to CF in Amhara region Effect of MT compared to CF in Tigray region 0 .2 .4 .6 .8 1 Propensity Score Untreated Treated: On support Treated: Off support 0 .2 .4 .6 .8 1 Propensity Score Untreated Treated: On support Treated: Off support 0 .2 .4 .6 .8 1 Propensity Score Untreated Treated: On support Treated: Off support 0 .2 .4 .6 .8 1 Propensity Score Untreated Treated: On support Treated: Off support
  • Reminder: Three comparisons of net value of agricultural production Three comparisons undertaken to assess Minimum Tillage (MT) and Commercial Fertilizer impacts on productivity. 1. Commercial Fertilizer (CF) versus Farmers’ Traditional Practices (FTP) (i.e. traditional tillage without CF) 2. Minimum Tillage without commercial fertilizer (MTWOCF) versus Traditional Practices and, 3. Minimum Tillage versus Commercial Fertilizer
  • 1. Commercial Fertilizer (CF) vs Farmers’ Traditional Practices (FTP) (Average adoption effects - Semi-parametric method) High potential areas Low potential areas Amhara Amhara Tigray NNM KBM NNM KBM NNM KBM Average adoption effect 1377A 1083A 118 279 56 142 Standard error 349 257 488 399 234 186 Number of observations within common support Number of treated 313 313 46 45 356 356 Number of control 447 447 331 331 607 607 A significant at 1%; B significant at 5%. Notes: NNM = nearest neighbor matching; KBM = kernel based matching;
  • 2. Minimum tillage (MTWOCF) vs FTP (Average adoption effects (ATT)-Semi-parametric method) High potential areas Low potential areas Amhara Amhara Tigray NNM KBM NNM KBM NNM KBM Average adoption effect 19 253 510B 277 715A 694A Standard error 994 446 246 219 313 316 Number of observations within common support Number of treated 19 21 131 131 109 109 Number of control 391 391 349 349 606 606 A significant at 1%; B significant at 5%. Notes: NNM = nearest neighbor matching; KBM = kernel based matching;
  • 3. Minimum tillage (MTWOCF) vs Commercial Fertilizer (Average adoption effects (ATT)-Semi-parametric method) Amhara Tigray NNM KBM NNM KBM Average adoption effect -1240A -935A 949A 303 Standard error 519 412 372 465 Number of observations within common support Number of treated 370 370 92 92 Number of control 112 112 357 357 A significant at 1%; B significant at 5%. Notes: NNM = nearest neighbor matching; KBM = kernel based matching;
  • Results from switching regressions (Average adoption effect (ATT)-parametric method) AMHARA REGION TIGRAY REGION High potential areas Low potential areas Entire sample Entire sample Entire sample CF vs. FTP MTWOCF vs. FTP CF vs. FTP MTWOCF vs. FTP MTWOCF vs. CF Average adoption effect 1051A 293B 173 650B 785A Standard error 229 149 145 245 302 Number of matched observations Number of treated 313 131 356 109 92 Number of control 127 74 115 73 58 A significant at 1%; B significant at 5%. Notes: CF = commercial fertilizer; FTP = farmers’ traditional practices; MTWOCF = minimum tillage without commercial fertilizer. Source: Own calculation
  • Conclusions-1 • Minimum tillage gives higher productivity gains compared to commercial fertilizer in the low agricultural potential areas • Commercial fertilizer gives higher productivity gains compared to minimum tillage in high agricultural potential areas • A one-size-fits-all approach in developing and promoting technologies not recommended: different strategies are needed for different environments
  • Conclusions-2 • Relying on external inputs (such as chemical fertilizers) in low- potential areas, which has been the strategy in the past, is not likely to be beneficial unless moisture availability issues are addressed. • Future research should investigate the combined effects of minimum tillage or other moisture conservation practices and commercial fertilizer.
  • Thank you!
  • Extra stuff
  • Results-3 • Covariate balancing indictors before and after matching. Amhara region Tigray region High potential Low potential High potential Low potential Entire sample Entire sample Entire sample Entire sample CF vs FTP CF vs. FTP MTWOCF vs. FTP MTWOCF vs. FTP MT VS CF MTWOCF vs. FTP CF vs FTP MT Vs. CF Before matching Mean standardized difference 19.37 20.47 23.05 22.46 37.96 16.40 14.33 23.89 Pseudo 2 R 0.295 0.374 0.285 0.287 0.580 0.249 0.122 0.358 P-value of LR 2 χ 0.000 0.000 0.031 0.000 0.000 0.000 0.000 0.000 After matching Mean standardized difference 6.03 11.68 12.80 9.79 11.94 7.67 3.83 10.13 Pseudo 2 R 0.055 0.029 0.112 0.090 0.139 0.086 0.015 0.106 P-value of LR 2 χ 0.111 0.815 1.000 0.650 0.208 0.973 0.998 0.997
  • Results-6 • Rosenbaum bounds sensitivity test to hidden bias Critical value of hidden bias ( )Γ CF vs. FTP MTWO CF vs. FTP MT Vs. FPT MTWOC F vs. FTP MT VS CF High potential areas Low potential areas Entire sample Entire sample Entire sample 1 0001.0< 001.0< 001.0< 001.0< 001.0< 1.10 001.0< 001.0< 001.0< 001.0< 001.0< 1.20 001.0< 001.0 001.0< 001.0< 001.0 1.30 001.0< 004.0 001.0< 001.0< 003.0 1.40 001.0< 026.0 001.0< 001.0 007.0 1.50 001.0< 026.0 001.0< 002.0 014.0 1.60 001.0< 050.0 001.0< 005.0 025.0 1.70 001.0< 085.0 001.0< 012.0 042.0 1.80 001.0< 135.0 001.0< 021.0 065.0 1.90 002.0 196.0 001.0< 034.0 096.0 2.00 006.0 267.0 001.0< 053.0 132.0