Anghileri, Daniela, Chibarabada, Tendai Polite, Gadedjisso-Tossou, Agossou, Craig, Ailish, Li, Chengxiu, Lu, Yang, Chimimba, Ellasy Gulule, Kambombe, Oscar, Musa, Frank, Ngongondo, Cosmo, Eneya, Levis, Onema, Jean Marie Kileshye, Ali, Abdou, Chiotha, Sosten, Dash, Jadunandan and Sheffield, Justin (2024) Understanding the maize yield gap in Southern Malawi by integrating ground and remote-sensing data, models, and household surveys. Agricultural Systems, 218, [103962]. (doi:10.1016/j.agsy.2024.103962).
Abstract
Context: improving the productivity of smallholder farmers in sub-Saharan Africa is a key component in reducing poverty and increasing food security as crop production is a significant source of livelihood for the majority of the population. Still, crop yields show a huge variability in smallholder farming systems whose productivity is poorly measured and understood.
Objective: in this work, we estimate maize (Zea Mays) yield gap in Southern Malawi (Phalombe district) and assess drivers of productivity gap under different socio-economic and biophysical contexts.
Methods: we use a mixed-method approach which integrates multi-source datasets (including primary ground-truth data we collected in the maize growing season 2019–2020 and secondary remote-sensing data), empirical and process-based crop-growth models (AquaCrop) to calculate the water-limited yield gap. In addition, we analyse the relationship between the relative yield (defined as the actual yield observed at the farmers' plots normalised by the AquaCrop simulated water-limited potential yield) and possible socio-economic drivers which we collected through surveys administered to households iin the same season 2019–2020.
Results and Conclusions: we obtained a water-limited potential yield for the maize hybrid SC649 of 9.5 t/ha during the season 2019–2020 in the Malawian trial site. The observed actual yield at the households in the season 2019–2020 varied from 0.8 to 10.9 t/ha. The estimate of the yield gap ranged between 15% and 85% thus showing a large variability due to the high resolution, but low accuracy of the empirical model. Results suggest that with higher income and increased fertiliser application there is potential to increase the relative yield and that the marginal increase is spatially differentiated. SIGNIFICANCE: Our spatially-explicit approach to yield-gap analysis is valuable in identifying high-productive areas and differentiated policy interventions aimed at closing the yield and income gaps for smallholder farmers.
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