Assessing yield and fertilizer response in heterogeneous smallholder fields with UAVs and satellites

Schut, A G T and Traore, P C S and Blaes, X and de By, R A (2018) Assessing yield and fertilizer response in heterogeneous smallholder fields with UAVs and satellites. Field Crops Research (TSI), 221. pp. 98-107. ISSN 03784290

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Abstract

Agricultural intensification and efficient use and targeting of fertilizer inputs on smallholder farms is key to sustainably improve food security. The objective of this paper is to demonstrate how high-resolution satellite and unmanned aerial vehicle (UAV) images can be used to assess the spatial variability of yield, and yield response to fertilizer. The study included 48 and 50 smallholder fields monitored during the 2014 and 2015 cropping seasons south-east of Koutiala (Mali), cropped with the five major crops grown in the area (cotton, maize, sorghum, millet and peanuts). Each field included up to five plots with different fertilizer applications and one plot with farmer practice. Fortnightly, in-situ in each field data were collected synchronous with UAV imaging using a Canon S110 NIR camera. A concurrent series of very high-resolution satellite images was procured and these images were used to mask out trees. For each plot, we calculated vegetation index means, medians and coefficients of variation. Cross-validated general linear models were used to assess the predictability of relative differences in crop yield and yield response to fertilizer, explicitly accounting for the effects of fertility treatments, between-field and within-field variabilities. Differences between fields accounted for a much larger component of variation than differences between fertilization treatments. Vegetation indices from UAV images strongly related to ground cover (R2 = 0.85), light interception (R2 = 0.79) and vegetation indices derived from satellite images (R2 values of about 0.8). Within-plot distributions of UAV-derived vegetation index values were negatively skewed, and within-plot variability of vegetation index values was negatively correlated with yield. Plots on shallow soils with poor growing conditions showed the largest within-plot variability. GLM models including UAV derived estimates of light interception explained up to 78% of the variation in crop yield and 74% of the variation in fertilizer response within a single field. These numbers dropped to about 45% of the variation in yield and about 48% of the variation in fertilizer response when lumping all fields of a given crop, with Q2 values of respectively 22 and 40% respectively when tested with a leave-field-out procedure. This indicates that remotely sensed imagery doesn’t fully capture the influence of crop stress and management. Assessment of crop fertilizer responses with vegetation indices therefore needs a reference under similar management. Spatial variability in UAV-derived vegetation index values at the plot scale was significantly related to differences in yields and fertilizer responses. The strong relationships between light interception and ground cover indicate that combining vertical photographs or high-resolution remotely sensed vegetation indices with crop growth models allows to explicitly account for the spatial variability and will improve the accuracy of yield and crop production assessments, especially in heterogeneous smallholder conditions.

Item Type: Article
Divisions: Research Program : West & Central Africa
CRP: CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS)
Uncontrolled Keywords: UAV, Agriculture, Ground coverage, Spatial variability, Smallholder landscapes, Unmanned Aerial Vehicle, Satellite images, Smallholder fields, Yields , UAV systems, Remote sensing technology, Smallholder farming
Subjects: Others > Smallholder Farmers
Others > GIS Techniques/Remote Sensing
Others > Smallholder Agriculture
Others > Crop Yield
Others > Fertilizer Applications
Others > Digital Agriculture
Depositing User: Mr Ramesh K
Date Deposited: 27 Mar 2018 06:39
Last Modified: 27 Mar 2018 06:39
URI: http://oar.icrisat.org/id/eprint/10504
Official URL: http://dx.doi.org/10.1016/j.fcr.2018.02.018
Projects: STARS Project
Funders: Bill and Melinda Gates Foundation and CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS)
Acknowledgement: This publication was made possible (in part) by the STARS project, an integrated effort to improve our understanding of the use of remote sensing technology in monitoring smallholder farming supported by the Bill and Melinda Gates Foundation (www.stars-project.org). Additional support was provided by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), which is carried out with support from CGIAR Fund Donors and through bilateral funding agreements. For details please visit https://ccafs.cgiar.org/donors. The views expressed in this document cannot be taken to reflect the official opinions of these organisations. The authors would like to acknowledge the field team members: Daouda Sanou (AMEDD), Nema Dembélé (AMEDD), Nouhoum Dembélé (AMEDD), Oumar Diabaté (AMEDD), Gilbert Dembélé (AMEDD), Birama Sissoko (AMEDD), Ousmane Dembélé (AMEDD), Bougouna Sogoba (AMEDD), Issa Kassogué (ICRISAT), Ousmane Ndiaye (ICRISAT), Fatoumata Sagounta (ICRISAT), Adja R. Sangaré (ICRISAT) and students Wilmar van Ommeren, Cass Gooskens, Bastiaen Boekelo, Joel Davidse, Frederic de Schaetzen and Guillaume Chomé for their contributions.
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