Research on Dynamic Change of Regional Cultivated Land Quality and Its Influence Factors

According to the reason of quality change,cultivated land is divided into four change types: decrease,increase,land construction project and gradual change. Farmland grading factor method is used to evaluate the quality of cultivated land in Zhengzhou City. The results show that(1) the quality of cultivated land in Zhengzhou City has slightly increased during the study period. Due to the decrease in the amount of cultivated land,the production capacity has declined.(2) The impact of different types of cultivated land change on the quality of regional cultivated land is in the order of gradual change> decrease > increase > land construction project.(3) Zhengzhou City is able to maintain the quality of regional cultivated land through new cultivated land and project construction,but fails to maintain the production capacity. By analyzing the mechanism of regional cultivated land quality change in different types,the relationship between quality changes in various types of areas and overall quality changes could be analyzed,and it could provide policy recommendations for managers to formulate farmland quality and capacity protection,with significant application value.

Comparisons of cropland area from multiple datasets over the past 300 years in the traditional cultivated region of China

Land use/cover change is an important parameter in the climate and ecological simulations. Although they had been widely used in the community, SAGE dataset and HYDE dataset, the two representative global historical land use datasets, were little assessed about their accuracies in regional scale. Here, we carried out some assessments for the traditional cultivated region of China （TCRC） over last 300 years, by comparing SAGE2010 and HYDE （v3.1） with Chinese Historical Cropland Dataset （CHCD）. The comparisons were performed at three spatial scales： entire study area, provincial area and 60 km by 60 km grid cell. The results show that （1） the cropland area from SAGE2010 was much more than that from CHCD moreover, the growth at a rate of 0.51% from 1700 to 1950 and -0.34% after 1950 were also inconsistent with that from CHCD. （2） HYDE dataset （v3.1） was closer to CHCD dataset than SAGE dataset on entire study area. However, the large biases could be detected at provincial scale and 60 km by 60 km grid cell scale. The percent of grid cells having biases greater than 70% （〈-70% or 〉70%） and 90% （〈-90% or 〉90%） accounted for 56%-63% and 40%-45% of the total grid cells respectively while those having biases range from -10% to 10% and from -30% to 30% account for only 5%-6% and 17% of the total grid cells respectively. （3） Using local historical archives to reconstruct historical dataset with high accuracy would be a valu- able way to improve the accuracy of climate and ecological simulation.

Gridded reconstruction of the population in the traditional cultivated region of China from 1776 to 1953

Using modern census and environmental factor data,this study first identified the environmental factors that significantly affect the population distribution through Geodetector analysis and then constructed a population spatial distribution model based on the random forest regression algorithm.Finally,with this model and historical population data that were examined and corrected by historians,gridded population distributions with a spatial resolution of 10 km by 10 km in the traditional cultivated region of China(TCRC,hereafter)were reconstructed for six time slices from 1776 to 1953.Using the reconstruction dataset,the spatiotemporal characteristics of the population distribution were depicted.The results showed that(1)the environmental factors that significantly affected the population density differences among counties in the TCRC mainly consisted of elevation,slope,relief amplitude,distances to the nearest prefectural and provincial capitals,distance to the nearest river and the climatology moisture index.(2)Using the census data of 1934 counties in the TCRC in 2000 and the abovementioned environmental factor data,a random forest regression algorithm-based population spatial distribution model was constructed.Its determination coefficient(R^(2))is 0.81.In 88.4%of the counties(districts),the relative errors of the model predictions were less than 50%.(3)From 1776 to 1953,the total population in the study area showed an uptrend.Prior to 1851,the population increased mainly in the Yangtze River Delta.During this period,the number of grid cells in which the population densities were greater than 500 persons per km^(2) increased from 292 to 683.From 1851 to 1953,the population increased extensively across the study area.In the North China Plain and the Pearl River Delta,the number of grid cells in which the population densities were greater than 500 persons per km^(2) increased from 36 to 88 and from 4 to 35,respectively.The spatial clustering pattern of the population distribution varied temporally.The potential reasons included the shifts in economic development hot spots,traditional beliefs,wars,famine,and immigration policies.(4)Between our reconstructions and the HYDE dataset,there are large differences in the data sources,selected environmental factors and modeling methods.As a consequence,in comparison to our reconstructions,there were fewer populations in the eastern area and more populations in the western area from 1776 to 1851 and more populations in urban areas and fewer populations in rural areas after 1851 in the HYDE dataset.