Agriculture

20 pages, 2107 KB

Open AccessArticle

Mild Salt Stress Impacts Physio-Chemical Attributes and Promotes Rebaudioside a Accumulation in Stevia rebaudiana Bertoni Cultivated in Floating Systems

by Clarissa Clemente, Silvia Tavarini, Marco Landi, Andrea Martini, Luca Incrocci, Lucia Guidi and Luciana G. Angelini

Agriculture 2026, 16(2), 159; https://doi.org/10.3390/agriculture16020159 - 8 Jan 2026

Abstract

Salt stress is one of the most harmful abiotic stresses that strongly affects plant growth and crop yield, limiting agricultural production across the Mediterranean area. Consequently, there is a growing need to identify resilient crops capable of adapting to saline conditions and enhancing [...] Read more.

Salt stress is one of the most harmful abiotic stresses that strongly affects plant growth and crop yield, limiting agricultural production across the Mediterranean area. Consequently, there is a growing need to identify resilient crops capable of adapting to saline conditions and enhancing desirable qualitative traits through a wide spectrum of physiological, biochemical, and molecular mechanisms. Therefore, this study aimed to investigate the effects of four different NaCl concentrations (0, 12.5, 25, and 50 mM) on the growth rates, biometric and productive characteristics, leaf gas exchange, and biochemical traits of Stevia rebaudiana Bertoni plants grown hydroponically (in a floating raft system) in a glasshouse. The results showed that NaCl-treated plants exhibited reduced growth parameters and productivity and a lower content of photosynthetic pigment content compared to the control. On the other hand, an increase in antioxidant capacity was observed due to the significant accumulation of total phenols and flavonoids, especially when stevia plants were treated with 50 mM NaCl. Similarly, the leaf concentration of ascorbic acid and glutathione remarkably increased. This provides new insight into the antioxidant defense strategy of S. rebaudiana under salt stress, demonstrating that stevia plants rely mainly on non-enzymatic mechanisms to counter oxidative stress. Although the highest salinity level (50 mM NaCl) resulted in the lowest content of steviol glycosides (stevioside + rebaudioside A), plants treated with 25 mM NaCl showed both the highest rebaudioside A content and Reb A/Stev ratio, which are desirable properties for the production of high-quality natural sweeteners. Overall, these findings underline that stevia can be considered a moderately salt-tolerant species, and mild stress conditions are able to promote the biosynthesis of interesting secondary metabolites, such as polyphenols and rebaudioside A. Full article

(This article belongs to the Section Crop Production)

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17 pages, 668 KB

Open AccessArticle

Tannin Tolerance in Lactic Acid Bacteria Modulates Whole-Plant Sorghum Silage Quality and In Vitro Methane Mitigation

by Zhenpeng Zhu, Siqi Wang, Yili Wang and Yunhua Zhang

Agriculture 2026, 16(2), 158; https://doi.org/10.3390/agriculture16020158 - 8 Jan 2026

Abstract

Although tannins generally inhibit the growth of lactic acid bacteria, different strains vary significantly in their tolerance to this inhibitory effect. However, it remains unclear whether the differences in tannin tolerance among various lactic acid bacteria (LAB) lead to variations in the fermentation [...] Read more.

Although tannins generally inhibit the growth of lactic acid bacteria, different strains vary significantly in their tolerance to this inhibitory effect. However, it remains unclear whether the differences in tannin tolerance among various lactic acid bacteria (LAB) lead to variations in the fermentation outcomes during the silage process and in vitro fermentation. Therefore, this study investigated the correlation between the fermentation effects of LAB with varying tannin tolerances and the tannin content of sorghum. Four LAB strains (Lactococcus garvieae, LG; Lactococcus lactis, LL; Lactiplantibacillus plantarum, LP; Pediococcus pentosaceus, PP) were selected and identified from whole sorghum and mulberry leaves, and their tannin tolerance was assessed. The results demonstrated that LG exhibited the highest tolerance to sorghum tannins, followed by LL and LP, while PP displayed the lowest tolerance. Upon addition of LAB to whole sorghum for silage, LG showed the most effective ability to lower pH, reduce ammonia nitrogen content, decrease neutral detergent fiber content, diminish microbial diversity, and enhance the abundance of firmicutes. Concurrently, during in vitro fermentation, they significantly reduced rumen fluid pH and suppressed gas emissions (CH₄, CO₂). Conversely, PP performed poorly across all parameters. These findings suggest that the fermentation effects of LAB on sorghum silage are closely related to their tannin tolerance. Full article

(This article belongs to the Topic Advances in Animal-Derived Non-Cow Milk and Niche Cow Milk. Properties, Processing, Dairy Products and Environmental Impact, 2nd Edition)

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18 pages, 4153 KB

Open AccessArticle

Straw Biochar Optimizes ¹⁵N Distribution and Nitrogen Use Efficiency in Dryland Foxtail Millet

by Zhiwen Cui, Jiling Bai, Fang Gao, Qiyun Ji, Xiaolin Wang, Panpan Zhang and Xiong Zhang

Agriculture 2026, 16(2), 157; https://doi.org/10.3390/agriculture16020157 - 8 Jan 2026

Abstract

The combined application of straw biochar and nitrogen fertilizer is an increasingly studied strategy to enhance soil fertility and crop yield. Optimizing the biochar-nitrogen interaction could be a choice for increasing nitrogen use efficiency (NUE) and reducing nitrogen loss in dryland agriculture. However, [...] Read more.

