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. In this work, we estimate maize () yield gap in Southern Malawi (Phalombe district) and assess drivers of productivity gap under different socio-economic and biophysical contexts. 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. 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. 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.

中文翻译：

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通过整合地面和遥感数据、模型和家庭调查了解马拉维南部的玉米产量差距


提高撒哈拉以南非洲小农的生产力是减少贫困和加强粮食安全的关键组成部分，因为农作物生产是大多数人口的重要生计来源。尽管如此，小农耕作系统的作物产量仍存在巨大差异，其生产力的衡量和了解甚少。在这项工作中，我们估计了马拉维南部（Phalombe 地区）的玉米产量差距，并评估了不同社会经济和生物物理背景下生产力差距的驱动因素。我们采用混合方法，整合了多源数据集（包括我们在 2019-2020 年玉米生长季收集的主要地面实况数据和二次遥感数据）、经验和基于过程的作物生长模型 (AquaCrop)计算水限产量差距。此外，我们分析了相对产量（定义为通过 AquaCrop 模拟限水潜在产量标准化的农民地块上观察到的实际产量）与我们通过对家庭进行的调查收集的可能的社会经济驱动因素之间的关系。 2019-2020 年同一赛季。我们在 2019-2020 年季节在马拉维试验地点获得了玉米杂交种 SC649 的水限制潜在产量为 9.5 吨/公顷。 2019-2020 年季节观察到的家庭实际产量在 0.8 至 10.9 吨/公顷之间。产量差距的估计范围在 15% 到 85% 之间，因此由于经验模型的分辨率较高但精度较低，因此显示出较大的变异性。结果表明，随着收入的增加和化肥施用的增加，相对产量有可能增加，而且边际增加在空间上存在差异。 我们的空间明确的产量差距分析方法对于确定高产地区和旨在缩小小农产量和收入差距的差异化政策干预措施非常有价值。