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Native Grass Enhances Bird, Dragonfly, Butterfly and Plant Biodiversity Relative to Conventional Crops in Midwest, USA -
Making the Connection Between PFASs and Agriculture Using the Example of Minnesota, USA: A Review -
LiDAR-IMU Sensor Fusion-Based SLAM for Enhanced Autonomous Navigation in Orchards -
Toward Sustainable Broiler Production: Evaluating Microbial Protein as Supplementation for Conventional Feed Proteins -
Different Responses to Salinity of Pythium spp. Causing Root Rot on Atriplex hortensis var. rubra Grown in Hydroponics
Journal Description
Agriculture
Agriculture
is an international, scientific peer-reviewed open access journal published semimonthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubAg, AGRIS, RePEc, and other databases.
- Journal Rank: JCR - Q1 (Agronomy) / CiteScore - Q1 (Plant Science)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18 days after submission; acceptance to publication is undertaken in 1.9 days (median values for papers published in this journal in the first half of 2025).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journals for Agriculture include: Poultry, Grasses, Crops and AIPA.
Impact Factor:
3.6 (2024);
5-Year Impact Factor:
3.8 (2024)
Latest Articles
Disrupting Path Dependence: Tariff-Induced Import Substitution in China’s Soybean Market
Agriculture 2025, 15(21), 2312; https://doi.org/10.3390/agriculture15212312 (registering DOI) - 6 Nov 2025
Abstract
Soybeans are of strategic importance to China, yet the country’s heavy reliance on imports leaves it highly exposed to policy and market disruptions. Existing studies have largely focused on the initial 2018 tariff episode, while the evolving impacts of subsequent and intensified tariff
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Soybeans are of strategic importance to China, yet the country’s heavy reliance on imports leaves it highly exposed to policy and market disruptions. Existing studies have largely focused on the initial 2018 tariff episode, while the evolving impacts of subsequent and intensified tariff measures remain insufficiently explored. This study investigates how tariff shocks transmit through import reduction, structural reallocation, and price pass-through by employing a multi-phase difference-in-differences (DID) framework in combination with a continuous-intensity DID model. Using monthly data from January 2015 to June 2025, the analysis evaluates the effects of tariff escalation on import volumes, source-country shares, and landed import prices, thereby capturing both stage-specific dynamics and intensity-dependent responses. Robustness is verified through event-study parallel trend tests and placebo validations. The results show that (1) import volumes from the United States declined sharply and did not fully revert, indicating that tariffs disrupted long-standing trade path dependence; (2) source-country shares reallocated away from the U.S. toward South American suppliers, reinforcing diversification in China’s supply structure; and (3) tariff costs were asymmetrically passed through to prices, with U.S. soybean prices rising by approximately 43 percent, while non-U.S. prices remained relatively stable. Overall, the findings demonstrate that tariff shocks functioned as structural catalysts rather than temporary disturbances, accelerating China’s transition toward a more diversified and resilient soybean import architecture under heightened geopolitical uncertainty.
Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
Open AccessArticle
Comprehensive Analysis of Full-Length Transcriptome Profiling, Genetic and Phenotypic Variation in Multiplier Onion (Allium cepa var. aggregatum) Accessions in China
by
Huixia Jia, Jiangping Song, Yuru Huang, Tingting Zhang, Mengzhen Wang, Yumin Tan, Jiyan Zang, Xiaohui Zhang, Wenlong Yang, Yanhui Pang, Yanfei Yang and Haiping Wang
Agriculture 2025, 15(21), 2311; https://doi.org/10.3390/agriculture15212311 (registering DOI) - 6 Nov 2025
Abstract
Multiplier onion (Allium cepa L. var. aggregatum) is an important bulbous vegetable widely utilized for culinary, condimental, and medicinal purposes. However, limited research on its genetic diversity and phenotypic variation has hindered the development and utilization of superior cultivars. In this
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Multiplier onion (Allium cepa L. var. aggregatum) is an important bulbous vegetable widely utilized for culinary, condimental, and medicinal purposes. However, limited research on its genetic diversity and phenotypic variation has hindered the development and utilization of superior cultivars. In this study, we conducted full-length transcriptome profiling to obtain unique transcripts and develop large-scale simple sequence repeat (SSR) markers. Subsequently, we employed integrative analysis to characterize the genetic and phenotypic variation of 263 multiplier onion accessions in China. Full-length transcriptome sequencing utilizing PacBio technology generated 61,108 high-quality non-redundant transcripts with an average length of 1816 bp, from which we developed 4124 SSR markers encompassing 100 motif types. Population structure, principal component analysis, and neighbor-joining phylogenetic analysis classified the 263 multiplier onion accessions into two distinct subpopulations: Pop1, consisting of 236 accessions primarily from Heilongjiang Province, and Pop2, comprising 27 accessions mostly from Shaanxi Province. Phenotypic evaluation demonstrated significant variation in bulb traits, with single bulb weight (SBW) exhibiting the highest variability (0.75–29.94 g; CV = 70.10%), followed by total bulb weight per plant (BW) (5.00–168.83 g; CV = 58.34%), indicating considerable potential for breeding high-yield varieties. Correlation analysis indicated that the SBW and BW had significantly positive correlations with multiple traits, including bulb height, bulb transverse diameter, diameter of basal plate of bulb, diameter of bulb neck, and number of cloves per bulb. Our findings provide a valuable genetic and phenotypic resource for the conservation and utilization of multiplier onion germplasms.
