Xuesong Gao

Microplastics persist in the environment due to their resistance to degradation and pose an increasing concern for terrestrial ecosystems, threatening soil physicochemical properties and nitrogen (N) cycling. Biochar is widely used to improve soil quality and mitigate contamination. However little is known about how biochar modulates microplastic effects on soil N cycling. Therefore, we conducted a soil incubation experiment to investigate the effects of different polyethylene microplastic particle sizes (6.5, 600 and 1700 μm) on soil N dynamics and to evaluate the combined influence of biochar in microplastic-contaminated soils. Although significant differences among individual microplastic-size treatments were observed at some sampling times, no consistent particle-size dependent effect on soil nitrogen pools was observed throughout the incubation period. However, the addition of biochar significantly increased soil total N, nitrate-N, and alkali-hydrolyzable N contents. Microbial community analysis revealed that the 1700 μm polyethylene microplastic combined with biochar resulted in distinct microbial community structures compared with the control treatment, particularly after 45 days of incubation. Microbial community analysis revealed that the combination of polyethylene microplastics and biochar altered the composition of the dominant bacterial genera, with the greatest community shift observed under the 1700 μm microplastic treatment after 45 days of incubation. Overall, both microplastic presence and biochar amendment altered soil microbial community composition. These findings suggest that corn straw biochar may improve soil nitrogen availability under the experimental conditions of this study and provide insight into microbial responses to polyethylene microplastics. Additional studies across different soil types, biochar feedstocks, microplastic polymers, and field conditions are needed to evaluate the broader applicability of these findings.

Biochar-based organic fertilizer is increasingly used to improve soil quality, yet its effects on soil aggregation and organic carbon (SOC) fractions remain poorly understood. This study aimed to elucidate the effects of biochar-based organic fertilizer on SOC accumulation and soil aggregation and their underlying mechanisms. A microcosm experiment was conducted using a Cambisol under four treatments: no addition, organic fertilizer alone, biochar alone, and biochar-based organic fertilizer. We determined aggregate size distribution and stability, SOC and its fractions, as well as microbial community composition. Biochar-based organic fertilizer increased the proportion of macroaggregates, geometric mean diameter, and SOC content (from 0.9% to 1.9%, p < 0.05), along with increases in mineral-associated organic carbon (MAOC) and particulate organic carbon (POC, an increase of 8.1 g kg⁻¹, p < 0.05). The biochar component played a dominant role in promoting MAOC accumulation and aggregate stability, both of which were closely linked to shifts in bacterial community composition. In contrast, organic fertilizer primarily contributed to POC content associated with fungal communities. Overall, biochar-based organic fertilizer is effective for improving soil physical structure and SOC accumulation via biochar–organic fertilizer synergy. These findings have important implications for its use as a sustainable soil amendment for degraded arable soils.

The soybean–maize strip intercropping system enhances soybean yield while maintaining maize production, improving nitrogen use efficiency, and fostering intercropping mutualism. However, vigorous weed growth in warm and humid regions competes for nitrogen, while elevated soil temperatures accelerate nitrification, promoting nitrogen loss, especially during the peak nitrogen demand period of maize. Plastic film mulching, which conserves moisture, regulates temperature, and suppresses weeds, can improve the soil environment. A two-year field experiment was conducted with polyethylene (PE) films of various thicknesses (0.01, 0.014, 0.02 millimeters) and colors (black, white, silver-black) with an un-mulched control plot. Soil nitrogen content, microbial diversity, soil properties, and crop productivity were analyzed. The results indicated that plastic film mulching significantly altered soil nutrient availability and rhizosphere microbial community structures, while simultaneously enhancing crop productivity. The 0.014 mm black and white films performed best, showing a positive association with enhanced nitrogen transformation indices, which coincided with increased available nitrogen, biomass, and crop yield. However, long-term soil nutrient depletion remains a risk, suggesting the need for strategies like organic fertilizers or crop rotation to maintain soil fertility and ecological sustainability.

Model-based scenarios are essential for assessing the potential of agricultural management strategies to achieve sustainable development goals. However, to date, knowledge of the trade-offs and synergies between greenhouse gas (GHG) emissions and nitrogen (N) reduction, carbon sequestration, and food provisioning under different agricultural practices remains limited, with most studies focusing on global and national scales. The present study implements the generalized representation of agro-food system model coupled with the soil organic carbon (SOC) AMG model in the Tuojiang River Basin, China, to assess the effects of 24 agricultural scenarios on SOC stock, the GHG budget, nitrogen (N) surplus, and export capacity at the county level in 2035. We considered viable options by modifying four levers: (i) synthetic fertilizer inputs, (ii) livestock population size and the fraction of animal proteins in the human diet, (iii) the share of legumes in crop rotation, and (iv) the proportion of straw used for bioenergy production. We found that the potential of biofuels to substitute fossil fuel emissions remains low across all scenarios, reducing by 2.9%–5.3% of current emissions. Our results also reveal synergies in reducing GHG emissions and N pollution, with reductions of 39%–43% and 26%–52%, respectively, under agro-ecological scenarios with zero N fertilizer application and halving of the livestock population. In contrast, trade-offs were identified between SOC sequestration and export capacity, both of which were lower in agro-ecological scenarios than in the others.

