Abstract Agroforestry is pointed out by the Intergovernmental Panel on Climate Change report as a key option to respond to climate change and land degradation while simultaneously improving global food security ( IPCC, 2019 ). Faidherbia albida parklands are widespread in Sub-Saharan Africa and provide several ecosystem services to populations, notably an increase in crop productivity. While remote sensing has been proven useful for crop yield assessment in smallholder farming system, it has so far ignored the woody component. We propose an original approach combining remote sensing, landscape ecology and statistical modelling to i) improve the accuracy of millet yield prediction in parklands and ii) identify the main drivers of millet yield spatial variation. The parkland of Central Senegal was chosen as a case study. Firstly, we calibrated a remote sensing-based linear model that accounted for vegetation productivity and tree density to predict millet yield. Integrating parkland structure improved the accuracy of yield estimation. The best model based on a combination of Green Difference Vegetation Index and number of trees in the field explained 70% of observed yield variability (relative Root Mean Squared Error (RRMSE) of 28%). The best model based solely on vegetation productivity (no information on parkland structure) explained only 46% of the observed variability (RRMSE = 34%). Secondly we investigated the drivers of the spatial variability in estimated yield using Gradient Boosting Machine algorithm (GBM) and biophysical and management factors derived from geospatial data. The GBM model explained 81% of yield spatial variability. Predominant drivers were soil nutrient availability (i.e. soil total nitrogen and total phosphorous) and woody cover in the surrounding landscape of fields. Our results show that millet yield increases with woody cover in the surrounding landscape of fields up to a woody cover of 35%. These findings have to be strengthened by testing the approach in more diversified and/or denser parklands. Our study illustrates that recent advances in earth observations open up new avenues to improve the monitoring of parkland systems in smallholder context.

中文翻译：

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使用遥感评估树木对塞内加尔中部复杂公园内小米产量的影响

摘要 政府间气候变化专门委员会报告指出农林业是应对气候变化和土地退化同时改善全球粮食安全的关键选择（IPCC，2019）。Faidherbia albida 公园在撒哈拉以南非洲广泛分布，为人们提供多种生态系统服务，尤其是提高作物生产力。虽然遥感已被证明对小农农业系统中的作物产量评估有用，但迄今为止它忽略了木质成分。我们提出了一种结合遥感、景观生态学和统计建模的原创方法，以 i) 提高公园用地小米产量预测的准确性，以及 ii) 确定小米产量空间变化的主要驱动因素。塞内加尔中部的公园被选为案例研究。首先，我们校准了一个基于遥感的线性模型，该模型考虑了植被生产力和树木密度，以预测小米产量。整合绿地结构提高了产量估算的准确性。基于绿色差异植被指数和田间树木数量组合的最佳模型解释了 70% 的观察到的产量变异性（相对均方根误差 (RRMSE) 为 28%）。仅基于植被生产力的最佳模型（没有关于公园绿地结构的信息）仅解释了 46% 的观测变异（RRMSE = 34%）。其次，我们使用梯度提升机算法 (GBM) 以及来自地理空间数据的生物物理和管理因素研究了估计产量的空间变异性的驱动因素。GBM 模型解释了 81% 的产量空间变异性。主要驱动因素是土壤养分有效性（即土壤总氮和总磷）和田地周围景观中的木质覆盖。我们的结果表明，谷子产量随着田地周围景观中的木本覆盖而增加，高达 35% 的木本覆盖。必须通过在更多样化和/或更密集的公园内测试该方法来加强这些发现。我们的研究表明，地球观测的最新进展开辟了改善小农背景下公园绿地系统监测的新途径。必须通过在更多样化和/或更密集的公园内测试该方法来加强这些发现。我们的研究表明，地球观测的最新进展开辟了改善小农背景下公园绿地系统监测的新途径。必须通过在更多样化和/或更密集的公园内测试该方法来加强这些发现。我们的研究表明，地球观测的最新进展开辟了改善小农背景下公园绿地系统监测的新途径。