基于县域尺度的粮食主产区耕地压力空间分异及其影响因子实证研究与预警

基于县域尺度对中国粮食主产区157个产粮大县(含县级市、市辖区、自治县、旗、自治旗)2000—2015年的耕地压力空间属性及其影响因子进行实证研究,并运用GM(1,1)模型对其2016—2030年的耕地压力进行拟合、预测。结果显示,157个产粮大县的耕地负荷较重,但随着粮食单产水平的不断提升,157个产粮大县的总体耕地压力指数由2000年的0.88下降到了2015年的0.66。截至2015年,研究对象中共99个产粮大县处于安全压力区,20个产粮大县处于潜在压力区,31个产粮大县处于轻度压力区,而江苏省邳州市,四川省安岳县、简阳市、宣汉县、岳池县、南部县、资中县7个产粮大县处于中度压力区,存在潜在耕地压力风险。157个产粮大县的耕地压力空间分异显著,马太效应增强,呈南高北低、西高东低态势。研究期内,总体耕地压力承载系数持续减小,耕地压力敏感系数波动显著,呈“W”形波动态势。在不考虑严重自然灾害的情况下预测到2030年157个产粮大县的耕地压力将进一步缓解。建立耕地压力变动影响因素模型,实证分析了耕地质量、化肥投入、种植结构、城镇化水平、农民收入等因素对耕地压力变动的显著影响,认为改良种植结构、改善耕地质量、提高粮食单产仍是缓解耕地压力的重要途径。

Empirical study and early warning on spatial differentiation of arable land pressure and its influencing factors in main grain-producing regions based on county scale

Based on the county scale, an empirical study was carried out on the spatial attributes and influencing factors of arable land pressure of 157 major grain-producing counties (including county-level city, district, autonomous county, banner and autonomous banner) in China's main grain-producing regions from 2000 to 2015. The arable land pressure from 2016 to 2030 was also fitted and forecasted with the help of GM(1,1) model. It was shown that the 157 major grain-producing counties had a heavy load of arable land. Due to the continuous increase in grain yield per unit area, the overall arable land pressure index of 157 major grain-producing counties dropped from 0.88 in 2000 to 0.66 in 2015. In 2015, a total of 99 major grain-producing counties in the study were in safe stress zones, 20 major grain-producing counties were in potential stress zone, and 31 major grain-producing counties were in mild stress zones. Whereas, seven major grain-producing counties, including Pizhou City in Jiangsu Province, and Anyue County, Jianyang City, Xuanhan County, Yuechi County, Nanbu County, Zizhong County in Sichuan Province were in moderate stress zones, and were under a potential risk of arable land pressure. The spatial distribution of arable land pressure in the 157 major grain-producing counties varied significantly, and their Matthew effect increased, and showed a trend of high in the South and low in the North, high in the West and low in the East. During the study period, the overall bearing coefficient of arable land pressure continued to decrease, yet the sensitivity coefficient of arable land pressure fluctuated significantly, showing a “W” wave dynamic trend. Without considering serious natural disasters, it was predicted that by 2030, the arable land pressure in 157 major grain-producing counties would ease. A model of influencing factors that could change the arable land pressure was established, and was used to empirically analyze the significant effects of factors such as arable land quality, fertilizer input, planting structure, urbanization level, and farmers' income on arable land pressure. Therefore, it was still the priority to ease the arable land pressure by improving planting structure and arable land quality and increasing the grain yield per unit area.