Straw returning is an effective management measure to improve or maintain soil fertility in agricultural ecosystems. This study investigated the effects of straw returning combined with compound fertilizer on the bacterial community, enzyme activities, and soil nutrients’ contents in a rape-rice rotation soil aggregates. To do so, a 5-year field trial (November 2016 to October 2021) was carried out in a paddy soil with three treatments: no straw + no fertilization (CK), compound fertilizer (F), and straw returning + compound fertilizer (SF). Soil aggregates were classified into mega-aggregates (> 2 mm), macro-aggregates (0.25–2 mm), micro-aggregates (0.053–0.25 mm), and silt–clay (< 0.053 mm) using the wet sieve method. High-throughput sequencing was employed to characterize the bacterial community, and Pearson correlation coefficient was used to identify the relationships among bacterial community, organic carbon, nitrogen, phosphorus, and enzyme activities in soil aggregates. Compared with F, the results showed that straw returning increased the content of > 2 mm aggregates by 3.17% and significantly decreased the content of 0.053–0.25 mm aggregates by 20.27%. The contents of organic carbon and total nitrogen in > 0.053 mm straw amended aggregates increased by 15.29 and 18.25%, respectively. Straw returning significantly increased the urease activity of > 0.053 mm aggregates with an average of 43.08%, while it decreased the phosphatase and invertase activities of soil aggregates by 7.71–40.66%. The Shannon indices of the bacterial community in each particle sizes soil aggregates decreased by an average of 1.16% and the Chao indices of the bacterial community in < 2 mm aggregates increased by an average of 3.90% in straw amended soils. Nevertheless, the relative abundances of Chloroflexi and Nitrospirotain in all soil aggregates increased by 6.17–71.77% in straw amended soils. Altogether, our findings suggest that straw returning is an efficient approach to enhance soil structure, carbon and nitrogen contents, and the richness of soil bacterial diversity.

The use of degradable chelating agents to facilitate phytoextraction is a promising low-cost method for the remediation of heavy metal-contaminated soils. However, there are few studies on how plants and soils respond to the chelating agents. In this study, the responses of French marigold (Tagetes patula L.) and soil cadmium (Cd) to the chelator tetrasodium glutamate (GLDA) was investigated in a 180 d field trial. Five GLDA treatments (0, 292.5, 585, 1170, and 2340 kg hm⁻²) were carried out in a Cd-contaminated soil (0.47 mg kg⁻¹) under French marigold plantation. The results showed that the application of GLDA promoted the transformation of other forms of Cd in soil to exchangeable state, and the exchangeable Cd and Fe–Mn oxide bound state increased by 42.13% and 32.97% (p < 0.05), respectively. The cell wall Cd accumulations significantly increased 9.39% (p < 0.05) and the percentages of soluble fractions increased by 460.33% (p < 0.05). Furthermore, increases occurred in soil pH, as well as DOC and DTPA-Cd contents with increasing the total amount of GLDA. The composite application of GLDA (2340 kg hm⁻²) with French marigold reduced the total soil Cd content by 7.59% compared with the soil background. Altogether, results of this study suggested that the application of GLDA can effectively activate soil Cd and enhance the capability of French marigold for the remediation of Cd-contaminated soils.

Cultivated land quality is an essential measure of cultivated land production capability. Establishing a cultivated land quality inversion model based on high-resolution remote sensing data provides a scientific basis for regional cultivated land resource management and sustainable utilization. Utilizing field survey data, cultivated land quality evaluation data, and high-resolution remote sensing data, a spectral index-cultivated land quality model was constructed and optimized with the machine learning method, and cultivated land quality inversion and verification in Chuzhou City in 2021 were carried out. The results showed that the distribution of cultivated land quality in the study area depicted with the remote sensing inversion model based on random forest was consistent with the actual cultivated land quality. Although the accuracy of the SVT-CLQ inversion model established using four spectral indices is slightly lower than that of the MSVT-CLQ group established using 15 indices, it can still accurately reflect the distribution of cultivated land quality in the study area. Compared with the two models of the MSVT-CLQ and SVT-CLQ groups, the field survey data of sampling points is reduced, the time and energy of field sampling and analysis are correspondingly saved, the efficiency of cultivated land quality evaluation is improved, and the dynamic monitoring and rapid evaluation of cultivated land quality are realized.