The combined application of straw biochar and nitrogen fertilizer is an increasingly studied strategy to enhance soil fertility and crop yield. Optimizing the biochar-nitrogen interaction could be a choice for increasing nitrogen use efficiency (NUE) and reducing nitrogen loss in dryland agriculture. However, the mechanisms by which it regulates nitrogen allocation and absorption in foxtail millet (Setaria italica) are still limited in terms of mechanical understanding. Based on preliminary experiments, the optimal biochar-nitrogen interaction for soil nutrient absorption was identified. A field experiment was conducted with six treatments in an arid region of northwestern China: N1C1 (N1: 130 kg ha⁻¹ + C1: 100 kg ha⁻¹, control group), N2C4 (N2: 195 kg ha⁻¹ + C4: 250 kg ha⁻¹), N3C1 (N3: 260 kg ha⁻¹ + C1: 100 kg ha⁻¹), N3C2 (N3: 260 kg ha⁻¹ + C2: 150 kg ha⁻¹), N3C3 (N3: 260 kg ha⁻¹ + C3: 200 kg ha⁻¹), and N3C4 (N3: 260 kg ha⁻¹ + C4: 250 kg ha⁻¹). The results demonstrated that the biochar–nitrogen ratio significantly influenced topsoil total nitrogen, microbial biomass carbon (SMBC), and microbial biomass nitrogen (SMBN). All biochar-to-nitrogen combinations sharply increased soil total nitrogen by 133.11–151.52% compared to pre-sowing levels, providing a fundamental base for microbial-driven nitrogen transformation. Low nitrogen addition is more conducive to biomass accumulation, with N2C4 significantly increasing by 62.82%. Although a high biochar-to-nitrogen ratio reduced leaf relative chlorophyll content (SPAD) by 5.72–16.18% and net photosynthetic rate (Pn) by 16.09–52.65% at the heading stage, these did not compromise final yield. Importantly, N2C4, N3C1, and N3C4 significantly increased spike ¹⁵N abundance by 71.45%, 13.21%, and 19.43%, respectively. N2C4 grain production increases by 53.77–110.57% in two years and was positively correlated with spike ¹⁵N abundance, reflecting high nitrogen partial factor productivity. In conclusion, a reasonable biochar-nitrogen interaction enhances nitrogen allocation and grain yield by stimulating microbial activity and strengthening soil–plant synergy, the certified strategy effectively supports sustainable dryland agriculture by simultaneously increasing productivity and improving soil health. Full article

(This article belongs to the Section Agricultural Soils)

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19 pages, 2648 KB

Open AccessArticle

Connection Between the Microbial Community and the Management Zones Used in Precision Agriculture Cultivation

by Mátyás Cserháti, Dalma Márton, Ádám Csorba, Milán Farkas, Neveen Almalkawi, Ádám Hegyi, Balázs Kriszt and Tamás Szegi

Agriculture 2026, 16(2), 156; https://doi.org/10.3390/agriculture16020156 - 8 Jan 2026

Abstract

In precision agriculture, the delineation of Management Zones (MZs) is essential for optimizing input use efficiency and site-specific nutrient management. MZs are established based on spatial variability derived from remote sensing data—such as Normalized Difference Vegetation Index (NDVI) from satellite or UAV-based imagery—and [...] Read more.

In precision agriculture, the delineation of Management Zones (MZs) is essential for optimizing input use efficiency and site-specific nutrient management. MZs are established based on spatial variability derived from remote sensing data—such as Normalized Difference Vegetation Index (NDVI) from satellite or UAV-based imagery—and yield maps collected during harvest. However, the microbial community composition of the soil is often overlooked in MZ delineation. To address this gap, we investigated the soil bacterial community structure across different MZs in an arable field. The zones were delineated using NDVI data, soil profiles were described, and bulk soil samples were collected. Soil physicochemical parameters were analyzed in parallel with 16S rRNA gene amplicon sequencing to characterize bacterial community composition and diversity. The results demonstrated that soil texture and soil organic matter content were the primary drivers influencing bacterial community structure across the field. Moreover, patterns in microbial composition aligned closely with MZ delineations, indicating that microbial profiles could aid in better understanding and supporting the nutrient management practices. Our findings suggest that soil microbiological data can enhance the stability and biological relevance of MZ definitions, thereby improving resource allocation, soil health management, and overall sustainability in precision farming systems. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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31 pages, 21618 KB

Open AccessArticle

Cohesion-Based Flocking Formation Using Potential Linked Nodes Model for Multi-Robot Agricultural Swarms

by Kevin Marlon Soza-Mamani, Marcelo Saavedra Alcoba, Felipe Torres and Alvaro Javier Prado-Romo

Agriculture 2026, 16(2), 155; https://doi.org/10.3390/agriculture16020155 - 8 Jan 2026

Abstract

Accurately modeling and representing the collective dynamics of large-scale robotic systems remains one of the fundamental challenges in swarm robotics. Within the context of agricultural robotics, swarm-based coordination schemes enable scalable and adaptive control of multi-robot teams performing tasks such as crop monitoring [...] Read more.

