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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
Effect of Different Light–Dark Cycles on the Growth and Nutritional Quality of Celery
Agriculture 2025, 15(21), 2228; https://doi.org/10.3390/agriculture15212228 (registering DOI) - 25 Oct 2025
Abstract
Celery (Apium graveolens L.) is a widely cultivated leafy vegetable of significant agronomic and nutritional importance. Owing to its high nutritional value, global demand for celery has steadily increased. However, under natural cultivation conditions, uncontrolled light exposure often prolongs the seedling stage
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Celery (Apium graveolens L.) is a widely cultivated leafy vegetable of significant agronomic and nutritional importance. Owing to its high nutritional value, global demand for celery has steadily increased. However, under natural cultivation conditions, uncontrolled light exposure often prolongs the seedling stage and impairs celery growth quality. Improving the nutritional quality of celery through artificial regulation of the light environment has therefore become an important research focus. This work aimed to elucidate the impact of varying light–dark cycles on the growth characteristics and nutritional attributes of celery. Six light–dark cycle treatments (4 h/2 h, 8 h/4 h, 16 h/8 h, 24 h/12 h, 32 h/16 h, and 40 h/20 h) were applied, using ‘Oster Ziyu Xiangqin’ as the plant material under a constant light intensity of 400 μmol·m−2·s−1. The results revealed that the 24 h/12 h light–dark treatment significantly enhanced plant height, total fresh weight, and root vigor and showed superior performance in photosynthetic and chlorophyll fluorescence parameters. The 32 h/16 h treatment significantly enhanced the accumulation of soluble sugars, proteins, total phenolic compounds, and flavonoids, as well as the activities of antioxidant enzymes, while reducing nitrate-nitrogen levels. In conclusion, the 24 h/12 h light–dark cycle was most conducive to the growth and photosynthetic performance of celery, whereas the 32 h/16 h treatment optimally enhanced its nutritional quality and antioxidant capacity.
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(This article belongs to the Section Crop Production)
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Open AccessArticle
Simple and Affordable Vision-Based Detection of Seedling Deficiencies to Relieve Labor Shortages in Small-Scale Cruciferous Nurseries
by
Po-Jui Su, Tse-Min Chen and Jung-Jeng Su
Agriculture 2025, 15(21), 2227; https://doi.org/10.3390/agriculture15212227 (registering DOI) - 25 Oct 2025
Abstract
Labor shortages in seedling nurseries, particularly in manual inspection and replanting, hinder operational efficiency despite advancements in automation. This study aims to develop a cost-effective, GPU-free machine vision system to automate the detection of deficient seedlings in plug trays, specifically for small-scale nursery
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Labor shortages in seedling nurseries, particularly in manual inspection and replanting, hinder operational efficiency despite advancements in automation. This study aims to develop a cost-effective, GPU-free machine vision system to automate the detection of deficient seedlings in plug trays, specifically for small-scale nursery operations. The proposed Deficiency Detection and Replanting Positioning (DDRP) machine integrates low-cost components including an Intel RealSense Depth Camera D435, Raspberry Pi 4B, stepper motors, and a programmable logic controller (PLC). It utilizes OpenCV’s Haar cascade algorithm, HSV color space conversion, and Otsu thresholding to enable real-time image processing without GPU acceleration. The proposed Deficiency Detection and Replanting Positioning (DDRP) machine integrates low-cost components including an Intel RealSense Depth Camera D435, Raspberry Pi 4B, stepper motors, and a programmable logic controller (PLC). It utilizes OpenCV’s Haar cascade algorithm, HSV color space conversion, and Otsu thresholding to enable real-time image processing without GPU acceleration. Under controlled laboratory conditions, the DDRP-Machine achieved high detection accuracy (96.0–98.7%) and precision rates (82.14–83.78%). Benchmarking against deep-learning models such as YOLOv5x and Mask R-CNN showed comparable performance, while requiring only one-third to one-fifth of the cost and avoiding complex infrastructure. The Batch Detection (BD) mode significantly reduced processing time compared to Continuous Detection (CD), enhancing real-time applicability. The DDRP-Machine demonstrates strong potential to improve seedling inspection efficiency and reduce labor dependency in nursery operations. Its modular design and minimal hardware requirements make it a practical and scalable solution for resource-limited environments. This study offers a viable pathway for small-scale farms to adopt intelligent automation without the financial burden of high-end AI systems. Future enhancements, adaptive lighting and self-learning capabilities, will further improve field robustness and including broaden its applicability across diverse nursery conditions.