Full article
(This article belongs to the Section Crop Genetics, Genomics and Breeding)
Open AccessArticle
A Low-Rank Coal-Derived Soil Amendment Promotes Plant Growth and Shapes Rhizosphere Microbial Communities of Lettuce (Lactuca sativa)
by
Xing-Feng Huang, Paul H. Fallgren, Song Jin and Kenneth F. Reardon
Agriculture 2025, 15(21), 2310; https://doi.org/10.3390/agriculture15212310 (registering DOI) - 6 Nov 2025
Abstract
Coal-derived soil amendments have been shown to improve soil physicochemical properties and promote plant growth; however, their effects on rhizosphere microbial communities remain insufficiently understood. In this study, a comprehensive assessment of the impacts of a lignite coal-based, microbially processed amendment on lettuce
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Coal-derived soil amendments have been shown to improve soil physicochemical properties and promote plant growth; however, their effects on rhizosphere microbial communities remain insufficiently understood. In this study, a comprehensive assessment of the impacts of a lignite coal-based, microbially processed amendment on lettuce (Lactuca sativa) growth, soil properties, and rhizosphere microbiota was conducted. Application of the coal-based amendment resulted in a more than two-fold increase in plant fresh weight compared to untreated soil. The amendment significantly improved soil organic matter content but did not increase phosphate or potassium levels. Rhizosphere bacterial and fungal communities were profiled using 16S rRNA and ITS gene sequencing. Principal coordinate analysis revealed that the coal-based amendment, commercial organic fertilizer, and raw coal each induced distinct shifts in microbial community structure. Notably, treatment with the coal-based amendment reduced the relative abundance of Proteobacteria while increasing Acidobacteriota and Chloroflexi in the bacterial community. In fungal communities, the amendment decreased Basidiomycota and enriched Ascomycota. These results suggest that the observed enhancement in plant growth is closely linked to changes in rhizosphere microbial composition and soil organic matter content, highlighting the potential of microbially processed coal products as sustainable soil amendments in agriculture.
Full article
(This article belongs to the Section Agricultural Soils)
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Open AccessArticle
Plant Desiccation and Root Rot in Rosemary: Insight into Macrophomina phaseolina, Ceratobasidium sp. and Fusarium falciforme Roles in Co-Infection
by
Eliana Dell’Olmo, Giovanna Serratore, Alessandro Mataffo, Giovanni Ragosta, Giovanna Napoletano and Loredana Sigillo
Agriculture 2025, 15(21), 2309; https://doi.org/10.3390/agriculture15212309 - 6 Nov 2025
Abstract
Rosmarinus officinalis (rosemary) is one of the most cultivated Lamiaceae species because of its medicinal, aromatic and ornamental properties. In the late summer of 2023, symptoms of desiccation were observed in a rosemary field located in southern Italy and the isolation from necrotic
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Rosmarinus officinalis (rosemary) is one of the most cultivated Lamiaceae species because of its medicinal, aromatic and ornamental properties. In the late summer of 2023, symptoms of desiccation were observed in a rosemary field located in southern Italy and the isolation from necrotic roots revealed three different fungal species: Ceratobasidium sp., Fusarium falciforme and Macrophomina phaseolina. All the isolates were morphologically and molecularly identified and Koch’s postulates assays were performed to establish the pathogenicity and the effect of each fungus on the disease incidence and severity. A dual plate method was used to investigate the interaction between Ceratobasidium sp., F. falciforme and M. phaseolina, mixing them in pairs and all together. Similarly, in vivo inoculations with the pathogens in combination were used to verify the role of each isolate in the interaction with the others and the contribution in plant disease expression. The results allowed to identify M. phaseolina and F. falciforme as causal agents of desiccation and root rot of rosemary, with M. phaseolina being the most aggressive between the two. Conversely, Ceratobasidium sp. behavior was influenced by the interaction with the other pathogens and the plant. Notably, this is the first report of M. phaseolina and F. falciforme on rosemary plants worldwide. This disease, caused by multiple pathogens, highlighted the crucial role of the combination of traditional in vivo assays and the use of molecular methods to achieve an accurate diagnosis.