Accurately modeling and representing the collective dynamics of large-scale robotic systems remains one of the fundamental challenges in swarm robotics. Within the context of agricultural robotics, swarm-based coordination schemes enable scalable and adaptive control of multi-robot teams performing tasks such as crop monitoring and autonomous field maintenance. This paper introduces a cohesive Potential Linked Nodes (PLNs) framework, an adjustable formation structure that employs Artificial Potential Fields (APFs), and virtual node–link interactions to regulate swarm cohesion and coordinated motion (CM). The proposed model governs swarm formation, modulates structural integrity, and enhances responsiveness to external perturbations. The PLN framework facilitates swarm stability, maintaining high cohesion and adaptability while the system’s tunable parameters enable online adjustment of inter-agent coupling strength and formation rigidity. Comprehensive simulation experiments were conducted to assess the performance of the model under multiple swarm conditions, including static aggregation and dynamic flocking behavior using differential-drive mobile robots. Additional tests within a simulated cropping environment were performed to evaluate the framework’s stability and cohesiveness under agricultural constraints. Swarm cohesion and formation stability were quantitatively analyzed using density-based and inter-robot distance metrics. The experimental results demonstrate that the PLN model effectively maintains formation integrity and cohesive stability throughout all scenarios. Full article

(This article belongs to the Topic Object Detection and Control of Networked Autonomous Systems: Theories, Analysis Tools and Applications)

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27 pages, 3479 KB

Open AccessArticle

The Water Lifting Performance of a Photovoltaic Sprinkler Irrigation System Regulated by Solar-Coupled Compressed-Air Energy Storage

by Xiaoqing Zhong, Maosheng Ge, Zhengwen Tang, Pute Wu, Xin Hui, Qianwen Zhang, Qingyan Zhang and Khusen Sh. Gafforov

Agriculture 2026, 16(2), 154; https://doi.org/10.3390/agriculture16020154 - 8 Jan 2026

Abstract

Solar-driven irrigation, a promising clean technology for agricultural water conservation, is constrained by mismatched photovoltaic (PV) pump outflow and irrigation demand, alongside unstable PV output. While compressed-air energy storage (CAES) shows mitigation potential, existing studies lack systematic explorations of pump water-lifting characteristics and [...] Read more.

Solar-driven irrigation, a promising clean technology for agricultural water conservation, is constrained by mismatched photovoltaic (PV) pump outflow and irrigation demand, alongside unstable PV output. While compressed-air energy storage (CAES) shows mitigation potential, existing studies lack systematic explorations of pump water-lifting characteristics and supply capacity under coupled meteorological and air pressure effects, limiting its practical promotion. This study focuses on a solar-coupled compressed-air energy storage regulated sprinkler irrigation system (CAES-SPSI). Integrating experimental and theoretical methods, it establishes dynamic flow models for three DC diaphragm pumps considering combined PV output and outlet back pressure, introduces pressure loss and drop coefficients to construct a nozzle pressure dynamic model via calibration and iteration, and conducts a 1-hectare corn field case study. The results indicate the following: pump flow increases with PV power and decreases with outlet pressure (model deviation < 9.24%); nozzle pressure in pulse spraying shows logarithmic decline; CAES-SPSI operates 10 h/d, with hourly water-lifting capacity of 0.317–1.01 m³/h and daily cumulation of 6.71 m³; and the low-intensity and long-duration mode extends irrigation time, maintaining total volume and optimal soil moisture. This study innovatively incorporates dynamic air pressure potential energy into meteorological-PV coupling analysis, providing a universal method for quantifying pump flow changes, clarifying CAES-SPSI’s water–energy coupling mechanism, and offering a design basis for its agricultural application feasibility. Full article

(This article belongs to the Section Agricultural Water Management)

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15 pages, 317 KB

Open AccessArticle

Use of Tropical Legume Tree and Coffee Pulp to Reduce Enteric Methane Emission by Cattle Fed a Low-Quality Forage Diet

by Cristian Cruz-Matías, Francisca Avilés-Nova, José Nahed-Toral, José Herrera-Camacho, Romeo Josué Trujillo-Vázquez, Manuel González-Ronquillo and Octavio Alonso Castelán-Ortega

Agriculture 2026, 16(2), 153; https://doi.org/10.3390/agriculture16020153 - 8 Jan 2026

Abstract

Tanniferous forages, leaves and pods from legume trees can be used as feed additives to reduce enteric CH₄ in tropical regions of the world where smallholder farmers cannot afford to purchase commercial anti-methanogenic feed additives. The present work aimed to evaluate the [...] Read more.

Tanniferous forages, leaves and pods from legume trees can be used as feed additives to reduce enteric CH₄ in tropical regions of the world where smallholder farmers cannot afford to purchase commercial anti-methanogenic feed additives. The present work aimed to evaluate the impact of small doses of Gliricidia sepium (G. sepium) alone or in combination with coffee pulp (COP) on enteric CH₄ production in cattle. A 4 × 4 Latin square experimental design was used, where four Holstein x Charolais heifers of 390 ± 50 kg body weight were used. Four treatments were evaluated, with G. sepium (GSep) and COP used as additives. The control treatment (CON) had no additives and was offered ad libitum, the COP treatment contained 1.0 kg DM d⁻¹ of COP, the treatment with G. sepium contained 0.342 kg DM d⁻¹ of this plant, and the treatment with both plants (COP + GSep) had 0.505 and 0.171 kg DM d⁻¹, respectively. The lowest CH₄ production was observed for the COP + GSep treatment, followed by GSep, with 17% and 14.2% less CH₄, respectively, compared to the CON treatment (p < 0.05). We concluded that supplementation with G. sepium, alone or in combination with COP, can be used as part of a strategy to mitigate enteric CH₄ production in tropical cattle production systems. To the best of our knowledge, this is the first time two natural additives have been used together to reduce enteric methane in cattle fed a low-quality forage. Full article

