Worldwide evidence indicates an obvious reduction in the rate of yield growth for many key food crops since the end of the last century, which has received global concerns as it may decrease the ability of agriculture to support growing population. Understanding the causes for the yield stagnation is therefore important for devising relevant responding actions to secure the food supply in the future, particularly in China due to its fast loss of arable land and increasing food demand. However, very limited studies to date have tried to tackle this issue at both national and regional scales. This study attempts to investigate the reasons of observed yield slowing of the main food crops (rice, wheat, and maize) in China. We will explore the physical mechanisms for the observed slowing of yield growth through process-based models, based on a combination of a number of simulation methods such as up-scaling application of site-specific crop models, isolating simulation of affecting factors, re-sampling and scenarios-based mass simulation. The objectives of the study are: (1) to reproduce the actual changing trends of reported yields for China's main food crops, by using a couple of process-based crop models and a newly developed dynamic parameterization procedure; (2) to untangle and measure the contributions of five important affecting factors that have contributed to past yield growth (increase of fertilizer use, change of cultivation land, irrigation expansion and intensification, technology development, and climate change); (3) to understand the reasons and underlying mechanisms for the prevailing yield stagnation; and (4) to evaluate the possibilities of using climate change adaptation strategies to reverse the slowing of observed yield growth, gauge the maximum yield potential of specific adaptation options under a series of climate change scenarios. This 4-yr study are theoretically targeting on filling the gaps of our understanding, in perspective of what reasons and causes are behind the observed slowing yield growth for the major food crops. Additional endeavors are being emphasized on the development of a fussy simulation method by which the roles of different drivers for past crop yield growth could be easily isolated and explored. Furthermore, we will apply the method to quantitatively identify the potential of ongoing and proposed adaptation options in promoting the national crop yields, facilitating the implementation of climate-smart agriculture in the near future.
气候变化背景下,近年来全球主要粮食作物单产增长速度明显放缓,已引起世界各国的广泛关注。开展粮食单产增速放缓的物理驱动机制研究,探讨粮食单产增速变化的定量原因,提出气候变化背景下粮食单产增长的有效途径,有利于实现我国中近期粮食生产目标,保障国家粮食安全。研究以我国主要粮食作物为对象,基于作物模型的升尺度模拟技术,通过不同驱动因子的分离、抽样与情景模拟相结合等多种手段和方法,着重于:(1)主要粮食作物单产变化趋势的模型拟合;(2)主要粮食作物单产变化的驱动因子分离及贡献分析;(3)主要粮食作物单产增速放缓的物理机制分析;(4)气候变化背景下,农业技术和重点适应措施的增产潜力分析。预期通过4年研究,在理论上回答1980-2012年我国主要粮食作物单产增速放缓的驱动机制和定量原因;在方法上建立基于作物模型模拟的不同驱动因子分离技术;在应用上提出气候变化背景下保障粮食单产持续增加的有效技术和途径。
近年来全球主要粮食作物单产增长速度明显放缓,已引起世界各国的广泛关注。本研究以我国主要粮食作物为对象,基于作物模型的升尺度模拟技术,通过不同驱动因子的分离、抽样与情景模拟相结合等多种手段和方法,研究主要粮食作物单产变化趋势,分离影响粮食作物单产变化的五大驱动因子(肥料投入、种植面积、灌溉条件的改善、农业技术的进步和气候变化),评估不同驱动因子对粮食单产的贡献率,探讨主要粮食作物单产增速放缓的原因和物理机制。本项目建立了基于作物模型模拟的不同驱动因子分离技术,提出了气候变化背景下保障粮食单产持续增加的有效技术和途径。. 结果表明,1981-2010年间,我国水稻产量增长出现了明显的放缓趋势,肥料用量的增加和品种进步起到了主导作用,分别为64%和37%,水稻种植面积(3%)和气候(2%)的变化对水稻产量增长贡献最小。我国小麦产量呈现出显著的线性增长,未出现变缓趋势,品种和肥料对全国小麦产量增加的贡献达到了显著水平,占总产量增长的90%以上。而灌溉,种植面积和气候变化对小麦产量的增加均未达到显著水平。我国玉米单产整体呈增长趋势,但是随时间推移,单产增加到一定程度出现了增速变缓现象。肥料一直是玉米单产增长最重要的因素(77.52%),但是肥料在玉米单产增长中的地位逐步下降,玉米种植面积的增长不利于玉米单产增长(-6.67%),灌溉对玉米单产增长的正面贡献处于增长趋势(2.43%),且增速加快,但是相较于肥料,它对我国玉米单产增长的贡献较小,而气候变化对玉米单产的影响不显著。品种改良对我国玉米单产的贡献所占比例为 26.17%,仅次于肥料,且它的作用和地位在逐步提高。品种的改良和更新对产量的贡献率逐年增加,将是未来可选的应对气候变化技术措施之一。
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数据更新时间:2023-05-31
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