Estimating Groundnut Yield in Smallholder Agriculture Systems Using PlanetScope Data
Estimating Groundnut Yield in Smallholder Agriculture Systems Using PlanetScope Data
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DOI：
10.3390/land11101752
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发表时间：
2022-10
期刊：
影响因子：
3.9
通讯作者：
Daniel Kpienbaareh;Kamaldeen Mohammed;I. Luginaah;Jinfei Wang;R. Bezner Kerr;E. Lupafya;L. Dakishoni
中科院分区：
文献类型：
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作者：
Daniel Kpienbaareh;Kamaldeen Mohammed;I. Luginaah;Jinfei Wang;R. Bezner Kerr;E. Lupafya;L. Dakishoni
作物产量与家庭粮食安全和社区抵御力有关，特别是在小农农业系统中。因此，准确估计季内产量是至关重要的，以便为农场管理和决策提供关键信息。因此，本文的主要目的是评估预测马拉维北部小农农业系统花生产量的最合适方法、指标和生长阶段。我们利用观测到的产量和植被指数（VIs）估算了两个小农农场的花生产量，这些指数来源于多时相PlanetScope卫星数据。采用简单线性、多元线性（MLR）和随机森林（RF）回归进行预测。采用留一交叉验证法对模型进行验证。结果表明：(1)5个最重要变量的RF模型（RF5）是预测花生产量的最佳方法，其决定系数（R2）为0.96，均方根误差（RMSE）为0.29 kg/ha，其次是MLR模型（R2 = 0.84, RMSE = 0.84 kg/ha）；此外，（ii）准确预测花生产量的季内最佳时期是R5/初种期。RF5模型用于估算四个不同农场的产量。将估计产量与农场报告的总产量进行比较。结果表明，RF5模型对花生产量的估计较为准确，误差范围在0.85% ~ 11%之间。这些误差在马拉维的收获后损失幅度之内。结果表明，利用开源遥感数据获得的观测产量和能见度可用于估算产量，为农业生产和粮食安全规划提供依据。
Crop yield is related to household food security and community resilience, especially in smallholder agricultural systems. As such, it is crucial to accurately estimate within-season yield in order to provide critical information for farm management and decision making. Therefore, the primary objective of this paper is to assess the most appropriate method, indices, and growth stage for predicting the groundnut yield in smallholder agricultural systems in northern Malawi. We have estimated the yield of groundnut in two smallholder farms using the observed yield and vegetation indices (VIs), which were derived from multitemporal PlanetScope satellite data. Simple linear, multiple linear (MLR), and random forest (RF) regressions were applied for the prediction. The leave-one-out cross-validation method was used to validate the models. The results showed that (i) of the modelling approaches, the RF model using the five most important variables (RF5) was the best approach for predicting the groundnut yield, with a coefficient of determination (R2) of 0.96 and a root mean square error (RMSE) of 0.29 kg/ha, followed by the MLR model (R2 = 0.84, RMSE = 0.84 kg/ha); in addition, (ii) the best within-season stage to accurately predict groundnut yield is during the R5/beginning seed stage. The RF5 model was used to estimate the yield for four different farms. The estimated yields were compared with the total reported yields from the farms. The results revealed that the RF5 model generally accurately estimated the groundnut yields, with the margins of error ranging between 0.85% and 11%. The errors are within the post-harvest loss margins in Malawi. The results indicate that the observed yield and VIs, which were derived from open-source remote sensing data, can be applied to estimate yield in order to facilitate farming and food security planning.