Agriculture, Volume 14, Issue 5 (May 2024) – 76 articles

The realization of the value of ecological products has led to an economic means for reducing carbon emissions in China. Tea is one of the most important cash crops and one of the most popular beverages in the world. Due to the complex the tea industrial chain, it is considered to be an industry with high carbon emissions. Ecological tea products with low-carbon attributes can be considered a linkage of ecology, economy, and society. Based on this, this paper presents research on low-carbon ecological tea (LCT). Herein, we construct the formational logic of low-carbon ecological products, explore the connotations of LCT, and form a conceptual pathway for realizing LCT to contribute to climate change mitigation and adaptation. This paper starts from the upstream, midstream, and downstream of the industrial chain; it establishes three value realization pathways that keep, as a priority, the promotion of ecological industrialization, focus on restoration to improve the ecology of the industrial chain, and innovate technology to expand the industrial chain. The pathways are a set of low-emission production solutions that use techniques to enhance carbon sequestration in soil, reduce the use of fertilizers and pesticides, and help shift to clean energy from low-emission sources in the stages of plantation, processing, and distribution. In the process of realizing LCT, the government plays an important role, and its support and guidance are needed. Based on stakeholder theory, this paper builds an implementation mechanism that focuses on the micro perspective (users, organizations), integrates the mesoscopic perspective (industry), and relies on the macro perspective (government). Full article

The oxidative stability index (OSI) and fatty acid (FA) composition of extra virgin olive oils (EVOOs) are key parameters in the characterization of new varieties in breeding programs. Their determination through traditional methods (Rancimat and gas chromatography, respectively) is expensive and time-consuming. Therefore, there is a need to develop rapid and cost-effective analytical procedures. This study aimed to evaluate the potential use of near-infrared spectroscopy (NIRS) for analyzing OSI and FA composition in EVOOs. A total of 318 samples sourced from different origins were evaluated using both FT-NIR MPA and MicroNIR instruments in transmittance mode, with wavelengths ranging from 1100 to 2500 nm and 908 to 1676 nm, respectively. Different accuracies were obtained in the models developed for the different evaluated traits, with simpler models (using a lower number of latent variables) for the MPA analyzer in all cases. Additionally, consistent results between instruments for the partitioning of the variance and heritability estimation, and the reliable ranking of genotypes were obtained from one of the sample sets tested. In summary, models derived from PLS regression using spectroscopic data of both instruments demonstrated promising results in determining these EVOO traits, facilitating their evaluation and selection of genotypes, particularly in breeding programs. Full article

In animal husbandry applications, segmenting live pigs in complex farming environments faces many challenges, such as when pigs lick railings and defecate within the acquisition environment. The pig’s behavior makes point cloud segmentation more complex because dynamic animal behaviors and environmental changes must be considered. This further requires point cloud segmentation algorithms to improve the feature capture capability. In order to tackle the challenges associated with accurately segmenting point cloud data collected in complex real-world scenarios, such as pig occlusion and posture changes, this study utilizes PointNet++. The SoftPool pooling method is employed to implement a PointNet++ model that can achieve accurate point cloud segmentation for live pigs in complex environments. Firstly, the PointNet++ model is modified to make it more suitable for pigs by adjusting its parameters related to feature extraction and sensory fields. Then, the model’s ability to capture the details of point cloud features is further improved by using SoftPool as the point cloud feature pooling method. Finally, registration, filtering, and extraction are used to preprocess the point clouds before integrating them into a dataset for manual annotation. The improved PointNet++ model’s segmentation ability was validated and redefined with the pig point cloud dataset. Through experiments, it was shown that the improved model has better learning ability across 529 pig point cloud data sets. The optimal mean Intersection over Union (mIoU) was recorded at 96.52% and the accuracy at 98.33%. This study has achieved the automatic segmentation of highly overlapping pigs and pen point clouds. This advancement enables future animal husbandry applications, such as estimating body weight and size based on 3D point clouds. Full article

Microbial communities are not only an important indicator of soil status but also a determinant of plant nutrition and health levels. Loss of microbial community ecosystem control can directly lead to microbial disease occurrence. During the process of Yuanyang Nanqi wild imitation planting, root rot diseases frequently occur, seriously affecting their yield and quality. Via amplicon sequencing, this study mainly compared the microbial community composition between the rhizosphere soil and roots of healthy and diseased Yuanyang Nanqi with root rot. The α-diversity showed that the microbial community diversity and abundance in the roots of diseased Yuanyang Nanqi were much lower than those of those in healthy specimens, while no significant difference was found in the rhizosphere soil. The β－diversity showed that the bacterial community in the Gejiu region and the fungal community in the Honghe region were significantly different from those in other regions. The species relative abundance map showed that there was no obvious difference in microbial community composition between the rhizosphere soil and roots of healthy and diseased Nanqi, but in diseased specimens with root rot, the proportions of Pseudomonas and Fusarium increased. Based on a functional prediction analysis of FUNGuild, the results showed that the Nanqi roots were mainly pathological saprophytic type and that their rhizosphere soil was mainly saprophytic type. The microorganisms in the roots of Yuanyang Nanqi tubers with root rot were also isolated and identified through the use of the culture method. The possible pathogenic strains were tested via anti－inoculation, and Fusarium oxysporum was identified as one of the main pathogenic fungi of Nanqi root rot, which was consistent with the amplicon sequencing results. These results will help us understand the change trend of microbial communities in healthy and diseased plants and analyze the pathogens involved, the pathogenesis, and the beneficial microorganisms, which would provide a theoretical basis for effective biological control. Full article

