ARTÍCULO
TITULO

A Quantitative Analysis of Factors Influencing Organic Matter Concentration in the Topsoil of Black Soil in Northeast China Based on Spatial Heterogeneous Patterns

Zhenbo Du    
Bingbo Gao    
Cong Ou    
Zhenrong Du    
Jianyu Yang    
Bayartungalag Batsaikhan    
Battogtokh Dorjgotov    
Wenju Yun and Dehai Zhu    

Resumen

Black soil is fertile, abundant with organic matter (OM) and is exceptional for farming. The black soil zone in northeast China is the third-largest black soil zone globally and produces a quarter of China?s commodity grain. However, the soil organic matter (SOM) in this zone is declining, and the quality of cultivated land is falling off rapidly due to overexploitation and unsustainable management practices. To help develop an integrated protection strategy for black soil, this study aimed to identify the primary factors contributing to SOM degradation. The geographic detector, which can detect both linear and nonlinear relationships and the interactions based on spatial heterogeneous patterns, was used to quantitatively analyze the natural and anthropogenic factors affecting SOM concentration in northeast China. In descending order, the nine factors affecting SOM are temperature, gross domestic product (GDP), elevation, population, soil type, precipitation, soil erosion, land use, and geomorphology. The influence of all factors is significant, and the interaction of any two factors enhances their impact. The SOM concentration decreases with increased temperature, population, soil erosion, elevation and terrain undulation. SOM rises with increased precipitation, initially decreases with increasing GDP but then increases, and varies by soil type and land use. Conclusions about detailed impacts are presented in this paper. For example, wind erosion has a more significant effect than water erosion, and irrigated land has a lower SOM content than dry land. Based on the study results, protection measures, including conservation tillage, farmland shelterbelts, cross-slope ridges, terraces, and rainfed farming are recommended. The conversion of high-quality farmland to non-farm uses should be prohibited.

 Artículos similares

       
 
Thoralf Reis, Lukas Dumberger, Sebastian Bruchhaus, Thomas Krause, Verena Schreyer, Marco X. Bornschlegl and Matthias L. Hemmje    
Manual labeling and categorization are extremely time-consuming and, thus, costly. AI and ML-supported information systems can bridge this gap and support labor-intensive digital activities. Since it requires categorization, coding-based analysis, such a... ver más

 
Håkon Harnes and Donn Morrison    
WebAssembly is a low-level bytecode language that enables high-level languages like C, C++, and Rust to be executed in the browser at near-native performance. In recent years, WebAssembly has gained widespread adoption and is now natively supported by al... ver más
Revista: Future Internet

 
Anestis Kousis and Christos Tjortjis    
In recent years, the emergence of the smart city concept has garnered attention as a promising innovation aimed at addressing the multifactorial challenges arising from the concurrent trends of urban population growth and the climate crisis. In this stud... ver más
Revista: Future Internet

 
Daniela Castagna, Luzinete Scaunichi Barbosa, Charles Campoe Martim, Rhavel Salviano Dias Paulista, Nadja Gomes Machado, Marcelo Sacardi Biudes and Adilson Pacheco de Souza    
The Amazon biome plays a crucial role in the hydrological cycle, supplying water vapor for the atmosphere and contributing to evapotranspiration (ET) that influences regional humidity across Brazil and South America. Remote sensing (RS) has emerged as a ... ver más
Revista: Hydrology

 
Cai Wu, Yanwen Wang, Jiong Wang, Menno-Jan Kraak and Mingshu Wang    
This study introduces a machine learning-based framework for mapping street patterns in urban morphology, offering an objective, scalable approach that transcends traditional methodologies. Focusing on six diverse cities, the research employed supervised... ver más