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Design of an integrated climatic assessment indicator (ICAI) for wheat production: A case study in Jiangsu Province, China

机译:小麦生产综合气候评估指标(ICAI)的设计:以中国江苏省为例

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摘要

Agro-meteorological condition plays a fundamental role in crop production. For a specific region, the comprehensive effects of multiple meteorological factors are important indicators for the climatic suitability of the crops. To evaluate the synthetic effects, an integrated climatic assessment indicator (ICAI) are developed in Jiangsu Province, China. A newly produced meteorological assimilation driving datasets (CMADS V1.0) combined with observation data are used in establishing the indicator. The procedure to construct the indicator involves building statistical crop models by meteorological factors and determining the indicator values by classification. In modeling, two machine learning algorithms: Random Forest (RF) and Support Vector Machine (SVM) are compared and the classification model of RF is chosen to build ICAI due to its better performance in the independent test set. To determine a reasonable division in classification, distribution detection of climatic yield is carried out and Monte Carlo simulations are applied for the Kolmogorov-Smirnov (KS) test. The generated indicator includes three values: yield loss, normal and yield increment, with the spatial and temporal prediction accuracy from 67.86% to 100% in the test set for the Northern, Central and Southern Jiangsu. The ICAI are used to estimate the past climatic suitability of winter wheat and the future suitability under global warming conditions in Jiangsu Province. The results show that the climate in 1990s has more adverse effects on wheat production than the other two sub-periods in Northern and Southern Jiangsu. The adaptability of wheat production in Southern Jiangsu has improved greatly to the local environments during the past three decades. In addition, when annual temperature accelerates upwards, both possibilities of yield loss in Northern Jiangsu and yield increment in Southern Jiangsu will increase. Therefore, more concerns should be given to the North in future warming climate, while yield potential in the South may be further improved in this circumstance.
机译:农业气象条件在作物生产中起着重要作用。对于特定区域,多种气象因素的综合影响是作物气候适应性的重要指标。为了评估综合影响,在中国江苏省开发了综合气候评估指标(ICAI)。建立了新生成的气象同化驱动数据集(CMADS V1.0)和观测数据。构造指标的过程包括通过气象因素建立统计作物模型并通过分类确定指标值。在建模中,比较了两种机器学习算法:随机森林(RF)和支持向量机(SVM),并且由于RF的分类模型在独立测试集中具有更好的性能,因此选择了RF的分类模型来构建ICAI。为了确定分类的合理划分,进行了气候产量的分布检测,并将蒙特卡洛模拟应用于Kolmogorov-Smirnov(KS)测试。生成的指标包括三个值:产量损失,正常值和产量增量,在江苏北部,中部和南部的测试集中,时空预测准确度从67.86%到100%。 ICAI用于估算江苏省过去冬小麦的气候适应性和全球变暖条件下的未来适应性。结果表明,与苏北和苏南的其他两个子时期相比,1990年代的气候对小麦生产的不利影响更大。在过去的三十年中,苏南地区小麦生产的适应性已大大改善。此外,当年气温上升时,苏北地区产量损失和苏南地区产量增加的可能性都会增加。因此,在未来的变暖气候下,应该更多地关注北方,而在这种情况下,南方的单产潜力可能会进一步提高。

著录项

  • 来源
    《Ecological indicators》 |2019年第6期|943-953|共11页
  • 作者单位

    Yangzhou Univ, Wheat Res Inst, Jiangsu Prov Key Lab Crop Genet & Physiol, Yangzhou, Jiangsu, Peoples R China;

    Meteorol Bur Jiangsu Prov, Nanjing, Jiangsu, Peoples R China;

    Yangzhou Univ, Wheat Res Inst, Jiangsu Prov Key Lab Crop Genet & Physiol, Yangzhou, Jiangsu, Peoples R China;

    Yangzhou Univ, Wheat Res Inst, Jiangsu Prov Key Lab Crop Genet & Physiol, Yangzhou, Jiangsu, Peoples R China;

    Yangzhou Univ, Wheat Res Inst, Jiangsu Prov Key Lab Crop Genet & Physiol, Yangzhou, Jiangsu, Peoples R China;

    Yangzhou Univ, Wheat Res Inst, Jiangsu Prov Key Lab Crop Genet & Physiol, Yangzhou, Jiangsu, Peoples R China;

    Yangzhou Univ, Wheat Res Inst, Jiangsu Prov Key Lab Crop Genet & Physiol, Yangzhou, Jiangsu, Peoples R China;

    Yangzhou Univ, Coll Informat Engn, Yangzhou, Jiangsu, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Winter wheat; Agro-meteorological indicator; Yield prediction; Random Forest; Support Vector Machine;

    机译:冬小麦;农业气象指标;产量预报;随机森林;支持向量机;

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