Crop water production functions (quantifying crop yield as a function of irrigation rate) can help in the design of management systems that reduce the water footprint. We examined the role of parameter uncertainties in characterizing production functions using the DayCent agroecosystem model. A global sensitivity analysis was conducted to identify the model parameters associated with the greatest uncertainties in model responses. Under both irrigated and non-irrigated conditions, growth/production-related parameters had relatively more impact on grain yield than did soil-related parameters. Under non-irrigated conditions, there was greater sensitivity to evapotranspiration related parameters. We then used the DREAM method, a Markov Chain-Monte Carlo (MCMC) Bayesian approach, to determine the posterior distributions of the selected parameters. The DREAM method produced good estimates for the posterior distribution of the critical parameters. The utility of water production functions as predictive tools to guide water management decisions is greatly enhanced by incorporating rigorous estimates of uncertainty.

作物水分生产功能（根据灌溉速率对作物产量进行量化）可以帮助设计减少水足迹的管理系统。我们使用DayCent农业生态系统模型研究了参数不确定性在表征生产功能中的作用。进行了全局敏感性分析，以确定与模型响应中最大不确定性相关的模型参数。在灌溉和非灌溉条件下，生长/生产相关参数对谷物产量的影响都比土壤相关参数更大。在非灌溉条件下，对蒸散相关参数的敏感性更高。然后，我们使用DREAM方法（马尔可夫链蒙特卡罗（MCMC）贝叶斯方法）来确定所选参数的后验分布。DREAM方法对关键参数的后验分布产生了很好的估计。通过结合严格的不确定性估算，极大地提高了水生产作为指导水管理决策的预测工具的效用。