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Potential Use of Da...
Potential Use of Data-Driven Models to Estimate and Predict Soybean Yields at National Scale in Brazil
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- Monteiro, Leonardo A. (author)
- Food and Agriculture Organization of the United Nations,University of Kentucky
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Ramos, Rafael M. (author)
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- Battisti, Rafael (author)
- Universidade Federal de Goias,Federal University of Goiás
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Soares, Johnny R. (author)
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- de Castro Oliveira, Julianne, 1987 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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Figueiredo, Gleyce K.D.A. (author)
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Lamparelli, Rubens A.C. (author)
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- Nendel, Claas (author)
- Czech Academy of Sciences,Universität Potsdam,University of Potsdam,Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF),Leibniz Centre for Agricultural Landscape Research (ZALF)
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- Lana, Marcos Alberto (author)
- Swedish University of Agricultural Sciences,Sveriges lantbruksuniversitet,Institutionen för växtproduktionsekologi,Department of Crop Production Ecology,Sveriges lantbruksuniversitet (SLU),Swedish University of Agricultural Sciences (SLU)
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(creator_code:org_t)
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- 2022-09-07
- 2022
- English.
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In: International Journal of Plant Production. - : Springer Science and Business Media LLC. - 1735-8043 .- 1735-6814. ; 16:4, s. 691-703
- Related links:
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https://doi.org/10.1...
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Abstract
Subject headings
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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
- NATURVETENSKAP -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
- NATURVETENSKAP -- Geovetenskap och miljövetenskap -- Naturgeografi (hsv//swe)
- NATURAL SCIENCES -- Earth and Related Environmental Sciences -- Physical Geography (hsv//eng)
- LANTBRUKSVETENSKAPER -- Lantbruksvetenskap, skogsbruk och fiske -- Jordbruksvetenskap (hsv//swe)
- AGRICULTURAL SCIENCES -- Agriculture, Forestry and Fisheries -- Agricultural Science (hsv//eng)
Keyword
- Machine learning approaches
- Public databases
- Large-scale analysis
- Geospatial and temporal variability
- Climatic and soil variables
Publication and Content Type
- art (subject category)
- ref (subject category)
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