(This article belongs to the Section Farm Animal Production)

15 pages, 1782 KB

Open AccessArticle

Impact of Meteorological Conditions on the Bird Cherry–Oat Aphid (Rhopalosiphum padi L.) Flights Recorded by Johnson Suction Traps

by Kamila Roik, Sandra Małas, Paweł Trzciński and Jan Bocianowski

Agriculture 2026, 16(2), 152; https://doi.org/10.3390/agriculture16020152 - 7 Jan 2026

Abstract

Due to its abundance, bird cherry–oat aphid is the most important vector in Poland of the complex of viruses causing barley yellow dwarf virus (BYDV). These viruses infect all cereals. During the growing season, cereal plants are exposed to many species of agrophages, [...] Read more.

Due to its abundance, bird cherry–oat aphid is the most important vector in Poland of the complex of viruses causing barley yellow dwarf virus (BYDV). These viruses infect all cereals. During the growing season, cereal plants are exposed to many species of agrophages, which can limit their growth, development and yield. As observed for many years, global warming contributes to changes in the development of many organisms. Aphids (Aphidoidea), which are among the most important pests of agricultural crops, respond very dynamically to these changes. Under favorable conditions, their populations can increase several-fold within a few days. The bird cherry–oat aphid (Rhopalosiphum padi L.) is a dioecious species that undergoes a seasonal host shift during its life cycle. Its primary hosts are trees and shrubs (Prunus padus L.), while secondary hosts include cereals and various grass species. R. padi feeds directly on bird cherry tree, reducing its ornamental value, and on cereals, where it contributes to yields losses. The species can also damage plants indirectly by transmitting harmful viruses. Indirect damage is generally more serious than direct feeding injury. Monitoring aphid flights with a Johnson suction trap (JST) is useful for plant protection, which enables early detection of their presence in the air and then on cereal crops. To provide early detection of R. padi migrations and to study the dynamics of abundance, flights were monitored in 2020–2024 with Johnson suction traps at two localities: Winna Góra (Greater Poland Province) and Sośnicowice (Silesia Province). The aim of the research conducted in 2020–2024 was to study the dynamics of the bird cherry–oat aphid (Rhopalosiphum padi L.) population in relation to meteorological conditions as recorded by a Johnson suction trap. Over five years of research, a total of 129,638 R. padi individuals were captured using a Johnson suction trap at two locations (60,426 in Winna Góra and 69,212 in Sośnicowice). In Winna Góra, the annual counts were as follows: 5766 in 2020, 6498 in 2021, 36,452 in 2022, 5598 in 2023, and 6112 in 2024. In Sośnicowice, the numbers were as follows: 6954 in 2020, 9159 in 2021, 49,120 in 2022, 3855 in 2023, and 124 in 2024. The year 2022 was particularly notable for the exceptionally high abundance of R. padi, especially in the autumn. Monitoring crops for the presence of pests is the basis of integrated plant protection. Climate change, modern cultivation technologies, and increasing restrictions on chemical control are the main factors contributing to the development and spread of aphids. Therefore, measures based on monitoring the level of threat and searching for control solutions are necessary. Full article

(This article belongs to the Special Issue Agriculture and Global Climate Change: Threats, Challenges and Adaptations)

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25 pages, 7956 KB

Open AccessArticle

A Lightweight Facial Landmark Recognition Model for Individual Sheep Based on SAMS-KLA-YOLO11

by Yangfan Bai, Xiaona Zhao, Xinran Liang, Zhimin Zhang, Yuqiao Yan, Fuzhong Li and Wuping Zhang

Agriculture 2026, 16(2), 151; https://doi.org/10.3390/agriculture16020151 - 7 Jan 2026

Abstract

Accurate and non-contact identification of individual sheep is important for intelligent livestock management, but remains challenging due to subtle inter-individual differences, breed-dependent facial morphology, and complex farm environments. This study proposes a lightweight sheep face detection and keypoint recognition framework based on an [...] Read more.

Accurate and non-contact identification of individual sheep is important for intelligent livestock management, but remains challenging due to subtle inter-individual differences, breed-dependent facial morphology, and complex farm environments. This study proposes a lightweight sheep face detection and keypoint recognition framework based on an improved YOLO11 architecture, termed SAMS-KLA-YOLO11. The model incorporates a Sheep Adaptive Multi-Scale Convolution (SAMSConv) module to enhance feature extraction across breed-dependent facial scales, a Keypoint-Aware Lightweight Attention (KLAttention) mechanism to emphasize biologically discriminative facial landmarks, and the Efficient IoU (EIoU) loss to stabilize bounding box regression. A dataset of 3860 images from 68 individuals belonging to three breeds (Hu, Dorper, and Dorper × Hu crossbreeds) was collected under unconstrained farm conditions and annotated with five facial keypoints. On this dataset, the proposed model achieves higher precision, recall, and mAP than several mainstream YOLO-based baselines, while reducing FLOPs and parameter count compared with the original YOLO11. Additional ablation experiments confirm that each proposed module provides complementary benefits, and OKS-based evaluation shows accurate facial keypoint localization. All results are obtained on a single, site-specific dataset without external validation or on-device deployment benchmarks, so the findings should be viewed as an initial step toward practical sheep face recognition rather than definitive evidence of large-scale deployment readiness. Full article