Full article
(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)
Open AccessArticle
The Impact of Information Acquisition Channels and Risk Preferences on Farmers’ Chemical Pesticide Reduction Behavior
by
Muhao Jin, Lei Xu and Chao Chen
Agriculture 2025, 15(21), 2226; https://doi.org/10.3390/agriculture15212226 (registering DOI) - 25 Oct 2025
Abstract
Farmers, as the primary decision makers in agricultural production, are crucial to ensuring food safety and ecological security through chemical pesticide reduction, thereby contributing to agricultural sustainability. While existing research has acknowledged the influence of information factors on farmers’ pesticide reduction behavior, there
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Farmers, as the primary decision makers in agricultural production, are crucial to ensuring food safety and ecological security through chemical pesticide reduction, thereby contributing to agricultural sustainability. While existing research has acknowledged the influence of information factors on farmers’ pesticide reduction behavior, there remains a lack of comprehensive consideration of multiple information acquisition channels. The differential impacts and underlying mechanisms among these channels require further exploration. This study focuses on cash crops with higher chemical pesticide usage, utilizing field survey data from 573 peach farmers across seven province-level regions (including provinces, autonomous regions, and municipalities) in China in 2023 to assess the impact of information acquisition channels on farmers’ chemical pesticide reduction behavior. The results indicate the following: (1) Information acquisition channels significantly promote farmers’ implementation of chemical pesticide reduction behavior. (2) Information acquisition channels encourage the adoption of agricultural and biological control technologies, but have no significant impact on physical control technologies. (3) Information acquisition channels have a more substantial impact on older farmers and those in the eastern-central regions compared to other demographic groups. (4) Information acquisition channels alter farmers’ risk preferences, thereby facilitating chemical pesticide reduction behavior. Based on the above conclusions, government agencies should diversify information acquisition channels and enhance the dissemination of information related to chemical pesticide reduction. Furthermore, given the characteristics of different green control technologies, government agencies should select appropriate information acquisition channels to conduct targeted promotion and outreach to farmers.
Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Open AccessArticle
Yield Performance and Physicochemical Properties of Selected Honey Berry (Lonicera caerulea L. var. kamtschatica Sevast.), Under Central Polish Conditions
by
Ewa Szpadzik, Julia Trzcińska, Karolina Molska-Kawulok, Łukasz Seliga and Stanisław Pluta
Agriculture 2025, 15(21), 2225; https://doi.org/10.3390/agriculture15212225 (registering DOI) - 24 Oct 2025
Abstract
Until recently, the blue honeysuckle (Lonicera caerulea L. var. kamtschatica Sevast.) was considered a niche species, but Poland is now one of the largest producers of this fruit in the world. The purpose of this study was to assess the yield, quality of
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Until recently, the blue honeysuckle (Lonicera caerulea L. var. kamtschatica Sevast.) was considered a niche species, but Poland is now one of the largest producers of this fruit in the world. The purpose of this study was to assess the yield, quality of the fruits, and the health promotion value of the fruits of selected honey berry cultivars grown under the conditions of central Poland. Six cultivars (‘Morena’, ‘Vostorg’, ‘Honeybee’, ‘Wojtek’, ‘Boreal Beast’, and ‘Boreal Beauty’) were evaluated for yield and physical fruit characteristics: average fruit weight (g), dry matter content (%), fruit shape, fruit colour (CIE lab), firmness (N), soluble solid content (°Brix), pH, titratable acidity (% citric acid), as well as biologically active compounds including polyphenols, flavonoids, anthocyanins, vitamin C, and antioxidant activity (DPPH+). The studies also determined the degree of correlation between different variables using Pearson’s linear correlation coefficients. The highest yields were obtained for the ‘Wojtek’ and ‘Boreal Beauty’ cultivars, while in terms of health-promoting properties, the ‘Morena’ cultivar stood out, characterised by the darkest fruit colour, the highest content of polyphenols, anthocyanins, vitamin C, and the highest antioxidant activity. The correlation analysis showed relationships between the vitamin C content, antioxidant activity, and fruit colour and the accumulation of bioactive compounds. The differences observed among the cultivars tested indicated their different potential for use in the fresh consumption, food processing, and pharmaceutical industries.
Full article
(This article belongs to the Special Issue Adapting Horticultural Plant Cultivation Technology and Storage to Changing Conditions)
Open AccessArticle
Modeling the Thermal Conditions in a Piglet Area with Infrared Heating
by
Aleksey Kuzmichev, Aleksei Khimenko, Dmitry Tikhomirov and Dmitry Budnikov
Agriculture 2025, 15(21), 2224; https://doi.org/10.3390/agriculture15212224 (registering DOI) - 24 Oct 2025
Abstract
A pressing task is to develop a mathematical model and calculation method that most accurately describes the radiant component of heat exchange between an animal and its environment. This will help determine the optimal design parameters and temperature conditions for infrared (IR) heaters
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A pressing task is to develop a mathematical model and calculation method that most accurately describes the radiant component of heat exchange between an animal and its environment. This will help determine the optimal design parameters and temperature conditions for infrared (IR) heaters in livestock premises. The mathematical models considered describe the animal's heat exchange with the environment during IR heating. However, they do not take into account the hidden surface temperature of the premises’ enclosing structures and their emissivity factor, or the relationship between animal thermal comfort and the IR heater surface temperature. The proposed radiant heat exchange mathematical model is applicable to diffusely absorbing and radiating isothermic surface system typical of pigsties. It takes into account the emissivity factors of all of the enclosing structures’ surfaces and determines the effective (apparent) premises temperature value tef, corresponding to the thermal comfort conditions. The IR heater surface temperature’s dependence on the emissivity of the pigsty’s enclosing structures (walls, ceiling, and floor) is given, calculated using three methods. As the emissivity of the premises’ enclosing structures decreases, the difference between the results obtained via methods 1, 2, and 3 increases significantly and reaches 50…60% at ε = 0.8. The IR heater radiating surface temperature range is defined in order to create suitable thermal conditions on premises designed for keeping 1- to 4-week-old newborn piglets depending on the enclosing structure temperature and emissivity, taking into account hidden heat exchange surfaces.
