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  1. Home
  3. Application Of The Jules-crop Model And Agrometeorological Indicators For Forecasting Off-season Maize Yield In Brazil.
  1. Home
  3. Application Of The Jules-crop Model And Agrometeorological Indicators For Forecasting Off-season Maize Yield In Brazil.

Related Experiment Video

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
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Published on: March 28, 2025

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Application of the JULES-crop model and agrometeorological indicators for forecasting off-season maize yield in

Amauri Cassio Prudente Junior1, Murilo S Vianna2, Karina Willians2,3

  • 1Institute of Physics- University of São Paulo, São Paulo-SP, 05508-090, Brazil.

Heliyon
|April 25, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

A new maize yield forecast model for Brazil

Keywords:
Large-scale analysisYield forecastZea mays.L

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Area of Science:

  • Agricultural Science
  • Crop Modeling
  • Climate Science

Background:

  • Maize (Zea mays L.) is a vital crop for Brazil, ranking second in production and sixth in exports.
  • Brazil's off-season maize production, occurring after soybeans, constitutes 80% of the national output.
  • Accurate maize yield forecasting is crucial for Brazil's economy and food security.

Purpose of the Study:

  • To develop and implement a maize yield forecast model for Brazil's off-season.
  • To integrate agrometeorological indicators and land surface model outputs for improved prediction accuracy.
  • To provide timely yield predictions throughout the maize growing cycle.

Main Methods:

  • Developed a model using multiple linear regressions connecting JULES-crop (a land surface model) outputs with agrometeorological indicators.
  • Applied the model every 10 days from sowing to maturation.
  • Validated the model against official off-season maize yields from 2003-2016.

    • Main Results:

    • Agrometeorological indicators during the reproductive phase explained 60% of interannual maize production variability.
    • The model achieved a Nash-Sutcliffe efficiency (EF) of 0.77 at maturation and 0.72 during grain filling when JULES-crop outputs were included.
    • JULES-crop outputs improved vegetative stage modeling, reducing prediction error standard deviation from 0.59 to 0.49 Mg ha⁻¹.

    Conclusions:

    • The developed model provides useful maize yield predictions starting from the 80th day of the cycle.
    • Integrating land surface model simulations significantly enhances forecasting accuracy.
    • This approach supports economic and food security planning in Brazil.