Full article
(This article belongs to the Special Issue Exploring Sustainable Strategies That Control Fungal Plant Diseases)
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Open AccessArticle
The Effect of Long-Term Organic Amendments on Soil Organic Carbon Accumulation via Regulating Microbial Traits in a Paddy Soil
by
Jing Ye, Zhaoming Chen, Jinchuan Ma, Junwei Ma, Ping Zou, Wanchun Sun, Feng Wang, Qiaogang Yu and Qiang Wang
Agriculture 2025, 15(21), 2308; https://doi.org/10.3390/agriculture15212308 - 6 Nov 2025
Abstract
Understanding how organic amendments affect microbial carbon use efficiency (CUE) and necromass C (MNC) is crucial for understanding soil organic C (SOC) formation and accrual in paddy fields, but the underlying mechanisms remain largely unclear. In this study, the microbial CUE, MNC, and
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Understanding how organic amendments affect microbial carbon use efficiency (CUE) and necromass C (MNC) is crucial for understanding soil organic C (SOC) formation and accrual in paddy fields, but the underlying mechanisms remain largely unclear. In this study, the microbial CUE, MNC, and microbial community composition, as well as SOC fractions and chemical composition, were measured under long-term organic amendments: rice straw (RS), green manure (GM), and pig manure (PM) in paddy soils. Four treatments were included: (1) chemical fertilizers (CF); (2) CF plus RS (CF + RS); (2) CF plus GM (CF + GM); and (4) CF plus PM (CF + PM). The CUE, MNC, and microbial community were determined by 18O-H2O incubation, amino sugars levels, and phospholipid fatty acids (PLFAs) content, respectively. Results showed that SOC, particulate organic C (POC), and mineral-associated organic C (MAOC) concentrations were significantly increased by organic amendments compared with chemical fertilization alone. The O-alkyl C decreased, but aromatic C increased with long-term organic amendments, suggesting enhanced SOC hydrophobicity. GM and PM inputs significantly enhanced microbial CUE, but straw return did not affect microbial CUE compared to CF. Microbial growth and C uptake increased by 25.2–42.4% and 19.8–30.0% under organic amendments relative with CF. Microbial respiration was increased by RS and GM amendments. Turnover time was more rapid in CF + RS and CF + GM than in CF and CF + PM. Compared to CF, organic amendments increased the MNC concentration due to the increase in microbial biomass. In addition, CF + RS and CF + GM enhanced the MNC contribution to SOC, but PM had no effect, suggesting that PM contributed more organic C from non-microbial sources. The SOC, POC, and MAOC increased with microbial CUE and MNC, indicating that microbial traits play a crucial role in SOC accrual. Higher microbial CUE and biomass explained the increased MNC accumulation under organic amendments. Our study highlights the crucial role of microbe-mediated processes in SOC accrual under long-term organic amendments in paddy soils. Our findings show that organic amendments are an effective management practice for accumulating more SOC in paddy soils.
Full article
(This article belongs to the Topic Recent Advances in Soil Health Management)
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Open AccessArticle
Distinguishing True from False Estrus in Hanwoo Cows Using Neck-Mounted IMU Sensors: Quantifying Behavioral Differences to Reduce False Positives
by
Seong-Jin Kim, Xue-Cheng Jin, Rajaraman Bharanidharan and Na-Yeon Kim
Agriculture 2025, 15(21), 2307; https://doi.org/10.3390/agriculture15212307 - 5 Nov 2025
Abstract
This study aimed to characterize behavioral differences between true estrus (TE) and false estrus (FE) in cows using neck-mounted six-axis inertial measurement unit sensors to reduce false positives in automated detection systems. A retrospective analysis was conducted on 1464 validated estrus alerts from
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This study aimed to characterize behavioral differences between true estrus (TE) and false estrus (FE) in cows using neck-mounted six-axis inertial measurement unit sensors to reduce false positives in automated detection systems. A retrospective analysis was conducted on 1464 validated estrus alerts from 414 Hanwoo cows across 13 commercial farms in South Korea. Alerts were classified as TE (625 alerts) or FE (839 alerts) based on comprehensive validation criteria, including standing heat observation, artificial insemination records, ovulation confirmation, and pregnancy outcomes. Mounting activity, rumination time, and lying time were analyzed. True estrus exhibited significantly higher (p < 0.0001) total number of mounts and maximum mounting duration compared to FE over the entire observation period. Notably, the maximum number of mounts per hour was higher (p < 0.0001) in FE before alert generation but higher (p < 0.0001) in TE afterward, with FE declining rapidly. Coefficients of variation for rumination and lying time were significantly higher (p < 0.0001) in TE than in FE, indicating greater behavioral disruption. These findings revealed that secondary behavioral signs exhibit distinct quantitative and temporal patterns between TE and FE, suggesting potential criteria that could be integrated into automated detection algorithms to reduce false-positive rates.
