Quantitative Assessment Of The Effects Of Climate Factors On Grain Yield In China

The anomaly of climate change has affected the stability and sustainable development of Chinese agricultural production,the most direct and obvious effect is reflected in grain output.Grain yield is affected by social,economic,climate and other factors.How to quantitatively assess the impact of climate change on grain yield has become a hot and difficult issue involving the intersection of meteorological and economic fields.Based on the Cobb-Douglas production function C-D economic model,which takes seeding area,total power of agricultural machinery and conversion amount of fertilizer application as variables of capital input,labor force and technological progress,this paper introduces climate factor as "input factor" into the C-D model to build a C-D-C model.Based on CN05.1 grid precipitation data and grid surface temperature data from the National Climate Center during 1978-2020,six meteorological elements were selected:extreme hot days,extreme precipitation days,extreme precipitation intensity,average temperature,average precipitation and accumulated temperature,and classified into three combination schemes to study their quantitative effects on grain yield in China.Only the extreme meteorological elements are considered:the number of extreme high temperature days,the number of extreme precipitation days and the intensity of extreme precipitation;Consider only the mean meteorological elements:mean temperature,mean precipitation and accumulated temperature;Based on the comprehensive consideration of the mean and extreme meteorological elements,the extreme climate factors,the mean climate factors and the comprehensive climate factors were constructed respectively through the principal component analysis.Compared with the C-D model,the goodness of fit of the economic climate model C-D-C model after the introduction of climate factors improved to varying degrees,indicating that the constructed climate factors had a certain impact on China’s grain yield and improved the model’s characterization accuracy of grain yield.The improvement of goodness of fit was mainly concentrated in the arid and semi-arid regions and the coastal areas of South China and East China.These areas are themselves highly vulnerable to extreme weather and climate events.The influence of extreme climate factors on grain yield in most provinces(autonomous regions directly under the Central Government)is positive,that is,the increase of extreme climate factors will promote the increase of grain yield,mainly in Chongqing,Shanxi and Jiangsu,but the increase of extreme climate factors will reduce grain yield in Qinghai,Anhui and Guangdong.The linear regression coefficient between climate factors and corresponding meteorological factors constructed based on principal component analysis can be regarded as the contribution degree of each factor to climate factors.Extreme precipitation intensity had the greatest impact on grain yield in most provinces,which were mainly distributed in southern coastal areas and central areas.The second is the number of extreme precipitation days,which is close to and has the same effect as the extreme precipitation intensity.The provinces with the greatest contribution of extreme heat days are located in western China.Among the eight agricultural regions,the negative effect of extreme climate factors on grain yield in East China was the most significant,and the positive effect of extreme climate factors on grain yield in northwest China was the most significant.The average climate factor has a negative effect on the grain yield of most provinces(autonomous regions directly under the Central Government),that is,the increase of the average climate factor reduces the grain yield,and the impact is more obviously concentrated in the Yangtze River basin and North China.The influence of average temperature and accumulated temperature on grain yield in most regions is greater than that of average precipitation.The regions that are more sensitive to average temperature are distributed in South China and Southwest China,while the regions that are more sensitive to accumulated temperature are distributed in North China.The negative effect of average climate factors was the most significant in East China,and the positive effect of average climate factors was the most significant in North China.Comprehensive climate factors have a positive effect on grain yield in most provinces(autonomous regions directly under the Central Government),and the areas sensitive to comprehensive climate factors are distributed in the Yangtze River Basin,North China and South China.The comprehensive climate factor affects grain output in two types including temperature and precipitation.Grain output in most areas of our country is more sensitive to precipitation.The negative effect of comprehensive climate factors is the most significant in northeast China,and the positive effect of comprehensive climate factors is the most significant in Northwest China.From the improvement of the fitting degree of different climate factors,the fitting degree of grain yield in North China improved greatly with the addition of extreme climate factors,while the fitting degree of grain yield in the Yangtze River Basin and North China improved greatly with the addition of average climate factors.The fitting degree of grain yield in the middle and upper reaches of Yangtze River and North China was improved greatly by adding the C-D-C model with comprehensive climate factors.The results showed that the grain yield of most provinces(autonomous regions directly under the Central Government)in China was negatively affected by the average climate factors,and was concentrated in East China and Northwest China.The second is the extreme climate factor,these provinces(autonomous regions directly under the Central Government)concentrated in North and South China;Finally,the comprehensive climate factor,these provinces(autonomous regions directly under the central Government)concentrated distribution in the southwest,northeast.