Understanding soil loss pathways in karst regions is crucial for erosion control. Combining high-density measurements of grid points with runoff plot monitoring, we attempt to use a new indirect method to study the characteristics of soil loss in karst rocky desertification areas of Salaxi Town, Guizhou province. One cycle year monitoring data of 12640 grid points were applied in the soil loss analysis. This study identifies underground leakage as the primary pathway of soil loss, with an mean soil leakage of 21.51 kg in potential areas, accounting for 83.12%, and an average leakage of 22.69 kg in in mild karst rocky desertification areas accounting for 81.48%. Mixed vegetation types (forest, shrub, and grass) were better at preventing surface soil loss but increased underground leakage compared to single vegetation types. The rainy season significantly influences soil erosion, accounting for 67.88% of total loss, with slight variations among different karst rocky desertification grades and vegetation types. Mean underground leakage rates during the rainy and dry seasons are 63.34% and 36.66%, respectively. Although this method still has certain limitations, it plays a positive role in revealing the mechanism of soil erosion processes in karst regions. Full article

In this paper, a cotton precision seed-taking dibbler device was designed to address the problems of congestion and leakage of the hole-type dibbler during high-speed operation (more than 4 km/h). Firstly, the motion trajectory of the seed in the limited gear-shaped space was analyzed and a motion model was established to analyze the relationship between the motion trajectory and seed-filling performance. Secondly, a central combination test with four factors and five levels was implemented using the discrete element software EDEM2018, which simulated the seed-filling performance of the seed-holding space with different structural dimensions. The optimal parameters impacting the seed-filling behavior of the designed dibbler were derived via response surface optimization and multiple regression analyses. Under optimal conditions, three bench tests were repeatedly conducted, and the average qualified index was 93.67%, the leakage index Y₃ was 2.67%, and the multiple index Y₂ was 3.66%, which was close to the simulation results. Finally, for the speed adaptability test of the seed-holding space with optimal structural parameters, the qualified index was more than 90% when the rotating speed ranged from 1.0 to 2.0 r/s (the speed of the corresponding dibbler was 5.4 km/h to 7.2 km/h), indicating that the dibbler could meet the requirements of high-speed operation and had good speed adaptability. The results can not only provide a reference for the development of precision hole-type dibblers but also have theoretical significance for the quantitative separation of the individual from the population of irregularly rotating agricultural materials and ore materials such as cotton seeds. Full article

The current literature considers agritourism as a valid option for promoting the development of rural areas. This would be achieved by increasing agricultural incomes. However, numerous scientific studies have revealed the difficulty in reaching a consensus on the very concept of agritourism. In addition, the definition of agritourism is rarely related to the opinion of the demand. For this reason, this research aimed to understand the idea that tourists have about this variety. To this end, more than 500 surveys were carried out, from which the tourists’ conception of agritourism and the activities it entails were deduced. Other questions were also analyzed to determine whether the conception varies between those who have already performed this type of activity and those who have not yet had the opportunity to do so. From this, we can deduct the interest that visitors have in agritourism products, evidencing their potential. This interest is deduced through the visualization of different landscapes and activities of interest to tourists. Under these four central points, the aim was to understand the aims of agritourism in Extremadura (Spain). At the methodological level, a combination of descriptive statistics and spatial statistics was used, highlighting the use of cluster analysis. The results show a significant lack of knowledge of the meaning of agritourism, especially among those who have never practiced it, and of the activities associated with it. At the same time, the selection of landscapes preferred by tourists makes it possible to establish areas with potential for the development of these activities. Likewise, knowing which activities are of interest to tourists also helps to generate complementary activities and to better target the design of agrotourism products. Full article

Heavy metal soil pollution severely threatens human health and food safety. This study used PRISMA to systematically review heavy metal-contaminated soil remediation patents in the Derwent Patent Database from 2003 to 2023. A total of 1744 patents were selected. The results of the analysis show that related patent applications are growing around the world. Among them, China has the most significant number of patents, but the layout of transnational patents needs to be revised. Countries have different preferences in transnational patent technology. Technological development is generally balanced, and there is no apparent monopoly. However, the need for continuous in-depth research on inventors is an obstacle to technological development. In addition, the technology in this field is concentrated in chemistry and engineering. Currently, the mainstream technology is soil remediation agents, and thermal desorption technology has also attracted much attention. Future technologies will use new polymer materials and advanced machinery to improve efficiency and control repair costs. In addition, remediation has shifted from the total amount of heavy metals to the control of practical parts. This study summarizes the current status of heavy metal-contaminated soil remediation technology and analyzes future development trends, providing a reference for technology development. Full article

