Walnut blight, caused by Xanthomonas arboricola pv. juglandis (Xaj), occurs worldwide in almost all areas where the Persian walnut (Juglans regia) is grown, causing significant reductions in nut yield via defoliation and fruit drop. The disease control relies on the calendar-based, repeated use of chemical bactericides, negatively impacting economic and environmental sustainability and potentially inducing Xaj resistance to chemicals. This study developed and validated a mechanistic model incorporating the main stages of the pathogen’s life cycle and the influence of weather under orchard conditions to improve the scheduling of disease control interventions. The model can simulate (i) the mobilization of primary inoculum; (ii) the infection caused by bacteria; and (iii) the lesion formation and production of secondary inoculum. We evaluated the model against 21 independent walnut blight epidemics in Italy (9 epidemics on leaves in five orchards) and the United States (12 epidemics on fruit in six orchards). Overall, the model provided accurate predictions for both the occurrence and nonoccurrence of infection, with a precision and F1-score of 0.866 and 0.844, respectively. The model could accurately predict disease progression across the season, with a concordance correlation coefficient between observed and predicted disease severities of 0.951, a root mean square error of 0.069, and a coefficient of residual mass of 0.024. After further validation, the model can serve as a decision-making tool for the risk-based timing of chemical sprays in walnut blight management.

Greenhouse microclimates exhibit substantial spatial heterogeneity. Conventional evaluations based on average measurements cannot adequately describe the environmental conditions experienced by crops. This study developed a spatial frequency analysis method to quantify long-term temperature and relative humidity heterogeneity. This method was coupled with cucumber yield and downy mildew models to evaluate the biological consequences of environmental variability. Environmental conditions in four representative greenhouse structures were monitored using distributed sensor networks under different weather conditions. The proposed method successfully identified persistent environmental hotspots. The spatial distribution of environmental heterogeneity differed among greenhouse structures, whereas solar radiation primarily controlled its intensity. Long-season monitoring data indicate that the maximum spatial variations in temperature and relative humidity within the brick-wall solar greenhouse, the assembled greenhouse, the glass greenhouse, and the plastic multi-span greenhouse reach up to 10 °C and 46%, 8.5 °C and 46%, 12 °C and 50%, and 7 °C and 32%, respectively. The assembled solar greenhouse and plastic greenhouse exhibited higher environmental uniformity, while the brick-wall solar greenhouse and glass multi-span greenhouse showed pronounced spatial gradients. Environmental heterogeneity resulted in significant within-greenhouse differences in simulated cucumber yield, with the smallest variation occurring in the assembled solar greenhouse (5.3%) and the largest in the glass multi-span greenhouse (39.2%). Disease simulations further revealed clear spatial aggregation of cucumber downy mildew risk, with the southern region of the solar greenhouse exhibiting 17–67% higher cumulative infection risk than other positions. This study establishes an integrated framework linking greenhouse environmental heterogeneity with crop productivity and disease risk, providing a practical basis for spatially differentiated precision greenhouse management and greenhouse structural optimization.