Spatiotemporal Dynamics and Drivers of Farmland Changes in Panxi Mountainous Region, China

Abstract

Due to the multiple impacts of landform effects, spatial heterogeneity and land use policies, farmland dynamics in mountainous areas are complicated. This study investigated farmland dynamics based on land use data from a typical mountainous area, Panxi, in China for 1990, 2000 and 2010, discussed the relationship between altitude, slope and farmland changes and presented an analysis of the driving forces of farmland change. Our findings are as follows: (1) from 1990 to 2010, the area of converted farmland was relatively small (313 km 2 ), accounting for only 2.6% of the region's farmland. Farmland was mainly converted to forest and grassland as a result of the Returning Farmland to Forest Program; (2) The spatial distribution of land use types differed significantly. The presence of water bodies influenced farmland and built-up land, while forest and grassland showed "landform-oriented" characteristics. Built-up land was especially variable in distribution, indicating that it was more vulnerable to human activities; (3) The vertical differentiation of farmland changes was obvious. At altitudes <2500 m, the data displayed a trend of conversion from forest to farmland, while at >2500 m, this trend reversed. Thus, 2500 m serves as an altitudinal boundary between farmland and forest in Panxi. The largest area of forest-farmland transfer occurred on slopes steeper than 15 • , not 25 • , as defined by China's policy of Returning Farmland to Forest; (4) The driving forces of farmland changes varied. Decreases in farmland were negatively correlated with proximity to rivers and roads. Increases in farmland were positively related to temperature and negatively related to both population density and altitude.

Exploring the Factors Driving Changes in Farmland within the Tumen/Tuman River Basin

“…The examined study region is characterized by a typical temperate monsoonal climate and experiences average temperatures between 2 • C and 6 • C [30], the rising temperatures in this area become more favorable for crop growth; the positive effects of AMT on farmland expansions in different regions might therefore be due to the fact that areas where temperature is rising tend to be more favorable for agricultural production. These positive effects of temperature on farmland expansions also appeared in existing study [16] in Panxi mountainous region, China. At the same time, the positive effects of AMT on farmland contractions might also be due to urbanization and reforestation, as these regions are also favorable for human activities and plant growth [16].…”

“…Mountain regions tend to be more sensitive to LUCC [15] and farmland dynamics in these areas are also complicated by the multiple impacts of terrain, spatial heterogeneity, and land use policies [16]. Understanding farmland changes (both expansion and contraction referred to the cultivated surface) and their underlying driving factors is therefore important for modeling future dynamics, developing food security management strategies, and sustainable development.…”

“…The relative influence of these variables on farmland changes may, however, take place at different rates and at various stages [15] in different regions and time periods. A range of neural network models [18] as well as analytic hierarchy process-based approaches [19], multivariate regression models [8], principal component analysis [16], and canonical correlation analysis [20], alongside logistic regression (LR) models [21,22], have been utilized in this field to estimate the forces driving LUCC. In terms of spatial sampling (SS), LR models have been widely applied to estimate the quantitative relationship between the spatial distribution of LUCC and driving forces by incorporating physical, socioeconomic, climatic, and accessibility factors [22][23][24].…”

The Effect of Urbanization and Farmland Transfer on the Spatial Patterns of Non-Grain Farmland in China

“…Boncinelli et al found that proximity reduces transaction and information costs [45]. Studies in China also showed that agglomeration of population, industries and technology affected on spatial pattern of farmland [48,49]. These evidences, however, are mostly based on county level data [47], rural household level data [46], and plot level data [50].…”

Research on Barriers and Government Driving Force in Technological Innovation of Architecture Based on BIM

“…To this end, taking the implementation of this study as an example, the relationship between the main obstacles in the process of knowledge transfer (taking BIM) is established. For example, if the organization of knowledge transfer cannot adapt to the implementation of innovative technology, it will inevitably lead to the increase in the cost of knowledge transfer, the formation of organizational entities operating efficiency barriers (Peng, L., 2016).…”