This study focused on downy mildew in hop caused by the pathogen Pseudoperonospora humuli. A systematic literature review was conducted using bibliometric analysis to explore trends in publishing, prominent research themes, and where research is being conducted on hop downy mildew. The databases Scopus, Web of Science, and ScienceDirect were used to identify publications spanning from 1928 to 2023. The analysis yielded 54 publications, with the most cited studies primarily focusing on disease management and host resistance. Additionally, these studies explored the genetic and pathogenic relationship between P. cubensis and P. humuli. A word co-occurrence map revealed that the main themes addressed in the publications included “hop”, “disease”, “downy”, “humuli”, “mildew”, and “Pseudoperonospora”. Notably, there was a particular emphasis on subtopics such as disease management, the disease reaction of hop cultivars, and the influence of weather factors on hop downy mildew. Notably, there was limited knowledge about the disease in regions with tropical climates. This study provides valuable information that can support and guide future research endeavors concerning downy mildew in hop cultivation. Full article

The application of ecological techniques by farmers is important for ensuring the environmentally sustainable advancement of the grain sector. Based on micro-level survey data from 921 Chinese wheat growers in the Hebei and Henan provinces, this study employed an endogenous switching probit model and counterfactual analysis to investigate the impact and mechanisms of digital information utilization on ecological production technology adoption. The results indicated that 43.87% of sample wheat farmers had a low level of adoption of ecological techniques. The utilization of digital information significantly promoted farmers’ adoption. If farmers who currently used digital information were to opt-out, the probability of their high adoption would decrease by 11.26%. The utilization of digital information significantly enhanced the adoption of ecological technologies through three mediating factors: technological cognition, production monitoring, and market channels. Therefore, it is imperative to encourage farmers to broaden their social networks and enhance their perception of the importance of digital information. Additionally, it is essential to promote the industrialization and scale operation of wheat production, direct policy subsidies towards new types of management entities, and ensure the accuracy of the supply of digital information for green production through multiple channels. Therefore, it is imperative to expand farmers’ social networks and leverage rural communities to increase their perceived importance of digital information. Governments should increase subsidies and promote the scale and industrialization of wheat production. Moreover, the accuracy of digital information supply for sustainable production should be promoted through digital learning platforms, production monitoring systems, and e-commerce networks. Full article

The pricing behavior of agricultural processing firms in input markets has large impacts on farmers’ and processors’ prosperity as well as the overall market structure. Despite analytical approaches to food processors’ pricing in agricultural input markets, the need for models to represent complex market features is urgent. Agent-based models (ABMs) serve as computational laboratories to understand complex markets emerging from autonomously interacting agents. Yet, individual agents within ABMs must be equipped with intelligent learning algorithms. In this paper, we propose supervised and unsupervised learning agents to simulate the pricing behavior of firms in agricultural markets’ ABMs. Supervised learning firms are pre-trained to accurately best respond to their competitors and are deemed to result in the market Nash equilibria. Unsupervised learning firms play a course of pricing interaction with their competitors without any pre-knowledge but based on deep reinforcement learning. The simulation results show that unsupervised deep learning firms are capable of approximating the pricing equilibria obtained by the supervised firms in different spatial market settings. Optimal discriminatory and uniform delivery pricing emerges in agricultural input markets with the high and intermediary importance placed on space. Free on board pricing emerges in agricultural input markets with small importance placed on space. Full article

Maize residue cover (MRC) is an important parameter to quantify the degree of crop residue cover in the field and its spatial distribution characteristics. It is also a key indicator of conservation tillage. Rapid and accurate estimation of maize residue cover (MRC) and spatial mapping are of great significance to increasing soil organic carbon, reducing wind and water erosion, and maintaining soil and water. Currently, the estimation of maize residue cover in large areas suffers from low modeling accuracy and poor working efficiency. Therefore, how to improve the accuracy and efficiency of maize residue cover estimation has become a research hotspot. In this study, adaptive threshold segmentation (Yen) and the CatBoost algorithm are integrated and fused to construct a residue coverage estimation method based on multispectral remote sensing images. The maize planting areas in and around Sihe Town in Jilin Province, China, were selected as typical experimental regions, and the unmanned aerial vehicle (UAV) was employed to capture maize residue cover images of sample plots within the area. The Yen algorithm was applied to calculate and analyze maize residue cover. The successive projections algorithm (SPA) was used to extract spectral feature indices from Sentinel-2A multispectral images. Subsequently, the CatBoost algorithm was used to construct a maize residue cover estimation model based on spectral feature indices, thereby plotting the spatial distribution map of maize residue cover in the experimental area. The results show that the image segmentation based on the Yen algorithm outperforms traditional segmentation methods, with the highest Dice coefficient reaching 81.71%, effectively improving the accuracy of maize residue cover recognition in sample plots. By combining the spectral index calculation with the SPA algorithm, the spectral features of the images are effectively extracted, and the spectral feature indices such as NDTI and STI are determined. These indices are significantly correlated with maize residue cover. The accuracy of the maize residue cover estimation model built using the CatBoost model surpasses that of traditional machine learning models, with a maximum determination coefficient (R²) of 0.83 in the validation set. The maize residue cover estimation model constructed based on the Yen and CatBoost algorithms effectively enhances the accuracy and reliability of estimating maize residue cover in large areas using multispectral imagery, providing accurate and reliable data support and services for precision agriculture and conservation tillage. Full article

