Wheat yield gaps across smallholder farming systems in Ethiopia

AbstractWheat yields in Ethiopia need to increase considerably to reduce import dependency and keep up with the expected increase in population and dietary changes. Despite the yield progress observed in recent years, wheat yield gaps remain large. Here, we decompose wheat yield gaps in Ethiopia into efficiency, resource, and technology yield gaps and relate those yield gaps to broader farm(ing) systems aspects. To do so, stochastic frontier analysis was applied to a nationally representative panel dataset covering the Meher seasons of 2009 and 2013 and crop modelling was used to simulate the water-limited yield (Yw) in the same years. Farming systems analysis was conducted to describe crop area shares and the availability of land, labour, and capital in contrasting administrative zones. Wheat yield in farmers’ fields averaged 1.9 t ha− 1 corresponding to ca. 20% of Yw. Most of the yield gap was attributed to the technology yield gap (> 50% of Yw) but narrowing efficiency (ca. 10% of Yw) and resource yield gaps (ca. 15% of Yw) with current technologies can nearly double actual yields and contribute to achieve wheat self-sufficiency in Ethiopia. There were small differences in the relative contribution of the intermediate yield gaps to the overall yield gap across agro-ecological zones, administrative zones, and farming systems. At farm level, oxen ownership was positively associated with the wheat cultivated area in zones with relatively large cultivated areas per household (West Arsi and North Showa) while no relationship was found between oxen ownership and the amount of inputs used per hectare of wheat in the zones studied. This is the first thorough yield gap decomposition for wheat in Ethiopia and our results suggest government policies aiming to increase wheat production should prioritise accessibility and affordability of inputs and dissemination of technologies that allow for precise use of these inputs.

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Technical efficiency and yield gap of smallholder wheat producers in Ethiopia: A Stochastic Frontier Analysis

African Journal of Agricultural Research ◽

10.5897/ajar2016.12050 ◽

2018 ◽

Vol 13 (28) ◽

pp. 1407-1418 ◽

Cited By ~ 1

Author(s):

Mamo Tadele ◽

Getahun Wudineh ◽

Chebil Ali ◽

Tesfaye Agajie ◽

Debele Tolessa ◽

...

Keyword(s):

Technical Efficiency ◽

Stochastic Frontier Analysis ◽

Stochastic Frontier ◽

Yield Gap ◽

Frontier Analysis

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Unravelling the variability and causes of smallholder maize yield gaps in Ethiopia

Food Security ◽

10.1007/s12571-019-00981-4 ◽

2019 ◽

Vol 12 (1) ◽

pp. 83-103 ◽

Cited By ~ 4

Author(s):

Banchayehu Tessema Assefa ◽

Jordan Chamberlin ◽

Pytrik Reidsma ◽

João Vasco Silva ◽

Martin K. van Ittersum

Keyword(s):

Stochastic Frontier Analysis ◽

National Level ◽

Total Yield ◽

Maize Yield ◽

Sub Saharan Africa ◽

Yield Gap ◽

Potential Yield ◽

Frontier Analysis ◽

Yield Gaps ◽

Maize Yields

AbstractEthiopia has achieved the second highest maize yield in sub-Saharan Africa. Yet, farmers’ maize yields are still much lower than on-farm and on-station trial yields, and only ca. 20% of the estimated water-limited potential yield. This article provides a comprehensive national level analysis of the drivers of maize yields in Ethiopia, by decomposing yield gaps into efficiency, resource and technology components, and accounting for a broad set of detailed input and crop management choices. Stochastic frontier analysis was combined with concepts of production ecology to estimate and explain technically efficient yields, the efficiency yield gap and the resource yield gap. The technology yield gap was estimated based on water-limited potential yields from the Global Yield Gap Atlas. The relative magnitudes of the efficiency, resource and technology yield gaps differed across farming systems; they ranged from 15% (1.6 t/ha) to 21% (1.9 t/ha), 12% (1.3 t/ha) to 25% (2.3 t/ha) and 54% (4.8 t/ha) to 73% (7.8 t/ha), respectively. Factors that reduce the efficiency yield gap include: income from non-farm sources, value of productive assets, education and plot distance from home. The resource yield gap can be explained by sub-optimal input use, from a yield perspective. The technology yield gap comprised the largest share of the total yield gap, partly due to limited use of fertilizer and improved seeds. We conclude that targeted but integrated policy design and implementation is required to narrow the overall maize yield gap and improve food security.

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Closing yield gaps in Colombian direct seeding rice systems: a stochastic frontier analysis

Agronomía Colombiana ◽

10.15446/agron.colomb.v38n1.79470 ◽

2020 ◽

Vol 38 (1) ◽

pp. 110-119

Author(s):

David Arango-Londoño ◽

Julián Ramírez-Villegas ◽

Camilo Barrios-Pérez ◽

Osana Bonilla-Findji ◽

Andy Jarvis ◽

...

Keyword(s):

Technical Efficiency ◽

Stochastic Frontier Analysis ◽

Stochastic Frontier ◽

Rice Production ◽

Direct Seeding ◽

Limiting Factors ◽

Nitrogen Application ◽

Irrigated Rice ◽

Frontier Analysis ◽

Yield Gaps

Rice is one of the most important crops in terms of harvested area and food security both globally and for Colombia. Improvement of technical efficiency levels in rice production in order to close yield gaps in a context in which rice demand increases, natural resources are depleted, and where there are growing expectations about both climate changes and trade agreements is likely the most important challenge that farmers confront. This research assessed the main management factors that limit both rice crop productivity and the likely drivers of non-optimal technical efficiency levels (a proxy for yield gaps). This study focused on both upland and irrigated direct seeding systems across a variety of environments in Colombia. Stochastic frontier models were used to integrate microeconomic theory and empirical regression analysis in conjunction with a large commercial rice production database developed by the Colombian rice growers’ federation (Fedearroz). A large variation was found in technical efficiency (from 40 to 95%) levels for both upland and irrigated systems, and major differences were obtained in the limiting factors of the two systems (e.g. seed availability, variety type, market accessibility, fertilizer type, and use rate). This suggests both substantial and varied opportunities for improvements in current technical efficiency levels. Across systems, the correct choice of variety was identified as a common key factorfor maximizing yield for a particular environment. For upland systems, optimal choices were F174 and F2000, whereas for irrigated rice F473 was found to produce the highest yield. Additionally, numerical analysis suggests a yield impact of ca. 0.18% for each 1% increase in the nitrogen application rate for upland systems. For irrigated rice, phosphorous rather than nitrogen application rates were found to be more important. Since our analysis is based on farm-scale commercial production data, we argue that once our results are brought to consensus with local extension agents, technicians and agronomists, then management recommendations for closing yield gaps can be used to improve rice productivity.

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