Full article
(This article belongs to the Special Issue Application of Intelligent Technologies in Farm Animal Disease, Feeding and Building Environmental Control)
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Open AccessArticle
Numerical Simulation Study on Hydraulic Characteristics Experiment and Opening Process of Hydraulically Driven Irrigation Control Valve
by
Xiaoyu Yang, Ming Hong, Gengchen Nian, Jiale Wang, Wenxin Yang and Shifeng Fan
Agriculture 2025, 15(21), 2306; https://doi.org/10.3390/agriculture15212306 - 5 Nov 2025
Abstract
To address the issue of existing automatic irrigation systems’ excessive reliance on electrical power and communication networks, a one-inlet, four-outlet Hydraulically Actuated Irrigation Control Valve (HAICV) was designed that operates based on water pressure variations. Its hydraulic characteristics and flow field features were
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To address the issue of existing automatic irrigation systems’ excessive reliance on electrical power and communication networks, a one-inlet, four-outlet Hydraulically Actuated Irrigation Control Valve (HAICV) was designed that operates based on water pressure variations. Its hydraulic characteristics and flow field features were investigated through experimental and numerical simulation methods. The results indicated that power–function relationships exist between pressure and flow rate, as well as between flow rate and head loss. The flow coefficient and resistance coefficient were found to range within [77.46, 81.06] and [15.94, 17.46], respectively. Dynamic simulations based on User-Defined Functions (UDF) revealed that during the opening process, the internal pressure of the valve spool remains high, with the primary pressure drop concentrated in the outlet region, and the low-pressure zone shrinks as the opening degree increases. A high-velocity band forms at the outlet, with jet flow and turbulence observed at small to medium openings, while the flow field stabilizes after full opening. The unique spool shape and non-straight flow passage structure of the HAICV result in relatively high energy loss, making it suitable for self-pressure irrigation systems. This study provides a theoretical foundation for evaluating its performance and broader applications.
Full article
(This article belongs to the Special Issue Innovative Machinery for Climate-Smart Agriculture)
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Open AccessReview
European Digital Innovation Hubs and the Agri-Food Sector: A Scoping Review of Current Knowledge and Sectoral Gaps
by
Irina Toma-Constantin, Ioan Sebastian Brumă, Oana Coca and Gavril Ștefan
Agriculture 2025, 15(21), 2305; https://doi.org/10.3390/agriculture15212305 - 5 Nov 2025
Abstract
The European Digital Innovation Hub (EDIH) network represents the EU’s first program for accelerating digital technology adoption by SMEs and public organizations. However, academic literature on EDIHs remains fragmented with notable sectoral imbalances—particularly underrepresentation of the agri-food sector. This scoping review systematically examines
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The European Digital Innovation Hub (EDIH) network represents the EU’s first program for accelerating digital technology adoption by SMEs and public organizations. However, academic literature on EDIHs remains fragmented with notable sectoral imbalances—particularly underrepresentation of the agri-food sector. This scoping review systematically examines existing EDIH publications to identify knowledge gaps and propose a research agenda. Searches were conducted in Web of Science, Google Scholar, and MDPI databases (Jan–May 2025), complemented by snowballing techniques, identifying 84 publications categorized by methodology, interaction levels, and sectoral relevance. Results reveal uneven distribution across four EDIH interaction levels, with ecosystem-level research dominating while internal dynamics and European network-level coordination remain understudied. Only 1.7% of EDIH beneficiaries receiving services by September 2024 originate from agriculture, confirming the sector’s marginal participation. Most contributions lack empirical validation, relying on theoretical frameworks or isolated case studies with limited geographic scope. As the first comprehensive review addressing EDIHs in agri-food contexts, this study provides timely insights at the end of the first EDIH implementation cycle and outlines priorities for future research to support equitable and evidence-based digital transformation.
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(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Open AccessArticle
Nominal Evaluation of Automatic Multi-Sections Control Potential in Comparison to a Simpler One- or Two-Sections Alternative with Predictive Spray Switching
by
Mogens Plessen
Agriculture 2025, 15(21), 2304; https://doi.org/10.3390/agriculture15212304 - 5 Nov 2025
Abstract
Automatic Section Control (ASC) promises to minimize spray overlap areas. The idea is to (i) switch off spray nozzles on areas that have already been sprayed, and (ii) to dynamically adjust nozzle flow rates along the boom bar that holds the spray nozzles
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Automatic Section Control (ASC) promises to minimize spray overlap areas. The idea is to (i) switch off spray nozzles on areas that have already been sprayed, and (ii) to dynamically adjust nozzle flow rates along the boom bar that holds the spray nozzles when velocities of boom sections vary during turn maneuvers. Spraying and the movement of modern wide boom bars are highly dynamic processes with many uncertainty factors. Therefore, an Automatic Multi-Sections Control method is compared to a proposed simpler one- or two-sections alternative that uses a predictive spray switching. The comparison is provided under nominal conditions. Combinations of two area coverage path planning and switching logics as well as three sections-setups are compared. These differ by controlling 48 sections, 2 sections or controlling all nozzles uniformly with the same control signal as one single section. Methods are evaluated on 10 diverse real-world field examples. An economic cost analysis is provided. A preferred method is suggested that (i) minimizes area coverage pathlength, (ii) is suitable for manual driving by following a pre-planned predictive spray switching logic for an area coverage path plan, and (iii) and in contrast to ASC can be implemented sensor-free and therefore at low cost. Surprisingly strong economic arguments are found to not recommend ASC for small farms.