The objective of this study was to verify the genetic dissimilarity and validate image phenotyping using RGB (red, green, and blue) sensors in tropical carrot germplasms. The experiment was conducted in the city of Carandaí-MG, Brazil, using 57 tropical carrot entries from Seminis and three commercial entries. The entries were evaluated agronomically and two flights with Remotely Piloted Aircraft (RPA) were conducted. Clustering was performed to validate the existence of genetic variability among the entries using an artificial neural network to produce a Kohonen’s self-organizing map. The genotype–ideotype distance index was used to verify the best entries. Genetic variability among the tropical carrot entries was evidenced by the formation of six groups. The Brightness Index (BI), Primary Colors Hue Index (HI), Overall Hue Index (HUE), Normalized Green Red Difference Index (NGRDI), Soil Color Index (SCI), and Visible Atmospherically Resistant Index (VARI), as well as the calculated areas of marketable, unmarketable, and total roots, were correlated with agronomic characters, including leaf blight severity and root yield. This indicates that tropical carrot materials can be indirectly evaluated via remote sensing. Ten entries were selected using the genotype–ideotype distance (2, 15, 16, 22, 34, 37, 39, 51, 52, and 53), confirming the superiority of the entries. Full article

The accurate and rapid identification of rice diseases is crucial for enhancing rice yields. However, this task encounters several challenges: (1) Complex background problem: The rice background in a natural environment is complex, which interferes with rice disease recognition; (2) Disease region irregularity problem: Some rice diseases exhibit irregular shapes, and their target regions are small, making them difficult to detect; (3) Classification and localization problem: Rice disease recognition employs identical features for both classification and localization tasks, thereby affecting the training effect. To address the aforementioned problems, an enhanced rice disease recognition model leveraging the improved YOLOv7-Tiny is proposed. Specifically, in order to reduce the interference of complex background, the YOLOv7-Tiny model’s backbone network has been enhanced by incorporating the Convolutional Block Attention Module (CBAM); subsequently, to address the irregularity issue in the disease region, the RepGhost bottleneck module, which is based on structural reparameterization techniques, has been introduced; Finally, to resolve the classification and localization issue, a lightweight YOLOX decoupled head has been proposed. The experimental results have demonstrated that: (1) The enhanced YOLOv7-Tiny model demonstrated elevated F1 scores and [email protected], achieving 0.894 and 0.922, respectively, on the rice pest and disease dataset. These scores exceeded the original YOLOv7-Tiny model’s performance by margins of 3.1 and 2.2 percentage points, respectively. (2) In comparison to the YOLOv3-Tiny, YOLOv4-Tiny, YOLOv5-S, YOLOX-S, and YOLOv7-Tiny models, the enhanced YOLOv7-Tiny model achieved higher F1 scores and [email protected]. The improved YOLOv7-Tiny model boasts a single image inference time of 26.4 ms, satisfying the requirement for real-time identification of rice diseases and facilitating deployment in embedded devices. Full article

Ensuring national food security is a perennial topic, and securing the grain planting area is an essential solution. Cost savings at scale from agricultural insurance policy adjustments could be a powerful incentive for grain production. In this study, 527 data sets from 31 provinces in China from 2006 to 2022 were used as the sample, and the author applied a multi-stage DID model to measure the effects of agricultural insurance policy adjustments on the grain planting area and planting structure, as well as the influence mechanisms behind them. The results can be summarized as follows: Firstly, agricultural insurance policy adjustments can make a significant contribution to increasing the grain planting area, with some positive impact on the `grain-oriented’ planting structure. Secondly, agricultural insurance policy adjustments can significantly increase the grain planting area by increasing the application of agricultural machinery, but this mechanism does not affect the `grain orientation’ planting structure. Thirdly, agricultural insurance policy adjustments can have a significant positive impact on the grain planting area and `grain—oriented’ planting structure in both high- and low-risk areas, with low-risk areas being more affected than high-risk areas. Full article

The technology of seed coating with superabsorbent polymer (SAP) has the potential to mitigate the negative impact of drought on seed germination and crop establishment. However, their application on the seed surface can affect the effectiveness of pesticides used for seed treatment in the protection against phytopathogens. In our work, the influence of the Aquaholder^®Seed polymer coating on the effectiveness of fungicides in the protection of germinating seeds of spring barley cv. Bojos and Laudis against the fungal pathogen Bipolaris sorokiniana was studied. One-half of the seeds were first treated with fungicides, and then a polymer was applied. Fungicide efficacy was evaluated in a Petri dish test and pot test under the pathogen attack. Seed coating with SAP did not negatively affect fungicide efficacy. The percentage of germinated seeds, seedling emergence, plant height, and symptoms of the disease in the fungicide-treated variants were not significantly changed by the SAP application. Moreover, in cv. Laudis, the application of SAP alone partially protected germinating seeds against pathogen attack. The amount of pathogen DNA in plant tissues of cv. Laudis was not significantly different among seed treatments, while in cv. Bojos, the pathogen DNA increased in seeds coated with SAP alone but decreased in combined treatment with fungicides. These results demonstrated that SAP seed coating does not negatively affect the efficacy of fungicides used for seed protection against fungal pathogens. Full article