Full article
(This article belongs to the Section Farm Animal Production)
Open AccessArticle
A Simple Method Using High Matric Suction Calibration Points to Optimize Soil–Water Characteristic Curves Derived from the Centrifuge Method
by
Bo Li, Hongyi Pan, Yue Tian and Xiaoyan Jiao
Agriculture 2025, 15(21), 2223; https://doi.org/10.3390/agriculture15212223 (registering DOI) - 24 Oct 2025
Abstract
The centrifuge method serves as an efficient and rapid approach for determining the soil–water characteristic curve (SWCC). However, soil shrinkage during centrifugation remains overlooked and prior modified methods may suffer from complex operations, high costs, time consumption, and limited applicability. To address these
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The centrifuge method serves as an efficient and rapid approach for determining the soil–water characteristic curve (SWCC). However, soil shrinkage during centrifugation remains overlooked and prior modified methods may suffer from complex operations, high costs, time consumption, and limited applicability. To address these issues, this study introduces a simple correction scheme (G3) for determining drying SWCCs using the centrifuge method based on high matric suction calibration points. The performance of the proposed G3 method was systematically evaluated against a modified method considering soil shrinkage (G1) and the conventional uncorrected method (G2). Results revealed significant soil linear shrinkage post-centrifugation, accompanied by a reduction in total soil porosity and an increase in soil bulk density. SWCCs from all methods exhibited strong consistency at low matric suction ranges but diverged markedly at high matric suction segments. High matric suction data dominated the SWCC fitting. The G1 method achieved the highest fitting accuracy, while the G3 method performed the worst yet maintained acceptable reliability. The G2 method yielded optimal SWCC for simulating saturated soil water content, field capacity, and permanent wilting point. Conversely, Hydrus-1D simulations revealed superior performance of the G3 method in simulating farmland soil moisture dynamics during the dehumidification process. Values of R2 across methods followed G3 > G1 > G2, while mean absolute error, mean absolute percentage error, and root mean square error exhibited the opposite trend. These findings highlight that the previous modified approaches are more suitable for low and medium matric suction ranges. The proposed correction method enhances drying SWCC performance across the full matric suction range, offering a practical refinement for the centrifuge method. This advancement could enhance the reliability in soil hydraulic characterization and contribute to a better understanding of the hydraulic–mechanical–chemical behavior in soils.
Full article
(This article belongs to the Section Agricultural Soils)
Open AccessArticle
Fusion of LSTM-Based Vertical-Gradient Prediction and 3D Kriging for Greenhouse Temperature Field Reconstruction
by
Zhimin Zhang, Xifeng Liu, Xiaona Zhao, Zihao Gao, Yaoyu Li, Xiongwei He, Xinping Fan, Lingzhi Li and Wuping Zhang
Agriculture 2025, 15(21), 2222; https://doi.org/10.3390/agriculture15212222 (registering DOI) - 24 Oct 2025
Abstract
This paper presents a proposed LSTM-based vertical-gradient prediction combined with three-dimensional kriging that enables reconstruction of greenhouse 3D temperature fields under sparse-sensor deployments while capturing temporal dynamics and spatial correlations. In northern China, winter solar greenhouses rely on standardized structures and passive climate-control
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This paper presents a proposed LSTM-based vertical-gradient prediction combined with three-dimensional kriging that enables reconstruction of greenhouse 3D temperature fields under sparse-sensor deployments while capturing temporal dynamics and spatial correlations. In northern China, winter solar greenhouses rely on standardized structures and passive climate-control strategies, which often lead to non-uniform thermal conditions that complicate precise regulation. To address this challenge, 24 sensors were deployed, and their time-series data were used to train a long short-term memory (LSTM) model for vertical temperature-gradient prediction. The predicted values at multiple heights were fused with in situ observations, and three-dimensional ordinary kriging (3D-OK) was applied to reconstruct the spatiotemporal temperature field. Compared with conventional 2D monitoring and computationally intensive CFD, the proposed approach balances accuracy, efficiency, and deployability. LSTM–Kriging validation showed Trend + Residual Kriging had the lowest RMSE (0.45558 °C) and bias (−0.03148 °C) (p < 0.01), outperforming Trend-only RMSE (3.59 °C) and Kriging-only RMSE (0.48 °C); the 3D model effectively distinguished sunny and rainy dynamics. This cost-effective framework balances accuracy, efficiency, and deployability, overcoming limitations of 2D monitoring and CFD. It provides critical support for adaptive greenhouse climate regulation and digital-twin development, directly advancing precision management and yield stability in CEA.
Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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Open AccessArticle
Integrated Metabolomic and Transcriptomic Analysis of Antimony (Sb) Stress Response in Common Bermudagrass (Cynodon dactylon [L.] Pers.)
by
Qian Liu, Maryam Noor, Yuanhang Xiang, Yao Chen, Shang Gao, Fangming Wu, Xiaoqin Li, Xutong Hu, Xuebing Yan, Bing Wen and Jibiao Fan
Agriculture 2025, 15(21), 2221; https://doi.org/10.3390/agriculture15212221 (registering DOI) - 24 Oct 2025
Abstract
Antimony (Sb) is a toxic metalloid and has become an increasingly prevalent contaminant in ecosystems. Previous studies have reported that Sb has severe toxic effects on plant growth. However, the molecular mechanisms of the response to Sb stress in plants still remain unclear.
