Study on the Factors Affecting the Humus Horizon Thickness in the Black Soil Region of Liaoning Province, China DOI Creative Commons
Yingying Jiang, Jiayi Tang, Zhong‐Xiu Sun

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(9), P. 2106 - 2106

Published: Sept. 15, 2024

Understanding the spatial variability and driving mechanisms of humus horizon thickness (HHT) degradation is crucial for effective soil prevention in black regions. The study compared ordinary kriging interpolation (OK), inverse distance weighted (IDW), regression (RK) using mean error (ME), absolute (MAE), root square (RMSE), relative RMSE to select most accurate model. Environmental variables were then integrated predict HHT characteristics. Results indicate that: (1) RK was superior OK IDW characterizing with smallest ME (11.45), (14.98), MAE RRMSE (0.44). (2) average annual temperature (0.29), precipitation (0.27), digital elevation model (DEM) (0.21) primary factors influencing HHT. (3) exhibited notable variability, an increasing trend from southeast towards central northern directions, being thinnest southeast. It thicker northeast southwest regions, but less dense along southern Bohai coast, yet sporadically distributed northwest (especially Chaoyang Fuxin), thick aggregated distribution over a smaller area northeastern direction (e.g., Tieling). These findings provide scientific basis management Liaoning Province.

Language: Английский

Study on the Factors Affecting the Humus Horizon Thickness in the Black Soil Region of Liaoning Province, China DOI Creative Commons
Yingying Jiang, Jiayi Tang, Zhong‐Xiu Sun

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(9), P. 2106 - 2106

Published: Sept. 15, 2024

Understanding the spatial variability and driving mechanisms of humus horizon thickness (HHT) degradation is crucial for effective soil prevention in black regions. The study compared ordinary kriging interpolation (OK), inverse distance weighted (IDW), regression (RK) using mean error (ME), absolute (MAE), root square (RMSE), relative RMSE to select most accurate model. Environmental variables were then integrated predict HHT characteristics. Results indicate that: (1) RK was superior OK IDW characterizing with smallest ME (11.45), (14.98), MAE RRMSE (0.44). (2) average annual temperature (0.29), precipitation (0.27), digital elevation model (DEM) (0.21) primary factors influencing HHT. (3) exhibited notable variability, an increasing trend from southeast towards central northern directions, being thinnest southeast. It thicker northeast southwest regions, but less dense along southern Bohai coast, yet sporadically distributed northwest (especially Chaoyang Fuxin), thick aggregated distribution over a smaller area northeastern direction (e.g., Tieling). These findings provide scientific basis management Liaoning Province.

Language: Английский

Citations

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