Rice straw presents challenges as livestock feed due to its low digestibility and the presence of chemical residues. One potential solution is to focus on breeding brittle varieties that possess disease-resistance traits. In this study, AZ1803, a brittle mutant line isolated from the IR64 mutant pool, was chosen for gene identification and breeding. The AZ1803 mutant was crossed to the TNG67 variety to generate a mapping population and to the CS11 variety for fine mapping and breeding. The gene was mapped on chr. 10 between RM467 and RM171 SSR markers and was narrowed down to RM271 and RM5392 with 600 kb proximately interval. The AZ1803 and IR64 sequencing results revealed a substitution mutant in the Exon 9th of the OsCesA7 gene, resulting in an amino acid mutation at the end of the transmembrane domain 5th of the CESA7, responsible for cellulose synthesis for the secondary cell wall. The cellulose content of AZ1803 was reduced by 25% compared with the IR64. A new brittle and disease-resistant variety was bred by using developed markers in marker-assisted selection. In addition, bending tests and bacterial blight inoculation were applied. The bacterial lesion length of the bred variety is 64% lower than that of AZ1803. The rice straw of the new variety can be used for livestock feeding, which increases farmer income and reduces pesticide residues and air pollution from straw burning. Full article

Water and soil salinization significantly reduce crop yields. Among the strategies developed to counteract salt stress, biostimulants can maintain crop productivity, reversing its impact. In this context, there is interest in finding new substances that could act as biostimulants. Recently, the biostimulatory potential of Lemna minor L. (duckweed) extracts has been shown. This work aimed to highlight whether an extract from duckweed (Lemna extract—LE) could protect maize grown in salinity, exploring the mechanisms induced to improve crop resistance. Plants were grown by applying two concentrations of NaCl (150 and 300 mM), and some physiological, morphological, and biochemical traits were studied in control and salt-stressed samples, treated or not with LE. Salinity decreased shoots, roots, pigment, and soluble protein. LE prompted ameliorative changes at the root level and increased photosynthetic pigment and soluble protein. Furthermore, concerning the oxidative impairment provoked by salt stress, LE enhanced the cellular redox state, contrasting H₂O₂ and MDA accumulation and positively affecting the activity of superoxide dismutase (SOD—EC 1.15.1.1) and catalase (CAT—EC 1.11.1.6). The assessment of some mineral nutrients showed that LE stimulated their acquisition, especially for the highest salt dosage, explaining some benefits found for the parameters investigated. Full article

This paper addresses challenges in the application of existing colters in Chinese ecological tea plantations due to abundant straw roots and insufficient tillage depth. Aligned with the agronomic requirements of hilly eco-tea plantations, our study optimizes the structural advantages of the joint use of rotary tillage blades and double-disc colters to design an efficient trenching device. Our investigation explores the motion characteristics of a double-disc colter during deep trenching operations, in conjunction with rotary tillage blades. Employing discrete element method (DEM) simulations, this paper aims to minimize the working resistance and enhance the tillage depth stability. Single-factor experiments are conducted to determine the impact of key structural parameters on the tillage depth stability and working resistance. The optimal parameters are determined as a relative height of 80 mm to 120 mm, a 280 mm to 320 mm diameter for the double-disc colter, and a 10° to 14° angle between the two discs. The central composite design method is used to optimize the structural parameters of the double-disc colter. The results indicate that when the relative height is 82 mm, the diameter of the double-disc colter is 297 mm, and the angle between the two discs is 14°, the tillage depth stability performance reaches 91.64%. With a working resistance of merely 93.93 N, the trenching device achieves optimal operational performance under these conditions. Field validation testing shows a tillage depth stability coefficient of 92.37% and a working resistance of 104.2 N. These values deviate by 0.73% and 10.93%, respectively, from the simulation results, confirming the reliability of the simulation model. A field validation test further confirms that the operational performance of the colter aligns with the agronomic requirements of ecological tea plantations, offering valuable insights for research on trenching devices in such environments. Full article

Reducing greenhouse gas (GHG) emissions and increasing the size of carbon sinks are closely related to food security in agricultural systems. This study conducted an in-depth data analysis of previous studies to explore the dynamic causal relationships among the reduction of emissions, carbon sink increases, and food security in agricultural systems. The fixed-effect regression model, causality tests, PVAR model, impulse response functions, and variance decomposition were used to explore correlations among the three variables. The results show that the national average carbon sinks surged from 2662.194 Mg in 2000 to 4010.613 Mg in 2020, with the food security index concurrently climbing from 0.198 to 0.308. Moreover, GHG emissions exhibited a negative growth rate from 2016 onwards, yet the 2020 mean remained 142.625 Mg above the 2000 baseline. The agricultural “three subsidies” reform has not directly promoted food security, but significantly inhibited GHG emissions. However, conflicts exist between emissions reduction and carbon sinks increase in agricultural systems and food security. At the whole level, changes in carbon sinks only have a positive effect on the increase in GHG emissions, whereas changes in GHG emissions have a positive effect on both carbon sinks and food security. Changes in food security strongly inhibit the increase in carbon sinks. This relationship varies among distinct grain functional zones. Policy objectives should be coordinated, target thresholds set, and policies classified according to different functional orientations, to achieve a win–win situation for food supply and low-carbon development. Full article

