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

## TL;DR

A new model combining crop simulations and weather data improves predictions of Brazil's off-season maize yields.

## Contribution

A national-scale forecasting model for off-season maize using JULES-crop and agrometeorological indicators was developed and validated.

## Key findings

- Agrometeorological indicators during the reproductive phase explain 60% of interannual yield variability.
- Including JULES-crop outputs improved forecast accuracy with EF values of 0.77 at maturation and 0.72 at grain filling.
- Modeling performance improved during the vegetative stage, reducing prediction error.

## Abstract

Zea mays L is a crucial crop for Brazil, ranking second in terms of production and sixth in terms of exports. In Brazil, the second season, or off-season, accounts for 80 % of the overall maize output, which primarily occurs after the soybean main season. A maize yield forecast model for the off-season was developed and implemented throughout Brazilian territory due to its importance to the country's economy and food security. The model was built using multiple linear regressions that connected outputs simulated from a land surface model used in large-scale analysis for agriculture (JULES-crop), to agrometeorological indicators. The application of the developed model occurred every 10 days from the sowing until the maturation. A comparison of the forecasting model was verified with the official off-season maize yields for the years 2003–2016. Agrometeorological indicators during the reproductive phase accounted for 60 % of the interannual variability in maize production. When outputs simulated by JULES-crop were included, the forecasting model achieved Nash-Sutcliffe modeling efficiency (EF) of 0.77 in the maturation and EF = 0.72 in the filling-grain stage, suggesting that this approach can generate useful predictions for final maize yield beginning on the 80th day of the cycle. Outputs of JULES crop enhanced modeling performance during the vegetative stage, reducing the standard deviation error in prediction from 0.59 to 0.49 Mg ha−1.

•A yield-predicting approach was proposed based on JULES-crop outputs and agrometeorological indicators.•The yield forecast model was tested at the national scale for off-season maize.•The forecasting approach demonstrated high performance in predicting yield from the 80th day of the cycle.•Maize yield inter-annual variability was explained by agrometeorological indicators in 60 %, during the reproductive stage.

A yield-predicting approach was proposed based on JULES-crop outputs and agrometeorological indicators.

The yield forecast model was tested at the national scale for off-season maize.

The forecasting approach demonstrated high performance in predicting yield from the 80th day of the cycle.

Maize yield inter-annual variability was explained by agrometeorological indicators in 60 %, during the reproductive stage.