Ali M. Oumer. Adoption of Sustainable Agricultural Intensification Technologies in Ethiopia: Drivers and Synergies

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1 UWA Institute of Agriculture Postgraduate Showcase Adoption of Sustainable Agricultural Intensification Technologies in Ethiopia: Drivers and Synergies Ali M. Oumer UWA School of Agriculture and Environment

2 Acknowledgments Supervisors Associate Professor Michael Burton Associate Professor Atakelty Hailu Senior Lecturer Amin Mugera Professor Salvatore Di Falco Dr Menale Kassie Project facilitation Dr Chilot Yirga Debra Basanovic Funding Australian Government through ACIAR - Australian Centre for International Agricultural Research Data access EIAR - Ethiopian Institute of Agricultural Research CIMMYT - International Maize and Wheat Improvement Centre NMA- National Meteorological Agency of Ethiopia

3 Research context: Maize in Ethiopia Farming: >95% smallholders Average farm size: <1 hectare Fertilizer: low (34 kg/ha) Land degradation, soil fertility depletion o Mono-cropping, removal of farm resources, top soil erosion o Extractive practices: soil nutrients mining Mainly rain-fed; prone to weather variability o 1% of total maize area irrigated

4 Wide yield gaps Ethiopian maize yield (tons/ha) On-station trials On-farm trials National average Global yield gap Atlas (

5 Solution: Sustainable Intensification Sustainable agricultural technologies. Raise crop yields while enhancing natural resources o o o Input-intensive: e.g. improved seeds, chemical fertilizers Natural resource management: e.g. crop residues, minimum tillage, organic manure, etc. No consensus on most appropriate Public debate: these two strategies often perceived as incompatible. Environmental NGOs: low external input strategies the only sustainable form of agriculture (Wainaina et al., 2016).

6 Research question: What determines the adoption of sustainable agricultural technologies?

7 Motivation of the study Limitations of empirical research on adoption (Doss 2006): o Analyse drivers of individual technologies: interdependency between technologies overlooked o Use different methods and data: comparisons between multiple technologies are not easily possible o Lack of intertemporal dynamics among different types of technologies But in practice, farmers technology adoptions are inherently dynamic, sequential and interdependent Doss, C. R Analyzing technology adoption using microstudies: limitations, challenges, and opportunities for improvement. Agricultural Economics, 34,

8 The study area and data Multi-stage random sampling procedure Sample size: Panel of 2031 households in 2009/2010 and 2012/2013 Household level data matched with village level climate data Sample villages from maize growing districts of Ethiopia

9 Sustainable agricultural technologies Proportion of households adopted / /2013

10 The empirical model Dynamic multivariate probit y* = x' β + ε, y itm itm itm m itm Observable unobservable 1 if y * > 0 = itm, 0 otherwise Where: i = household, t = time period=1,2 m = sustainable agricultural technology=1,2.8 in each period with a total of 16 binary choices y* is a latent variable that captures the expected benefit from adopting a technology in each period εε X is a vector of covariates (drivers) is unobserved error term capturing unobserved effect of interrelationships ββ is unknown parameter

11 Significant drivers in period one Main drivers Climate factors Hist. Rainfall abundance Hist. temperature Hist. rainfall variability Lag rainfall variability Hist. temperature variability Sustainable agricultural technologies CF IS M SWC CR MT LR LI CF=Chemical fertilizer IS=Improved seed M=Manure SWC=Soil-water conservation CR=Crop residue MT=Minimum tillage LR=Legume rotation LI=Legume intercrop

12 Significant drivers in period one cont. Main drivers Sustainable agricultural technologies CF IS M SWC CR MT LR LI Farm characteristics Age of farm manager Farm size Infertile soils Steep fields Farm distance Altitude CF=Chemical fertilizer IS=Improved seed M=Manure SWC=Soil-water conservation CR=Crop residue MT=Minimum tillage LR=Legume rotation LI=Legume intercrop

13 Significant drivers in period one cont. Main drivers Household characteristics Sustainable agricultural technologies CF IS M SWC CR MT LR LI Off-farm income Lack of oxen Credit use + + Institutions Tenure security + + Family education Education of head + Family labour CF=Chemical fertilizer IS=Improved seed M=Manure SWC=Soil-water conservation CR=Crop residue MT=Minimum tillage LR=Legume rotation LI=Legume intercrop

14 Significant drivers in period one cont. Main drivers Sustainable agricultural technologies CF IS M SWC CR MT LR LI Economic factors Fertilizer price Seed price Labour price CF=Chemical fertilizer IS=Improved seed M=Manure SWC=Soil-water conservation CR=Crop residue MT=Minimum tillage LR=Legume rotation LI=Legume intercrop

15 Temporal dynamics of adoption Sustainable agricultural technologies Intertemporal correlation Chemical fertilizer 0.52*** Improved variety seed 0.22*** Manure 0.18*** Soil and water conservation structures 0.19*** Crop residue retention 0.13*** Minimum tillage 0.47*** Legume rotation 0.21*** Legumes intercropping 0.27*** We observe persistence in the adoption of technologies: Positive spill-over effect of time in adoption behaviour.

