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[Objective] To investigate the soil infiltration characteristics of different agroforestry composite modes and their influencing factors types in the purple soil area. [Methods] We analyzed the changes in the soil physical properties and infiltration processes under four agroforestry types in the purple soil area, namely, no crop (CK), peanut (HS), pepper (HJ), and peanut + pepper (HS + HJ), through field surveys and laboratory experiments, and evaluated the applicability of three soil infiltration models including Kostiakov, Horton, and Philip. [Results] (1) The total soil porosity in HJ was not significantly different from that in CK and HS + HJ, but was considerably higher than that in HS (p

Wheat is an important food crop in China. The quality of wheat affects the development of the agricultural economy. However, the high-quality wheat produced in China cannot meet the demand, so it would be an important direction for research to develop high-quality wheat. Grain protein content (GPC) is an important criterion for the quality of winter wheat and its content directly affects the quality of wheat. Studying the spatial heterogeneity of wheat grain proteins is beneficial to the prediction of wheat quality, and it plays a guiding role in the identification, grading, and processing of wheat quality. Due to the complexity and variability of wheat quality, conventional evaluation methods have shortcomings such as low accuracy and poor applicability. To better predict the GPC, geographically weighted regression (GWR) models, multiple linear regression, random forest (RF), BP neural networks, support vector machine, and long-and-short-term memory algorithms were used to analyze the meteorological data and soil data of Jiangsu Province from March to May in 2019–2022. It was found that the winter wheat GPC rises by 0.17% with every 0.1° increase in north latitude at the county level in Jiangsu. Comparison of the prediction accuracy of the coefficient of determination, mean deviation error, root mean square error, and mean absolute error by analyzing multiple algorithms showed that the GWR model was the most accurate, followed by the RF model. The regression coefficient of precipitation in April showed the smallest range of variation among all factors, indicating that precipitation in April had a more stable effect on GPC in the study area than the other meteorological factors. Therefore, consideration of spatial information might be beneficial in predicting county-level winter wheat GPC. GWR models based on meteorological and soil factors enrich the studies regarding the prediction of wheat GPC based on environmental data. It might be applied to predict winter wheat GPC and improve wheat quality to better guide large-scale production and processing. [ABSTRACT FROM AUTHOR]

Biochar has the potential to effectively improve yield and nitrogen use efficiency (NUE) of upland crops in different soil types. However, it is unclear for rapeseed in purple soil under N reduction. A 2 years field experiment was conducted in southwest China, to evaluate the combined effects of nitrogen (180, 144 and 108 kg ha−1, named N100, N80 and N60, respectively) and biochar (0, 10, 20 and 40 t ha−1, named B0, B10, B20 and B40, respectively) on rapeseed growth, NUE and yield. Compared with B0, B10 significantly improved growth, NUE and yield, while nitrogen reduction had an opposite effect. The better management practices were N80B10 and N100B10, which increased NUE by 10.0% and 10.2%, and grain yield by 16.1% and 21.8%, respectively, compared with N100B0. Path analysis revealed that effective pods and root surface area, root volume had significant and direct path coefficients with variances in seed yield and NUE, respectively. Besides, positive residual effectiveness of biochar was preserved for at least 2 years. This study indicated that B10 could replace 20% nitrogen to promote rapeseed root development and yield formation, thus achieving both high yield and high NUE for at least 2 years in upland purple soil. [ABSTRACT FROM AUTHOR]