vLeaf@DSSAT: integrating leaf energy balance and biochemistry into CERES-Maize to reassess water-efficient ideotypes

Future food security will increasingly depend on the development of crop ideotypes that produce higher yields per unit of water used. Stomata are central to developing water-efficient crop ideotypes, as they serve as the primary gateway for carbon and water exchange. Process-based crop models are essential tools for testing crop phenotypes with favorable stomatal traits, as they can explain how changes in stomatal traits propagate to whole canopy carbon gain and water use. Yet, current models still struggle to connect leaf-level physiology to season-long canopy performance (e.g., yield) under realistic climate variability.

Current process-based models have one of these limitations: (i) lack of explicit biochemical photosynthesis module for C3 or C4 crops, preventing mechanistic analysis of crop phenotypes; and (ii) models that explicitly represent biochemistry ignore leaf energy balance dynamics and assume leaf temperature (T_leaf) equal to air temperature (T_air), ignoring the feedback between stomatal conductance, transpiratory cooling. As a result, they require extensive empirical calibration and are not recommended for exploring novel stomatal phenotypes, such as lower stomatal density and pore size. In particular, current efforts to manipulate stomatal traits in crops cannot be reliably evaluated using these simplifications, as they do not account for how changes in stomata affect CO₂ diffusion and canopy energy balance.

This study presents a novel cross-scale framework, vLeaf@DSSAT, where we couple a process-based leaf model with the CERES-Maize growth model and introduce a two-leaf (sunlit–shaded) canopy representation. The explicit consideration of energy balance makes this framework distinct from similar attempts in the past. CERES-Maize provides daily crop state variables such as leaf area index (LAI), phenology, soil water status, and nitrogen status. Using these, vLeaf then computes hourly net assimilation and transpiration rates for both sunlit and shaded leaf areas. It computes photosynthesis, stomatal conductance, boundary-layer conductance, and leaf energy balance simultaneously in an iterative loop. Root water uptake from CERES-Maize constrains canopy transpiration; vLeaf then reruns under these constraints and updates T_leaf and gas exchange rates. The resulting canopy-scale assimilation from vLeaf drives the biomass accumulation in CERES-Maize on the next day, closing the loop between leaf biophysics and crop growth.

Simulations for climates based on a US Midwest reference site show that neglecting leaf energy balance results in sizeable errors in both carbon gain and water use. For cooler climates, forcing T_leaf = T_air underestimates seasonal carbon gain by ≈ 9% and transpiration by ≈ 30%. For warmer climates, the bias in carbon gain is small, but transpiration is overestimated by 5–10%. These errors can create uncertainty in ranking crop phenotypes with favorable stomatal traits. vLeaf@DSSAT provides a practical approach to testing stomatal manipulation, irrigation strategies, and climate-resilient ideotypes under realistic climate conditions, while also connecting leaf biophysics to field-scale yield and water use.