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Potential Use of Data-Driven Models to Estimate and Predict Soybean Yields at National Scale in Brazil
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 evaluat...
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| Published in: | International journal of plant production 2022-12, Vol.16 (4), p.691-703 |
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| creator | Monteiro, Leonardo A. Ramos, Rafael M. Battisti, Rafael Soares, Johnny R. Oliveira, Julianne C. Figueiredo, Gleyce K. D. A. Lamparelli, Rubens A. C. Nendel, Claas Lana, Marcos Alberto |
| description | 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. |
| doi_str_mv | 10.1007/s42106-022-00209-0 |
| format | article |
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–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.</description><identifier>ISSN: 1735-6814</identifier><identifier>ISSN: 1735-8043</identifier><identifier>EISSN: 1735-8043</identifier><identifier>DOI: 10.1007/s42106-022-00209-0</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Agricultural Science ; Agriculture ; Biomedical and Life Sciences ; Climatic and soil variables ; Geospatial and temporal variability ; Jordbruksvetenskap ; Large-scale analysis ; Life Sciences ; Machine learning approaches ; Plant Ecology ; Plant Physiology ; Public databases</subject><ispartof>International journal of plant production, 2022-12, Vol.16 (4), p.691-703</ispartof><rights>Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c406t-f0afc81162566f22fb587ebe518d187128b3a9d3847cef0cc2c3b71e832ed9c63</citedby><cites>FETCH-LOGICAL-c406t-f0afc81162566f22fb587ebe518d187128b3a9d3847cef0cc2c3b71e832ed9c63</cites><orcidid>0000-0003-3889-6095</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,315,786,790,891,27957,27958</link.rule.ids><backlink>$$Uhttps://research.chalmers.se/publication/532111$$DView record from Swedish Publication Index$$Hfree_for_read</backlink><backlink>$$Uhttps://res.slu.se/id/publ/119035$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><creatorcontrib>Monteiro, Leonardo A.</creatorcontrib><creatorcontrib>Ramos, Rafael M.</creatorcontrib><creatorcontrib>Battisti, Rafael</creatorcontrib><creatorcontrib>Soares, Johnny R.</creatorcontrib><creatorcontrib>Oliveira, Julianne C.</creatorcontrib><creatorcontrib>Figueiredo, Gleyce K. D. A.</creatorcontrib><creatorcontrib>Lamparelli, Rubens A. C.</creatorcontrib><creatorcontrib>Nendel, Claas</creatorcontrib><creatorcontrib>Lana, Marcos Alberto</creatorcontrib><creatorcontrib>Sveriges lantbruksuniversitet</creatorcontrib><title>Potential Use of Data-Driven Models to Estimate and Predict Soybean Yields at National Scale in Brazil</title><title>International journal of plant production</title><addtitle>Int. J. Plant Prod</addtitle><description>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.</description><subject>Agricultural Science</subject><subject>Agriculture</subject><subject>Biomedical and Life Sciences</subject><subject>Climatic and soil variables</subject><subject>Geospatial and temporal variability</subject><subject>Jordbruksvetenskap</subject><subject>Large-scale analysis</subject><subject>Life Sciences</subject><subject>Machine learning approaches</subject><subject>Plant Ecology</subject><subject>Plant Physiology</subject><subject>Public databases</subject><issn>1735-6814</issn><issn>1735-8043</issn><issn>1735-8043</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9kctuFDEQRVsIJELgB1j5BzqUH-12LyEJEClApCELVlbZLhNHne7I9gSFr4-HGbGDVdWiztFV3a57y-GEA4zvihIcdA9C9AACph6edUd8lENvQMnnh10brl52r0q5BdBac3PUxau10lITzuy6EFsjO8OK_VlOD7SwL2ugubC6svNS0x1WYrgEdpUpJF_ZZn10hAv7kWgOhWFlX7GmdWmyjceZWFrYh4y_0_y6exFxLvTmMI-764_n308_95ffPl2cvr_svQJd-wgYveFci0HrKER0gxnJ0cBN4GbkwjiJU5BGjZ4ieC-8dCMnIwWFyWt53J3sveUX3W-dvc8tdX60KyZb5q3DvBu2kOV8Ajk0YPNPIFMhzP7G-huc7yiXHafUoOMwTdYIF6wiI6zRHKwaJIqgpZu4b1axt_q8lpIp_vW2y11hdl-YbYXZP4VZaJA8RGnHy0_K9nbd5vbM8j_qCdoTmRc</recordid><startdate>20221201</startdate><enddate>20221201</enddate><creator>Monteiro, Leonardo A.</creator><creator>Ramos, Rafael M.</creator><creator>Battisti, Rafael</creator><creator>Soares, Johnny R.</creator><creator>Oliveira, Julianne C.</creator><creator>Figueiredo, Gleyce K. 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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.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s42106-022-00209-0</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0003-3889-6095</orcidid></addata></record> |
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| subjects | Agricultural Science Agriculture Biomedical and Life Sciences Climatic and soil variables Geospatial and temporal variability Jordbruksvetenskap Large-scale analysis Life Sciences Machine learning approaches Plant Ecology Plant Physiology Public databases |
| title | Potential Use of Data-Driven Models to Estimate and Predict Soybean Yields at National Scale in Brazil |
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