Inteligent Data Ecosystems, Rothamsted Research, Harpenden, Hertfordshire, United Kingdom.
Net Zero and Resilient Farming, Rothamsted Research, Harpenden, Hertfordshire, United Kingdom.
PLoS One. 2024 Nov 5;19(11):e0312734. doi: 10.1371/journal.pone.0312734. eCollection 2024.
Cassava is a staple in the diet of millions of people in sub-Saharan Africa, as it can grow in poor soils with limited inputs and can withstand a wide range of environmental conditions, including drought. Previous studies have shown that the distribution of rural populations is an important predictor of cassava density in sub-Saharan Africa's landscape. Our aim is to explore relationships between the distribution of cassava from the cassava production disaggregation models (CassavaMap and MapSPAM) and rural population density, looking at potential differences between countries and regions. We analysed various properties of cassava cultivations collected from surveys at 69 locations in Côte d'Ivoire and 87 locations in Uganda conducted between February and March 2018. The relationships between the proportion of surveyed land under cassava cultivation and rural population and settlement data were examined using a set of generalized additive models within each country. Information on rural settlements was aggregated around the survey locations at 2, 5 and 10 km circular buffers. The analysis of the original survey data showed no significant correlation between rural population and cassava production in both MapSPAM and CassavaMap. However, as we aggregate settlement buffers around the survey locations using CassavaMap, we find that at a large scale this model does capture large-scale variations in cassava production. Moreover, through our analyses, we discovered country-specific spatial trends linked to areas of higher cassava production. These analyses are useful for validating disaggregation models of cassava production. As the certainty that existing cassava production maps increases, analyses that rely on the disaggregation maps, such as models of disease spread, nutrient availability from cassava with respect to population in a region, etc. can be performed with increased confidence. These benefit social and natural scientists, policymakers and the population in general by ensuring that cassava production estimates are increasingly reliable.
木薯是撒哈拉以南非洲数百万人饮食中的主食，因为它可以在投入有限且土壤贫瘠的条件下生长，并能耐受广泛的环境条件，包括干旱。先前的研究表明，农村人口的分布是预测撒哈拉以南非洲景观中木薯密度的一个重要因素。我们的目的是探索木薯生产分解模型（CassavaMap 和 MapSPAM）中的木薯分布与农村人口密度之间的关系，同时研究国家和地区之间的潜在差异。我们分析了 2018 年 2 月至 3 月在科特迪瓦的 69 个地点和乌干达的 87 个地点进行的调查中收集的各种木薯种植地的属性。在每个国家内，我们使用一组广义加性模型来检验木薯种植比例与农村人口和定居点数据之间的关系。农村定居点的信息在以调查地点为中心的 2、5 和 10 公里的圆形缓冲区中进行了汇总。对原始调查数据的分析表明，在 MapSPAM 和 CassavaMap 中，农村人口与木薯产量之间均无显著相关性。然而，随着我们使用 CassavaMap 对调查地点周围的定居点缓冲区进行汇总，我们发现该模型确实可以在较大范围内捕捉到木薯产量的大规模变化。此外，通过我们的分析，我们发现了与高木薯产量地区相关的特定于国家的空间趋势。这些分析对于验证木薯产量分解模型是有用的。随着现有木薯产量地图的可信度提高，依赖于分解地图的分析，例如疾病传播模型、该地区木薯的营养可用性与人口之间的关系等，可以更有信心地进行。这使社会和自然科学家、政策制定者以及一般民众受益，因为它可以确保木薯产量估计的可靠性越来越高。