Full article
(This article belongs to the Special Issue Innovative Machinery Systems and Technologies for Sustainable Agricultural and Biomass/Bioenergy Production)
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Open AccessReview
Antifungal Mechanisms of Plant Essential Oils: A Comprehensive Literature Review for Biofungicide Development
by
Michel Leiva-Mora, Diana Bustillos, Cristina Arteaga, Kattyta Hidalgo, Deysi Guevara-Freire, Orestes López-Hernández, Luis Rodrigo Saa, Paola S. Padilla and Alberto Bustillos
Agriculture 2025, 15(21), 2303; https://doi.org/10.3390/agriculture15212303 - 5 Nov 2025
Abstract
Plant pathogenic fungi pose a persistent global threat to food security, causing severe yield losses in staple crops and increasing dependence on chemical fungicides. However, the ecological and toxicological drawbacks of synthetic fungicides have intensified the search for safer, plant-derived alternatives. This review
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Plant pathogenic fungi pose a persistent global threat to food security, causing severe yield losses in staple crops and increasing dependence on chemical fungicides. However, the ecological and toxicological drawbacks of synthetic fungicides have intensified the search for safer, plant-derived alternatives. This review synthesizes current advances on the antifungal mechanisms of plant essential oils (EOs) and their prospects for biofungicide development. The literature reveals that the antifungal activity of EOs arises from their diverse phytochemical composition, principally terpenes, phenolics, and aldehydes that target multiple fungal cellular sites. These compounds disrupt membrane integrity through ergosterol depletion, inhibit chitin and β-glucan synthesis, interfere with mitochondrial energy metabolism, and induce oxidative stress, leading to lipid peroxidation and cell death. Morphological and transcriptomic evidence confirms that EOs alter hyphal growth, spore germination, and key gene expression pathways associated with fungal virulence. Furthermore, emerging nanotechnological and encapsulation strategies enhance EO stability, bioavailability, and field persistence, addressing major barriers to their large-scale agricultural application. The integration of EO-based biofungicides within sustainable and precision agriculture frameworks offers a promising route to reduce chemical inputs, mitigate resistance development, and promote ecological balance. This review underscores the need for interdisciplinary research linking phytochemistry, nanotechnology, and agronomy to translate EO-based antifungal mechanisms into next-generation, environmentally compatible crop protection systems.
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(This article belongs to the Special Issue Exploring Sustainable Strategies That Control Fungal Plant Diseases)
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Open AccessArticle
How Much Area of a Pear Orchard Can One Honey Bee Colony Pollinate?
by
Xinying Qu, Xinru Zhang, Rongshen Wang, Yuesen Wang, Qingfang Cheng, Yaxiong Xu, Lingjun Xin, Hanbing Lu and Xiao Chen
Agriculture 2025, 15(21), 2302; https://doi.org/10.3390/agriculture15212302 - 5 Nov 2025
Abstract
Pear trees, though self-pollinating, are self-incompatible and depend on insect pollination—primarily by honey bees. The optimal density of honey bee colonies per unit area in pear orchards remains uncertain, hindering scientific pollination management. This study in Zhao County, Hebei, compared honey bee (
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Pear trees, though self-pollinating, are self-incompatible and depend on insect pollination—primarily by honey bees. The optimal density of honey bee colonies per unit area in pear orchards remains uncertain, hindering scientific pollination management. This study in Zhao County, Hebei, compared honey bee (Apis mellifea ligustica), artificial, and natural pollination effects on pear yield and fruit size. Honey bee pollination achieved a higher, more stable fruit set (inflorescence fruit set rate was 71.52%), increased yield, and significantly improved fruit size—in transverse diameter (90.96 mm), longitudinal diameter (92.48 mm), and single-fruit weight (407.39 g)—compared with natural pollination. Although the fruit set rates and fruit quality of bee-pollinated pears were not significantly higher than those of artificially pollinated pears, the data still demonstrated the advantages of honey bee pollination. One bee colony (containing ~20,000 honey bees) was found to pollinate approximately 3846.5 m2 of a pear orchard. This provides data-supported guidance for the scientific allocation of pollinating honey bee colonies in future pear orchard pollination practices.
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(This article belongs to the Section Crop Production)
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Research on the Fallow Compensation Mechanism for Groundwater Overexploitation in the Tarim River Basin Under Bidirectional Collaboration
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Jiaxin Hao, Kangzheng Zhong, Liqiang Shen, Zengyi Cheng and Yuejian Wang
Agriculture 2025, 15(21), 2301; https://doi.org/10.3390/agriculture15212301 - 4 Nov 2025
Abstract
Exploring the differentiated fallow compensation (FC) standards in different regions is of great significance for formulating and improving the mechanism of fallow compensation and ensuring the sustainability of policies. The groundwater overexploitation area in the Tarim River Basin was selected as the research
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Exploring the differentiated fallow compensation (FC) standards in different regions is of great significance for formulating and improving the mechanism of fallow compensation and ensuring the sustainability of policies. The groundwater overexploitation area in the Tarim River Basin was selected as the research area; this study breaks through the perspective of a single subject and integrates the “opportunity cost” of the compensated subject and the “ecosystem service value” of the compensating subject into a unified analysis framework to obtain the fallow compensation standard, and the logistic model is used to analyze the influencing factors of farmers’ compensation method selection. The results are as follows: (1) The FC standards exhibit significant spatial heterogeneity. The range of FC standards in various counties is 5540.40 to 7770.53 CNY/hm2 (769.50 to 1079.24 USD/hm2), which is generally lower than the current standard. (2) There are three main compensation methods chosen by farmers, ranked in descending order of selection ratio: monetary compensation (72.06%) > physical compensation (19.37%) > technical compensation (8.57%). (3) The factors influencing the choice of compensation method are quite complex. The dependency ratio is the main influencing factor in the choice of monetary compensation (β = 0.738); the evaluation of economic conditions has a significant negative correlation with the choice of physical compensation (β = −0.562), and nonfarm household income is the main influencing factor for choosing technical compensation (β = 0.747). This study provides a new perspective for determining FC standards and aims to provide a theoretical basis for local governments to improve their fallow policies.
