Natural plant roots enrich a diverse array of soil microbes, collectively known as the root microbiota. This microbiota interacts synergistically with plants, modulating various physiological processes, including nutrient utilization, which influences plant growth and health. Environmental nutrient conditions and plant nutrient-related genes have been reported to regulate the composition of the root microbiota. Innovative analytical methods, such as microbiome genome- and microbiome-wide association studies, have advanced understanding of the relationships between plants and root microbiota. These methods systematically reveal the interactions between root microbiota and plant nutrient utilization, providing a theoretical foundation for applying root microbiota in agriculture.

The study emphasizes the significance of biochar-based nanocomposites (BNCs) in tackling waste management challenges and developing valuable materials for environmental remediation and energy generation. BNCs have enhanced adsorption and catalytic properties by incorporating nanoparticles into a charcoal matrix, offering a dual benefit for waste treatment and environmental preservation. Using waste biomass for BNC production repurposes resources and reduces the ecological impact of waste disposal. This study also addresses the existing research gaps and uncertainties hindering the widespread use of biochar and BNCs. After almost a decade of extensive research, it is crucial to address and fill the gaps in knowledge, such as long-term impacts, carbon sequestration rates, potential deforestation and economic viability. Thoroughly analyzing the entire system and establishing adaptable governance is need to realize the full benefits of BNCs. This article discusses the urgent need for sustainable technology and solutions to solve global concerns, including waste management, water quality, soil health, climate change and renewable energy. Its aim is to improve existing research by providing a comprehensive overview of the potential of biochar and BNCs in achieving sustainability objectives. It also identifies research gaps and challenges that must be addressed, directing future research directions. It extensively reviews biochar-based nanocomposites derived from waste biomass as a sustainable solution for wastewater treatment and renewable bioenergy. The constraints and future research directions have been highlighted, offering essential perspectives on the potential of biochar and BNCs in addressing global sustainability issues.

Analyzing the changes in agricultural carbon emissions (ACE) and their influencing factors can provide a sound basis for accurately estimating the carbon balance of agroecosystems. Such analyses can serve as a reference for developing policies to mitigate global climate change and promote sustainable agricultural development. Using the carbon emission calculation framework of the Intergovernmental Panel on Climate Change, this study examined the spatiotemporal characteristics of ACE, including total amount, intensity, structure and their influencing factors, in Fujian Province from 2002 to 2022. The logarithmic mean scale index model and Tapio decoupling model were used, with the GM (1,1) model to forecast carbon emissions from 2023 to 2040. The results indicate that both the total emissions and intensity of ACE had fluctuating downward trends and agricultural material inputs were the largest contributors to ACE. Additionally, total ACE was found to have a spatial pattern higher in the west and lower in the east and agricultural production efficiency was the primary factor in reducing ACE. ACE was clearly decoupled from economic development and is projected to continually decline after 2023.

Beneficial root-microbiome interactions offer enormous potential to improve crop performance and stress tolerance. Domestication and improvement reduced the genetic diversity of crops and reshaped their phenotypic traits and their associated microbiome structure and function. However, understanding of the genetic and physiological mechanisms how domestication and improvement modulated root function, microbiome assembly and even co-selective patterns remains largely elusive. This review summarizes the current status of how crop domestication and improvement (heterosis) affected root characteristics and their associated microbiome structure and function. Also, it assesses potential mechanisms how crop domestication and improvement reshaped root-microbiome association through gene regulation, root structure and function and root exudate features. A hypothetical strategy is proposed that entangles crop genetics and abiotic interactions with beneficial microbiomes to mitigate the effects of global climate change on crop performance. A comprehensive understanding of the role of crop domestication and improvement in root-associated microbiome interaction will advance future breeding efforts and agricultural management.

Chickens are one of the most important domesticated animals, serving as an important protein source. Studying genetic variations in chickens to enhance their production performance is of great potential value. The emergence of next-generation sequencing has enabled precise analysis of single nucleotide polymorphisms and insertions/deletions in chicken, while third-generation sequencing achieves the accurate structural variant identification. However, the high cost of third-generation sequencing technology limits its application in population studies. The graph-based pan-genome strategy can overcome this challenge by enabling the detection of structural variations using cost-effective next-generation sequencing data. This study constructed a graph-based pan-genome for chickens using 12 high-quality genomes. This pan-genome used linear genome GRCg6a as the reference genome, containing variant information from two commercial and nine native chicken breeds. Compared to the linear genome, the pan-genome provided significant improvements in the efficiency of structural variation identification. On the basis of the graph-based pan-genome, high-frequency structural variations related to high egg production in Leghorn chicken were predicted. Additionally, it was discovered that potential structural variations was associated with highland adaptation in Tibetan chickens according to next-generation sequencing and transcriptomics data. Using the pan-genome graph, a new strategy to identify structural variations related to traits of interest in chickens is presented.