Over the last decade, a large number of studies have been conducted on heavy metals and magnetic susceptibility () measurement in soils. Yet, a global understanding of soil contamination and magnetic responses remains elusive due to the limited scope or sampling sites of these studies. Hence, we attempted to explore a pollution proxy on a global scale. Through a meta-analysis of data from 102 published studies, our research aimed to provide a worldwide overview of heavy metal pollution and magnetic responses in agriculture soils. We mapped the geographic distribution of nine heavy metals (Cr, Cu, Zn, Pb, Ni, As, Cd, Mn, and Fe) in agricultural soils and explored their pollution sources and contributions. Since 2011, The accumulation of heavy metals has escalated, with industrial activities (31.5%) being the largest contributor, followed by agricultural inputs (27.1%), atmospheric deposition (22.66%), and natural sources (18.74%). The study reports ranging from 6.45 × 10⁻⁸ m³/kg to 319.23 × 10⁻⁸ m³/kg and from 0.59% and 12.85%, with the majority of the samples being below 6%, indicating heavy metal influence mainly from human activities. Pearson’s correlation and redundancy analysis show significant positive correlations of Pb, Zn, and Cu with (r = 0.51–0.53) and Mn and Fe with (r = 0.50–0.53), while Pb, Zn, Cu, and As metals were shown to be key factors of variation in magnetic response. The average heavy metal pollution load index of 2.03 suggests moderate global agricultural soil pollution, with higher heavy metal contamination in areas of high . Regression analysis confirms soil is considered to be non-polluted below of and polluted above this threshold, with all contamination factors of metals showing a linear correlation with (R = 0.72), indicating that a significant relationship between and the geochemical properties of soils continues to exist on a global scale. This study provides new insights for large-scale agricultural soil quality assessment and magnetic response. Full article

Traditional methods for harvesting medicinal materials with long roots, like Astragalus membranaceus, require extensive soil excavation, leading to problems like inefficient soil separation, low stemming rates, and blockages in conveyor chains. To address these challenges, this study introduces a prototype machine capable of digging, separating soil, crushing soil, and collecting the medicinal materials in one continuous process. The paper focuses on the machine’s design and working principle, with theoretical analysis and calculations for key components like the digging shovel, multi-stage conveyor, and soil-crushing device. Specific structural parameters were determined, and the screening efficiency of the roller screen was analyzed using EDEM 2020 software, comparing scenarios with and without rollers. A motion model for the medicinal materials during conveyance was established, allowing for the determination of optimal linear velocity and mounting angle for the conveyor. Additionally, a motion model for the second-stage conveyor chain and rear soil-crushing device was used to optimize their placement, ensuring efficient soil crushing without affecting the thrown Astragalus. Compared to traditional Chinese medicine diggers, this machine boasts superior resistance reduction and soil-crushing capabilities. Compared with traditional harvesters, the drag-reducing and soil-crushing device of this machine is more efficient, reducing the damage to Astragalus during the harvesting process, reducing the labor intensity of farmers, and improving the quality and efficiency of Astragalus harvesting. Field experiments have shown that when the operating speed of the prototype is 1.0 m/s and the roller-screen speed is 130~150 rpm, the operating performance is optimal, and comparative experiments can be conducted under the optimal parameters. From the experimental results, it can be seen that the improved equipment has increased the bright-stem rate by about 4%, the digging and loosening rate by 97.42%, and the damage rate by 2.44%. The equipment design meets the overall design requirements, and all experimental indicators meet national and industry standards. This provides a reference for the optimization and improvement of the soil-crushing device and the structure of the Astragalus membranaceus harvester. Full article

Sorting unreelable inferior cocoons during the reeling process is essential for obtaining high-quality silk products. At present, silk reeling enterprises mainly rely on manual sorting, which is inefficient and labor-intensive. Automated sorting based on machine vision and sorting robots is a promising alternative. However, the accuracy and computational complexity of object detection are challenges for the practical application of automatic sorting, especially for small stains of inferior cocoons in images of densely distributed cocoons. To deal with this problem, an efficient fine-grained object detection network based on attention mechanism and multi-scale feature fusion, called AMMF-Net, is proposed for inferior silkworm cocoon recognition. In this model, fine-grained object features are key considerations to improve the detection accuracy. To efficiently extract fine-grained features of silkworm cocoon images, we designed an efficient hybrid feature extraction network (HFE-Net) that combines depth-wise separable convolution and Transformer as the backbone. It captures local and global information to extract fine-grained features of inferior silkworm cocoon images, improving the representation ability of the network. An efficient multi-scale feature fusion module (EMFF) is proposed as the neck of the object detection structure. It improves the typical down-sampling method of multi-scale feature fusion to avoid the loss of key information and achieve better performance. Our method is trained and evaluated on a dataset collected from multiple inferior cocoons. Extensive experiments validated the effectiveness and generalization performance of the HFE-Net network and the EMFF module, and the proposed AMMF-Net achieved the best detection results compared to other popular deep neural networks. Full article

