Abstract
Predicting attainable yield under water-limiting conditions is an important goal in arid, semi-arid and drought-prone environments. To address this task, FAO has developed a model, AquaCrop, which simulates attainable yields of the major herbaceous crops in response to water. Compared to other models, AquaCrop has a significantly smaller number of parameters and attempts to strike a balance between simplicity, accuracy, and robustness. Root zone water content is simulated by keeping track of incoming and outgoing water fluxes. Instead of leaf area index, AquaCrop uses canopy ground cover. Canopy expansion, stomatal conductance, canopy senescence, and harvest index are the key physiological processes which respond to water stress. Low and high temperature stresses on pollination and harvestable yield are considered, as is cold temperature stress on biomass production. Evapotranspiration is simulated separately as crop transpiration and soil evaporation and the daily transpiration is used to calculate the biomass gain via the normalized biomass water productivity. The normalization is for atmospheric evaporative demand and carbon dioxide concentration, to make the model applicable to diverse locations and seasons, including future climate scenarios. AquaCrop accommodates fertility levels and water management systems, including rainfed, supplemental, deficit, and full irrigation. Simulations are routinely in thermal time, but can be carried out in calendar time. Future versions will incorporate salt balance and capillary raise. AquaCrop is aimed at users in extension services, consulting firms, governmental agencies, NGOs, farmers associations and irrigation districts, as well as economists and policy analysts in need of crop models for planning and assessing water needs and use of projects and regions.
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© 2009 Tsinghua University Press, Beijing and Springer-Verlag Berlin Heidelberg
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Steduto, P. et al. (2009). Concepts and Applications of AquaCrop: The FAO Crop Water Productivity Model. In: Cao, W., White, J.W., Wang, E. (eds) Crop Modeling and Decision Support. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01132-0_19
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DOI: https://doi.org/10.1007/978-3-642-01132-0_19
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