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(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Open AccessArticle
Enhancing Peanut Crop Quality Under Arsenic Stress Through Agronomic Amendments
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Soledad Martos, Mengchen Ye, Antonio Riofrío, Roser Tolrà and Eliana Bianucci
Agriculture 2025, 15(21), 2300; https://doi.org/10.3390/agriculture15212300 - 4 Nov 2025
Abstract
Arsenic (As) contamination poses a major challenge to sustainable crop production, particularly in legumes such as peanut (Arachis hypogaea L.), where it disrupts growth, nodulation, and redox homeostasis. This study evaluated the potential of circular-economy-based amendments derived from spent mushroom substrate (SMS)
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Arsenic (As) contamination poses a major challenge to sustainable crop production, particularly in legumes such as peanut (Arachis hypogaea L.), where it disrupts growth, nodulation, and redox homeostasis. This study evaluated the potential of circular-economy-based amendments derived from spent mushroom substrate (SMS) of Pleurotus djamor and plant growth-promoting bacteria (PGPB) to mitigate As stress in peanut plants. Six growth conditions were tested under 20 µM arsenate, including single and combined inoculations with P. djamor and Pseudomonas fluorescens, as well as a residue-only benchmark (E). Results showed that the unamended control (AP) exhibited the highest As accumulation, oxidative stress (H2O2, TBARs), and biomass loss, whereas SMS-based amendments attenuated these effects. Treatments HB (SMS + P. djamor + PGPB) and B (SMS + PGPB) combined low As translocation with enhanced antioxidant performance (SOD, CAT), maintaining growth and pigment stability. Amendment H (SMS + P. djamor) preferentially activated phytochelatin-related genes (PCS2, CAD1), while E minimized As uptake but lacked circular applicability. Overall, SMS-PGPB interactions promoted As retention in roots and strengthened ROS-scavenging defenses. These findings highlight SMS-based amendments as viable, sustainable strategies to enhance peanut quality and resilience under As stress, supporting their integration into circular agronomic systems.
Full article
(This article belongs to the Special Issue New Challenges and Trends in Agri-Environmental Management: Accomplishment of Sustainable Development Goals)
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Open AccessArticle
Characterization of Citrus Orchard Soil Improved by Green Manure Using the Discrete Element Method
by
Chen Ma, Liewang Cao, Jian Zhang, Gaozhen Liang, Chengsong Li, Chunlei Wang and Lihong Wang
Agriculture 2025, 15(21), 2299; https://doi.org/10.3390/agriculture15212299 - 4 Nov 2025
Abstract
Accurate determination of soil and contact parameters is crucial for tillage machinery design; however, the interactions among soil, tools, and roots in citrus orchards covered with green manure remain insufficiently defined. This study, therefore, combined physical experiments with DEM simulations to characterize these
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Accurate determination of soil and contact parameters is crucial for tillage machinery design; however, the interactions among soil, tools, and roots in citrus orchards covered with green manure remain insufficiently defined. This study, therefore, combined physical experiments with DEM simulations to characterize these interactions. Using significance analysis and response surface methodology (RSM), the effects of major factors on angle of repose (AoR) and initial slip angle (ISA) at varying soil depths were evaluated, enabling precise calibration of both external (soil–machinery) and internal (particle–particle) parameters. Subsequently, a GA-BP optimization model was constructed to enhance calibration accuracy, yielding optimal values for the soil-to-soil rolling friction coefficient (γ = 0.125–0.136), soil-to-65Mn static friction coefficient (μ′ = 0.431 − 0.540), and soil surface energy (JKR = 0.952 − 1.091 J·m−2). Shear tests using the bonding V2 model were conducted to calibrate the Bonding parameters of green manure stems and roots, while pull-out tests and simulations were used to validate the root–soil parameters. Direct shear tests confirmed the model’s reliability, with errors in internal friction angle and cohesion below 10%. These findings may contribute to improving DEM simulation accuracy for soil improvement under green manure coverage and support the optimization of soil tillage in citrus orchards.
