Identifying links between monsoon variability and rice production in India through machine learning.
Identifying links between monsoon variability and rice production in India through machine learning.
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DOI：
10.1038/s41598-023-27752-8
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发表时间：
2023-02-10
影响因子：
4.6
通讯作者：
中科院分区：
文献类型：
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作者：
气候变化对全球粮食安全构成重大威胁。依赖季风降雨的农业系统特别容易受到气候变化的影响。本文使用机器学习来加深对季风变化如何影响农业生产力的理解。我们证明，随机森林模型是有效的，代表水稻产量的变化，季风天气的变化。我们的随机森林建模发现，季风天气预报解释了水稻产量（33%）和收获面积（35%）的相似水平的去趋势异常变化。天气在解释水稻收获面积方面的作用突出表明，生产面积的变化是极端天气影响农业生产力的一个重要途径，这可能会加剧因单位面积产量变化而造成的损失。我们发现，下降流短波辐射通量是最重要的天气变量在解释产量异常的变化，灌溉面积的比例是最重要的预测整体。机器学习建模能够代表季风农业中的作物气候变化，并与传统的参数模型相比，揭示了更多的信息。例如，灌溉、季风开始和季节长度的非线性产量和面积反应都符合生物物理预期。总的来说，我们发现随机森林模型可以揭示复杂的非线性和气候和水稻产量变异之间的相互作用。
Climate change poses a major threat to global food security. Agricultural systems that rely on monsoon rainfall are especially vulnerable to changes in climate variability. This paper uses machine learning to deepen understanding of how monsoon variability impacts agricultural productivity. We demonstrate that random forest modelling is effective in representing rice production variability in response to monsoon weather variability. Our random forest modelling found monsoon weather predictors explain similar levels of detrended anomaly variation in both rice yield (33%) and area harvested (35%). The role of weather in explaining harvested rice area highlights that production area changes are an important pathway through which weather extremes impact agricultural productivity, which may exacerbate losses that occur through changes in per-area yields. We find that downwelling shortwave radiation flux is the most important weather variable in explaining variation in yield anomalies, with proportion of area under irrigation being the most important predictor overall. Machine learning modelling is capable of representing crop-climate variability in monsoonal agriculture and reveals additional information compared to traditional parametric models. For example, non-linear yield and area responses of irrigation, monsoon onset and season length all match biophysical expectations. Overall, we find that random forest modelling can reveal complex non-linearities and interactions between climate and rice production variability.
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影响因子：
6.2
作者：
Ben-Ari, Tamara;Adrian, Juliette;Makowski, David
通讯作者：
Makowski, David
影响因子：
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作者：
Bookhagen, Bodo;Burbank, Douglas W.
通讯作者：
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DOI：
10.1007/s12652-020-02752-y
发表时间：
2021-01-01
影响因子：
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作者：
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通讯作者：
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作者：
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通讯作者：
Stahl, K.
影响因子：
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作者：
Asseng, Senthold;Cammarano, Davide;Lobell, David B.
通讯作者：
Lobell, David B.