16 Significant correlations in period two CF IS M SWC CR MT LR LI CF 1 IS M SWC CR MT LR 1 LI CF=Chemical fertilizer IS=Improved seed M=Manure SWC=Soil-water conservation CR=Crop residue MT=Minimum tillage LR=Legume rotation LI=Legume intercrop

17 Summary 1. Significant dynamic interrelationships between different types of sustainable agricultural technologies. 2. Input-intensive and NRM technologies are compatible, contrary to the literature. 3. Drivers of adoption differ between technologies; reflect observed dynamic interdependencies [synergistic patterns] 4. Positive spill-over effect of time on adoption: enhance widespread use of the technologies if farmers get persistent and appropriate incentives.

18 Policy implications Policies could promote a combination (packages) of input-intensive and NRM technologies that could lead to beneficial synergies. The individual technology promotion approach may not work in every context as African agroecosystems are diverse and complex. Understanding the drivers and synergies of dynamic adoption would be the best way forward.

19 Further research If farmers realize the benefits of NRM technologies, why are then low adoption rates? Could farmers get benefits for their ecosystem services beyond the farm? What roles could sustainable agricultural technologies play in offsetting production risks and food insecurity?

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21 Backup slides, error correlation matrix and adoption drivers with significance values

22 Significant correlations in period one CF CR M IS MT LR SWC LI CF 1 CR M IS MT LR SWC LI CF=Chemical fertilizer CR=Crop residues M=Manure IS=Improved seed MT=Minimum tillage LR=Legume rotation SWC=Soil-water control LI=Legume intercrop

23 Results interrelationships-period one CF CR M IS MT LR SWC LI CF 1 CR -0.10** 1 M -0.12*** 1 IS 0.45*** -0.19*** -0.13*** 1 MT -0.41*** -0.33*** 1 LR 0.22*** 0.17*** 1 SWC 0.12*** 0.10** 1 LI 0.11** -0.34*** 0.22*** 0.12** 1 CF=Chemical fertilizer CR=Crop residues M=Manure IS=Improved seed MT=Minimum tillage LR=Legume rotation SWC=Soil-water conserve LI=Legume intercrop

24 Results interrelationships-period two CF CR M IS MT LR SWC LI CF 1 CR 1 M -0.09** -0.09** 1 IS 0.67*** -0.11** 1 MT -0.28*** -0.09* -0.33*** 1 LR 1 SWC 0.28*** 0.08* 0.14*** 1 LI 0.16*** 0.16*** 0.17*** 1 CF=Chemical fertilizer CR=Crop residues M=Manure IS=Improved seed MT=Minimum tillage LR=Legume rotation SWC=Soil-water conserve LI=Legume intercrop

25 Results temporal dynamics 2009/2010 [Period one] CF CR M IS MT LR SWC LI CF 0.52*** 0.13*** -0.14*** 0.08* 0.19*** 2012/2013 [Period two] CR -0.17*** 0.13*** -0.10** 0.09* M -0.08** 0.18*** -0.16*** 0.07* IS 0.34*** -0.12*** 0.22*** -0.22*** 0.19*** MT -0.19*** -0.15** 0.47*** 0.14** LR 0.16** 0.13** 0.21*** SWC 0.15*** 0.09** 0.19*** 0.09* LI 0.16*** -0.18*** 0.11** 0.17*** 0.27*** CF=Chemical fertilizer CR=Crop residues M=Manure IS=Improved seed MT=Minimum tillage LR=Legume rotation SWC=Soil-water conserve LI=Legume intercrop

26 Results key drivers-period one Main drivers Sustainable agricultural intensification technologies CF CR M IS MT LR SWC LI Climate factors H. rainfall 0.04*** 0.05*** 0.04*** -0.05*** 0.08*** -0.1*** 0.06*** H. temperature 0.06*** 0.06*** -0.04** H. rainfall variability 6.3*** -3.5* 6.2** -6.0** 9.0*** 6.7** Lagged rainfall variability -1.6*** -1.3*** 0.8*** 0.8*** 1.0*** -3.4*** H. temperature variability -6.4*** 3.3* 11.0*** -7.3*** Farm characteristics Farm size 0.16*** 0.09** 0.10** 0.14*** 0.08* 0.09* Soil fertility 0.21*** -0.15** -0.13** 0.28*** Slope of field 0.19*** 0.21*** -0.3*** 0.36*** Farm distance 0.07** 0.07** -0.2*** 0.08** 0.07** Altitude 0.10*** 0.05*** -0.18*** -0.03*

27 Results key drivers-period one cont. Main drivers Sustainable agricultural intensification technologies CF CR M IS MT LR SWC LI Household characteristics Off-farm income -0.35*** -0.32*** -0.24** 0.46*** -0.31** Lack of oxen -0.24** -0.27*** 0.50*** 0.27** Credit use 0.19** 0.20** Institutions 0.09*** -0.11*** 0.06*** -0.06** 0.05* 0.11*** Tenure security 0.31*** 0.27** Family education 0.05** -0.05** 0.12*** Education level of head 0.04*** Economic factors Fertilizer price -0.07** 0.10*** -0.15*** -0.08** Seed price 0.09*** 0.08*** -0.05*** -0.04*** -0.05*** Labour price -0.04*** 0.04*** -0.03*** 0.021** 0.04***

28 Sustainable agricultural technologies Proportion of households adopted / /2013

29 Thank you very much for your attention!