Crop productivity under heat stress: a structural analysis of light use efficiency models

Abstract

The increasing frequency and intensity of extreme heat events necessitate reliable global estimates of crop productivity under heat stress. Light use efficiency (LUE) models are commonly used for macroscale crop productivity estimation but exhibit uncertainties under high-temperature extremes related to the representation of model components and their interactions. They also struggle to isolate heat stress effects from other factors. This study reduced LUE model uncertainty for crop productivity estimation under heat stress by systematically assessing the representations of three essential components: the fraction of photosynthetically active radiation absorbed by the canopy (F_PAR), the temperature constraint (F_T), and the moisture constraint (F_M), and the synergy among them under heat-stressed and normal conditions. Model optimizations used data from 75 heat periods (HP) across 18 cropland flux sites worldwide for gross primary production (GPP) estimation, where crops were solely stressed by high temperatures, independent of low soil moisture and unfavorable light. By testing 200 LUE configurations in HP conditions, combing five F_PAR and F_T representations, and four F_M representations, we identified the best-performing model, which combined the Enhanced Vegetation Index (EVI)-based F_PAR, the evaporative fraction (EF)-based F_M, and an inverse double exponential F_T. This model notably improved GPP estimation under heat stress, comparable to three existing models under normal conditions, further enhancing aboveground biomass estimation across general conditions. Additionally, this study highlighted the limitations of five air temperature-based F_Ts, while emphasizing the critical contributions of EVI-based F_PAR and EF-based F_M under heat stress. These findings emphasize the importance of considering interactions among model components, such as the evapotranspiration effect on F_T and F_M, to reduce LUE model uncertainty under extreme conditions. Our findings offer valuable insights for improving crop productivity estimation under heat stress and developing adaptation strategies to mitigate heat stress impacts, thereby ensuring food security in the warming future.