(This article belongs to the Special Issue Computer Vision Analysis Applied to Farm Animals)

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13 pages, 3097 KB

Open AccessArticle

Genome-Wide Association Analysis Reveals Genetic Loci and Candidate Genes Related to Soybean Leaf Shape

by Yan Zhang, Yuan Li, Xiuli Rui, Yina Zhu, Jie Wang, Xue Zhao and Xunchao Zhao

Agriculture 2026, 16(2), 150; https://doi.org/10.3390/agriculture16020150 - 7 Jan 2026

Abstract

Soybean is the world’s foremost oilseed crop, and leaf morphology significantly influences yield potential by affecting light interception, canopy structure, and photosynthetic efficiency. In this study, leaf length, leaf width, maximum leaf width, leaf apex opening angle, and leaf area were measured in [...] Read more.

Soybean is the world’s foremost oilseed crop, and leaf morphology significantly influences yield potential by affecting light interception, canopy structure, and photosynthetic efficiency. In this study, leaf length, leaf width, maximum leaf width, leaf apex opening angle, and leaf area were measured in 216 soybean accessions, and genome-wide association studies (GWAS) were conducted using genomic resequencing data to identify genetic variants associated with leaf morphological traits. A total of 824 SNP loci were found to be significantly associated with leaf shape, and 130 candidate genes were identified in the genomic regions flanking these significant loci. KEGG enrichment analysis revealed that the above candidate genes were significantly enriched in arginine biosynthesis (ko00220), nitrogen metabolism (ko00910), carbon metabolism (ko01200), pyruvate metabolism (ko00620), glycolysis/glycogenolysis (ko00010), starch and sucrose metabolism (ko00500), plant–pathogen interaction (ko04626), and amino acid biosynthesis (ko01230). By combining KEGG and GO enrichment analysis as well as expression level analysis, four candidate genes related to leaf shape (Glyma.10G141600, Glyma.13G062700, Glyma.16G041200 and Glyma.20G115500) were identified. Further, through candidate gene association analysis, it was found that the Glyma.10G141600 gene was divided into two major haplotypes. The leaf area of haplotype 1 was significantly smaller than that of haplotype 2. Subsequently, the cutting amplification polymorphism sequence (CAPS) molecular marker was developed. The marker Chr.10:37502955 can effectively distinguish the differences in leaf size through enzymatic digestion technology, and has excellent typing ability and application potential. The above results can provide a theoretical basis for molecular-assisted selection (MAS) of soybean leaf morphology. Full article

(This article belongs to the Section Crop Genetics, Genomics and Breeding)

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34 pages, 477 KB

Open AccessReview

Revisiting Environmental Sustainability in Ruminants: A Comprehensive Review

by Yufeng Shang, Tingting Ju, Upinder Kaur, Henrique A. Mulim, Shweta Singh, Jacquelyn Boerman and Hinayah Rojas de Oliveira

Agriculture 2026, 16(2), 149; https://doi.org/10.3390/agriculture16020149 - 7 Jan 2026

Abstract

Ruminant livestock production faces increasing pressure to reduce environmental impacts while maintaining productivity and food security. This comprehensive review examines current strategies and emerging technologies for enhancing environmental sustainability in ruminant systems. The review synthesizes recent advances across four interconnected domains: genetic and [...] Read more.

Ruminant livestock production faces increasing pressure to reduce environmental impacts while maintaining productivity and food security. This comprehensive review examines current strategies and emerging technologies for enhancing environmental sustainability in ruminant systems. The review synthesizes recent advances across four interconnected domains: genetic and genomic approaches for breeding environmentally efficient animals, rumen microbiome manipulation, nutritional strategies for emission reduction, and precision management practices. Specifically, genetic and genomic strategies demonstrate significant potential for long-term sustainability improvements through selective breeding for feed efficiency, methane reduction, and enhanced longevity. Understanding host–microbe interactions and developing targeted interventions have also shown promising effects on optimizing fermentation efficiency and reducing methane production. Key nutritional interventions include dietary optimization strategies that improve feed efficiency, feed additives, and precision feeding systems that minimize nutrient waste. Furthermore, management approaches encompass precision livestock farming technologies including sensor-based monitoring systems, automated feeding platforms, and real-time emission measurement tools that enable data-driven decision making. Integration of these approaches through system-based frameworks offers the greatest potential for achieving substantial environmental improvements while maintaining economic viability. In addition, this review identifies key research gaps including the need for standardized measurement protocols, long-term sustainability assessments, and economic evaluation frameworks. Future directions emphasize the importance of interdisciplinary collaboration, policy support, and technology transfer to accelerate adoption of sustainable practices across diverse production systems. Full article

(This article belongs to the Special Issue The Threats Posed by Environmental Factors to Farm Animals)

20 pages, 59455 KB

Open AccessArticle

ACDNet: Adaptive Citrus Detection Network Based on Improved YOLOv8 for Robotic Harvesting

by Zhiqin Wang, Wentao Xia and Ming Li

Agriculture 2026, 16(2), 148; https://doi.org/10.3390/agriculture16020148 - 7 Jan 2026

Abstract

To address the challenging requirements of citrus detection in complex orchard environments, this paper proposes ACDNet (Adaptive Citrus Detection Network), a novel deep learning framework specifically designed for automated citrus harvesting. The proposed method introduces three key innovations: (1) Citrus-Adaptive Feature Extraction (CAFE) [...] Read more.