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Antimony (Sb) is a toxic metalloid and has become an increasingly prevalent contaminant in ecosystems. Previous studies have reported that Sb has severe toxic effects on plant growth. However, the molecular mechanisms of the response to Sb stress in plants still remain unclear. In the present study, common bermudagrass (Cynodon dactylon [L.] Pers.), ‘Yangjiang’ cultivar, was treated with 200 mg/mL of antimony potassium tartrate solution. Integrated metabolomic and transcriptomic analysis was conducted to investigate the mechanisms of the Sb stress response of bermudagrass. The results showed that, after Sb stress treatment, soluble protein content, malondialdehyde (MDA) content, and catalase (CAT) activity increased by 180.56%, 280%, and 112.61%, respectively, compared to the control. Meanwhile, transcriptomic and metabolomic analyses identified numerous differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) that were involved in the Sb stress response of bermudagrass, and many pathways, such as the carbon metabolism, photosynthesis and alanine, aspartate, and glutamate metabolism pathways, were also identified to be related to the Sb stress response of the bermudagrass plant by KEGG and GO enrichment. Overall, the present study revealed that photosynthesis and amino acid metabolism pathways play important roles in the Sb stress response of bermudagrass.
Full article
(This article belongs to the Special Issue Molecular Mechanisms and Breeding Techniques of Forage Crops)
Open AccessArticle
BudCAM: An Edge Computing Camera System for Bud Detection in Muscadine Grapevines
by
Chi-En Chiang, Wei-Zhen Liang, Jingqiu Chen, Xin Qiao, Violeta Tsolova, Zonglin Yang and Joseph Oboamah
Agriculture 2025, 15(21), 2220; https://doi.org/10.3390/agriculture15212220 (registering DOI) - 24 Oct 2025
Abstract
Bud break is a critical phenological stage in muscadine grapevines, marking the start of the growing season and the increasing need for irrigation management. Real-time bud detection enables irrigation to match muscadine grape phenology, conserving water and enhancing performance. This study presents BudCAM
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Bud break is a critical phenological stage in muscadine grapevines, marking the start of the growing season and the increasing need for irrigation management. Real-time bud detection enables irrigation to match muscadine grape phenology, conserving water and enhancing performance. This study presents BudCAM , a low-cost, solar-powered, edge computing camera system based on Raspberry Pi 5 and integrated with a LoRa radio board , developed for real-time bud detection. Nine BudCAMs were deployed at Florida A&M University Center for Viticulture and Small Fruit Research from mid-February to mid-March, 2024, monitoring three wine cultivars (A27, noble, and Floriana)with three replicates each. Muscadine grape canopy images were captured every 20 min between 7:00 and 19:00, generating 2656 high-resolution (4656 × 3456 pixels) bud break images as a database for bud detection algorithm development. The dataset was divided into 70% training, 15% validation, and 15% test. YOLOv11 models were trained using two primary strategies: a direct single-stage detector on tiled raw images and a refined two-stage pipeline that first identifies the grapevine cordon. Extensive evaluation of multiple model configurations identified the top performers for both the single-stage (mAP@0.5 = 86.0%) and two-stage (mAP@0.5 = 85.0%) approaches. Further analysis revealed that preserving image scale via tiling was superior to alternative inference strategies like resizing or slicing. Field evaluations conducted during the 2025 growing season demonstrated the system’s effectiveness, with the two-stage model exhibiting superior robustness against environmental interference, particularly lens fogging. A time-series filter smooths the raw daily counts to reveal clear phenological trends for visualization. In its final deployment, the autonomous BudCAM system captures an image, performs on-device inference, and transmits the bud count in under three minutes, demonstrating a complete, field-ready solution for precision vineyard management.
Full article
(This article belongs to the Special Issue Advanced Image Collection, Processing, and Analysis in Crop and Livestock Management)
Open AccessArticle
Preliminary Study of the Genetic Response of Grapevine Buds to a Preventive Natural Polysaccharide-Based Biogel Under Simulated Late Frost Conditions
by
Alessandra Zombardo, Simone Garavelloni, Chiara Biselli, Agostino Fricano, Paolo Bagnaresi, Marco Ammoniaci and Mauro Eugenio Maria D’Arcangelo
Agriculture 2025, 15(21), 2219; https://doi.org/10.3390/agriculture15212219 (registering DOI) - 24 Oct 2025
Abstract
Late spring frosts represent a major threat to grapevine (Vitis vinifera L.), a risk increasingly exacerbated by climate change-driven shifts in phenology. To explore sustainable strategies for frost mitigation, this study investigated the effect of a natural polysaccharide-based biogel, derived from carob
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Late spring frosts represent a major threat to grapevine (Vitis vinifera L.), a risk increasingly exacerbated by climate change-driven shifts in phenology. To explore sustainable strategies for frost mitigation, this study investigated the effect of a natural polysaccharide-based biogel, derived from carob (Ceratonia siliqua L.), on the molecular response of grapevine buds exposed to severe cold stress. To this aim, a preliminary RNA-Seq analysis was carried out to compare the transcriptomes of biogel-treated frozen buds (BIOGEL), untreated frozen buds (NTF), and unstressed controls (TNT). The transcriptomic analysis revealed extensive reprogramming of gene expression under freezing stress, highlighting the involvement of pathways related to membrane stabilization, osmotic adjustment, and metabolic regulation. Interestingly, the biogel treatment appeared to attenuate the modulation of several cold-responsive genes, particularly those associated with membrane functionality. Based on these preliminary transcriptomic data, twelve candidate genes, representative of the functional classes affected by biogel treatment, were selected for qRT-PCR validation. The expression patterns confirmed the RNA-Seq trends, further suggesting that biogel application might mitigate the typical transcriptional activation induced by frost, while supporting genes involved in cellular protection and integrity maintenance. The overall analyses suggest that the biogel may act through a dual mechanism: (i) providing a physical barrier that reduces cold-induced cellular damage and stress perception, and (ii) promoting a selective adjustment of gene expression that restrains excessive defense activation while enhancing membrane stability. Although further field validation is required, this natural and biodegradable formulation represents a promising and sustainable tool for mitigating late frost injuries in viticulture.
