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ABSTRACT The development of commercially available porometers has allowed for higher throughput measurement of stomatal conductance, but a body of evidence has suggested a persistent positive bias in their measurements relative to “reference” measurements from instrumentation based on infra‐red gas analysis. We compiled a data set comprised of 25 angiosperm species, across a range of field conditions and found that the LI‐COR LI‐600, an open flow‐through porometer, produced an exponentially increasing bias relative to the LI‐COR LI‐6800 infra‐red gas analyser‐based instrument in response to increasing stomatal conductance and decreasing relative humidity. This bias was minimal at lower stomatal conductance (below roughly 0.25 mol m s), but was pronounced for larger values. We hypothesised that this bias is the result of the assumption of a constant air temperature throughout the flow stream used by the instrument software to estimate stomatal conductance from raw sensor measurements. We relaxed this assumption, and applied psychrometrics to augment the typical gas exchange equations with an additional energy balance constraint to solve for the temperature change throughout the air flow stream. We found that including this temperature difference corrects the computed transpiration and stomatal conductance values, and brings the porometer measurement into agreement with that of the infra‐red gas analysis‐based system. Software is provided to apply the correction to LI‐600 output files. For future instrument design iterations, explicit measurement of temperature variation in the flow stream provides a potential opportunity for improvement in measurement accuracy at high stomatal conductance. 

Summary Stomatal conductance models are essential components of crop and land surface models, but collecting data to calibrate them remains challenging due to large leaf‐to‐leaf variability, slow stomatal kinetics, and a lack of consistent measurement protocols, leading to unknown reliability and representativeness of calibrated model parameter estimates.We combined field measurements, 3D biophysical simulations, and statistical power analyses to quantify parameter calibration discrepancies with different instruments under different conditions to provide recommendations for protocol development.Leaf‐to‐leaf physiological variability in measured steady‐state stomatal conductance exceeded threefold under identical conditions, calling into question the use of few steady‐state response curves to represent a canopy. Stomatal kinetics introduce systematic error in parameter calibration, and slower stomatal response times necessitated larger survey sample sizes to recover known stomatal model parameters of simulated data.Primary recommendations are as follows: survey measurements (c.100 samples) are needed to sample leaf‐to‐leaf variability and can be supplemented by steady‐state measurements to better represent environmental responses, survey measurements should maximize the range of leaf‐level environmental conditions while minimizing transient effects, and steady‐state measurements with controlled environmental conditions should maintain constant conditions for 15–45 min before measurement to allow for true stomatal steady state and not just instrument equilibrium. 

Abstract Integrating innovative technologies into plant breeding is critical to bolster food and nutritional security under biotic and abiotic stresses in changing climates. While breeding efforts have focused primarily on yield and stress tolerance, emerging evidence highlights the need to also prioritize nutritional quality. Advanced molecular breeding approaches have enhanced our ability to develop improved crop varieties and could be substantially informed by the routine integration of crop modeling and remote sensing technologies. This review article discusses the potential of combining crop modeling and sensing with molecular breeding to address the dual challenge of nutritional quality and stress tolerance. We provide overviews of stress response strategies, challenges in breeding for quality traits, and the use of environmental data in genomic prediction. We also describe the status of crop modeling and sensing technologies in grain legumes, rice, and leafy greens, alongside the status of -omics tools in these crops and the use of AI with directed evolution to identify novel resistance genes. We describe the pairwise and three-way integration of AI-enabled sensing and biophysically and empirically constrained crop modeling into breeding to enable prediction of phenotypic and breeding values and dissection of genotype-by-environment-by-management interactions with increasing fidelity, efficiency, and temporal/spatial resolution to inform selection decisions. This article highlights current initiatives and future trends that focus on leveraging these advancements to develop more climate-resilient and nutritionally dense crops, ultimately enhancing the effectiveness of molecular breeding. 

Abstract Background and AimsVariation in architectural traits related to the spatial and angular distribution of leaf area can have considerable impacts on canopy-scale fluxes contributing to water-use efficiency (WUE). These architectural traits are frequent targets for crop improvement and for improving the understanding and predictions of net ecosystem carbon and water fluxes. MethodsA three-dimensional, leaf-resolving model along with a range of virtually generated hypothetical canopies were used to quantify interactions between canopy structure and WUE by examining its response to variation of leaf inclination independent of leaf azimuth, canopy heterogeneity, vegetation density and physiological parameters. Key ResultsOverall, increasing leaf area index (LAI), increasing the daily-averaged fraction of leaf area projected in the sun direction (Gavg) via the leaf inclination or azimuth distribution and increasing homogeneity had a similar effect on canopy-scale daily fluxes contributing to WUE. Increasing any of these parameters tended to increase daily light interception, increase daily net photosynthesis at low LAI and decrease it at high LAI, increase daily transpiration and decrease WUE. Isolated spherical crowns could decrease photosynthesis by ~60 % but increase daily WUE ≤130 % relative to a homogeneous canopy with equivalent leaf area density. There was no observed optimum in daily canopy WUE as LAI, leaf angle distribution or heterogeneity was varied. However, when the canopy was dense, a more vertical leaf angle distribution could increase both photosynthesis and WUE simultaneously. ConclusionsVariation in leaf angle and density distributions can have a substantial impact on canopy-level carbon and water fluxes, with potential trade-offs between the two. These traits might therefore be viable target traits for increasing or maintaining crop productivity while using less water, and for improvement of simplified models. Increasing canopy density or decreasing canopy heterogeneity increases the impact of leaf angle on WUE and its dependent processes. 

Abstract Large‐scale canopy models utilizing satellite data typically rely on simplified models to describe radiation absorption by vegetation. However, these models' accuracy can be limited when applied to heterogeneous row‐oriented canopies because of assumptions of canopy homogeneity, or oversimplification of the impacts of heterogeneity. This study evaluated existing model assumptions and developed a novel geometric binomial model for radiation absorption in discontinuous canopies with an ellipsoidal crown envelope. The simple models considered include a one‐dimensional turbid medium model (i.e., Beer's law), two models incorporating constant or variable clumping factors, and the geometric binomial model adapted to predict radiation absorption for canopies with ellipsoidal shaped crowns. Compared to Beer's law and the models with clumping factors, the proposed binomial model accounts for scattering, variable radiative path lengths through vegetation, diffuse radiation, and crown shadow overlap. The simplified models were evaluated against a sophisticated three‐dimensional (3D) leaf‐resolving radiation model (Helios) and field measurements collected in almond and olive orchards. Results indicated that Beer's law considerably overpredicted radiation absorption for a wide range of virtually generated canopies and field observations. For the model with variable clumping factor, errors increased as the radiative path length through the canopy increased. Among the simple models, the proposed binomial model resulted in small errors across all canopies (index of agreement: 0.91–0.99). With additional inputs related to the canopy geometry, this approach could be integrated within the shortwave absorbed‐radiation component of large‐scale canopy models to improve the estimation of biophysical processes such as photosynthesis and transpiration.