To address the challenging requirements of citrus detection in complex orchard environments, this paper proposes ACDNet (Adaptive Citrus Detection Network), a novel deep learning framework specifically designed for automated citrus harvesting. The proposed method introduces three key innovations: (1) Citrus-Adaptive Feature Extraction (CAFE) module that combines fruit-aware partial convolution with illumination-adaptive attention mechanisms to enhance feature representation with improved efficiency; (2) Dynamic Multi-Scale Sampling (DMS) operator that adaptively focuses sampling points on fruit regions while suppressing background interference through content-aware offset generation; and (3) Fruit-Shape Aware IoU (FSA-IoU) loss function that incorporates citrus morphological priors and occlusion patterns to improve localization accuracy. Extensive experiments on our newly constructed CitrusSet dataset, which comprises 2887 images capturing diverse lighting conditions, occlusion levels, and fruit overlapping scenarios, demonstrate that ACDNet achieves superior performance with mAP@0.5 of 97.5%, precision of 92.1%, and recall of 92.8%, while maintaining real-time inference at 55.6 FPS. Compared to the baseline YOLOv8n model, ACDNet achieves improvements of 1.7%, 3.4%, and 3.6% in mAP@0.5, precision, and recall, respectively, while reducing model parameters by 11% (to 2.67 M) and computational cost by 20% (to 6.5 G FLOPs), making it highly suitable for deployment in resource-constrained robotic harvesting systems. However, the current study is primarily validated on citrus fruits, and future work will focus on extending ACDNet to other spherical fruits and exploring its generalization under extreme weather conditions. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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35 pages, 25567 KB

Open AccessArticle

Origin Warehouses as Logistics or Supply Chain Centers: Comparative Analysis of Business Models in Sustainable Agri-Food Supply Chains

by Yiwen Gao, Mengru Shen, Kai Yang, Xifu Wang, Lijun Jiang and Yang Yao

Agriculture 2026, 16(2), 147; https://doi.org/10.3390/agriculture16020147 - 7 Jan 2026

Abstract

Origin warehouses, positioned at the critical “first mile” of the agri-food supply chain, profoundly influence supply chain power structures and profit allocation, as well as supply chain stability and sustainable development. To explore the role of origin warehouses in the agri-food supply chain, [...] Read more.

Origin warehouses, positioned at the critical “first mile” of the agri-food supply chain, profoundly influence supply chain power structures and profit allocation, as well as supply chain stability and sustainable development. To explore the role of origin warehouses in the agri-food supply chain, this study develops a three-level game model comprising a “planter–origin warehouse operator–seller” framework. Notably, this study conceptualizes the dual-functional “origin warehouse” as observed in practice, proposing two theoretical modes: the Logistics Center (LC) and the Supply Chain Center (SCC). By treating quality level, service level, and selling price decisions as endogenous variables, this study further reveals the interconnected decision-making mechanisms under different operational modes. Overall, the LC mode performs better in quality-driven markets, generating higher system profits and greater social welfare, whereas the SCC mode is superior when consumers are more price-sensitive or place greater value on service. Based on these findings, this study provides decision-making guidance for origin warehouse operators aiming to select the optimal mode under varying market conditions and proposes targeted coordination strategies to promote the high-quality development and economic sustainability of the agri-food supply chain. Full article

(This article belongs to the Special Issue Building Resilience Through Sustainable Agri-Food Supply Chains)

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16 pages, 735 KB

Open AccessArticle

GGE Biplot Analysis for the Assessment and Selection of Bread Wheat Genotypes Under Organic and Low-Input Stress Environments

by Evangelos Korpetis, Elissavet Ninou, Ioannis Mylonas, Dimitrios Katsantonis, Nektaria Tsivelika, Ioannis N. Xynias, Alexios N. Polidoros, Dimitrios Roupakias and Athanasios G. Mavromatis

Agriculture 2026, 16(2), 146; https://doi.org/10.3390/agriculture16020146 - 7 Jan 2026

Abstract

Bread wheat variety development suited to organic farming conditions remains a major challenge mainly because of the high breeding costs involved and the few cultivars adapted to low-input systems. The present work explores whether early generation selection needs to take place under organic [...] Read more.