The demographic transition that accompanies the urbanization transformation has a key impact on land use efficiency. This study applies the PVAR, SDM, and threshold models to investigate the influence of China’s demographic dividend composition on urban land use efficiency, aiming to provide guidance for the strategic management and allocation of demographic resources, thereby optimizing urban land resource utilization. The key findings are as follows: (1) Spatial effects reveal that the scale and structural dividends of the population size dividend significantly inhibit urban land use efficiency, whereas the technological dividend of the population quality dividend initially restrains but ultimately enhances it. The combined impact of quantitative and qualitative demographic dividends on land use efficiency is most pronounced in the west and least in the east. High population provinces see significant land use efficiency benefits, contrasting with medium and low population provinces. (2) Both population size and quality dividends exhibit a single-threshold effect on land use efficiency. In summary, cultivating diverse talents with superior technical skills is essential for fostering the upgrade of new industrialization models, ultimately significantly enhancing land use efficiency.

Land-use and land-cover changes (LUCCs) significantly impact carbon sequestration by modifying the structure and function of terrestrial ecosystems. This study utilized GIS and remote sensing techniques to forecast future LUCC patterns and their influence on regional carbon budgets, which is essential for sustainable development. We devised a coupled system dynamics (SD) model integrated with a patch-generating land-use simulation (PLUS) model to simulate LUCCs under diverse future scenarios using multisource environmental data. Additionally, the InVEST model was employed to quantify carbon storage in terrestrial ecosystems. By establishing three scenarios—ecological priority (EP), highly urbanized (HU), and coordinated development (CD)—this study’s aim was to predict the LUCC patterns and carbon storage distribution of the Tuojiang River Basin (TRB), China, up to 2035. The results showed that (1) from 2000 to 2020, significant LUCCs occurred in the TRB, primarily involving the conversion of cultivated land into construction areas and forestland; (2) LUCCs had a substantial impact on carbon storage in the TRB, with the EP scenario demonstrating the highest carbon storage by 2035 due to extensive forest expansion, while the HU scenario indicated a decline in carbon storage associated with rapid urbanization; and (3) the mountainous regions of the TRB, dominated by forestland, consistently exhibited higher carbon storage, whereas the Chengdu Plain region in the upper basin displayed the lowest. In conclusion, we recommend prioritizing the CD scenario in future development strategies to balance economic growth with ecological protection while simultaneously enhancing carbon storage. Our findings offer valuable insights to shape future LUCC policies in the Tuojiang River Basin, underscoring the adaptability of the coupled model approach to a wide range of geographic scales and contexts.

Timely and accurate mapping of rice distribution is crucial to estimate yield, optimize agriculture spatial patterns, and ensure global food security. Feature selection (FS) methods have significantly improved computational efficiency by reducing redundancy in spectral and temporal feature sets, playing a vital role in identifying and mapping paddy rice. However, the optimal feature sets selected by existing methods suffer from issues such as information redundancy or local optimality, limiting their accuracy in rice identification. Moreover, the effects of these FS methods on rice recognition in various machine learning classifiers and regions with different climatic conditions and planting structures is still unclear. To overcome these limitations, we conducted a comprehensive evaluation of the potential applications of major FS methods, including the wrapper method, embedded method, and filter method for rice mapping. A novel hierarchical lustering sequential forward selection (HCSFS) method for precisely extracting the optimal feature set for rice identification is proposed. The accuracy of the HCSFS and other FS methods for rice identification was tested with nine common machine learning classifiers. The results indicated that, among the three FS methods, the wrapper method achieved the best rice mapping performance, followed by the embedded method, and lastly, the filter method. The new HCSFS significantly reduced redundant features compared with eleven typical FS methods, demonstrating higher precision and stability, with user accuracy and producer accuracy exceeding 0.9548 and 0.9487, respectively. Additionally, the spatial distribution of rice maps generated using the optimal feature set selected by HCSFS closely aligned with actual planting patterns, markedly outperforming existing rice products. This research confirms the effectiveness and transferability of the HCSFS method for rice mapping across different climates and cultivation structures, suggesting its enormous potential for classifying other crops using time-series remote sensing images.

Improving low nitrogen (LN) tolerance in barley (Hordeum vulgare L.) increases global barley yield and quality. In this study, a recombinant inbred line (RIL) population crossed between “Baudin × CN4079” was used to conduct field experiments on twenty traits of barley yield, agronomy, and nitrogen(N)-related traits under LN and normal nitrogen (NN) treatments for two years. This study identified seventeen QTL, comprising eight QTL expressed under both LN and NN treatments, eight LN-specific QTL, and one NN-specific QTL. The localized C2 cluster contained QTL controlling yield, agronomic, and N-related traits. Of the four novel QTL, the expression of the N-related QTL Qstna.sau-5H and Qnhi.sau-5H was unaffected by N treatment. Qtgw.sau-2H for thousand-grain weight, Qph.sau-3H for plant height, Qsl.sau-7H for spike length, and Qal.sau-7H for awn length were identified to be the four stable expression QTL. Correlation studies revealed a significant negative correlation between grain N content and harvest index (p < 0.01). These results are essential for barley marker-assisted selection (MAS) breeding.