Rapeseed blanket seedling transplanters have developed rapidly due to their high efficiency and adaptability to the soil in many areas of China. However, during the transplanter’s longitudinal seedling conveying process, seedling blanket compression leads to inaccurate conveying and thus declined seedling picking performance. In this paper, a mechanical compression test was carried out on rapeseed seedling blankets. The longitudinal compression force of the rapeseed seedling blanket on a transplanter was calculated through mechanical analysis. A compression model of the rapeseed seedling blanket was established to determine how the blanket’s mechanical characteristics and the device’s structural parameters affect blanket compression. In addition, with the index of longitudinal compression Y₁, the coefficient of variation in the longitudinal seedling conveying distance Y₂, and the qualified-block-cutting rate Y₃, the interactive influence between the seedling tray tilt angle A, the seedling blanket moisture content B, and the seedling blanket thickness C were analyzed using response surface analysis. Aiming to reduce blanket compression and enhance the accuracy of longitudinal seedling conveying and block-cutting quality, the optimized results show that the predicted optimal parameters were a 50.14° seedling tray tilt angle, a 71.86% seedling blanket moisture content, and a 22.13 mm seedling blanket thickness. Using these optimized parameters, the transplanter achieved a blanket longitudinal compression of 18.17 mm, a coefficient of variation in the longitudinal seedling conveying distance of 1.142, and a qualified-block-cutting rate of 90%. Subsequently, a validation test was performed, revealing a high degree of conformity between the optimization model and the experimental data. Thus, the predicted optimal parameters can provide significantly reduced compression and a high seedling conveying performance. The results of this study provide theoretical and empirical support for the optimized design and operation of mechanized rapeseed blanket seedling transplanting. Full article

Aflatoxin B1 (AFB1) is the most toxic mycotoxin and is widespread in moldy feed. The use of biological removal methods to reduce AFB1 has become a research hotspot. This study aimed to isolate lactic acid bacteria (LAB) capable of removing AFB1 from moldy feeds and assessed the removal capacity under various environmental conditions. A strain named Lactobacillus brevis DN-1 was isolated from feed samples and showed 71.38% AFB1 percent removal. Furthermore, DN-1 showed good antifungal activity against Aspergillus flavus BNCC336156 and Aspergillus parasiticus BNCC335939. The optimum growth temperature and pH of DN-1 were 37 °C and 6.0, respectively, and DN-1 grew well in the concentration range of 0–20 µg/L AFB1. Under a temperature of 20–40 °C, pH of 3.0–9.0, and anaerobic conditions, the percent removal of AFB1 was more than 60%. An analysis of the different components of DN-1 showed that cell wall adsorption was the main removal method and suggested the pathway for AFB1 removal by LAB. In addition, strain DN-1 was used as a biological preservative in artificially contaminated peanut and sunflower cakes, which significantly inhibited the growth of mold and production of AFB1. In brief, this study highlights the potential use of DN-1 as a preventive agent against aflatoxicosis via strong removal capability in the application of fermented feed or food. Full article

Sustainability credentialling is the communication of environmental, social, economic, or animal-welfare-related information about a producer or product. Demand for sustainability credentials has been increasing and the aim of this study was to describe the main drivers for this kind of information in Australian red meat value chains that reach consumers across Australia and internationally, mainly in Asia, the USA, and the Middle East. The mixed methods approach included consultation with red meat processors. Desk-based research explored drivers from outside the value chain identified in the consultation. Little evidence was found that consumers are a driver of sustainability credentialling. The main drivers were in the global financial system, expressed in coordinated climate action policies by financial service providers and emerging government climate-related financial legislation. The inclusion of Scope 3 emissions extends coverage to most value chain participants. Net zero transitioning presents many risks to red meat value chains, potentially involving costly interventions and greater difficulty accessing financial services, with direct implications for production costs and asset values. Urgent action is recommended to achieve the formal recognition and use of climate metrics that differentiate the management strategies that are applicable to short-lived biogenic methane compared to CO₂ to achieve the Paris Agreement goals. Full article

Livestock management is challenging for resource-poor (R-P) farmers due to unavailability of quality feed, limited professional advice, and rumor-spreading about animal health condition in a herd. This research seeks to improve animal health in southern Africa by promoting sericea lespedeza (Lespedeza cuneata), a nutraceutical forage legume. An automated geospatial model for precision agriculture (PA) can identify suitable locations for its cultivation. Additionally, a novel approach of radio-frequency identifier (RFID) supported telemetry technology can track animal movement, and the analyses of data using artificial intelligence can determine sickness of small ruminants. This RFID-based system is being connected to a smartphone app (under construction) to alert farmers of potential livestock health issues in real time so they can take immediate corrective measures. An accompanying Decision Support System (DSS) site is being developed for R-P farmers to obtain all possible support on livestock production, including the designed PA and RFID-based DSS. Full article

