Estimating Groundnut Yield in Smallholder Agriculture Systems Using PlanetScope Data

Daniel Kpienbaareh, Kamaldeen Mohammed (), Isaac Luginaah, Jinfei Wang, Rachel Bezner Kerr, Esther Lupafya and Laifolo Dakishoni
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Daniel Kpienbaareh: Department of Geography, Geology and the Environment, Illinois State University, 104 Felmley Hall, Normal, IL 61790-4000, USA
Kamaldeen Mohammed: Department of Geography, University of Western Ontario, 151 Richmond St, London, ON N6A 3K7, Canada
Isaac Luginaah: Department of Geography, University of Western Ontario, 151 Richmond St, London, ON N6A 3K7, Canada
Jinfei Wang: Department of Geography, University of Western Ontario, 151 Richmond St, London, ON N6A 3K7, Canada
Rachel Bezner Kerr: Department of Global Development, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853, USA
Esther Lupafya: Soils, Food and Healthy Communities (SFHC), Ekwendeni P.O. Box 36, Malawi
Laifolo Dakishoni: Soils, Food and Healthy Communities (SFHC), Ekwendeni P.O. Box 36, Malawi

Land, 2022, vol. 11, issue 10, 1-19

Abstract: 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 ( R 2 ) of 0.96 and a root mean square error (RMSE) of 0.29 kg/ha, followed by the MLR model ( R 2 = 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.

Keywords: food security; yield prediction; Malawi; random forest regression; PlanetScope; vegetation indices (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:11:y:2022:i:10:p:1752-:d:937037

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