Wheat yield gaps across smallholder farming systems in Ethiopia

Silva, João Vasco; Reidsma, P.; Baudron, Frédéric; Jaleta, Moti; Tesfaye, Kindie; Ittersum, M.K. van

doi:10.1007/s13593-020-00654-z

Cited by 31 publications

(27 citation statements)

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“…Therefore, the availability of high-resolution fertilizer recommendations, such as the one developed for wheat in this study, can increase nutrient efficiency, affordability, and economic returns to smallholder farmers. Moreover, government policies aimed at increasing wheat production in rainfed systems should focus on fostering the accessibility and affordability of inputs, particularly fertilizers (Silva et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Although Ethiopia is the largest wheat producer in SSA, the country imported 1.5 million tonnes of wheat, corresponding to a value of around $600 million (CSA, 2019). Currently, the national average yield of wheat is 2.9 t/ha, or roughly 20% of the crop's rainfed yield potential (Silva et al, 2021). Low and blanket fertilizer application has long been considered as the main cause of low yields in Ethiopia (Tamene et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

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A data-mining approach for developing site-specific fertilizer response functions across the wheat-growing environments in Ethiopia

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Summary The use of chemical fertilizers is among the main innovations brought by the 1960s Green Revolution. In Ethiopia, fertilizer application during the last four decades has led to significant yield gains, yet yield remains below its potential across much of the country. One of the main challenges responsible for low yield response to fertilizer application has been the use of ‘blanket’ recommendations, whereby no tailoring of fertilizer amount and frequency is done based on soil requirements. As a result, the amount of fertilizer applied ranges widely, and can be either sub- or supra-optimal. There is thus an increasing need for site-specific fertilizer recommendations which take into account site characteristics such as climate variables (temperature, rainfall, and solar radiation); soil factors (soil organic carbon, moisture, pH, texture, cation exchange capacity, and level of macro- and micronutrients); and topographic position indices. This article reports on a data-mining approach we developed on a large dataset of 6585 wheat (Triticum aestivum) field trials. The dataset includes detailed, site-specific biophysical variables to create nutrient response functions that can guide optimal site-specific fertilizer application. The approach used a machine-learning model (random forest) to capture the relationship between nutrients – nitrogen (N), phosphorous (P), potassium (K), and sulfur (S) – and wheat yield. The model explained about 83, 82, 47, and 69% of variances of yield for N, P, K, and S omission, respectively, with consistent performance across training and testing datasets. Expectedly, for N and P omission data, the most important explanatory variables are nutrient rate, followed by soil organic carbon and soil pH. For K and S, however, climatic variables played an important role alongside nutrient rates. The site-specific yield–fertilizer response curves derived from our model are highly variable from location to location, as they are affected by the climatic, soil, or topographic conditions of the site. Importantly, using principal component analysis, we showed that the shape of the fertilizer response curves is a result of the multiple environmental factors (including soil, topography, and climate) that are at play at a given site, rather than of a specific dominant one. The research output is expected to respond to the national policy demands for a sound method to identify the optimal fertilizer rate to increase economic returns of fertilizer investments and take fertilizer utilization research one step further.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A data-mining approach for developing site-specific fertilizer response functions across the wheat-growing environments in Ethiopia

et al. 2022

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“…(2010) , reported that the rice production drivers in sub-Saharan Africa are: depletion of soil fertility, weak information system, shortage of finance, lack of high yield varieties, shortage of N fertilizer, bird damage, weed competition, and inadequate water management. In relation to this, crop yield gap in Ethiopia is mainly attributed due to low input use ( Assefa et al., 2020 ) and technological limitations in variety selection, planting date and density, crop residue management, weeds, pests, and diseases problems ( Silva et al., 2021 ). As informed by Ringler et al.…”

Section: Resultsmentioning

confidence: 99%

“…As specific drivers dictate the productivity and yield performance of different regions, region-specific management changes and interventions are required to close the observed crop yield gap ( Mueller et al., 2012 ). Wheat yield gap in Ethiopia is attributed due to lack of appropriate technology, pests, diseases, weeds, and shortage of input levels for water-limited production system ( Silva et al., 2021 ). Maestrini and Basso (2018) reported that the drivers of spatial and temporal variability of crop yield depends on interaction between climate, soil, topography, and management practices.Understanding the small-scale farm production constraints is also essential in designing intervention plans and targets to enhance smallholder farm yield output ( Arias et al., 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Farmers’ perspectives on drivers of rice yield in the Fogera Plain of Ethiopia

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Tesfaye²,

Mekbib³

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“…Compared to the decade 1994 − 2003, wheat productivity increased by 85% for 2009−2018. Despite this increase, there is still a considerable yield gap (achievable yield under ideal management practices minus actual yield in farmers" fields) for wheat in Ethiopia (Silva et al, 2021). Improved wheat varieties in Ethiopia are delivered through both formal and informal channels.…”

Section: Wheat In Ethiopiamentioning

confidence: 99%

Gender, wheat trait preferences, and innovation uptake: Lessons from Ethiopia and India

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During the post-Green Revolution era, numerous improved wheat varieties were released and disseminated to enhance tolerance to biotic and abiotic stresses and increase productivity. Still, in the wheat-based farming systems of the Global South, gender-based and other social inequalities continue to undermine equitable access to improved varieties, especially for women, poor, and marginalized farmers. Here, we present a case for gender-sensitive technology development, dissemination, and evaluation as part of wheat varietal improvement programs. We take stock of the various challenges that persist in the uptake of modern wheat varieties by male and female smallholders. We focus on Ethiopia and India, two geographies with substantive wheat economies, widespread poverty, and gender inequalities. The socio-economic literature on wheat is relatively thin with limited and dated gender-sensitive evaluation studies on varietal technologies in these countries. Varietal technology evaluations could ideally cover gender differences in relation to wheat varietal trait preferences, technology adoption, and associated decision-making and labor-use changes related to new varieties and complementary technologies, as well as nutritional and economic benefits. The paper calls for a need to change the institutional arrangements in wheat research-and-development (R&D) programs to understand and pursue better paths for wheat improvement to proactively contribute toward gender equity and inclusivity.

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Wheat yield gaps across smallholder farming systems in Ethiopia

Cited by 31 publications

References 31 publications

A data-mining approach for developing site-specific fertilizer response functions across the wheat-growing environments in Ethiopia

A data-mining approach for developing site-specific fertilizer response functions across the wheat-growing environments in Ethiopia

Farmers’ perspectives on drivers of rice yield in the Fogera Plain of Ethiopia

Gender, wheat trait preferences, and innovation uptake: Lessons from Ethiopia and India

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