Assessment of Soil Quality in the Transformation from Pure Chinese Fir Plantation to Mixed Broad-Leaved and Cunninghamia lanceolata Plantation in Subtropical China

• Published in: Forests Vol. 14; no. 9; p. 1867
• Main Authors: Cao, Xiaoyu, Zhang, Zelian, Yan, Wende, Mo, Yongjun, Wu, Shuping, Wang, Menglei, Peng, Yuanying
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2023
• Subjects: Agricultural practices; broad-leaved tree species; Bulk density; Catalase; Chinese fir; Climate change; Coniferous forests; Cunninghamia lanceolata; Datasets; Deforestation; Forest management; Forest soils; Indicators; Intercropping; interplanting; minimal data set; Nutrients; Physicochemical properties; Plant species; Plantations; Potassium; principal component analysis; Principal components analysis; Quality assessment; Soil chemistry; Soil depth; Soil fertility; Soil investigations; Soil layers; Soil pH; Soil properties; Soil quality; Soil sciences; Soil structure; Species composition; SQI; Sustainability management; Sustainable forestry; Trees; China
• DOI: 10.3390/f14091867
• ISSN: 1999-4907

To assess the alterations in soil properties resulting from the interplanting of broad-leaved tree species within coniferous forests, we conducted an investigation into soil quality in a mixed Chinese fir and broad-leaved forest, as well as in a Chinese fir pure forest (used as a control) in subtropical China. A total of 15 soil physicochemical properties were assessed across three soil depths—0–15 cm, 15–30 cm, and 30–45 cm—for the two forest types in the experimental study. Principal component analysis in conjunction with the Norm value was employed to create a minimal data set (MDS) for assessing six indicators, including bulk density (BD), total nitrogen (TN), total phosphate (TP), available potassium (AK), soil pH, and catalase (CAT). The soil quality index (SQI) was calculated for both forest types. The results demonstrated that following the interplanting of broad-leaved tree species in the Chinese fir forest, all soil physicochemical indicators were significantly improved compared to the control, and significant differences were also observed in the 0–15 cm and 15–30 cm soil layers (p < 0.05). The overall average of the SQI of the mixed forest (0.8523, 0.6636) was significantly higher than that of the control (0.4477, 0.3823) (p < 0.05) in the 0–15 cm and 15–30 cm soil layers, respectively. However, there was no significant difference in the SQI in the 30–45 cm soil layer (p > 0.05) between the two forest types. The results indicated that the SQI based on the minimal dataset (MDS) can reflect the SQI of the total dataset (TDS) when assessing soil quality in forests. Our research provides valuable scientific insights into soil science and an understanding of the relationships between soil properties, forest structure, and species composition in sustainable forest management.