Bread wheat variety development suited to organic farming conditions remains a major challenge mainly because of the high breeding costs involved and the few cultivars adapted to low-input systems. The present work explores whether early generation selection needs to take place under organic conditions for subsequent adaptation or whether conventional testing at an early stage could be adequate. A diverse set of crosses involving Greek landraces and commercial cultivars were developed and advanced by honeycomb pedigree selection under both organic and conventional environments. Subsequently, F₄ progenies and an upgraded landrace were evaluated over two years in neighboring organic and conventional trials. Both statistical and GGE biplot analyses revealed significant genotype × environment interactions. The results clearly indicate that early selection under organic conditions did not provide a consistent advantage for subsequent performance under organic management compared with conventional early selection. Genotypes derived from the Africa × Atheras cross consistently showed the highest and most stable yields across the two environments, irrespective of the early selection environment. These results indicate that genetic background and landrace-derived diversity are more important than the early selection environment for the expression of performance. A staged breeding strategy involving initial selection in conventional management followed by multi-environment testing in organic conditions can provide a cost-effective approach to developing resilient, high-yielding wheat cultivars suitable for organic farming systems, which are typically characterized by low-input management practices, and in tune with the EU targets for expanded organic farming. Full article

(This article belongs to the Special Issue Sustainable Small Grain Cropping Systems: Circular Economy, Pollution Mitigation, and Farmer-Centric Innovation)

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15 pages, 2153 KB

Open AccessReview

Assessing the Potential for Modifying Certain Eradication Measures for Xylella fastidiosa subsp. pauca in Olive Groves of Apulia (Italy)

by Marco Scortichini

Agriculture 2026, 16(2), 145; https://doi.org/10.3390/agriculture16020145 - 6 Jan 2026

Abstract

Sometimes, mandatory rules for eradicating pathogens specifically target crops that hold intrinsic economic value, cultural heritage, and are a lucrative tourist attraction as well as an appealing part of the landscape due to their historical presence in the region. An example of this [...] Read more.

Sometimes, mandatory rules for eradicating pathogens specifically target crops that hold intrinsic economic value, cultural heritage, and are a lucrative tourist attraction as well as an appealing part of the landscape due to their historical presence in the region. An example of this is the introduction of Xylella fastidiosa subsp. pauca (Xfp), mainly vectored by Philaenus spumarius to olive groves in Apulia. Twelve years after the first official report on its presence and numerous studies, this review aims to reconsider some of the quarantine measures in place to prevent the spread of Xfp. Surveys carried out within the demarcated areas have shown a low incidence of Xfp over the years ranging from 0.06% to 0.70%. Furthermore, the bacterium is now present throughout the region, from the south to the north, potentially suggesting that the bacterium may be endemic in the region. Epidemiological models have indicated low or negligible infectivity for asymptomatic trees. Rigorous vector control, achieved through the mechanical removal of eggs and juvenile forms, coupled with the contemporary reduction in the Xfp load within the olive crown using bactericidal compounds, could effectively reduce the spread of Xfp in both infected and demarcated areas. These actions could also serve as preventive measures in current free areas. Once the prevalence of both vectors and Xfp is low, only olive trees in demarcated areas that test positive for the bacterium should be uprooted. Trees within a 50 m radius of an Xfp-positive olive tree should not be removed if they test negative for Xfp upon detection. Full article

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

23 pages, 1593 KB

Open AccessArticle

Research on the Coupling Coordination Degree and Obstacle Factors of Digital Inclusive Finance and Digital Agriculture in Rural China

by Lunqiu Huang, Jun Wen, Junzeng Liu and Dong Han

Agriculture 2026, 16(2), 144; https://doi.org/10.3390/agriculture16020144 - 6 Jan 2026

Abstract

In the context of advancing agricultural and rural modernization in China, digital agriculture has gained significant governmental attention. However, existing research has predominantly focused on examining the relationship from digital inclusive finance to digital agriculture, while in-depth investigations into their bidirectional coupled coordination, [...] Read more.

In the context of advancing agricultural and rural modernization in China, digital agriculture has gained significant governmental attention. However, existing research has predominantly focused on examining the relationship from digital inclusive finance to digital agriculture, while in-depth investigations into their bidirectional coupled coordination, spatiotemporal evolution, and underlying obstacle factors remain limited. To address this research gap, this study aims to construct innovative evaluation index systems for both domains and to establish a coupling coordination degree model alongside an obstacle degree model. This methodological framework is designed to examine the bidirectional coupled coordination, reveal its spatiotemporal evolution patterns, and identify key obstacle factors across 30 Chinese provinces. Results indicate a consistent annual improvement in the coupling coordination level across provinces. Many regions have progressed from moderate or mild dysfunction to marginal or primary coordination, with coordination degrees ranging between 0.5 and 0.6 by 2022. Specifically, the eastern region recorded 0.586, the central region 0.562, and the western region 0.531. Regional disparities are identified as the primary source of variation. Key obstacles include insufficient support from digital finance to agriculture, the east–west development gap, low actual usage of digital financial services, volatility in agricultural production price indices, and high agricultural carbon emissions. Recommendations focus on bridging regional gaps, strengthening financial support, and addressing these impediments, which are crucial for promoting sustainable development. Full article

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

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24 pages, 4739 KB

Open AccessArticle

Dynamics of Key Meteorological Variables and Their Impacts on Staple Crop Yields Across Large-Scale Farms in Heilongjiang, China

by Jingyang Li, Huanhuan Li, Xin Liu, Qiuju Wang, Qingying Meng, Jiahe Zou, Yifei Luo, Shuangchao Wang and Long Tan

Agriculture 2026, 16(2), 143; https://doi.org/10.3390/agriculture16020143 - 6 Jan 2026

Abstract

Against the backdrop of global warming and a reshaped hydrothermal regime, the albic soil belt of the Sanjiang Plain, a major grain base, requires farm-scale evidence of how meteorological variability couples with staple-crop yields. Using meteorological and yield records from 2000 to 2023 [...] Read more.