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(This article belongs to the Section Agricultural Technology)
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The Innovation Lifecycle of AI-Driven Agriculture: Causal Dynamics in University–Industry–Research Collaboration
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Giani Gradinaru, Madalina Zurini, Bogdan Florin Matei and Ioana-Diana Petre
Agriculture 2025, 15(21), 2298; https://doi.org/10.3390/agriculture15212298 - 4 Nov 2025
Abstract
Considering the current prediction from the Food and Agriculture Organization, food production needs an increase of over 70 percent by 2050, and the agriculture sector requires a boost that is also obtained by integrating novelty technologies and methods of the artificial intelligence (AI)
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Considering the current prediction from the Food and Agriculture Organization, food production needs an increase of over 70 percent by 2050, and the agriculture sector requires a boost that is also obtained by integrating novelty technologies and methods of the artificial intelligence (AI) field. This study investigates the innovation lifecycle of AI-driven agriculture through a causal inference analysis within the university–industry–research (UIR) collaboration framework. Using time series data from 1985 to 2023, collected from Web of Science (academic articles), CORDIS (research projects), WIPO (patents), and Crunchbase (start-ups), the study explores the causal dynamics among four key innovation pillars. Results from Granger causality and impulse-response analyses reveal a sequential innovation pathway in which start-up activity precedes research projects, followed by academic publications and patent filings. These findings highlight the interdependence of innovation stages and the evolving role of UIR collaboration in driving agricultural transformation. The study provides quantitative insights for policymakers, investors, and researchers to strategically foster innovation ecosystems in AI-driven agriculture.
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(This article belongs to the Special Issue Computers and IT Solutions for Agriculture and Their Application)
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Open AccessArticle
Development of an Intelligent Inspection System Based on YOLOv7 for Real-Time Detection of Foreign Materials in Fresh-Cut Vegetables
by
Hary Kurniawan, Muhammad Akbar Andi Arief, Braja Manggala, Hangi Kim, Sangjun Lee, Moon S. Kim, Insuck Baek and Byoung-Kwan Cho
Agriculture 2025, 15(21), 2297; https://doi.org/10.3390/agriculture15212297 - 4 Nov 2025
Abstract
Ensuring food safety in fresh-cut vegetables is essential due to the frequent presence of foreign material (FM) that threatens consumer health and product quality. This study presents a real-time FM detection system developed using the YOLO object detection framework to accurately identify diverse
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Ensuring food safety in fresh-cut vegetables is essential due to the frequent presence of foreign material (FM) that threatens consumer health and product quality. This study presents a real-time FM detection system developed using the YOLO object detection framework to accurately identify diverse FM types in cabbage and green onions. A custom dataset of 14 FM categories—covering various shapes, sizes, and colors—was used to train six YOLO variants. Among them, YOLOv7x demonstrated the highest overall accuracy, effectively detecting challenging objects such as transparent plastic, small stones, and insects. The system, integrated with a conveyor-based inspection setup and a Python graphical interface, maintained stable and high detection accuracy confirming its robustness for real-time inspection. These results validate the developed system as an alternative intelligent quality-control layer for continuous, automated inspection in fresh-cut vegetable processing lines, and establish a solid foundation for future robotic-based removal systems aimed at fully automated food safety assurance.
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(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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Open AccessReview
Computer Vision for Site-Specific Weed Management in Precision Agriculture: A Review
by
Puranjit Singh, Biquan Zhao and Yeyin Shi
Agriculture 2025, 15(21), 2296; https://doi.org/10.3390/agriculture15212296 - 4 Nov 2025
Abstract
Weed management is always a challenge in crop production, exacerbated by the issue of herbicide resistance. Excessive herbicide application not only leads to the development of herbicide resistance weeds but also causes environmental problems. In precision agriculture, innovative weed management methods, especially advanced
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Weed management is always a challenge in crop production, exacerbated by the issue of herbicide resistance. Excessive herbicide application not only leads to the development of herbicide resistance weeds but also causes environmental problems. In precision agriculture, innovative weed management methods, especially advanced remote sensing and computer vision technologies for targeted herbicide applications, i.e., site-specific weed management (SSWM), have recently drawn a lot of attention. Challenges exist in accurately and reliably detecting diverse weed species under varying field conditions. Significant efforts have been made to advance computer vision technologies for weed detection. This comprehensive review provides an in-depth examination of various methodologies used in developing weed detection systems. These methodologies encompass a spectrum ranging from traditional image processing techniques to state-of-the-art machine and deep learning models. The review further discusses the potential of these methods for real-time applications, highlighting recent innovations, and identifying future research hotspots in SSWM. These advancements hold great promise for further enhancing and innovating weed management practices in precision agriculture.
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(This article belongs to the Special Issue Advanced Image Collection, Processing, and Analysis in Crop and Livestock Management)
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Open AccessArticle
Research on Wheat Spike Phenotype Extraction Based on YOLOv11 and Image Processing
by
Xuanxuan Li, Zhenghui Zhang, Jiayu Wang, Lining Liu and Pingzeng Liu
Agriculture 2025, 15(21), 2295; https://doi.org/10.3390/agriculture15212295 - 4 Nov 2025
Abstract
With the aim of tuning the complexity of traditional image processing parameters, the automated extraction of spike phenotypes based on the fusion of YOLOv11 and image processing was proposed, with winter wheat in Lingcheng District, Dezhou City, Shandong Province as the research object.