Full article
(This article belongs to the Special Issue Biostimulants for Crop Growth and Abiotic Stress Mitigation)
Open AccessArticle
Erosion Assessment by a Fast and Low-Cost Procedure in a Vineyard Under Different Soil Management
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Maria Costanza Andrenelli, Sergio Pellegrini, Gianni Fila, Claudia Becagli, Giuseppe Valboa and Nadia Vignozzi
Agriculture 2025, 15(21), 2218; https://doi.org/10.3390/agriculture15212218 (registering DOI) - 24 Oct 2025
Abstract
Soil erosion in vineyards is a major environmental problem, particularly in hilly Mediterranean environments. Our study evaluated the effectiveness of permanent grass cover (PG), continuous tillage (CT), and green manure (GM) in reducing soil erosion. Furthermore, a new software tool (ISUMmate_1.1.xlsm), based on
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Soil erosion in vineyards is a major environmental problem, particularly in hilly Mediterranean environments. Our study evaluated the effectiveness of permanent grass cover (PG), continuous tillage (CT), and green manure (GM) in reducing soil erosion. Furthermore, a new software tool (ISUMmate_1.1.xlsm), based on the improved stock unearthing method (ISUM), was developed and tested to quantify soil mobilization between successive transects along vineyard inter-row. The field trial was carried out over a three-year period in a Tuscany (Italy) vineyard. The results showed that PG significantly improved aggregate stability and soil organic carbon (SOC) content, while exhibiting the lowest erosion rates. In contrast, GM showed the highest erosion rates as a result of soil disturbance associated with cultivation operations and the occurrence of unexpected intense rainfalls. ISUMmate_1.1 has proven to be a reliable tool for monitoring both water- and tillage-induced erosion, providing valuable information for sustainable vineyard management.
Full article
(This article belongs to the Special Issue Effects of Different Managements on Soil Quality and Crop Production)
Open AccessArticle
Biomarker-Based Evaluation of a Zearalenone-Degrading Enzyme in Broilers and Piglets Across Multiple Biological Matrices
by
Barbara Streit, Karin Schöndorfer, Manuela Killinger, Andreas Höbartner-Gussl, Veronika Nagl and Barbara Doupovec
Agriculture 2025, 15(21), 2217; https://doi.org/10.3390/agriculture15212217 (registering DOI) - 24 Oct 2025
Abstract
Zearalenone (ZEN) is an estrogenic mycotoxin that impairs animal health and productivity, necessitating effective mitigation strategies in livestock production. This study evaluated the efficacy of the ZEN lactonase ZenA, an enzyme that converts ZEN to non-estrogenic hydrolyzed ZEN (HZEN) and decarboxylated HZEN (DHZEN).
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Zearalenone (ZEN) is an estrogenic mycotoxin that impairs animal health and productivity, necessitating effective mitigation strategies in livestock production. This study evaluated the efficacy of the ZEN lactonase ZenA, an enzyme that converts ZEN to non-estrogenic hydrolyzed ZEN (HZEN) and decarboxylated HZEN (DHZEN). Broilers were fed either uncontaminated feed, feed contaminated with 1500 µg ZEN/kg, or ZEN-contaminated feed supplemented with 20 U ZenA/kg for 35 days. Piglets received 200 µg ZEN/kg feed, with or without 10 U ZenA/kg, for 43 days. ZEN biomarkers (ZEN, α-zearalenol, β-zearalenol, HZEN, and DHZEN) were quantified in plasma, urine, feces/excreta, and gastrointestinal contents using liquid chromatography–tandem mass spectrometry. While performance parameters remained unaffected, ZenA supplementation significantly reduced ZEN concentrations (by 19.6–66.2%) in all matrices and at all time points in both species. In addition, significant formation of HZEN was observed in gastrointestinal samples. Thus, in the present study, ZenA efficiently degraded ZEN in both broilers and piglets. Biomarker analysis in multiple matrices provided complementary insights: gastrointestinal samples confirmed the enzyme’s mode of action, while plasma and urine data showed a marked reduction in systemic ZEN exposure. Finally, the results reinforce that performance parameters are insufficient for assessing the efficacy of mycotoxin-detoxifying feed additives and support biomarker-based evaluation approaches.
