# Mapping Abandoned Cultivated Land in China: Implications for Grain Yield Improvement
## 3 Results
### Characterizing the Spatial Pattern of Cultivated Land Abandonment in China  
In this paper, the global and local spatial autocorrelation tests were conducted on the abandoned area and abandoned rate of cultivated land in 2017 using the GeoDa spatial statistical analysis method, and the spatial pattern was analyzed. The estimated value of the global Moran's I and its significance are shown in Table 2. The Moran scatterplot of the abandoned area/rate of cultivated land is shown in Figure 4, and the results of the LISA cluster maps are shown in Figure 5. The Moran's I indices were statistically significant at the 1% level, suggesting a positive spatial autocorrelation between abandoned area and abandoned rate of cultivated land in China; also, the LISA cluster map showed that there were four types of spatial autocorrelation: high-high, low-low, low-high, and high-low. \begin{table}
\begin{tabular}{c c c} \hline
**Comprehensive Agricultural Regionalization of China** & **Abandoned Area (10\({}^{4}\) ha)** & **Abandoned Rate (\%)** \\ \hline The Northeast Region & 103.67 & 3.43 \\ The regions along the Inner Mongolia and the Great Wall & 173.71 & 12.76 \\ The Huang-huai-hai Region & 70.24 & 6.25 \\ The Loess Plateau & 163.62 & 13.57 \\ The middle and lower reaches of the Yangtze River & 121.32 & 6.07 \\ The Southwest Region & 123.85 & 2.88 \\ The Southern Region & 67.79 & 7.95 \\ The Gansu-Xinjiang Region & 68.35 & 6.61 \\ The Qinghai-Tibet Region & 17.54 & 14.81 \\ \hline \end{tabular}
\end{table} 
Table 1: Abandoned area and abandoned rate of cultivated land in nine agricultural comprehensive regions. \begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & **Moran’s Index** & **Expected Index** & **Mean-Value** & **Z-Score** & **\(p\)-Value** \\ \hline Abandoned area & 0.444038 & \(-\)0.0030 & \(-\)0.0028 & 12.7180 & 0.00100 \\ Abandoned rate & 0.289077 & \(-\)0.0030 & \(-\)0.0041 & 9.3406 & 0.00100 \\ \hline \end{tabular}
\end{table} 
Table 2: Global Moran’s I estimate of abandoned area and abandoned rate. In addition, the cluster maps in Figure 5 show that the abandoned area and abandoned rate of cultivated land presented distribution patterns. In terms of the area of abandoned cultivated land, the regions characterized by high-high spatial clusters of abandoned cultivated land represent an area with high observation value for variables being analyzed. Most of them are primarily concentrated on the Loess Plateau, the Inner Mongolia Plateau, and the Daxing'anling region in the northeast. Abandoned cultivated land with low-high spatial outliers (that is, an area with low observation value, but is surrounded by areas with high observation value) is distributed to Shijiazhuang, Baoding, Xianyang, Luzhou, Guiyang, and Alxa League as the center of high-high spatial clusters; while abandoned cultivated land with low-low spatial clusters, which shows an area with low observation value and is surrounded by areas with low observation value, is more scattered throughout the country, mainly distributed in Heze, Zhengzhou, Chengdu, Suzhou and Haikou and other cities with better economic development, which are influenced by multiple factors such as a superior land policy environment and developed socio-economic conditions, as well as the demand for food security. Influenced by multiple factors such as superior land policy environment and developed socio-economic conditions, as well as its demand for food security, the degree of reliance on cultivated land is still large, thus making the cultivated land resources in and around the region highly utilized and the area of cultivated land. Figure 4: Moran scatterplot of the abandoned area and abandoned rate of cultivated land in China. Figure 5: LISA cluster map of abandoned area and abandoned rate of cultivated land in China. land abandonment relatively lower. There is no abandoned cultivated land showing high-low spatial outliers that indicate an area with high observation value, but it is surrounded by areas with low observation value.