Potential Use of Data-Driven Models to Estimate and Predict Soybean Yields at National Scale in Brazil

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F22%3A00567485" target="_blank" >RIV/86652079:_____/22:00567485 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/content/pdf/10.1007/s42106-022-00209-0.pdf?pdf=button" target="_blank" >https://link.springer.com/content/pdf/10.1007/s42106-022-00209-0.pdf?pdf=button</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s42106-022-00209-0" target="_blank" >10.1007/s42106-022-00209-0</a>

Result language

angličtina

Original language name

Potential Use of Data-Driven Models to Estimate and Predict Soybean Yields at National Scale in Brazil

Original language 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 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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

40101 - Agriculture

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

International Journal of Plant Production

ISSN

1735-6814

e-ISSN

1735-8043

Volume of the periodical

16

Issue of the periodical within the volume

4

Country of publishing house

IR - IRAN, ISLAMIC REPUBLIC OF

Number of pages

13

Pages from-to

691-703

UT code for WoS article

000851058700001

EID of the result in the Scopus database

2-s2.0-85137479259