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Estimation of the carbon dioxide (CO2) fertilization effect using growth rate anomalies of CO2 and crop yields since 1961

机译:自1961年以来使用CO2的增长率异常和农作物产量估算二氧化碳(CO2)的施肥效果

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The effect of elevated carbon dioxide (CO2) on crop yields is one of the most uncertain and influential parameters in models used to assess climate change impacts and adaptations. A primary reason for this uncertainty is the limited availability of experimental data on CO2 responses for crops grown under typical field conditions. However, because of historical variations in CO2, each year farmers throughout the world perform uncontrolled yield 'experiments' under different levels of CO2. In this study, measurements of atmospheric CO2 growth rates and crop yields for individual countries since 1961 were compared with empirically determine the average effect of a 1 ppm increase of CO2 on yields of rice, wheat, and maize. Because the gradual increase in CO2 is highly correlated with major changes in technology, management, and other yield controlling factors, we focused on first differences of CO2 and yield time series. Estimates of CO2 responses obtained from this approach were highly uncertain, reflecting the relatively small importance of year-to-year CO2 changes for yield variability. Combining estimates from the top 20 countries for each crop resulted in estimates with substantially less uncertainty than from any individual country. The results indicate that while current datasets cannot reliably constrain estimates beyond previous experimental studies, an empirical approach supported by large amounts of data may provide a potentially valuable and independent assessment of this critical model parameter. For example, analysis of reliable yield records from hundreds of individual, independent locations (as opposed to national scale yield records with poorly defined errors) may result in empirical estimates with useful levels of uncertainty to complement estimates from experimental studies.
机译:在用于评估气候变化影响和适应性的模型中,二氧化碳(CO2)升高对作物产量的影响是最不确定和最具影响力的参数之一。这种不确定性的主要原因是在典型田间条件下种植的农作物对二氧化碳反应的实验数据有限。但是,由于二氧化碳的历史变化,全世界的农民每年在不同的二氧化碳水平下都进行不受控制的产量“实验”。在这项研究中,比较了自1961年以来各个国家的大气CO2增长率和作物单产的测量值,并凭经验确定了CO2增加1 ppm对水稻,小麦和玉米单产的平均影响。由于CO2的逐渐增加与技术,管理和其他产量控制因素的重大变化高度相关,因此我们关注CO2和产量时间序列的最初差异。通过这种方法获得的CO2响应估计值非常不确定,这反映了逐年CO2变化对产量变异性的重要性相对较小。结合前20个国家/地区对每种作物的估计,得出的估计结果的不确定性要比任何单个国家/地区小得多。结果表明,尽管当前的数据集无法可靠地限制超出先前实验研究的估计,但是,由大量数据支持的经验方法可能会对该关键模型参数提供潜在有价值的独立评估。例如,对数百个独立地点的可靠产量记录进行分析(与误差定义不佳的国家规模产量记录相对)可能会产生经验估计,其不确定性水平会与实验研究的估计相辅相成。

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