Rhizosphere Fungal Dynamics in Sugarcane during Different Growth Stages

A potential slow-release fertilizer based on biogas residue biochar: Nutrient release patterns and synergistic mechanism for improving soil fertility

The large yield of anaerobic digestates and the suboptimal efficacy of nutrient slow-release severely limit its practical application. To address these issues, a new biochar based fertilizer (MAP@BRC) was developed using biogas residue biochar (BRC) to recover nitrogen and phosphorus from biogas slurry. The nutrient release patterns of MAP@BRC and mechanisms for enhancing soil fertility were studied, and it demonstrated excellent performance, with 59% total nitrogen and 50% total phosphorus nutrient release rates within 28 days. This was attributed to the coupling of the mechanism involving the dissolution of struvite skeletons and the release of biochar pores. Pot experiments showed that crop yield and water productivity were doubled in the MAP@BRC group compared with unfertilized planting. The application of MAP@BRC also improved soil nutrient levels, reduced soil acidification, increased microbial populations, and decreased soil heavy metal pollution risk. The key factors that contributed to the improvement in soil fertility by MAP@BRC were an increase in available nitrogen and the optimization of pH levels in the soil. Overall, MAP@BRC is a safe, slow-release fertilizer that exhibits biochar-fertilizer interactions and synergistic effects. This slow-release fertilizer was prepared by treating a phosphorus-rich biogas slurry with a nitrogen-rich biogas slurry, and it simultaneously addresses problems associated with livestock waste treatment and provides a promising strategy to promote zero-waste agriculture.

Analysis of rhizosphere fungal community of agricultural crops cultivated in laboratory experiments on Chernevaya taiga soil

Chernevaya taiga of Western Siberia, Russia, is a unique ecosystem characterized by fertile soil, exceptionally large herbaceous plant sizes, and extraordinarily rapid rates of plant residue degradation. We expected that growing crops on soil collected from Chernevaya taiga, which has never been used for agricultural purposes before, would result in a distinct rhizospheric fungal community. This community could potentially yield novel, potent biostimulators and biocontrol fungi for modern agriculture. To check this idea, we used high-throughput ITS sequencing to examine the microbial communities in the rhizosphere of spring wheat and radish grown in greenhouse experiments on Chernevaya and control soils. Additionally, representative fungal strains were isolated and assessed for their ability to promote growth in wheat seedlings. The study revealed that the most abundant phyla in the rhizospheric fungal community were Mortierellomycota, primarily consisting of Mortierella species, and Ascomycota. Mucor and Umbelopsis comprised the majority of Mucoromycota in the control soils. Fusarium and Oidiodendron, two potentially plant-pathogenic fungi, were only found in the rhizosphere of crops grown in the control soil. Conversely, Chernevaya soil contained a diverse range of potential biocontrol fungi for plants. Tested novel fungal isolates showed a stimulating effect on the development of wheat seedlings and positively affected their rate of biomass accumulation. The results of the study demonstrate that the soil of Chernevaya taiga do indeed contain fungi with prominent potential to stimulate agricultural plants growth.

SE-VisionTransformer: Hybrid Network for Diagnosing Sugarcane Leaf Diseases Based on Attention Mechanism

Sugarcane is an important raw material for sugar and chemical production. However, in recent years, various sugarcane diseases have emerged, severely impacting the national economy. To address the issue of identifying diseases in sugarcane leaf sections, this paper proposes the SE-VIT hybrid network. Unlike traditional methods that directly use models for classification, this paper compares threshold, K-means, and support vector machine (SVM) algorithms for extracting leaf lesions from images. Due to SVM's ability to accurately segment these lesions, it is ultimately selected for the task. The paper introduces the SE attention module into ResNet-18 (CNN), enhancing the learning of inter-channel weights. After the pooling layer, multi-head self-attention (MHSA) is incorporated. Finally, with the inclusion of 2D relative positional encoding, the accuracy is improved by 5.1%, precision by 3.23%, and recall by 5.17%. The SE-VIT hybrid network model achieves an accuracy of 97.26% on the PlantVillage dataset. Additionally, when compared to four existing classical neural network models, SE-VIT demonstrates significantly higher accuracy and precision, reaching 89.57% accuracy. Therefore, the method proposed in this paper can provide technical support for intelligent management of sugarcane plantations and offer insights for addressing plant diseases with limited datasets.

Climate change impacts on plant pathogens, food security and paths forward

Plant disease outbreaks pose significant risks to global food security and environmental sustainability worldwide, and result in the loss of primary productivity and biodiversity that negatively impact the environmental and socio-economic conditions of affected regions. Climate change further increases outbreak risks by altering pathogen evolution and host-pathogen interactions and facilitating the emergence of new pathogenic strains. Pathogen range can shift, increasing the spread of plant diseases in new areas. In this Review, we examine how plant disease pressures are likely to change under future climate scenarios and how these changes will relate to plant productivity in natural and agricultural ecosystems. We explore current and future impacts of climate change on pathogen biogeography, disease incidence and severity, and their effects on natural ecosystems, agriculture and food production. We propose that amendment of the current conceptual framework and incorporation of eco-evolutionary theories into research could improve our mechanistic understanding and prediction of pathogen spread in future climates, to mitigate the future risk of disease outbreaks. We highlight the need for a science-policy interface that works closely with relevant intergovernmental organizations to provide effective monitoring and management of plant disease under future climate scenarios, to ensure long-term food and nutrient security and sustainability of natural ecosystems.

First insights into diversity and potential metabolic pathways of bacterial and fungal communities in the rhizosphere of Argemonemexicana L. (Papaveraceae) from the water-level-fluctuation zone of Wudongde Reservoir of the upper Yangtze river, China

The water-level fluctuation zone (WLFZ) of Wudongde reservoir of the upper Yangtze river is a completely new aquatic-terrestrial transitional zone, and its plant degenerate issue is attracting global concerns. Uncovering the unknown rhizosphere microbiome of dominant plants of this zone is helpful in understanding the plant-microbe interactions and their growth under the largely varying environment. Here, a first exploration of the rhizosphere bacterial and fungal communities of wilted (JB) and unwilted (JA) Argemonemexicana L. individuals from the WLFZ of Wudongde reservoir was carried out using high-throughput sequencing and MetaCyc metabolic pathway analyses. The results showed that rhizosphere of wilted A.mexicana L individuals exhibited a higher microbial richness and diversity than the unwilted ones, irrespective of the bacterial and fungal communities. It was noted that 837 common bacterial amplicon sequence variants (ASV) and 92 common fungal ASV were presented in both JA and JB with 3108 bacteria and 212 fungi unique to JA, and 3569 bacteria and 693 fungi unique to JB. Linear discriminant analysis effect Size (LEfSe) analyses indicated that the taxa that had the most contribution to observed differences between both JA and JB was Proteobacteria, Actinobacteria and Ascomycota for JA, and Bacteroidetes, Firmicutes, Verrucomicrobia, Basidiomycota and Ascomycota for JB. Organic compound conversion pathway (degradation/reduction/oxidation) was consistently highly represented in the rhizosphere microbiomes of both JA and JB. Overall, this study provides insights into the rhizosphere microbiome composition, diversity and metabolic pathways of both wilted and unwilted A.mexicana L. individuals in the WLFZ of Wudongde reservoir, and the results give valuable clues for manipulating microbes to support plant growth in such a recently-formed WLFZ under a dry-hot valley environment.