Predictive Modelling of Amaranthus hybridus Emergence Under Climate Change: Implications for the Efficiency of Bean and Maize Crop Systems

Climate change poses a significant challenge to food security, as it alters crop productivity, distribution patterns, and the overall food supply. This study modelled the emergence of Amaranthus hybridus L. in bean (Phaseolus vulgaris L.) and maize (Zea mays L.) production systems in the Brazilian state of Minas Gerais, in the cities of Coimbra, Paracatu, São João del-Rei, and Uberaba, under the Coupled Model Intercomparison Project Phase 6 (CMIP6) SSP1-2.6 and SSP5-8.5 scenarios. Using Hydrothermal Time (HTT), computational modelling, and nonlinear Weibull regression, weed emergence was simulated under current and future climate scenarios for 2050 and 2070. Although biological triggers such as temperature and base water potential remain constant, higher average temperatures accelerate HTT accumulation. Thus, this results in earlier and more intense emergence flows. The highest and lowest cumulative emergence were observed in Uberaba and Paracatu, respectively. The SSP5-8.5 scenario projects high emergence windows for 2070. This reduces the time available for management interventions. The root-mean-square error (RMSE) associated with the coefficient of determination (R2) of the models validates HTT as an essential tool in computational agriculture. The integration of these models into decision-support systems is essential to mitigating productivity losses and it will increase control efficiency amid future climate uncertainties.