The return of livestock and poultry manure (LPM) to fields is necessary for sustainable agricultural development, but it is also a primary source of heavy metal contamination in agricultural land, which potentially threatens soil and crops. Conflicting results have been reported in previous studies on the impacts of returning LPM to fields on cadmium (Cd) accumulation in the soil and crops. Herein, we investigated the impacts of applying such manure on Cd accumulation in the soil and crops through meta-analysis. We also explored the relationships of Cd content in the soil and crops with the soil properties, experimental conditions, and manure properties. Moreover, we evaluated the primary reasons for the differences in the results of different studies and the factors influencing them. Upon applying LPM, the contents of soil total Cd, soil available Cd, and crop grain Cd increased by 30.96%, 86.91%, and 20.43%, respectively, and the crop root Cd content decreased by 16.91%. Random forest analysis and decision tree analysis further quantified the importance of each influencing factor and identified the primary factors influencing soil available Cd and grain Cd contents. From the perspective of safe production, some suggestions were made for returning LPM to fields: (1) applying higher pH LPM or increasing the dosage in soils with low Cd content and pH of <5.7; (2) applying LPM to soils with Cd content of 5.7, and soil organic matter content of >19 g/kg, which was associated with a low risk of Cd accumulation in crop grains; and (3) applying LPM while planting low-Cd-accumulating crop varieties. This study provides scientific guidance for the safe use of such manure resources and helps to reduce the risk of cadmium accumulation.

Cadmium (Cd) pollution poses a severe threat to rice safety and human health, while traditional linear models exhibit significant limitations in predicting rice Cd accumulation due to environmental complexities. This study systematically evaluated the predictive performance of Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Residual Neural Networks (ResNet), using a multi-source soil–rice dataset comprising 57,200 samples from Hunan Province. The results showed that the RF model performed best on the test set (R² = 0.62), with the dominant features being soil’s available Cd (contributing 9.74%) and precipitation during the rice-filling stage (joint contribution of 15.96%). However, the model’s predictive performance experienced a sharp decline on the independent 2023 validation set comprising 393 samples from Yizhang County and Lengshuitan District, with R² values ranging from −0.12 to −0.31. This highlighted the fundamental limitations of static data-driven paradigms. Agronomic management measures, simplified by heterogeneous data and binary encoding, failed to effectively represent the actual intervention intensity. The study demonstrated that while machine learning models captured nonlinear relationships in laboratory environments, they struggled to adapt to the dynamic interactions and spatiotemporal heterogeneity of farmland systems. Future efforts should focus on developing hybrid models guided by mechanistic insights, integrating dynamic environmental processes and real-time data, and promoting localized “one model per region” strategies to enhance predictive robustness. This study provides methodological insights for the technological transformation of agricultural artificial intelligence, emphasizing that the deep integration of data-driven approaches and mechanistic understanding is crucial for overcoming the “last mile” challenge.

Context: Biochar can affect the storage and forms of nitrogen; thus, it may also play a role in altering the nitrogen cycle during the fermentation process of cow dung into organic fertilizer. Objective: To elucidate the mechanism and process of nitrogen transformation during the composting of cow manure with biochar, a comparative experiment was conducted. Method: This study investigates the use of biochar as a medium to enhance nitrogen storage during the aerobic composting of cow manure. The effectiveness was verified through a rapid composting experiment. Result and Conclusions: The results demonstrated that adding 5% biochar to the compost pile increased the total nitrogen content in manure by 12%. Specifically, the pyrrolic nitrogen in the composted cow manure increased from 38% to 44%, and the carbon-nitrogen ratio improved from 35% to 37%. Analysis of surface functional groups indicated that the C=O and C=C bonds in biochar played a key role in modifying nitrogen storage. Microbial analysis showed that biochar could significantly enhance the regional competitiveness of microorganisms, such as Cellvibrio, thereby boosting the expression of functional genes involved in the nitrification process, including amoABC, hao, and nxrAB. Therefore, adding 5% biochar not only enhances nitrogen storage in organic fertilizer but also changes the microbial population structure. Significance: This study carries substantial implications for the application of Biochar in the field, as well as for the development of microbial fertilizers based on cow manure.

The appropriate use of manure is important for the sustainable development of dairy farms. To identify more advantageous ways of using manure and key factors in the recycling process, this study investigated differences in soil fertility between solid and liquid manure waste recycling at an integrated farm. Both types of manure waste recycling promote soil fertility. However, solid manure exerted a more pronounced effect on soil fertility, especially on available phosphorus (P), which increased by 93.83%, but only 26.67% with liquid manure. As the amount of solid manure was increased, a clear logarithmic relationship (correlation coefficient = 0.90) formed between manure use and available P, indicating that P was a key index for manure recycling. Solid manure had a more positive influence on soil fertility in terms of microbial community change, as revealed by redundancy analysis and Pearson’s correlation analysis. The results of this study can be applied to most large-scale breeding farms, that the combination of solid and liquid manure fertilizer can be used to meet sustainable development goals. And the findings highlight solid manure as a key component for improving soil fertility.

Cow dung liquid mulch (CDLM), which uses cow dung as a raw material, has a good degradability and is a potential alternative to traditional plastic agricultural mulch, but there is a lack of research on the effects of CDLM on rhizosphere soil physicochemical properties, rhizosphere soil microbial functions, and crop yields. In this study, the link between maize yield, environmental factors, and functional genes as well as the responses of microbial community functions to CDLM and polyethylene mulch (PE) were studied using metagenomic sequencing. Functional annotation was also performed on clusters of orthologous groups of proteins, the Kyoto Encyclopedia of Genes and Genomes, and carbohydrate-active enzyme sequencing data. The results showed that CDLM significantly increased maize yield by 30.9% compared to CK while maintaining lower soil microplastic levels. CDLM promotes the enrichment of beneficial microorganisms such as Mycolicibacterium and Pseudomonas. The relative abundance of functional genes related to microbial metabolism, soil element cycling pathways, and organic matter degradation was significantly higher in CDLM than in CK. Microbial functional genes were positively correlated with maize yield and environmental factors such as soil nutrients. These results suggested that CDLM can improve maize yield by enriching beneficial microorganisms, reducing rhizosphere soil environmental risks, and enhancing rhizosphere soil microbial function. Rhizosphere soil nutrients and microbial functional genes together mediated the positive response of maize yield to CDLM. This study can provide a scientific basis and data support for the safe use of mulch in the future.