A Large-Scale Dataset of Conservation and Deep Tillage in Mollisols, Northeast Plain, China

文献类型： 外文期刊

第一作者： Jiang, Fahui

作者： Jiang, Fahui;Huang, Shangshu;Huang, Shangshu;Wu, Yan;Ul Islam, Mahbub;Dong, Fangjin;Cao, Zhen;Chen, Guohui;Guo, Yuming

作者机构：

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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