Full article
(This article belongs to the Special Issue Mycotoxin Contamination in Farm Animals: Innovative Reduction Strategies)
Open AccessArticle
Exploring the Impact of Wheat Prices and Annual Income on Pig Carcass Prices in European Countries: A Spatial Panel Regression Analysis
by
Mihai Dinu, Silviu Ionuț Beia, Simona Roxana Pătărlăgeanu, Alina Florentina Gheorghe, Irina Denisa Munteanu and Mihail Dumitru Sacală
Agriculture 2025, 15(21), 2216; https://doi.org/10.3390/agriculture15212216 (registering DOI) - 24 Oct 2025
Abstract
In this study, we investigated the spatial and temporal dynamics of pork carcass prices across European Union Member States, focusing on the influence of wheat prices and population income levels between 2014 and 2023. Our analysis revealed that both input costs (reflected by
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In this study, we investigated the spatial and temporal dynamics of pork carcass prices across European Union Member States, focusing on the influence of wheat prices and population income levels between 2014 and 2023. Our analysis revealed that both input costs (reflected by wheat price fluctuations) and income-driven demand factors exert significant and spatially correlated effects on pork carcass prices. The results demonstrate the existence of spatial interdependencies among neighboring countries, indicating that price changes in one region may propagate through the broader European market. By integrating spatial econometric techniques within a panel data framework, this research provides empirical evidence of the interconnected nature of EU agricultural markets, advancing the existing literature by demonstrating how input markets and consumer income dynamics jointly shape price behavior within an integrated regional economy. Our findings contribute to a deeper understanding of price transmission mechanisms in the livestock sector and offer valuable insights for policymakers seeking to enhance market efficiency and resilience within the Common Agricultural Policy context.
Full article
(This article belongs to the Special Issue Sustainability and Energy Economics in Agriculture—2nd Edition)
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Open AccessReview
Precision Feeding Systems in Animal Husbandry: Guiding Rabbit Farming from Concept to Implementation
by
Wei Jiang, Guohua Li, Jitong Xu, Yinghe Qin, Liangju Wang and Hongying Wang
Agriculture 2025, 15(21), 2215; https://doi.org/10.3390/agriculture15212215 (registering DOI) - 24 Oct 2025
Abstract
Precision Feeding Systems (PFS) demonstrate transformative potential in advancing sustainable and efficient production within modern animal husbandry. However, existing research lacks a synthesis of PFS applications in livestock farming and offers little targeted guidance for China’s rapidly growing rabbit industry. The objective of
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Precision Feeding Systems (PFS) demonstrate transformative potential in advancing sustainable and efficient production within modern animal husbandry. However, existing research lacks a synthesis of PFS applications in livestock farming and offers little targeted guidance for China’s rapidly growing rabbit industry. The objective of this review is to bridge this gap by synthesizing current knowledge on PFS technologies—including sensor networks, artificial intelligence (AI), automated controls, and data analytics—and providing a structured framework for their implementation in rabbit production. This study selects and analyzes 112 core references, establishing a foundational database for comprehensive evaluation. The key contributions of this work are threefold: first, it outlines the core components and operational mechanisms of PFS; second, it identifies major challenges such as sensor reliability in dynamic environments, data security risks, limited explainability of AI models, and interoperability barriers; and third, it proposes a customized strategy for PFS adoption in rabbit farming, emphasizing phased implementation, cross-system integration, and iterative optimization. The primary outcomes and advantages of adopting such a system include significant improvements in feed efficiency, resource utilization, animal welfare, and waste reduction—critical factors given rabbits’ sensitive digestive systems and precise nutritional needs. Furthermore, this review outlines a future research agenda aimed at developing resilient sensors, explainable AI frameworks, and multi-objective optimization engines to enhance the commercial scalability and sustainability of PFS in rabbit husbandry and beyond.
Full article
(This article belongs to the Section Farm Animal Production)
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The Use of Selected Essential Oils as an Alternative Method of Controlling Pathogenic Fungi, Weeds and Insects on Oilseed Rape (Brassica napus L.)
by
Jakub Danielewicz, Joanna Horoszkiewicz, Ewa Jajor, Marek Korbas, Joanna Zamojska, Daria Dworzańska, Paweł Węgorek, Monika Grzanka, Łukasz Sobiech, Robert Idziak, Jan Bocianowski, Kinga Stuper-Szablewska and Maciej Buśko
Agriculture 2025, 15(21), 2214; https://doi.org/10.3390/agriculture15212214 (registering DOI) - 24 Oct 2025
Abstract
The increasing demand for sustainable agricultural practices has led researchers to explore alternative methods for controlling plant diseases and pests. Among these alternatives, essential oils (EOs) derived from various plant species have gained significant attention due to their broad-spectrum antimicrobial properties, which can
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The increasing demand for sustainable agricultural practices has led researchers to explore alternative methods for controlling plant diseases and pests. Among these alternatives, essential oils (EOs) derived from various plant species have gained significant attention due to their broad-spectrum antimicrobial properties, which can be utilized in plant protection. Essential oils are volatile compounds that possess strong aromatic characteristics and are found in many medicinal and aromatic plants. They are known for their antifungal, antibacterial, and insecticidal activities, making them viable candidates for eco-friendly pest and disease management strategies. In this research, six essential oils—pine, patchouli, geranium, spruce, coriander, and eucalyptus oil—have been tested in vitro for controlling mycelium growth of Sclerotinia sclerotiorum, Botrytis cinerea, Alternaria brassicicola, and Cylindrosporium concentricum. The study also covers experiments in controlling pollen beetle and cabbage seed weevil (laboratory trials). In greenhouse conditions, the phytotoxicity of EOs to oilseed rape (Brassica napus L.) and the effect of these substances on the control of cornflower (Centaurea cyanus) were also tested. The results obtained indicate a large diversity of different essential oils in terms of their action on pathogens, pests, weeds, and winter rapeseed. Differences in their effectiveness were also found, depending on the applied dose.
