Document Type


Publication Date


Publication Title



food security, yield prediction, Malawi, random forest regression, PlanetScope, vegetation indices


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.

Funding Source

This research was funded as part of the FARMS4Biodiversity project through the 2017–2018 Belmont Forum and BiodivERsA joint call for research proposals, under the BiodivScen ERA-Net COFUND program, and with the funding organizations German Federal Ministry of Education and Research (BMBF Förderkennzeichen 01LC11804A), Research Council of Norway (no: 295442), National Science Foundation (NSF project 1852587) and the Natural Sciences and Engineering Research Council of Canada (NSERC Grant#: 523660-2018).




First published in Land 2022, 11(10), 1752;

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (