Agriculture

24 pages, 6242 KiB

Open AccessArticle

Synergistic Reduction and Common Driving Forces of Agricultural Pollution and Carbon Emissions Based on Agricultural Grey Water Footprint

by Hua Zhu, Qing Zhang and Junfeng Xiong

Agriculture 2025, 15(7), 782; https://doi.org/10.3390/agriculture15070782 (registering DOI) - 4 Apr 2025

Abstract

Managing agricultural water pollution (AWP) and agricultural carbon emissions (ACE) together is crucial for addressing the global water resources crisis and climate challenges. Traditional water quality indicators are limited in large-scale evaluations of AWP. The common trends of ACE and AWP, as well [...] Read more.

Managing agricultural water pollution (AWP) and agricultural carbon emissions (ACE) together is crucial for addressing the global water resources crisis and climate challenges. Traditional water quality indicators are limited in large-scale evaluations of AWP. The common trends of ACE and AWP, as well as the spatial heterogeneity of their common driving factors also remain unclear. This study introduces a novel framework for analyzing the synergistic reduction of AWP and ACE from the perspective of agricultural grey water footprint (AGWF) and examines disparities in common driving factors across areas with differing levels of economic development and pollution intensities in Zhejiang Province. The results indicate that ACE and AGWF in Zhejiang showed an upward trend from 2010 to 2012, followed by a significant decline from 2013 to 2020. A consistent synergistic reduction trend in grey water footprint and carbon emissions was identified in both the planting and livestock husbandry sectors across Zhejiang. Socio-economic factors jointly influenced the reductions in ACE and AGWF, with technological level and the labor-to-research-and-development (labor-R&D) ratio being the primary drivers, accounting for 79.41% and 78.38% of these reductions, respectively. The impact of agricultural R&D expenditure intensity on AGWF and ACE exhibited spatiotemporal heterogeneity and sectoral disparities. The key to promoting the synergistic reduction of AGWF and ACE lies in advancing the research, development, and application of green agricultural technologies especially in regions where such technologies are not yet fully developed. The results provide a theoretical framework and practical implementation for the integrated management of AWP and ACE, as well as sustainable agricultural policy formulation. Full article

(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)

15 pages, 5814 KiB

Open AccessArticle

Whole-Genome Identification and Analysis of Carbohydrate Esterase Gene Family in Colletotrichum graminicola

by Wenting Zhu, Limin Wang, Honglian Li, Yan Shi, Jiaxin Chang, Senbo Wang, Xu Liu, Penghao Ma, Jinzhang Zhao, Yan Liu and Yafei Wang

Agriculture 2025, 15(7), 781; https://doi.org/10.3390/agriculture15070781 (registering DOI) - 3 Apr 2025

Abstract

Colletotrichum graminicola can cause leaf spots and stalk rot in maize. The primary function of carbohydrate esterases (CEs) is to eliminate ester modifications from monosaccharides, oligosaccharides, and polysaccharides, thereby facilitating the hydrolysis of sugars. We identified 128 CE genes through whole-genome analysis and [...] Read more.

Colletotrichum graminicola can cause leaf spots and stalk rot in maize. The primary function of carbohydrate esterases (CEs) is to eliminate ester modifications from monosaccharides, oligosaccharides, and polysaccharides, thereby facilitating the hydrolysis of sugars. We identified 128 CE genes through whole-genome analysis and functional annotation of C. graminicola TZ–3 here. We further analyzed the physicochemical properties, subcellular localization, conserved motifs, gene structures, promoter regulatory elements of these 128 C. graminicola CE (CgCE) genes. Our results indicated that half of the CgCE proteins were located extracellularly. The CgCE proteins demonstrated diversity in both their structures and motifs. Furthermore, the CgCE gene family contained numerous conserved domains, suggesting potential functional diversity. Regulatory elements associated with various stresses and plant hormones were identified in this study. GO enrichment and expression pattern analysis indicated that the CgCE genes were involved in metabolic processes and might contribute to the establishment of fungal infections and lesion expansion. These results enhance our understanding of the CE family genes in C. graminicola and provide a foundation for further investigations into their roles in fungal pathogenesis. Full article

(This article belongs to the Special Issue Detection, Diagnostics and Management Control Strategies of Plant Pathogens)

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20 pages, 3549 KiB

Open AccessArticle

Analysis of Damage Characteristics and Fragmentation Simulation of Soybean Seeds Based on the Finite-Element Method

by Yuxuan Chen, Zhong Tang, Bin Li, Shiguo Wang, Yang Liu, Weiwei Zhou, Jianpeng Jing and Xiaoying He

Agriculture 2025, 15(7), 780; https://doi.org/10.3390/agriculture15070780 (registering DOI) - 3 Apr 2025

Abstract

Soybeans are a crucial crop, and it is therefore necessary to make accurate predictions of their mechanical properties during harvesting to optimize the design of threshing cylinders, minimize the breakage rate during threshing, and enhance the quality of the final product. However, a [...] Read more.

Soybeans are a crucial crop, and it is therefore necessary to make accurate predictions of their mechanical properties during harvesting to optimize the design of threshing cylinders, minimize the breakage rate during threshing, and enhance the quality of the final product. However, a precise model for the mechanical response of soybean seeds under stress conditions is currently lacking. To establish an accurate finite-element model (FEM) for soybeans that can predict their mechanical behavior under various loading conditions, an ellipsoidal modeling approach tailored for soybeans is proposed. Soybeans harvested in Xinjiang were collected and processed as experimental materials; the average moisture content was 11.77%, there was an average density of 1.229 g/cm³, and the average geometric specifications (height, thickness, and width) were 8.50 mm, 7.92 mm, and 7.10 mm, respectively. Compression tests were conducted on the soybeans in vertical, horizontal, and lateral orientations at the same loading speed to analyze the load and damage stages of these soybeans harvested in Xinjiang. The experimental results indicate that as the contact area decreases, the crushing load increases, with soybeans in the horizontal orientation being able to withstand the highest ultimate pressure. When placed vertically, the soybeans are not crushed; in horizontal and lateral orientations, however, they exhibit varying degrees of breakage. The Hertz formula was simplified based on the geometric characteristics of soybeans, and the elastic moduli in the X, Y, and Z directions of the soybean seeds were calculated as 42.8821 MPa, 40.4342 MPa, and 48.7659 MPa, respectively, using this simplified Hertz formula. A model of the soybeans was created in SolidWorks Ver.2019 and imported into ANSYS WORKBENCH for simulation verification. The simulation results were consistent with the experimental findings. The research findings enhance the understanding of the mechanical behavior of soybean seeds and provide robust scientific support for the optimization of soybean processing technologies and the improvement of storage and transportation efficiency. Full article

(This article belongs to the Section Agricultural Technology)

15 pages, 3941 KiB

Open AccessArticle

StrawberryNet: Fast and Precise Recognition of Strawberry Disease Based on Channel and Spatial Information Reconstruction

by Xiang Li, Lin Jiao, Kang Liu, Qihuang Liu and Ziyan Wang

Agriculture 2025, 15(7), 779; https://doi.org/10.3390/agriculture15070779 (registering DOI) - 3 Apr 2025

Abstract

Timely and effective identification and diagnosis of strawberry diseases play essential roles in the prevention of strawberry diseases. Nevertheless, various types of strawberry diseases with high similarity pose a great challenge to the accuracy of strawberry diseases, and the recent module with high [...] Read more.

Timely and effective identification and diagnosis of strawberry diseases play essential roles in the prevention of strawberry diseases. Nevertheless, various types of strawberry diseases with high similarity pose a great challenge to the accuracy of strawberry diseases, and the recent module with high parameter counts is not suitable for real-time identification and monitoring. Therefore, in this paper, we propose a lightweight strawberry disease identification method, termed StrawberryNet, to achieve accurate and real-time identification of strawberry diseases. First, to decrease the number of parameters, instead of standard convolution, a partial convolution is selected to construct the backbone for extracting the features of strawberry disease, which can significantly improve efficiency. And then, a discriminative feature extractor, including channel information reconstruction network (CIR-Net) and spatial information reconstruction network (SIR-Net) modules, is designed for abstracting the identifiable features of different types of strawberry disease. A large number of experimental results were conducted on the constructed strawberry disease dataset, containing 2903 images and 10 common strawberry diseases and normal leaves and fruits. Extensive experiments show that the recognition accuracy of the proposed method can reach 99.01% with only 3.6 M parameters, which have good balance between the identification precision and speed compared to other excellent modules. Full article

(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)

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22 pages, 1737 KiB

Open AccessArticle

Land Use Rather than Microplastic Type Determines the Diversity and Structure of Plastisphere Bacterial Communities

by Yangyang Wang, Zixuan Zhang, Shuang Zhang, Wanlin Zhuang, Zhaoji Shi, Ziqiang Liu, Hui Wei and Jiaen Zhang

Agriculture 2025, 15(7), 778; https://doi.org/10.3390/agriculture15070778 (registering DOI) - 3 Apr 2025

Abstract

Microplastic (MP) pollution has raised global concerns, and biodegradable plastics have been recommended to replace conventional ones. The “plastisphere” has been considered a hotspot for the interactions among organisms and environments, but the differences in the properties of soil microbial communities in the [...] Read more.

Microplastic (MP) pollution has raised global concerns, and biodegradable plastics have been recommended to replace conventional ones. The “plastisphere” has been considered a hotspot for the interactions among organisms and environments, but the differences in the properties of soil microbial communities in the plastisphere of conventional and biodegradable MPs remain unclear. This in situ experiment was conducted to compare the diversity and structure of the bacterial community in the plastisphere of conventional MPs (polyethylene [PE]) and biodegradable MPs (polylactic acid [PLA]) in vegetable fields, orchards, paddy fields, and woodlands. It was discovered that the bacterial α-diversity within the plastisphere was significantly lower than that in the soil across all land use. Significant differences between plastic types were only found in the vegetable field. Regarding the community composition, the relative abundances of Actinobacteriota (43.2%) and Proteobacteria (70.9%) in the plastisphere were found to exceed those in the soil, while the relative abundances of Acidobacteriota (45.5%) and Chloroflexi (27.8%) in the soil were significantly higher. The complexity of the microbial network within the plastisphere was lower than that of the soil. Compared with the soil, the proportion of dispersal limitation in the PLA plastisphere significantly decreased, with the greatest reduction observed in the vegetable field treatment, where it dropped from 57.72% to 3.81%. These findings indicate that different land use types have a greater impact on bacterial community diversity and structure than plastics themselves, and that biodegradable MPs may pose a greater challenge to the ecological function and health of soil ecosystems than conventional MPs. Full article

(This article belongs to the Special Issue Innovative Conservation Cropping Systems and Practices—2nd Edition)

18 pages, 1368 KiB

Open AccessArticle

Mineral Oil Hydrocarbons in Feed: Corn Silage Contamination in a Romanian Dairy Farm

by Mădălina Matei, Daniel Simeanu and Ioan Mircea Pop

Agriculture 2025, 15(7), 777; https://doi.org/10.3390/agriculture15070777 (registering DOI) - 3 Apr 2025

Abstract

This study investigates the presence of mineral oil hydrocarbons (MOHs) in corn silage, aiming to assess contamination levels and identify potential sources, including technological and environmental factors. Given the increasing concern regarding the presence of MOHs all over the food chain, this research [...] Read more.

This study investigates the presence of mineral oil hydrocarbons (MOHs) in corn silage, aiming to assess contamination levels and identify potential sources, including technological and environmental factors. Given the increasing concern regarding the presence of MOHs all over the food chain, this research provides important data on feed safety. A total of 15 corn silage samples were collected from the feed base of a dairy farm. Sampling was performed systematically across silos (top, middle, bottom layers). The analysis was conducted using LC-GC-FID to quantify mineral oil saturated hydrocarbon (MOSH) and aromatic hydrocarbon (MOAH) fractions. Statistical evaluation was applied to determine contamination patterns and potential influencing factors. The findings confirmed the presence of MOSH and MOAH in the analyzed silage, averaging 23.3 mg/kg MOSH and 1.4 mg/kg MOAH, exceeding European Commission guideline limits. Notably, the MOAH fraction, known for its potential toxicity, was detected at significant levels in several samples. The study highlights that corn silage can act as a source of MOSH/MOAH contamination in livestock feed. Technological processes, especially mechanized harvesting and ensiling, and environmental pollution factors appear to be likely the main contributors, emphasizing the need for improved monitoring and preventive measures to mitigate risks in the feed-to-food chain. Full article

(This article belongs to the Section Farm Animal Production)

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14 pages, 5252 KiB

Open AccessArticle

Physico-Mechanical Properties of Male and Female Hemp Plants

by Hüseyin Duran

Agriculture 2025, 15(7), 776; https://doi.org/10.3390/agriculture15070776 (registering DOI) - 3 Apr 2025

Abstract

Hemp (Cannabis sativa L.) is one of the oldest annual fiber crops cultivated throughout human history. Addressing the challenges encountered during the harvesting of hemp for seed and fiber purposes requires further investigation. Studies are also needed to determine plant characteristics in [...] Read more.

Hemp (Cannabis sativa L.) is one of the oldest annual fiber crops cultivated throughout human history. Addressing the challenges encountered during the harvesting of hemp for seed and fiber purposes requires further investigation. Studies are also needed to determine plant characteristics in terms of both variety and gender. This study aimed to determine the physico-mechanical properties of hemp plants. The stems of male and female hemp plants were divided into three sections along their length: lower, middle, and upper regions. Samples measuring 25.4 mm in length were collected from each section, and measurements of thickness and inner and outer diameter were conducted. The same samples were subjected to axial and lateral compression tests to determine load, elongation, and energy values. According to the results, the thickness of hemp ranged from 2.347 mm to 2.628 mm, the inner diameter varied between 3.986 mm and 4.452 mm, while the outer diameter ranged from 8.861 mm to 9.708 mm. The results showed that male hemp plants have an increase in thickness and inner and outer diameter values from the lower to the upper region compared to female hemp plants. The compressive loads in the axial and lateral directions were found to be higher in male hemp plants compared to female hemp plants. Moreover, elongation and energy requirements during axial and lateral compressions showed trends consistent with the load values across the stem samples. This study determined that the results of axial and lateral compression applied at three different positions (lower, middle, and upper) on male and female hemp stalks varied significantly based on both sex and position. Full article

(This article belongs to the Special Issue Research Progress on Agricultural Equipments for Precision Planting and Harvesting)

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17 pages, 9599 KiB

Open AccessArticle

Research on Walnut (Juglans regia L.) Yield Prediction Based on a Walnut Orchard Point Cloud Model

by Heng Chen, Jiale Cao, Jianshuo An, Yangjing Xu, Xiaopeng Bai, Daochun Xu and Wenbin Li

Agriculture 2025, 15(7), 775; https://doi.org/10.3390/agriculture15070775 - 3 Apr 2025

Abstract

This study aims to develop a method for predicting walnut (Juglans regia L.) yield based on the walnut orchard point cloud model, addressing issues such as low efficiency, insufficient accuracy, and high costs in traditional methods. The walnut orchard point cloud is [...] Read more.

This study aims to develop a method for predicting walnut (Juglans regia L.) yield based on the walnut orchard point cloud model, addressing issues such as low efficiency, insufficient accuracy, and high costs in traditional methods. The walnut orchard point cloud is reconstructed using unmanned aerial vehicle (UAV) images, and the semantic segmentation technique is applied to extract the individual walnut tree point cloud model. Furthermore, the tree height, canopy projection area, and volume of each walnut tree are calculated. By combining these morphological features with statistical models and machine learning methods, a prediction model between tree morphology and yield is established, achieving prediction accuracy with a mean absolute error (MAE) of 2.04 kg, a mean absolute percentage error (MAPE) of 17.24%, a root mean square error (RMSE) of 2.81 kg, and a coefficient of determination (R²) of 0.83. This method provides an efficient, accurate, and economically feasible solution for walnut yield prediction, overcoming the limitations of existing technologies. Full article

(This article belongs to the Topic Advances in Smart Agriculture with Remote Sensing as the Core and Its Applications in Crops Field)

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Graphical abstract

5 pages, 148 KiB

Open AccessCorrection

Correction: Wang, M.; Li, T. Pest and Disease Prediction and Management for Sugarcane Using a Hybrid Autoregressive Integrated Moving Average—A Long Short-Term Memory Model. Agriculture 2025, 15, 500

by Minghui Wang and Tong Li

Agriculture 2025, 15(7), 774; https://doi.org/10.3390/agriculture15070774 - 3 Apr 2025

Abstract

In the original publication [...] Full article

(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)

19 pages, 7260 KiB

Open AccessArticle

Calibration of Parameters for Leaf-Stem-Cutting Model of Tuber Mustard (Brassica juncea L.) Based on Discrete Element Method

by Man Gu, Haiyang Shen, Weiwen Luo, Jie Ling, Bokai Wang, Fengwei Gu, Shumin Song, Liang Pan and Zhichao Hu

Agriculture 2025, 15(7), 773; https://doi.org/10.3390/agriculture15070773 - 2 Apr 2025

Abstract

The cutting of leaf stems is a critical step in the mechanized harvesting of tuber mustard (Brassica juncea L.). This study focuses on the calibration of parameters for the discrete element model of mustard leaf stems to visualize the cutting process and [...] Read more.

The cutting of leaf stems is a critical step in the mechanized harvesting of tuber mustard (Brassica juncea L.). This study focuses on the calibration of parameters for the discrete element model of mustard leaf stems to visualize the cutting process and facilitate numerical simulations. Intrinsic material properties were measured based on mechanical testing, and EDEM2022 simulation software was utilized to calibrate the model parameters. The Hertz–Mindlin (no-slip) model was employed to simulate the stacking angle of mustard leaf stems, and the contact parameters for the discrete element model were determined using a combination of two-level factorial design, steepest ascent, and CCD (central composite design) tests. The results showed that the coefficient of restitution, coefficient of static friction, and coefficient of rolling friction for the leaf stems were 0.45, 0.457, and 0.167, respectively, while for interactions between the leaf stems and the working parts, these values were 0.45, 0.55, and 0.175, respectively. Based on the Hertz–Mindlin with bonding model, the primary bonding parameters were calculated, and a BBD (Box–Behnken design) test was applied for optimization. The comparison between the simulation and experimental results showed that the relative error in the maximum shear force was within 5%, indicating that the calibrated model can serve as a reliable theoretical reference for the design and optimization of tuber mustard harvesting and cutting equipment. Full article

(This article belongs to the Section Agricultural Technology)

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21 pages, 4012 KiB

Open AccessArticle

The Impact of Dissolved Biochar on Oxidative Stress and Its Effect on the Virulence of Steinernema feltiae: Implications for Biocontrol Efficiency

by Xinrui Wang, Jie Li, Jing Li, Lan Luo, Gang Li, Weibin Ruan and Guilong Zhang

Agriculture 2025, 15(7), 772; https://doi.org/10.3390/agriculture15070772 - 2 Apr 2025

Abstract

Dissolved biochar (DBC) can make a significantly impact on soil ecosystems and the associated biota due to its high environmental bioavailability. However, the impact of DBC on the adaptability of entomopathogenic nematodes (EPNs), such as Steinernema feltiae, remains uncertain. This study investigates [...] Read more.

Dissolved biochar (DBC) can make a significantly impact on soil ecosystems and the associated biota due to its high environmental bioavailability. However, the impact of DBC on the adaptability of entomopathogenic nematodes (EPNs), such as Steinernema feltiae, remains uncertain. This study investigates the impact of DBC on oxidative stress, antioxidant enzyme activity, virulence, and gene expression in EPNs through culture assays and RNA-seq analysis. Results showed that DBC exposure significantly increased the accumulation of reactive oxygen species (ROS) accumulation. The nematodes treated with DBC700 exhibited 64.34% higher ROS levels, while those treated with DBC400 had 51.13% higher levels compared to the control. Superoxide dismutase (SOD) and catalase (CAT) activities were significantly suppressed, with a stronger inhibition observed in the DBC700 group. As revealed by virulence assays, DBC treatment reduced the infectivity of EPNs against Galleria mellonella larvae. Transcriptome analysis revealed that DBC primarily affected oxidative stress response, membrane transport, and longevity regulation pathways. Moreover, DBC400 predominantly inhibited carbohydrate metabolism, whereas DBC700 significantly impacted oxidative metabolism, protein processing, and neuronal signaling pathways, suggesting the presence of distinct metabolic adaptation mechanisms between the two DBCs. Overall, this study suggests that DBC may impair the biocontrol efficacy of S. feltiae through oxidative stress and genetic perturbations, providing new insights into its long-term ecological impacts on soil ecosystems. Full article

(This article belongs to the Special Issue Biochar Applications in Agricultural Soil Restoration)

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18 pages, 2248 KiB

Open AccessArticle

Real-Time Caterpillar Detection and Tracking in Orchard Using YOLO-NAS Plus SORT

by Sumesh Nair, Guo-Fong Hong, Chia-Wei Hsu, Chun-Yu Lin and Shean-Jen Chen

Agriculture 2025, 15(7), 771; https://doi.org/10.3390/agriculture15070771 - 2 Apr 2025

Abstract

Detecting and tracking caterpillars in orchard environments is crucial for advancing precision agriculture but remains challenging due to occlusions, variable lighting, wind interference, and the need for precise small-object detection. This study presents a real-time deep learning approach that integrates the YOLO-NAS object [...] Read more.

Detecting and tracking caterpillars in orchard environments is crucial for advancing precision agriculture but remains challenging due to occlusions, variable lighting, wind interference, and the need for precise small-object detection. This study presents a real-time deep learning approach that integrates the YOLO-NAS object detection model with the SORT tracking algorithm to overcome these challenges. Evaluated in a jujube orchard, the proposed method significantly improved small caterpillar detection and tracking. Using an RGB-D camera operating at 30 frames per second, the system successfully detected caterpillars measuring 2–5 cm at distances of 20–35 cm, corresponding to resolutions of 21 × 6 to 55 × 10 pixels. The integration of YOLO-NAS with SORT enhanced detection performance, achieving a ~9% increase in true positive detections and an ~8% reduction in false positives compared to YOLO-NAS alone. Even for the smallest caterpillars (21 × 6 pixels), the method achieved over 60% true positive detection accuracy without false positives within 1 s inference. With an inference time of just 0.2 milliseconds, SORT enabled real-time tracking and accurately predicted caterpillar positions under wind interference, further improving reliability. Additionally, selective corner tracking was employed to identify the head and tail of caterpillars, paving the way for future laser-based precision-targeting interventions focused on the caterpillar head. Full article

(This article belongs to the Section Digital Agriculture)

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21 pages, 2979 KiB

Open AccessArticle

Investigating the Impact of Sowing Date on Wheat Leaf Morphology Through Image Analysis

by Junfan Chen, Jianliang Wang, Jiacheng Wang, Zhian Wang, Lihan Zhao, Yaohua Yan, Jiayue Li, Hanzeyu Xu, Chengming Sun and Tao Liu

Agriculture 2025, 15(7), 770; https://doi.org/10.3390/agriculture15070770 - 2 Apr 2025

Abstract

The morphology of wheat leaves is a key indicator of crop stand quality and photosynthetic capacity, with sowing date being a critical factor influencing leaf morphology. To investigate the effects of sowing time on wheat growth, development, and leaf phenotypes, this study utilized [...] Read more.

The morphology of wheat leaves is a key indicator of crop stand quality and photosynthetic capacity, with sowing date being a critical factor influencing leaf morphology. To investigate the effects of sowing time on wheat growth, development, and leaf phenotypes, this study utilized image analysis technology to systematically extract key phenotypic traits of winter wheat leaves, including effective leaf area, leaf color, and leaf shape. The results demonstrated that delayed sowing significantly affected the morphology and color characteristics of winter wheat leaves. Specifically, leaf length and width exhibited a quadratic decreasing trend, resulting in an average reduction in leaf area of over 59%. Additionally, the greenness index (EXG) decreased by 25.84%, while the red pigment index (EXR) increased by 21.69%. Significant differences in leaf color changes were observed among the varieties. This study provides reliable data for determining the optimal sowing period for winter wheat and offers valuable guidance for optimizing field management strategies to enhance crop yield and quality. Full article

(This article belongs to the Special Issue AI-Powered UAVs and Imaging Systems for Precision Wheat and Rice Management)

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22 pages, 5516 KiB

Open AccessArticle

Optimization Design and Experiment of Soil-Covering Device for Astragalus Mulching Transplanting Machine

by Bin Feng, Wei Sun, Shanglong Xin, Guanping Wang, Wenjing Lv and Junzeng Wang

Agriculture 2025, 15(7), 769; https://doi.org/10.3390/agriculture15070769 - 2 Apr 2025

Abstract

In response to the low efficiency and poor soil quality of the mechanized transplanting of Astragalus, and in combination with the agronomic requirements of Astragalus mulching and outcrop cultivation, an Astragalus film mulching transplanting machine was designed, which integrates functions such as trenching, [...] Read more.

In response to the low efficiency and poor soil quality of the mechanized transplanting of Astragalus, and in combination with the agronomic requirements of Astragalus mulching and outcrop cultivation, an Astragalus film mulching transplanting machine was designed, which integrates functions such as trenching, seedling feeding, mulching, and seed row soil covering. Firstly, based on the analysis of the overall structure of the transplanting machine, the structure and working principle of the soil-covering device are expounded, and the structure and working parameters of the soil-covering disc and soil-covering drum are clarified. In order to optimize the performance of the soil-covering device of the mulching transplanting machine and improve the quality of the covering soil, the Box–Behnken response surface test design method was adopted. The depth of disc extraction, the disc deflection angle, and the rotation speed of the soil-covering drum were selected as the main influencing factors. The quantity of soil cover and variation coefficient of soil cover quantity uniformity were used as the evaluation indicators for the quality of the operation, and parameter optimization experiments were conducted. By establishing a regression mathematical model between influencing factors and evaluation indicators, analyzing the interactive effects of each factor on response values, and comprehensively optimizing the model, the optimal parameter combination was obtained. The results of field experiments show that when the depth of disc extraction is 95 mm, the disc deviation angle is 40°, and the rotation speed of the soil-covering drum is 30 r/min, the corresponding quantity of soil cover and variation coefficient of soil cover quantity uniformity are 10.61 kg/m and 1.79%, respectively, which can meet the soil covering requirements. The research results can provide technical references for the structural optimization and performance improvement of the soil-covering device of the traditional Chinese medicine mulching transplanting machine. Full article

(This article belongs to the Section Agricultural Technology)

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23 pages, 1334 KiB

Open AccessReview

Research Progress on the Improvement of Farmland Soil Quality by Green Manure

by Yulong Wang, Aizhong Yu, Yongpan Shang, Pengfei Wang, Feng Wang, Bo Yin, Yalong Liu, Dongling Zhang and Qiang Chai

Agriculture 2025, 15(7), 768; https://doi.org/10.3390/agriculture15070768 - 2 Apr 2025

Abstract

Long-term intensive agricultural management practices have led to a continuous decline in farmland soil quality, posing a serious threat to food security and agricultural sustainability. Green manure, as a natural, cost-effective, and environmentally friendly cover crop, plays a significant role in enhancing soil [...] Read more.

Long-term intensive agricultural management practices have led to a continuous decline in farmland soil quality, posing a serious threat to food security and agricultural sustainability. Green manure, as a natural, cost-effective, and environmentally friendly cover crop, plays a significant role in enhancing soil quality, ensuring food security, and promoting sustainable agricultural development. The improvement of soil quality by green manure is primarily manifested in the enhancement of soil physical, chemical, and biological properties. Specifically, it increases soil organic matter content, optimizes soil structure, enhances nutrient cycling, and improves microbial community composition and metabolic activity. The integration of green manure with agronomic practices such as intercropping, crop rotation, conservation tillage, reduced fertilizer application, and organic material incorporation demonstrates its potential in addressing agricultural development challenges, particularly through its contributions to soil quality improvement, crop yield stabilization, water and nutrient use efficiency enhancement, fertilizer input reduction, and agricultural greenhouse gas emission mitigation. However, despite substantial evidence from both research and practical applications confirming the benefits of green manure, its large-scale adoption faces numerous challenges, including regional variability in application effectiveness, low farmer acceptance, and insufficient extension technologies. Future research should further clarify the synergistic mechanism between green manure and agronomic measures such as intercropping, crop rotation, conservation tillage, reduced fertilization and organic material return to field. This will help explore the role of green manure in addressing the challenges of soil degradation, climate change and food security, develop green manure varieties adapted to different ecological conditions, and optimize green manure planting and management technologies. Governments should comprehensively promote the implementation of green manure technologies through economic incentives, technology extension, and educational training programs. The integration of scientific research, policy support, and technological innovation is expected to establish green manure as a crucial driving force for facilitating the global transition towards sustainable agriculture. Full article

(This article belongs to the Special Issue Soil Chemical Properties and Soil Conservation in Agriculture)

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36 pages, 943 KiB

Open AccessArticle

Irrigation, Nitrogen Supplementation, and Climatic Conditions Affect Resistance to Aspergillus flavus Stress in Maize

by Heltan M. Mwalugha, Krisztina Molnár, Csaba Rácz, Szilvia Kovács, Cintia Adácsi, Tamás Dövényi-Nagy, Károly Bakó, István Pócsi, Attila Dobos and Tünde Pusztahelyi

Agriculture 2025, 15(7), 767; https://doi.org/10.3390/agriculture15070767 - 2 Apr 2025

Abstract

Maize production is increasingly challenged by climate change, which affects plant physiology, fungal colonization, and mycotoxin contamination. Aspergillus flavus, a saprophytic fungus, thrives in warm, dry conditions, leading to aflatoxin B1 (AFB1) accumulation, and posing significant food safety risks. Macro- and micro-climatic [...] Read more.

Maize production is increasingly challenged by climate change, which affects plant physiology, fungal colonization, and mycotoxin contamination. Aspergillus flavus, a saprophytic fungus, thrives in warm, dry conditions, leading to aflatoxin B1 (AFB1) accumulation, and posing significant food safety risks. Macro- and micro-climatic factors, including temperature, humidity, and precipitation, influence kernel development, leaf wetness duration, and mycotoxin biosynthesis. Nitrogen availability and irrigation play crucial roles in modulating plant responses to these stressors, affecting chlorophyll content, yield parameters, and fungal interactions. To investigate these interactions, a Completely Randomized Design (CRD) was employed from 2020 to 2022 to assess physiological changes in SY Orpheus maize hybrid under varying climatic conditions. Rising temperatures and declining relative humidity (RH) significantly reduced kernel number per ear length from 25.60 ± 0.34 in 2020 to 17.89 ± 0.39 in 2022 (p < 0.05), impacting yield. The AFB1 levels peaked in 2021 (156.88 ± 59.02 µg/kg), coinciding with lower humidity and increased fungal stress. Water availability improved kernel numbers and reduced AFB1 accumulation (p < 0.05) but did not significantly affect the total fungal load (p > 0.05). Nitrogen supplementation enhanced plant vigor, suppressed AFB1 biosynthesis, and influenced spectral indices. Potential confounding factors such as soil variability and microbial interactions may require further investigations. Full article

(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)

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23 pages, 2129 KiB

Open AccessArticle

The Effect of Climate Change on Important Climate Variables in Taiwan and Its Potential Impact on Crop Production

by Shih-Lun Fang, Bing-Yun Tsai, Chun-Yi Wu, Sheng-Chih Chang, Yi-Lung Chang and Bo-Jein Kuo

Agriculture 2025, 15(7), 766; https://doi.org/10.3390/agriculture15070766 - 2 Apr 2025

Abstract

Alterations in reference evapotranspiration (ET₀) and precipitation (PP) resulting from global warming substantially affect water resources and agriculture. This study analyzed trends in ET₀, PP, and key climate variables—including air temperature (T), vapor pressure deficit (VPD), wind speed, and [...] Read more.