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(This article belongs to the Special Issue Strategies to Improve the Security and Nutritional Quality of Crop Species—2nd Edition)
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Open AccessArticle
Automated Crop Measurements with UAVs: Evaluation of an AI-Driven Platform for Counting and Biometric Analysis
by
João Victor da Silva Martins, Marcelo Rodrigues Barbosa Júnior, Lucas de Azevedo Sales, Regimar Garcia dos Santos, Wellington Souto Ribeiro and Luan Pereira de Oliveira
Agriculture 2025, 15(21), 2213; https://doi.org/10.3390/agriculture15212213 (registering DOI) - 24 Oct 2025
Abstract
Unmanned aerial vehicles (UAVs) are transforming agriculture through enhanced data acquisition, improved monitoring efficiency, and support for data-driven decision-making. Complementing this, AI-driven platforms provide intuitive and reliable tools for advanced UAV analytics. However, their integration remains underexplored, particularly in specialty crops. Therefore, in
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Unmanned aerial vehicles (UAVs) are transforming agriculture through enhanced data acquisition, improved monitoring efficiency, and support for data-driven decision-making. Complementing this, AI-driven platforms provide intuitive and reliable tools for advanced UAV analytics. However, their integration remains underexplored, particularly in specialty crops. Therefore, in this study, we evaluated the performance of an AI-driven web platform (Solvi) for automated plant counting and biometric trait estimation in two contrasting systems: pecan, a perennial nut crop, and onion, an annual vegetable. Ground-truth measurements included pecan tree number, tree height, and canopy area, as well as onion bulb number and diameter, the latter used for market class classification. Counting performance was assessed using precision, recall, and F1 score, while trait estimation was evaluated with linear regression analysis. UAV-based counts showed strong agreement with ground-truth data, achieving precision, recall, and F1 scores above 97% for both crops. For pecans, UAV-derived estimates of tree height (R2 = 0.98, error = 11.48%) and canopy area (R2 = 0.99, error = 23.16%) demonstrated high accuracy, while errors were larger in young trees compared with mature trees. For onions, UAV-derived bulb diameters achieved an R2 of 0.78 with a 6.29% error, and market class classification (medium, jumbo, colossal) was predicted with <10% error. These findings demonstrate that UAV imagery integrated with a user-friendly AI platform can deliver accurate, scalable solutions for biometric monitoring in both perennial and annual specialty crops, supporting applications in harvest planning, orchard management, and market supply forecasting.
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(This article belongs to the Special Issue Image Analysis Techniques in Quality Assessment of Agricultural Products)
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Open AccessArticle
Research on Strawberry Visual Recognition and 3D Localization Based on Lightweight RAFS-YOLO and RGB-D Camera
by
Kaixuan Li, Xinyuan Wei, Qiang Wang and Wuping Zhang
Agriculture 2025, 15(21), 2212; https://doi.org/10.3390/agriculture15212212 (registering DOI) - 24 Oct 2025
Abstract
Improving the accuracy and real-time performance of strawberry recognition and localization algorithms remains a major challenge in intelligent harvesting. To address this, this study presents an integrated approach for strawberry maturity detection and 3D localization that combines a lightweight deep learning model with
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Improving the accuracy and real-time performance of strawberry recognition and localization algorithms remains a major challenge in intelligent harvesting. To address this, this study presents an integrated approach for strawberry maturity detection and 3D localization that combines a lightweight deep learning model with an RGB-D camera. Built upon the YOLOv11 framework, an enhanced RAFS-YOLO model is developed, incorporating three core modules to strengthen multi-scale feature fusion and spatial modeling capabilities. Specifically, the CRA module enhances spatial relationship perception through cross-layer attention, the HSFPN module performs hierarchical semantic filtering to suppress redundant features, and the DySample module dynamically optimizes the upsampling process to improve computational efficiency. By integrating the trained model with RGB-D depth data, the method achieves precise 3D localization of strawberries through coordinate mapping based on detection box centers. Experimental results indicate that RAFS-YOLO surpasses YOLOv11n, improving precision, recall, and mAP@50 by 4.2%, 3.8%, and 2.0%, respectively, while reducing parameters by 36.8% and computational cost by 23.8%. The 3D localization attains millimeter-level precision, with average RMSE values ranging from 0.21 to 0.31 cm across all axes. Overall, the proposed approach achieves a balance between detection accuracy, model efficiency, and localization precision, providing a reliable perception framework for intelligent strawberry-picking robots.
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(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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Open AccessArticle
An Analysis of the Circular Economy Performance of the Romanian Agri-Food System
by
Steliana Rodino, Rodica Chetroiu and Vili Dragomir
Agriculture 2025, 15(21), 2211; https://doi.org/10.3390/agriculture15212211 (registering DOI) - 24 Oct 2025
Abstract
The circular economy represents one of the key pillars of European Union strategies aiming to decouple growth from resource utilization. The circular economy has emerged as a key flagship for European policies related to sustainable agri-food systems, potentially decreasing pressures on resources and
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The circular economy represents one of the key pillars of European Union strategies aiming to decouple growth from resource utilization. The circular economy has emerged as a key flagship for European policies related to sustainable agri-food systems, potentially decreasing pressures on resources and the environment while ensuring economic competitiveness. In this context, this study proposed to measure the circularity performance of the Romanian agri-food system compared with average European Union performance, based on Eurostat data indicators for the years 2014 and 2022 and a normalized composite index composed of the economic, environmental, and social pillars. Indicator scores were categorized by higher-is-better or lower-is-better, constrained in the interval [0, 5] and then aggregated with equal weights. The composite index for Romania exhibited values ranging from 3.14 in 2014 to 3.45 in 2022, showing moderate progress. The results indicate a fragmentary transition where areas of strength for Romania were material resilience and trade. At the same time, areas of weakness were the economic integration of circularity practices. The study’s main limitations arise from the limited agri-food specificity of available indicators and the sensitivity of results to weighting choices. Overall, the findings highlight the need for stronger institutional mechanisms and targeted investments to accelerate Romania’s transition toward a circular agri-food economy.
