| Full text | |
| Author(s): |
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
Total Authors: 9
|
| Document type: | Journal article |
| Source: | INTERNATIONAL JOURNAL OF PLANT PRODUCTION; v. 16, n. 4, p. 13-pg., 2022-09-07. |
| Abstract | |
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 x 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. (AU) | |
| FAPESP's process: | 14/26767-9 - Global Sustainable Bioenergy Initiative: geospatial & environmental analysis of pastureland intensification for bioenergy |
| Grantee: | John Joseph Sheehan |
| Support Opportunities: | Research Projects - SPEC Program |
| FAPESP's process: | 17/08970-0 - Pasture potential and subsequent production of energy crops |
| Grantee: | Leonardo Amaral Monteiro |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |