Modeling Nonlinear Dynamics of CAM Productivity and Water Use for Global Predictions
This work was supported through the USDA Agricultural Research Service cooperative agreement 58‐6408‐3‐027 and National Institute of Food and Agriculture (NIFA) grant 12110061; National Science Foundation (NSF) grants EAR‐1331846, FESD‐1338694, EAR‐1316258, and GRFP‐1106401; the Carbon Mitigation Initiative (CMI) at Princeton University; the AFRI Postdoctoral Fellowship program Grant No. 2017‐67012‐26106/Project Accession No. 1011029 from the USDA National Institute of Food and Agriculture; and the National Natural Science Foundation of China (41877158 and 51739009). Supporting Information
Plant, Cell & Environment
Crassulacean acid metabolism (CAM) crops are important agricultural commodities in water-limited environments across the globe, yet modeling of CAM productivity lacks the sophistication of widely used C3 and C4 crop models, in part due to the complex responses of the CAM cycle to environmental conditions. This work builds on recent advances in CAM modeling to provide a framework for estimating CAM biomass yield and water use efficiency from basic principles. These advances, which integrate the CAM circadian rhythm with established models of carbon fixation, stomatal conductance, and the soil-plant-atmosphere continuum, are coupled to models of light attenuation, plant respiration, and biomass partitioning. Resulting biomass yield and transpiration for Opuntia ficus-indica and Agave tequilana are validated against field data and compared with predictions of CAM productivity obtained using the empirically-based Environmental Productivity Index (EPI). By representing regulation of the circadian state as a nonlinear oscillator, the modeling approach captures the diurnal dynamics of CAM stomatal conductance, allowing the prediction of CAM transpiration and water use efficiency for the first time at the plot scale. This approach may improve estimates of CAM productivity under light-limiting conditions when compared with previous methods. This article is protected by copyright. All rights reserved.
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Hartzell, S., Bartlett, M. S., Inglese, P., Consoli, S., Yin, J., & Porporato, A. (2020). Modeling nonlinear dynamics of productivity and water use for global predictions. Plant, Cell & Environment, pce.13918. https://doi.org/10.1111/pce.13918