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With the aim of tuning the complexity of traditional image processing parameters, the automated extraction of spike phenotypes based on the fusion of YOLOv11 and image processing was proposed, with winter wheat in Lingcheng District, Dezhou City, Shandong Province as the research object. The keypoint detection of spikes was studied, and the integration of FocalModulation and TADDH modules improved the feature extraction ability, solved the problems of light interference and spike awn occlusion under the complex environment in the field, and the detection accuracy of the improved model reached 96.00%, and the mAP50 reached 98.70%, which were 6.6% and 2.8% higher than that of the original model, respectively. On this basis, this paper integrated morphological processing and a watershed algorithm, and innovatively constructed an integrated extraction method for spike length, spike width, and number of grains in the spike to realize the automated extraction of phenotypic parameters in the spike. The experimental results show that the extraction accuracy of spike length, spike width, and number of grains reached 98.08%, 96.21%, and 93.66%, respectively, which provides accurate data support for wheat yield prediction and genetic breeding research, and promotes the development of intelligent agricultural phenomic technology innovation.
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(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)
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Influence of Meteorological Factors and Sowing Dates on Growth and Yield Traits of Summer Maize in Northeastern Sichuan, China
by
Yun Long, Yun Yang, Chuan He and Xiaohong Liu
Agriculture 2025, 15(21), 2294; https://doi.org/10.3390/agriculture15212294 - 3 Nov 2025
Abstract
This study investigates meteorological factors’ effects on summer maize growth, agronomic traits and yield in northeastern Sichuan, China, under different sowing dates. A five-gradient sowing date experiment was conducted with three varieties from 2023 to 2024. The results showed delayed sowing prolonged total
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This study investigates meteorological factors’ effects on summer maize growth, agronomic traits and yield in northeastern Sichuan, China, under different sowing dates. A five-gradient sowing date experiment was conducted with three varieties from 2023 to 2024. The results showed delayed sowing prolonged total growth period mainly in the joint–tasseling and silking–maturity stages. Early sowing (5th May and 20th May) significantly improved key agronomic traits and increased grain yield, with Xianyu 1171 achieving the highest yield of 9.77 t ha−1 under early sowing. Meteorological factors had limited influence during vegetative growth but strongly affected reproductive growth. Among them, average temperature (AT) and growing degree days (GDDs) were critical throughout the growth cycle, though their effects varied by stage. These findings suggest that adjusting sowing dates to align key growth stages with favorable weather—particularly by avoiding high-temperature stress during flowering and ensuring sufficient warmth during grain filling—can enhance yield stability. This study provides a basis for constructing a climate-resilient cultivation system and promoting stable and high summer maize yields in the hilly areas of northeastern Sichuan.
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(This article belongs to the Special Issue Effect of Cultivation Practices on Crop Yield and Quality)
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Open AccessArticle
Estimate the Pre-Flowering Specific Leaf Area of Rice Based on Vegetation Indices and Texture Indices Derived from UAV Multispectral Imagery
by
Jingjing Huang, Sunan Wang, Yuexia Pei, Quan Yin, Zhi Ding, Jianjun Wang, Weiling Wang, Guisheng Zhou and Zhongyang Huo
Agriculture 2025, 15(21), 2293; https://doi.org/10.3390/agriculture15212293 - 3 Nov 2025
Abstract
Rice ranks among the most significant staple crops worldwide. Precise and dynamic monitoring of specific leaf area (SLA) provides essential information for evaluating rice growth and yield. While previous remote sensing studies on SLA estimation have primarily focused on crops such as wheat
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Rice ranks among the most significant staple crops worldwide. Precise and dynamic monitoring of specific leaf area (SLA) provides essential information for evaluating rice growth and yield. While previous remote sensing studies on SLA estimation have primarily focused on crops such as wheat and soybeans, studies on rice SLA remain limited. This study aims to evaluate the predictive potential of several machine learning algorithms for estimating rice SLA across different growth stages, planting densities, and nitrogen treatments at the pre-flowering stage. By utilizing UAV-based multispectral remote sensing data, a high-precision rice SLA monitoring model was developed. The feasibility of using vegetation indices (VIs), texture indices (TIs), and their combinations to predict rice SLA was explored. VIs and TIs were derived from UAV imagery, and the recursive feature elimination was conducted on these indices individually as well as their combined fusion (VIs + TIs). Four machine learning algorithms were employed to predict SLA values. The results indicate that random forest-based models utilizing VIs, TIs, and their fusion can all predict rice SLA effectively with high accuracy. Among these models, the RF model utilizing the combined variables (VIs + TIs) exhibited the highest performance, with R2 = 0.9049, RMSE = 0.0694 m2/g, RRMSE = 0.1042, and RPD = 3.2419. This study demonstrates that individual VIs can provide effective spectral information for SLA estimation, especially during the crucial pre-flowering growth phase of rice. The fusion of VIs and TIs enhances the model’s adaptability to complex field conditions by integrating both canopy biochemical and structural characteristics, thus improving model stability. This technology offers a swift and efficient approach for monitoring crop growth in the field, offering a theoretical foundation for subsequent crop yield estimation.
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(This article belongs to the Special Issue Plant Diagnosis and Monitoring for Agricultural Production)
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