Alterations in reference evapotranspiration (ET₀) and precipitation (PP) resulting from global warming substantially affect water resources and agriculture. This study analyzed trends in ET₀, PP, and key climate variables—including air temperature (T), vapor pressure deficit (VPD), wind speed, and solar radiation (R_s)—across Taiwan from 1995 to 2022. Trends were assessed using the modified Mann–Kendall test and the multivariate Man–Kendall test at both station-wise and multi-station scales. Results indicated that ET₀ was primarily influenced by R_s, followed by T, wind speed, and VPD. Station-wise analysis revealed increasing trends in annual and seasonal T, R_s, and ET₀, while over 50% of wind speed series showed significant declines. Multi-station analysis confirmed an overall rise in ET₀. In eastern Taiwan, rising T and declining VPD and wind speed may increase the risk of pest and disease outbreaks. The arid index exhibited a general downward trend, particularly in summer, with 75% of the stations in eastern Taiwan exhibiting significant declines, suggesting a shift toward drier conditions. These findings imply that fewer crop options may be suitable for cultivation in eastern Taiwan due to water resource constraints. Additionally, seasonal and annual PP showed slight decreases, with a more uneven distribution observed in central Taiwan. Therefore, improving hydraulic facilities and irrigation systems will become important. Furthermore, comparisons between the multivariate Mann–Kendall test and the traditional univariate approach revealed some different results, indicating the need for further research to identify a more reliable approach. Full article

(This article belongs to the Section Agricultural Water Management)

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25 pages, 14345 KiB

Open AccessArticle

Research on an Apple Recognition and Yield Estimation Model Based on the Fusion of Improved YOLOv11 and DeepSORT

by Zhanglei Yan, Yuwei Wu, Wenbo Zhao, Shao Zhang and Xu Li

Agriculture 2025, 15(7), 765; https://doi.org/10.3390/agriculture15070765 - 2 Apr 2025

Abstract

Accurate apple yield estimation is essential for effective orchard management, market planning, and ensuring growers’ income. However, complex orchard conditions, such as dense foliage occlusion and overlapping fruits, present challenges to large-scale yield estimation. This study introduces APYOLO, an enhanced apple detection algorithm [...] Read more.

Accurate apple yield estimation is essential for effective orchard management, market planning, and ensuring growers’ income. However, complex orchard conditions, such as dense foliage occlusion and overlapping fruits, present challenges to large-scale yield estimation. This study introduces APYOLO, an enhanced apple detection algorithm based on an improved YOLOv11, integrated with the DeepSORT tracking algorithm to improve both detection accuracy and operational speed. APYOLO incorporates a multi-scale channel attention (MSCA) mechanism and an enhanced multi-scale prior distribution intersection over union (EnMPDIoU) loss function to enhance target localization and recognition under complex environments. Experimental results demonstrate that APYOLO outperforms the original YOLOv11 by improving mAP@0.5, mAP@0.5–0.95, accuracy, and recall by 2.2%, 2.1%, 0.8%, and 2.3%, respectively. Additionally, the combination of a unique ID with the region of line (ROL) strategy in DeepSORT further boosts yield estimation accuracy to 84.45%, surpassing the performance of the unique ID method alone. This study provides a more precise and efficient system for apple yield estimation, offering strong technical support for intelligent and refined orchard management. Full article

(This article belongs to the Section Digital Agriculture)

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27 pages, 1531 KiB

Open AccessArticle

Driving Mechanisms of the Integration of Ecological Farms and Rural Tourism: A Mixed Method Study

by Xia Xiao, Pingan Xiang, Haisong Wang and Maosen Xia

Agriculture 2025, 15(7), 764; https://doi.org/10.3390/agriculture15070764 - 2 Apr 2025

Abstract

Integration with rural tourism is an important way to achieve the sustainable development of ecological farms. Existing literature on the integration of agriculture and tourism lacks discussion from the microscopic farm level, making it difficult to capture the complex mechanisms of the integration [...] Read more.

Integration with rural tourism is an important way to achieve the sustainable development of ecological farms. Existing literature on the integration of agriculture and tourism lacks discussion from the microscopic farm level, making it difficult to capture the complex mechanisms of the integration of ecological farms and rural tourism. This paper attempts to address this problem by exploring the driving factors of the integration of ecological farms and rural tourism. The research aim of this paper is to construct a theoretical framework for driving the integration of ecological farms and rural tourism. We first conducted research on farms in four ecological agriculture demonstration zones: Ziquejie in Loudi, Hunan Province; Heshi in Shilin, Yunnan Province; Rongjiang in Dali, Yunnan Province; and Youxiqiao Village in Hunan Province. We interviewed 64 stakeholders in ecotourism and used grounded theory methods to construct a model and propose hypotheses. On this basis, a measurement scale was designed, and data was collected from 1041 Chinese ecological farms (ecological farm operators) using a structured questionnaire. The partial least squares structural equation model (PLS-SEM) was used to model and analyze the data to test the constructed model. The study found that higher market demand, regional economic level, intrinsic development needs, intrinsic resource endowments, technical support, and resource integration can promote the integration of ecological farms and rural tourism. Market demand and intrinsic development needs constitute the generative force of agritourism integration, while resource integration and intrinsic resource endowments constitute the development force of agritourism integration, and technical support and the regional economic level constitute the supporting force of agritourism integration. Full article

(This article belongs to the Section Agricultural Economics, Policies and Rural Management)

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