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Estimating Groundnut Yield in Smallholder Agriculture Systems Using PlanetScope Data

Author

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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)

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.

Suggested Citation

  • Daniel Kpienbaareh & Kamaldeen Mohammed & Isaac Luginaah & Jinfei Wang & Rachel Bezner Kerr & Esther Lupafya & Laifolo Dakishoni, 2022. "Estimating Groundnut Yield in Smallholder Agriculture Systems Using PlanetScope Data," Land, MDPI, vol. 11(10), pages 1-19, October.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:10:p:1752-:d:937037
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    References listed on IDEAS

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    1. Kate Ambler & Alan de Brauw & Susan Godlonton, 2018. "Measuring postharvest losses at the farm level in Malawi," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 62(1), pages 139-160, January.
    2. Edith Olmos-Trujillo & Julián González-Trinidad & Hugo Júnez-Ferreira & Anuard Pacheco-Guerrero & Carlos Bautista-Capetillo & Claudia Avila-Sandoval & Eric Galván-Tejada, 2020. "Spatio-Temporal Response of Vegetation Indices to Rainfall and Temperature in A Semiarid Region," Sustainability, MDPI, vol. 12(5), pages 1-18, March.
    3. Rodney Lunduka & Jacob Ricker-Gilbert & Monica Fisher, 2013. "What are the farm-level impacts of Malawi's farm input subsidy program? A critical review," Agricultural Economics, International Association of Agricultural Economists, vol. 44(6), pages 563-579, November.
    4. Lowder, Sarah K. & Skoet, Jakob & Raney, Terri, 2016. "The Number, Size, and Distribution of Farms, Smallholder Farms, and Family Farms Worldwide," World Development, Elsevier, vol. 87(C), pages 16-29.
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    Cited by:

    1. Mohammed, Kamaldeen & Batung, Evans & Saaka, Sulemana Ansumah & Kansanga, Moses Mosonsieyiri & Luginaah, Isaac, 2023. "Determinants of mechanized technology adoption in smallholder agriculture: Implications for agricultural policy," Land Use Policy, Elsevier, vol. 129(C).

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