The development of Internet information technology has given digital agricultural technology extension services advantages over earlier agricultural technology extension models, rendering them more conducive to the pursuit of sustainable and environmentally friendly agricultural development. This study leveraged survey data from 1167 farmers in Shaanxi and Gansu Provinces and used the propensity score matching method to elucidate the impact and mechanism of the digital agricultural technology extension service on the adoption of organomineral fertilizer. The results indicate that farmers who had used digital agricultural technology extension services had a 7.2% to 10.2% increase in the probability of adopting organomineral fertilizer compared with their non-user counterparts. In addition, adoption intensity increased from 7.0% to 9.9%. Secondly, digital agricultural technology extension services indirectly influence farmer adoption behavior by shaping their perceptions of benefits and reducing transaction costs. Also, this study examined the heterogeneity in the adoption of organomineral fertilizer facilitated by digital agricultural technology extension services. The findings of this study provide policy recommendations for advancing the use of digital agricultural technology extension services and enhancing organomineral fertilize adoption rates of farmers.

To efficiently obtain P from soil, most terrestrial plants form symbiosis with arbuscular mycorrhizal (AM) fungi and thus have two P uptake pathways, i.e., the direct pathway (DP) via roots, particularly root hairs, and the mycorrhizal pathway (MP) via AM fungal hyphae. AM fungi form an extraradical hyphal network to expand their contact area with soil and release carbon-rich compounds, which provide a high-energy habitat for soil bacteria. The bacteria affected by AM fungi support P nutrition of AM fungi by secreting extracellular phosphatases. During the P acquisition process, both DP and MP function and require C fixed by plant photosynthesis to maintain P transport. Plants make trade-offs between DP and MP based on C inputs and P benefits. This review first systematically explores the potential trade-offs between plant C inputs and P gains of DP and MP as well as the factors that influence such trade-offs. Then the response of AM fungi to soil nutrient heterogeneity and the mechanisms by which AM fungi select bacteria to mineralize organic P and increase the P contribution of MP were analyzed. Future studies need to apply emerging methods and technologies to accurately quantify the contribution of DP and MP to plant P absorption under different conditions and provide the theoretical basis for optimizing sustainable agricultural production systems.

Plant roots are crucial for nitrogen uptake. To efficiently acquire N, root system architecture (RSA), which includes the length and quantity of primary roots, lateral roots and root hairs, is dynamically regulated by the surrounding N status. For crops, an ideotype RSA characterized by enhanced plasticity to meet various N demands under fluctuating N conditions is fundamental for high N utilization and subsequent yield. Therefore, exploring the genetic basis of N-dependent RSA, especially in crops, is of great significance. This review summarizes how plants sense both local and systemic N signals and transduce them to downstream pathways. Additionally, it presents the current understanding of genetic basis of N-dependent root plasticity in Arabidopsis and major crops. Also, to fully understand the mechanisms underlying N-dependent root morphogenesis and effectively identify loci associated with an ideotype RSA in crops, more attention should be paid to non-destructive, in situ phenotyping of root traits, cell-type-specific exploration of gene functions, and crosstalk between root architecture, environment and management in the future.

In response to the demand of automatic fruit identification and harvesting, this paper presents a human-robot collaborative picking robot based on somatosensory interactive servo control. The robot system mainly consisted of four parts: picking execution mechanism, hand information acquisition system, human-machine interaction interface, and human-robot collaborative picking strategy. A six-degree-of-freedom robotic arm was designed as the picking execution mechanism. The D-H method is employed for both forward and inverse kinematic modeling of the robotic arm. A four-step inverse kinematic optimal solution selection method, including mechanical interference, correctness, rationality, and smoothness of motion, is proposed. The working principle and use of the Leap Motion controller for hand information acquisition were introduced. Data from three types of hand movements were collected and analyzed. Spatial mapping method between the Leap Motion interaction space and operating range of the robotic arm was proposed to achieve a direct correspondence between the cubic interaction box and the cylindrical space of the fan ring of the robotic arm. The test results demonstrated that the average response time of the double-click picking command was 332 ms. The average time consumption for somatosensory control targeting was 6.5 s. The accuracy rate of the picking gesture judgment was 96.7%.

With the development of smart agriculture, accurately identifying crop diseases through visual recognition techniques instead of by eye has been a significant challenge. This study focused on apple leaf disease, which is closely related to the final yield of apples. A multiscale fusion dense network combined with an efficient multiscale attention (EMA) mechanism called Incept_EMA_DenseNet was developed to better identify eight complex apple leaf disease images. Incept_EMA_DenseNet consists of three crucial parts: the inception module, which substituted the convolution layer with multiscale fusion methods in the shallow feature extraction layer; the EMA mechanism, which is used for obtaining appropriate weights of different dense blocks; and the improved DenseNet based on DenseNet_121. Specifically, to find appropriate multiscale fusion methods, the residual module and inception module were compared to determine the performance of each technique, and Incept_EMA_DenseNet achieved an accuracy of 95.38%. Second, this work used three attention mechanisms, and the efficient multiscale attention mechanism obtained the best performance. Third, the convolution layers and bottlenecks were modified without performance degradation, reducing half of the computational load compared with the original models. Incept_EMA_DenseNet, as proposed in this paper, has an accuracy of 96.76%, being 2.93%, 3.44%, and 4.16% better than Resnet50, DenseNet_121 and GoogLeNet, respectively, proved to be reliable and beneficial, and can effectively and conveniently assist apple growers with leaf disease identification in the field.