Crop growth parameters are the basis for evaluation of crop growth status and crop yield. The aim of this study was to develop a more accurate estimation model for corn growth parameters combined with multispectral vegetation indexes (VI_opt) and the differential radar information (DRI) derived from SAR data. Targeting the estimation of corn plant height (H) and the BBCH (Biologische Bundesanstalt, Bundessortenamt and CHemical industry) phenological parameters, this study compared the estimation accuracies of various multispectral vegetation indexes (VI_opt) and the corresponding VI_DRI (vegetation index corrected by DRI) indexes in inverting the corn growth parameters. (1) When comparing the estimation accuracies of four multispectral vegetation indexes (NDVI, NDVIre1, NDVIre2, and S2REP), NDVI showed the lowest estimation accuracy, with a normalized root mean square error (nRMSE) of 20.84% for the plant height, while S2REP showed the highest estimation accuracy (nRMSE = 16.05%). In addition, NDVIre2 (nRMSE = 16.18%) and S2REP (16.05%) exhibited a higher accuracy than NDVIre1 (nRMSE = 19.27%). Similarly, for BBCH, the nRMSEs of the four indexes were 24.17%, 22.49%, 17.04% and 16.60%, respectively. This confirmed that the multispectral vegetation indexes based on the red-edge bands were more sensitive to the growth parameters, especially for the Sentinel-2 red-edge 2 band. (2) The constructed VI_DRI indexes were more beneficial than the VI_opt indexes in enhancing the estimation accuracy of corn growth parameters. Specifically, the nRMSEs of the four VI_DRI indexes (NDVI_DRI, NDVIre1_DRI, NDVIre2_DRI, and S2REP_DRI) decreased to 19.64%, 18.11%, 15.00%, and 14.64% for plant height, and to 23.24%, 21.58%, 15.79%, and 15.91% for BBCH, indicating that even in cases of high vegetation coverage, the introduction of SAR DRI features can further improve the estimation accuracy of growth parameters. Our findings also demonstrated that the NDVIre2_DRI and S2REP_DRI indexes constructed using red-edge 2 band information of Sentinel-2 and SAR DRI features had more advantages in improving the estimation accuracy of corn growth parameters. Full article

The autonomous navigation system of feed-pushing robots is one of the key technologies for the intelligent breeding of dairy cows, and its accuracy has a significant influence on the quality of feed-pushing operations. Currently, the navigation methods of feed-pushing robots in the complex environment of cattle barns mainly include visual, LiDAR, and geomagnetic navigation, but there are still problems relating to low navigation accuracy. An autonomous navigation system based on ultra-wideband (UWB) positioning utilizing the dynamic forward-looking distance pure pursuit algorithm is proposed in this paper. First, six anchor nodes were arranged in the corners and central feeding aisle of a 30 × 86 m rectangular standard barn to form a rectangular positioning area. Then, utilizing the 9ITL-650 feed-pushing robot as a platform and integrating UWB wireless positioning technology, a global coordinate system for the cattle barn was established, and the expected path was planned. Finally, the pure pursuit model was improved based on the robot’s two-wheel differential kinematics model, and a dynamic forward-looking distance pure pursuit controller based on PID regulation was designed to construct a comprehensive autonomous navigation control system. Subsequently, field experiments were conducted in the cattle barn. The experimental results show that the static positioning accuracy of the UWB system for the feed-pushing robot was less than 16 cm under no-line-of-sight conditions in the cattle barn. At low speeds, the robot was subjected to linear tracking comparative experiments with forward-looking distances of 50, 100, 150, and 200 cm. The minimum upper-line distance of the dynamic forward-looking distance model was 205.43 cm. In the steady-state phase, the average lateral deviation was 3.31 cm, with an average standard deviation of 2.58 cm and the average root mean square error (RMSE) of 4.22 cm. Compared with the fixed forward-looking distance model, the average lateral deviation, the standard deviation, and the RMSE were reduced by 42.83%, 37.07%, and 42.90%, respectively. The autonomous navigation experiments conducted on the feed-pushing robot at travel speeds of 6, 8, and 10 m/min demonstrated that the maximum average lateral deviation was 7.58 cm, the maximum standard deviation was 8.22 cm, and the maximum RMSE was 11.07 cm, meeting the autonomous navigation requirements for feed-pushing operations in complex barn environments. This study provides support for achieving high-precision autonomous navigation control technology in complex environments. Full article

The increased use of chemicals in rice farming poses significant issues regarding the emergence of pesticide/fungicide resistance and environmental sustainability concerns. This study was aimed at the genetic improvement of blast, bacterial leaf blight (BB) and gall midge resistance in a popular rice variety CO 51 which already harbours a blast resistance gene Pi54. Efforts were made to pyramid an additional blast resistance gene Pi9 along with two BB resistance genes (xa13 and Xa21) and two gall midge resistance genes (Gm1 and Gm4) into an elite rice variety CO 51 to enhance the resistance level to biotic stresses. The superior lines were selected using functional markers conferring resistance to blast (NBS4 and Pi54MAS linked to Pi9 and Pi54 genes, respectively) and BB [(xa13Prom (xa13) and pTA248 (Xa21)] and SSR markers linked to Gm1 (RM1328) and Gm4 (RM22550) for phenotypic screening and agronomic evaluation. The genotyping and phenotyping of F₆ and BC₂F₆ progenies of CO 51 X 562-4, for agronomic traits and resistance to BB and blast, identified ten superior progenies in F₆ and five superior progenies in BC₂F₆. The breeding lines harbouring both xa13+Xa21 exhibited high levels of resistance to BB (score ≤ 1 cm) and Pi9+Pi54 exhibited strong resistance to blast (score ≤ 2). Identified lines can be evaluated further for varietal improvement or utilised as genetic stocks in breeding programs. Full article