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(This article belongs to the Special Issue Strategies for Resilient and Sustainable Agri-Food Systems—2nd Edition)
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Open AccessArticle
Microclimate Condition Influence on the Physicochemical Properties and Antioxidant Activity of Pomegranate (Punica granatum L.): A Case Study of the East Adriatic Coast
by
Mira Radunić, Maja Jukić Špika, Jelena Gadže, Smiljana Goreta Ban, Juan Carlos Díaz-Pérez and Dan MacLean
Agriculture 2025, 15(21), 2210; https://doi.org/10.3390/agriculture15212210 - 24 Oct 2025
Abstract
The pomegranate cultivar Barski slatki, the most widely cultivated on the Eastern Adriatic coast, was evaluated over one growing season across four growing areas to assess its pomological and chemical properties and antioxidant activity. Results showed that location significantly influenced fruit weight, volume,
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The pomegranate cultivar Barski slatki, the most widely cultivated on the Eastern Adriatic coast, was evaluated over one growing season across four growing areas to assess its pomological and chemical properties and antioxidant activity. Results showed that location significantly influenced fruit weight, volume, number of arils per fruit, and both total and individual aril weight, with the Kaštela (CRO) site producing the largest fruits and highest aril yields. Climatic factors, such as precipitation during bud differentiation, flowering, and early fruit development, were found to impact fruit set, aril number, and fruit size. Aril and juice yields, however, remained relatively stable across sites. Notable differences were observed in total soluble solids, titratable acidity, pH, total phenolic content, and anthocyanin profiles. Location with higher rainfall occurring during fruit growth favored enhanced phenolic accumulation. Although total anthocyanin content remained consistent among locations, significant variation occurred in aril coloration and composition of individual anthocyanins. In conclusion, microclimatic factors, particularly rainfall distribution, temperature, and altitude, play a decisive role in shaping the physical, chemical, and visual attributes of ‘Barski slatki’. Despite being cultivated under similar Mediterranean conditions, the observed differences across sites highlight the strong adaptability of this cultivar to diverse agroecological environments, while maintaining stable quality.
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(This article belongs to the Special Issue Advanced Cultivation Technologies for Horticultural Crops Production)
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Open AccessArticle
Functional Characterization of Rubisco Activase Genes in Kandelia candel Under the Stress of Flooding and Salinity
by
Jianhong Xing, Dezhuo Pan, Changfu Li, Shufeng Yan, Wei Chen, Juncheng Zhang and Yansheng Zhang
Agriculture 2025, 15(21), 2209; https://doi.org/10.3390/agriculture15212209 - 24 Oct 2025
Abstract
Rubisco activase (RCA) is an ATP-dependent enzyme that plays a crucial role in plant stress responses by regulating the catalytic activity of Rubisco. However, the alternative splicing and functional characteristics of the RCA gene exhibit notable species-specific diversity. The variable splice forms and
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Rubisco activase (RCA) is an ATP-dependent enzyme that plays a crucial role in plant stress responses by regulating the catalytic activity of Rubisco. However, the alternative splicing and functional characteristics of the RCA gene exhibit notable species-specific diversity. The variable splice forms and functions of the RCA gene in mangrove plants remain poorly understood. Herein, we cloned the RCA cDNA in the leaves of mangrove plant Kandelia candel (L.) in response to combined flooding and salinity stress, and performed systematic expression analysis and functional validation. Our results demonstrated that the RCA gene undergoes alternative splicing to produce two isoforms, designated as KcRCAl (GenBank accession: MG492021) and KcRCAs (GenBank accession: MG492022), respectively. The KcRCAl encodes a 440-amino acid protein (42.49 kDa) belonging to the β-isoforms, while KcRCAs encodes a 474-amino acid protein (46.10 kDa) classified as the α-isoforms. Moreover, protein structure analysis revealed that both isoforms contain phosphorylation and lysine acetylation modification sites. Phylogenetic analysis indicated that KcRCA shares the closest evolutionary relationship with RCA from Cicer arietinum (chickpea) and Durio zibethinus (durian). Furthermore, RT-qPCR analysis revealed that the expression levels of KcRCAl and KcRCAs were significantly upregulated in K. Candel leaves under the combined stress condition. The following functional validation studies in transgenic Arabidopsis demonstrated that overexpression of the KcRCA cDNA enhances the plant’s tolerance to resist flooding and salinity stress while improving antioxidant capacity and increasing RCA and Rubisco activity, thereby maintaining photosynthetic efficiency under combined flooding and salinity stress. Our study not only provides new experimental evidence for understanding the molecular mechanisms of plant flooding and salinity stress, but also offers theoretical foundations for breeding flooding- and salinity-tolerant crops.
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(This article belongs to the Topic Innovative Strategies for Enhancing Plant Tolerance to Abiotic and Biotic Stresses and Ensuring Food Safety in Changing Climates)
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