A Large-Scale Dataset of Conservation and Deep Tillage in Mollisols, Northeast Plain, China
文献类型: 外文期刊
作者: Jiang, Fahui 1 ; Huang, Shangshu 1 ; Wu, Yan 2 ; Ul Islam, Mahbub 3 ; Dong, Fangjin 4 ; Cao, Zhen 5 ; Chen, Guohui 6 ; Guo, Yuming 7 ;
作者机构: 1.Jiangxi Agr Univ, Coll Land Resources & Environm, Nanchang 330045, Peoples R China
2.Jiangxi Inst Red Soil, Nanchang 330046, Peoples R China
3.Bangladesh Agr Res Inst, Gazipur 1701, Bangladesh
4.Chinese Acad Sci, Northeast Inst Geog & Agroeocol, Harbin 150081, Peoples R China
5.Chinese Acad Sci, Nanjing Inst Geog & Limnol, Nanjing 210008, Peoples R China
6.Jiujiang Acad Agr Sci, Jiujiang 332000, Peoples R China
7.Minist Agr & Rural Affairs, Cultivated Land Qual Monitoring & Protect Ctr, Beijing 100125, Peoples R China
关键词: conservation tillage; deep tillage; conventional tillage; random forest; meta-analysis; subsoiling; no-tillage; straw mulching; crop yield
期刊名称:DATA ( 影响因子:2.6; 五年影响因子:3.0 )
ISSN:
年卷期: 2023 年 8 卷 1 期
页码:
收录情况: SCI
摘要: One of the primary challenges of our time is to feed a growing and more demanding world population with degraded soil environments under more variable and extreme climate conditions. Conservation tillage (CS) and deep tillage (DT) have received strong international support to help address these challenges but are less used in major global food production in China. Hence, we conducted a large-scale literature search of English and Chinese publications to synthesize the current scientific evidence to evaluate the effects of CS and DT on soil protection and yield maintenance in the Northeast China Plain, which has the most fertile black soil (Mollisols) and is the main agricultural production area of China. As a result, we found that CS had higher soil bulk density, strong soil penetration resistance, greater water contents, and lower soil temperature, and was well-suited for dry and wind erosion-sensitive regions i.e., the southwest areas of the Northeast. Conversely, DT had better performance in the middle belt of the Northeast China Plain, which contained a lower soil temperature and humid areas. Finally, we created an original dataset from papers [dataset 1, including soil physio-chemical parameters, such as soil water, bulk density, organic carbon, sand, silt, clay, pH, total and available nitrogen (N), phosphorus (P), and potassium (K), etc., on crop biomass and yield], by collecting data directly from publications, and two predicted datasets (dataset 2 and dataset 3) of crop yield changes by developing random forest models based on our data. Dataset: https://www.mdpi.com/article/10.3390/data8010006/s1 Dataset License: Creative Commons Attribution 4.0 International.
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