Analysis of Driving Factors of Cropland Productivity in Northeast China Using OPGD-SHAP Framework

Runzhao Gao, Hongyan Cai () and Xinliang Xu
Additional contact information
Runzhao Gao: State Key Laboratory of Geographic Information Science and Technology, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Hongyan Cai: State Key Laboratory of Geographic Information Science and Technology, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Xinliang Xu: State Key Laboratory of Geographic Information Science and Technology, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Land, 2025, vol. 14, issue 5, 1-23

Abstract: In the context of climate change and ecological degradation, enhancing cropland productivity in Northeast China is essential for ensuring national food security. This study adopted an integrated framework combining the optimal parameter-based geographical detector (OPGD) and SHapley Additive exPlanations (SHAP) to identify key drivers of average and total cropland productivity at the county level from 2001 to 2020. Growing-season-based cropland Net Primary Productivity (NPP) was estimated using the CASA model to represent cropland productivity. Results indicated that natural and ecological factors significantly dominated the spatial variation of cropland productivity, with their interactions amplified through dual-factor or nonlinear enhancements. Various machine learning models were fine-tuned and compared, and optimal models were selected for subsequent SHAP analysis. The findings revealed that erosion intensity exhibited the most significant impact on cropland productivity, whereas the effect of precipitation shifted from negative to positive, with a clear threshold of around 400 mm—matching the boundary between China’s semi-arid and semi-humid regions. Low-elevation plains (<300 m) and gentle slopes (<0.5°) predominately promoted total cropland productivity. Interactions between erosion and fertilizer intensity highlighted the need for moderate fertilization to prevent ecological degradation in severely eroded counties. These findings provide scientific support for targeted cropland management aimed at achieving sustainable agriculture in Northeast China.

Keywords: Northeast China; cropland productivity; optimal parameter-based geographical detector; interpretable machine learning; SHapley Additive exPlanations (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/14/5/1010/pdf (application/pdf)
https://www.mdpi.com/2073-445X/14/5/1010/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:14:y:2025:i:5:p:1010-:d:1650611

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().