The macropores of biochar provide a suitable habitat for microbial growth, and its high carbon content serves as an energy source for beneficial microbes. This study evaluated the potential of biochar as a carrier for Trichoderma in managing Sclerotinia sclerotiorum in chickpeas. Biochar application reduced plant disease severity by 36.5% and increased plant root mass by 23.3%. For this, three types of biochar, wheat straw, organic kitchen waste, and hardwood were tested with Trichoderma, analyzing such as organic C, total N, P, K, Mg, and Ca; pH, and ash content. Trichoderma populations were monitored with biochar carrier of different mesh sizes (250, 150, 75, and 45 µm) for up to 6 weeks after inoculation. Hardwood biochar at 150 µm supported the highest Trichoderma population, reaching 33.5 × 10⁵ CFU·g⁻¹ after 6 weeks. Hardwood biochar also achieved the maximum disease suppression compared to other biochar types. This research highlights the dual role of biochar in enhancing plant growth and controlling disease, contributing to the standardization of biochar use in agricultural practices.

Non-point source (NPS) pollution has been the major cause of water quality degradation. However, there are still shortcomings in the current monitoring methods for NPS pollution, such as small monitoring range, error of monitoring data, time-consuming and laborious monitoring process. Although the established method, field experiment plots, was used effectively in the first and second national pollution source census in China. However, when the results obtained by monitoring experimental plots are extrapolated to a field or larger scale, there are considerable uncertainties because of the characteristics of large spatial and temporal variation of farmland. To optimize the farmland surface runoff monitoring methods, an online monitoring system for continuous cropping based on a serial pipeline was developed, which takes diversion trench, online flowmeter and dynamic acquisition device as the main body. Compared with the current farmland monitoring methods, this system can realize more precise automatic monitoring of water quantity and quality, and lower costs. This innovative method will provide greater confidence in the actual monitoring of NPS pollution from farmland and wider practical application. This new method could prove particularly valuable for the next national pollution source census in China.

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.

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.

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.

Adequate dietary zinc intake remains a public health challenge in China. Also, there is a lack of information on the relationship between Zn intake and food consumption patterns across provinces and over time. In this study, data from the China Health and Nutrition Survey 2004–2011 (21,266 individuals) was used to explore associations between dietary Zn intake and sociodemographic factors. Zn intake per person declined from 11.1 mg·d⁻¹ in 2004 to 9.89 mg·d⁻¹ in 2011, with reduction in cereal consumption the greatest contributor to this. However, the reduction resulting from the lower cereal consumption was only partly compensated by an increase in consumption of Zn-rich foods. The percentage of the study population with inadequate Zn intake increased from 23% in 2004 to 37% in 2011. While Zn intake was positively associated with income levels in each survey year, the time trend for all income levels was a gradually reducing Zn intake. In all years, males had an average higher dietary Zn intake, whereas no significant difference was found between living areas. In conclusion, this study shows that dietary Zn inadequacy was high and has increased over the studied period. Remediation could be sought by shifting dietary patterns toward more Zn-dense food or by enhancing Zn concentration through biofortification.

● Water erosion models mainly applied in central China at large scales.

Soil erosion models are effective tools for assessing soil erosion indicators and simulating erosion processes. China has some of the most severe soil erosion in the world. To better apply soil erosion models to address soil erosion issues, it is necessary to understand the development process and current status of soil erosion modeling research in China. In this study, a combination of bibliometric analysis and statistical methods was used to review and organize Chinese soil erosion models (1982–2022) from various perspectives, including keywords, model operations, model classification, model spatiotemporal scales, and model geographical applications. This findings of this analysis indicate that the study of soil erosion models in China mainly focuses on large scales (regional and large river basins) using empirical models including USLE, RUSLE, and CSLE. The research areas are primarily concentrated in southeastern and central China. The research content has gradually shifted from studying soil erosion characteristics to analyzing influencing factors, spatiotemporal evolution of erosion, and erosion process and morphology stages. However, there are several issues in current Chinese soil erosion modeling research. These include a lack of validation of model application results with field measurements, limited application areas for the models, and relatively weak research on erosion process mechanisms. On this basis, it is recommended that future research should increase the observation of soil erosion processes and establish methods for data or mathematical formula conversion based on different geographical environments. Also, there is a need to strengthen research on erosion process mechanisms. The findings of this study should provide a valuable resource for researchers to future understand the development process and current issues of Chinese soil erosion models, providing insights for future research directions.

● Drying is a crucial postharvest process for paddy grain and impacts the quality of both paddy and rice.

In the context of food security, drying is a crucial postharvest process for paddy grain because it significantly impacts the quality of both paddy and rice. To conserve energy during the drying process, deep bed dryers are used as convective dryers that use a combination of ambient airflow and heating, thus relying on airflow, temperature, and relative humidity (RH) as the primary drying parameters. Consequently, an aeration system is necessary so that the drying air can penetrate the thick pile of paddy grain and distribute evenly throughout the drying chamber. This analysis aimed to determine the most optimal aeration system by using computational fluid dynamics (CFD) and the AHP-TOPSIS method. The quantitative and visual analysis of the airflow velocity, pressure, temperature, and RH was conducted using CFD on four different dryer aeration systems models, which were then ranked by preference value using the AHP-TOPSIS method. Model 4, with a sloping floor and circular pipe formation, was found to have the most optimal aeration system (preference value of 0.788) for a paddy grain deep bed dryer prototype.