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首页> 外文期刊>European Journal of Agronomy >Combining input uncertainty and residual error in crop model predictions: a case study on vineyards.
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Combining input uncertainty and residual error in crop model predictions: a case study on vineyards.

机译：在作物模型预测中结合输入不确定性和残余误差：以葡萄园为例。

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摘要

As crop modelling has matured and been proposed as a tool for many practical applications, there is increased need to evaluate the uncertainty in model predictions. A particular case of interest that has not been treated before is that where one takes into account both uncertainty in the model explanatory variables and model residual error (the uncertainty in model predictions even when the explanatory variables are perfectly known). The specific case we consider is that of a model for predicting water stress of a vineyard. For many of the model explanatory variables, the vine grower (or the farmer advisor) has a choice between approximate values which are easily obtainable and more precise values that are more difficult (and more expensive) to obtain. We specifically discuss the explanatory variable "initial water stress" which is directly based on the initial soil water content and can be estimated or measured (precise but expensive). The vine grower is interested in the decrease in uncertainty that would result from measuring initial water stress, but it is the decrease in total uncertainty, including model residual error, that is of importance. We propose using accurate measurements of water stress over time in multiple vineyards, to estimate model residual error. The uncertainty in initial water stress can be estimated if one has approximate and precise values of initial water stress in several vineyards. We then combine the two sources of error by simulation thanks to an independence hypothesis; the model is run multiple times with a distribution of values for initial water stress, and on each day a distribution of model residual errors is added to the result. The results show that the resulting uncertainty is quite different in different fields. In some cases, uncertainty in initial water stress becomes negligible a short time after the start of simulations, in other cases that uncertainty remains important, compared to model residual error, throughout the growing season. In all cases, residual error is a substantial percentage of overall error and thus should be taken into account.
机译：随着作物建模的成熟和被提议作为许多实际应用的工具，越来越需要评估模型预测中的不确定性。之前未曾处理过的一种特殊情况是，它既要考虑模型解释变量中的不确定性，也要考虑模型残余误差（即使完全知道解释变量时，模型预测中的不确定性）。我们考虑的具体案例是用于预测葡萄园水分胁迫的模型。对于许多模型解释变量，葡萄种植者（或农民顾问）可以在易于获得的近似值与更难（且更昂贵）的更精确值之间进行选择。我们专门讨论解释变量“初始水分胁迫”，该变量直接基于初始土壤含水量，可以估算或测量（精确但昂贵）。葡萄种植者对降低因测量初始水分胁迫而导致的不确定性很感兴趣，但重要的是总不确定性的降低，包括模型残留误差。我们建议使用多个葡萄园中随时间变化的水分胁迫的准确测量值，以估计模型残留误差。如果几个葡萄园中的初始水分胁迫具有近似和精确的值，则可以估算初始水分胁迫的不确定性。然后，由于独立性假设，我们通过仿真将两个误差源结合在一起。该模型以初始水分应力的值分布多次运行，并且每天都会将模型残留误差分布添加到结果中。结果表明，在不同领域中，不确定性差异很大。在某些情况下，模拟开始后不久，初始水分胁迫的不确定性就可以忽略不计，在其他情况下，与模型残留误差相比，在整个生长季节中，不确定性仍然很重要。在所有情况下，残留误差均占整体误差的很大一部分，因此应予以考虑。

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