## 3 Results
### GPP Influencing Factors  
We further examined the dominant climatic factors through the decomposition of regression coefficients when regressing GPP against cropping intensity, irrigation area, fertilizer consumption, quantity of agricultural machinery, and climate factors (annual mean temperature and annual cumulative precipitation). The interannual trends of these factors are listed in Supplemental Table S2. These factors were effective in explaining the trend of GPP, and their contribution rates were above 62.8%, except for GX (46.4%) and QT (39.2%).  
The dominant drivers of the GPP trend varied widely across different regions (Figure 4). The increasing trend of GPP in northern China was primarily driven by increasing investment in agriculture and rising precipitation. For HHH, multiple influencing factors jointly promoted a rise in GPP. An increasing quantity of agricultural machinery was the largest contributor, explaining 26.9% of the GPP trend, followed by rising irrigation area (19.3%), fertilizer consumption (10.2%),  
Figure 3: (**a**) Interannual trends of total GPP for each region; (**b**) the spatial distribution of annual cropland GPP trends from 2000 to 2015; (**c**) the spatial distribution of the significant increases or decreases in GPP at a significant level of 0.05.  
and cropping intensity (11.7%). The results were relatively consistent in LP and NE. The GPP of NE and IM was also affected by precipitation, with contribution rates of 28.9% and 38.4%, respectively. The negative GPP trend in southern China was mainly attributed to the decreasing cropping intensity, while other factors had a relatively small impact. In the MLY, SW, and SC regions, the direct contribution rates of cropping intensity to the GPP trends were 54.1%, 53.9%, and 48.7%, respectively, indicating that decreasing cropping intensity is a predominant factor of changing GPP trends. Additionally, we further investigated the direct relationships between GPP and influencing factors for each region (Supplemental Table S3), and the results were consistent with the above data.  
Figure 4: The relative importance of the influencing factors in determining the GPP trend. CI = cropping intensity; IRR = irrigation area; FER = consumption of fertilizers; MAC = quantity of agricultural machinery; TAVG = annual mean temperature; PRCP = annual cumulative precipitation. The numbers in the upper right corner of each graph indicate the degree to which all factors explain GPP trends.  
Additionally, the spatial patterns of the correlation coefficients between cropping intensity and annual cropland GPP are shown in Figure 5. About 87.9% of pixels showed a positive correlation (42.4% had a significant positive correlation, \(p<0.05\)) between cropping intensity and GPP in southern China, which covered the MLY, SW, and SC regions. In contrast, about 86.5% of pixels showed decreasing trends (35.6% had significant decreasing trends, \(p<0.05\)) of cropping intensity in southern China. The pixels revealed decreasing cropping intensity; cropping intensity and GPP were positively correlated and almost coincided, indicating that the decreasing cropping intensity was the main reason for the changes in GPP in these regions. In contrast, 8.7% of pixels showed significant positive correlations between cropping intensity and GPP, and almost all of them were distributed in HHH.  
Figure 5: Spatial distribution of (**a**) trends in cropping intensity and (**b**) the correlation coefficients between cropping intensity and annual cropland GPP from 2000 to 2015.