Chuang Zhang

Microbial necromass carbon influences soil organic carbon (SOC) persistence, yet its response to soil moisture change in semi-arid rhizospheres remains unclear. Avena sativa and Leymus chinensis , which are widely cultivated in semi-arid areas, were selected for a three-month indoor pot cultivation experiment. The responses of soil microbial necromass carbon to water addition and the key drivers in the rhizosphere soil of these plants were identified. The results demonstrated that water addition significantly decreased the concentrations of bacterial necromass carbon (BNC), fungal necromass carbon (FNC), and SOC in the Avena sativa rhizosphere, but increased them in the Leymus chinensis rhizosphere. However, water addition consistently reduced the contribution of microbial necromass carbon to SOC across both rhizosphere soils. The FNC accounted for a larger proportion of SOC than BNC, but the dynamic of SOC was more closely associated with the change in BNC. Both BNC and FNC showed significant correlations with microbial properties (gene abundances and enzyme activities). However, the BNC was closely related to soil nutrient availability, whereas FNC was linked to the ratio of SOC to total nitrogen. These findings demonstrate that rhizosphere microbial necromass carbon dynamics in response to moisture change are plant-specific and driven by distinct factors. Therefore, accurately projecting future SOC stability in semi-arid regions requires models to incorporate plant-specific traits and microbial mediation.

Future global warming is expected to increase carbon (C) losses from terrestrial ecosystems to the atmosphere through soil respiration (SR). However, the underlying mechanisms involving microbial communities and enzymatic responses remain unclear. A long-term field experiment was started in October 2009 to assess intensively cultivated soil in the North China Plain with an infrared-heated warming system. Soil physic–chemical and microbial properties and absolute and specific activities of enzymes were measured in 2019 (the ninth year). Compared to the control, nine years of warming increased the annual average soil temperature at a 5 cm depth by approximately 1.5 °C, with soil water contents decreasing by about 3%. Long-term warming decreased the total contents of phospholipid fatty acid (PLFA) biomarkers by up to 40%. Microbial communities utilizing recalcitrant C were more sensitive to long-term warming compared to those targeting labile C, as indicated by the increased ratio of Gram-positive to Gram-negative bacteria in May and the increased ratio of fungi to bacteria in August. Long-term warming caused similar absolute activity of oxidase but higher absolute and specific activities of C-, nitrogen-, and phosphorus-acquiring hydrolase, reflecting high microbial nitrogen, phosphorus, and energy demands. We conclude that a nine-year warming period relatively enriched the oligotrophic communities and raised the nitrogen, phosphorus and energy requirements. Co-limitation of multiple nutrients and water inhibits the biomass of microbial communities, which may finally promote microbial acclimation to long-term warming.

Microbial residue nitrogen can indicate soil quality and is crucial for soil nitrogen retention and supply. However, it is still unclear how the dynamic changes in soil microbial residue nitrogen affect crop nitrogen uptake in agricultural practice. Based on a long-term wheat-maize rotation experiment with different nitrogen application rates (150 kg N ha⁻¹, 200 kg N ha⁻¹, and 300 kg N ha⁻¹), ¹⁵N-labeled nitrogen fertilizer was applied during the wheat season to track the dynamics of soil microbial residue nitrogen and its mediated fertilizer nitrogen. The results showed that nitrogen addition was beneficial to the accumulation of microbial residue nitrogen (mainly fungal microbial residue nitrogen). Its contribution rate to soil total nitrogen is 54.87–56.55%, and the fertilizer nitrogen allocated to it accounts for 27.10–47.50% of the remaining fertilizer nitrogen in the soil. Ultimately, 6.77–10.24% of the nitrogen fertilizer applied remained in the soil as microbial residue nitrogen. In addition, microbial residue nitrogen is mainly mineralized during the jointing and filling stages of wheat. In a word, the accumulation and mineralization of soil microbial residue nitrogen during the application of 200 kg N ha⁻¹ better guaranteed the uptake of nitrogen by wheat, which provided a reliable basis for guiding farmland fertility improvement and nitrogen fertilizer reduction in the North China Plain.

Accurate crop identification and spatial distribution mapping are important for crop production estimation and famine early warning, especially for food-deficit African agricultural countries. By evaluating existing preprocessing methods for classification using satellite image time series (SITS) in Kenya, this study aimed to provide a low-cost method for cultivated land monitoring in sub-Saharan Africa that lacks financial support. SITS were composed of a set of MODIS Vegetation Indices (MOD13Q1) in 2018, and the classification method included the Support Vector Machine (SVM) and Random Forest (RF) classifier. Eight datasets obtained at three levels of preprocessing from MOD13Q1 were used in the classification: (1) raw SITS of vegetation indices (R-NDVI, R-EVI, and R-NDVI + R-EVI); (2) smoothed SITS of vegetation indices (S-NDVI); and (3) vegetation phenological data (P-NDVI, P-EVI, R-NDVI + P-NDVI, and P-NDVI-1). Both SVM and RF classification results showed that the “R-NDVI + R-EVI” dataset achieved the highest performance, while the three pure phenological datasets produced the lowest accuracy. Correlation analysis between variable importance and rainfall time series demonstrated that the vegetation index SITS during rainfall periods showed higher importance in RF classifiers, thus revealing the potential of saving computational costs. Considering the preprocessing cost of SITS and its negative impact on the classification accuracy, we recommend overlaying the original NDVI with the original EVI time series to map the crop distribution in Kenya.