An empirical, Bayesian approach to modelling crop yield: Maize in USA
An empirical, Bayesian approach to modelling crop yield: Maize in USA
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DOI：
10.1088/2515-7620/ab67f0
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发表时间：
2020-01
影响因子：
2.9
通讯作者：
R. Shirley;E. Pope;Myles Bartlett;Seb Oliver;Novi Quadrianto;P. Hurley;S. Duivenvoorden;Philip J. Ro
中科院分区：
文献类型：
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作者：
R. Shirley;E. Pope;Myles Bartlett;Seb Oliver;Novi Quadrianto;P. Hurley;S. Duivenvoorden;Philip J. Ro
我们采用经验数据驱动的方法，通过采用生成概率模型和通过贝叶斯推理确定的参数，将作物产量描述为每月温度和降水的函数。我们的方法适用于 1981 年至 2014 年美国玉米种植带的州级玉米产量和气象数据作为范例，但也可以很容易地转移到其他作物、地点和空间尺度。多个模型的实验表明，玉米生长率可以用温度和降水的二维高斯函数来表征，并在生长期间累积每月的贡献。这种方法解释了对各个气象变量的非线性增长响应，并考虑到它们之间的相互作用。我们的模型正确地确定了收获前六个月的温度和降水对产量的影响最大，这与美国玉米的典型生长季节（四月至九月）一致。最大增长率发生在月平均温度 18 °C–19 °C 时，对应于日最高温度 24 °C–25 °C（与之前的工作基本一致）和月总降水量 115 毫米。我们的方法还提供了一种在缺乏适应措施的情况下调查气候变化对当前美国玉米品种影响的自洽方法。保持降水量和种植面积不变，相对于 1981-2014 年，气温升高 2°C，导致平均产量下降 8%，而产量方差增加约 3 倍。因此，我们提供了一个灵活的、数据驱动的框架，用于根据观察到的行为探索自然气候变率和气候变化对全球重要作物的影响。与其他方法相结合，这可以帮助制定适应战略，以确保气候变化下的粮食安全。
We apply an empirical, data-driven approach for describing crop yield as a function of monthly temperature and precipitation by employing generative probabilistic models with parameters determined through Bayesian inference. Our approach is applied to state-scale maize yield and meteorological data for the US Corn Belt from 1981 to 2014 as an exemplar, but would be readily transferable to other crops, locations and spatial scales. Experimentation with a number of models shows that maize growth rates can be characterised by a two-dimensional Gaussian function of temperature and precipitation with monthly contributions accumulated over the growing period. This approach accounts for non-linear growth responses to the individual meteorological variables, and allows for interactions between them. Our models correctly identify that temperature and precipitation have the largest impact on yield in the six months prior to the harvest, in agreement with the typical growing season for US maize (April to September). Maximal growth rates occur for monthly mean temperature 18 °C–19 °C, corresponding to a daily maximum temperature of 24 °C–25 °C (in broad agreement with previous work) and monthly total precipitation 115 mm. Our approach also provides a self-consistent way of investigating climate change impacts on current US maize varieties in the absence of adaptation measures. Keeping precipitation and growing area fixed, a temperature increase of 2 °C, relative to 1981–2014, results in the mean yield decreasing by 8%, while the yield variance increases by a factor of around 3. We thus provide a flexible, data-driven framework for exploring the impacts of natural climate variability and climate change on globally significant crops based on their observed behaviour. In concert with other approaches, this can help inform the development of adaptation strategies that will ensure food security under a changing climate.