Search: id:"swepub:oai:research.chalmers.se:4456f599-82bd-4e82-8610-453a2d63b91c" >
Potential Use of Da...
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Monteiro, Leonardo A.Food and Agriculture Organization of the United Nations,University of Kentucky
(author)
Potential Use of Data-Driven Models to Estimate and Predict Soybean Yields at National Scale in Brazil
- Article/chapterEnglish2022
Publisher, publication year, extent ...
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2022-09-07
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Springer Science and Business Media LLC,2022
Numbers
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LIBRIS-ID:oai:research.chalmers.se:4456f599-82bd-4e82-8610-453a2d63b91c
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https://doi.org/10.1007/s42106-022-00209-0DOI
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https://research.chalmers.se/publication/532111URI
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https://res.slu.se/id/publ/119035URI
Supplementary language notes
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Language:English
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Summary in:English
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Classification
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Subject category:art swepub-publicationtype
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Subject category:ref swepub-contenttype
Notes
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Large-scale assessment of crop yields plays a fundamental role for agricultural planning and to achieve food security goals. In this study, we evaluated the robustness of data-driven models for estimating soybean yields at 120 days after sow (DAS) in the main producing regions in Brazil; and evaluated the reliability of the “best” data-driven model as a tool for early prediction of soybean yields for an independent year. Our methodology explicitly describes a general approach for wrapping up publicly available databases and build data-driven models (multiple linear regression—MLR; random forests—RF; and support vector machines—SVM) to predict yields at large scales using gridded data of weather and soil information. We filtered out counties with missing or suspicious yield records, resulting on a crop yield database containing 3450 records (23 years × 150 “high-quality” counties). RF and SVM had similar results for calibration and validation steps, whereas MLR showed the poorest performance. Our analysis revealed a potential use of data-driven models for predict soybean yields at large scales in Brazil with around one month before harvest (i.e. 90 DAS). Using a well-trained RF model for predicting crop yield during a specific year at 90 DAS, the RMSE ranged from 303.9 to 1055.7 kg ha–1 representing a relative error (rRMSE) between 9.2 and 41.5%. Although we showed up robust data-driven models for yield prediction at large scales in Brazil, there are still a room for improving its accuracy. The inclusion of explanatory variables related to crop (e.g. growing degree-days, flowering dates), environment (e.g. remotely-sensed vegetation indices, number of dry and heat days during the cycle) and outputs from process-based crop simulation models (e.g. biomass, leaf area index and plant phenology), are potential strategies to improve model accuracy.
Subject headings and genre
Added entries (persons, corporate bodies, meetings, titles ...)
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Ramos, Rafael M.
(author)
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Battisti, RafaelUniversidade Federal de Goias,Federal University of Goiás
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Soares, Johnny R.
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de Castro Oliveira, Julianne,1987Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)deju
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Figueiredo, Gleyce K.D.A.
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Lamparelli, Rubens A.C.
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Nendel, ClaasCzech Academy of Sciences,Universität Potsdam,University of Potsdam,Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF),Leibniz Centre for Agricultural Landscape Research (ZALF)
(author)
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Lana, Marcos AlbertoSwedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för växtproduktionsekologi,Department of Crop Production Ecology,Sveriges lantbruksuniversitet (SLU),Swedish University of Agricultural Sciences (SLU)
(author)
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Food and Agriculture Organization of the United NationsUniversity of Kentucky
(creator_code:org_t)
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Sveriges lantbruksuniversitet
Related titles
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In:International Journal of Plant Production: Springer Science and Business Media LLC16:4, s. 691-7031735-80431735-6814
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