Against the backdrop of global warming and a reshaped hydrothermal regime, the albic soil belt of the Sanjiang Plain, a major grain base, requires farm-scale evidence of how meteorological variability couples with staple-crop yields. Using meteorological and yield records from 2000 to 2023 at three large farms (859, 850, and 852), this study applied the Mann–Kendall test, wavelet and cross-wavelet coherence, Pearson correlation, gray relational analysis, and principal component analysis to track the evolution of air temperature, precipitation, evaporation, sunshine duration, relative humidity, and surface temperature, and to assess their multi-scale impacts on rice, corn, and soybean yields. The region warmed and became wetter overall, with dominant periodicities near 21a and 8a. Across the three farms, yields were significantly and positively associated with precipitation and air temperature (R > 0.60). Rice yield correlated strongly and negatively with evaporation at Farm 850 (R = −0.61) and at Farm 852 (R = −0.503). At Farm 859, gray relational analysis ranked precipitation highest for rice, corn, and soybean (γ = 0.853, 0.844, and 0.826), followed by air temperature. The first two principal components explained 67.66% of the variance; PC1 (41.80%) loaded positively for air temperature, and PC2 (25.86%) for precipitation and relative humidity. Cross-wavelet coherence indicated stable coupling between yields and hydrothermal variables, with the strongest coupling for rice with precipitation and air temperature, prominent coupling for corn with air temperature and sunshine duration, and stage-dependent responses of soybean to precipitation and evaporation. These results show that long-term trends together with phase-specific oscillations jointly shape yield variability. The findings support translating phase identification and sensitive windows into crop-specific rules for sowing or transplanting arrangements, irrigation timing, and early warning, providing a quantitative basis for climate-adaptive management on the study farms and, where soils, management, and microclimate are comparable, for the wider Sanjiang Plain. Full article

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

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23 pages, 3422 KB

Open AccessArticle

Evolution of Urban–Agricultural–Ecological Spatial Structure Driven by Irrigation and Drainage Projects and Water–Heat–Vegetation Response

by Tianqi Su and Yongmei

Agriculture 2026, 16(2), 142; https://doi.org/10.3390/agriculture16020142 - 6 Jan 2026

Abstract

In the context of global climate change and intensified water resource constraints, studying the evolution of the urban–agricultural–ecological spatial structure and the water–heat–vegetation responses driven by large-scale irrigation and drainage projects in arid and semi-arid regions is of great significance. Based on multitemporal [...] Read more.

In the context of global climate change and intensified water resource constraints, studying the evolution of the urban–agricultural–ecological spatial structure and the water–heat–vegetation responses driven by large-scale irrigation and drainage projects in arid and semi-arid regions is of great significance. Based on multitemporal remote sensing data from 1985 to 2015, this study takes the Inner Mongolia Hetao Plain as the research area, constructs a “multifunctionality–dynamic evolution” dual-principle classification system for urban–agricultural–ecological space, and adopts the technical process of “separate interpretation of each single land type using the maximum likelihood algorithm followed by merging with conflict pixel resolution” to improve the classification accuracy to 90.82%. Through a land use transfer matrix, a standard deviation ellipse model, surface temperature (LST) inversion, and vegetation fractional coverage (VFC) analysis, this study systematically reveals the spatiotemporal differentiation patterns of spatial structure evolution and surface parameter responses throughout the project’s life cycle. The results show the following: (1) The spatial structure follows the path of “short-term intense disturbance–long-term stable optimization”, with agricultural space stability increasing by 4.8%, the ecological core area retention rate exceeding 90%, and urban space expanding with a shift from external encroachment to internal filling, realizing “stable grain yield with unchanged cultivated land area and improved ecological quality with controlled green space loss”. (2) The overall VFC shows a trend of “central area stable increase (annual growth rate 0.8%), eastern area fluctuating recovery (cyclic amplitude ±12%), and western area local improvement (key patches increased by 18%)”. (3) The LST-VFC relationship presents spatiotemporal misalignment, with a 0.8–1.2 °C anomalous cooling in the central region during the construction period (despite a 15% VFC decrease), driven by irrigation water thermal inertia, and a disrupted linear correlation after completion due to crop phenology changes and plastic film mulching. (4) Irrigation and drainage projects optimize water resource allocation, constructing a hub regulation model integrated with the Water–Energy–Food (WEF) Nexus, providing a replicable paradigm for ecological effect assessment of major water conservancy projects in arid regions. Full article

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

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28 pages, 913 KB

Open AccessArticle

The Impact of the Integration of Digital and Real Economies on Agricultural New Quality Productive Forces: Empirical Evidence from China’s Major Grain-Producing Areas

by Wei Li, Linlu Li, Wenxi Li, Chunguang Sheng and Xinyi Li

Agriculture 2026, 16(2), 141; https://doi.org/10.3390/agriculture16020141 - 6 Jan 2026

Abstract

As the digital economy becomes increasingly integrated with the real economy, agricultural production is experiencing fundamental transformation. Digital–real integration has emerged as strategically important for cultivating agricultural new quality productive forces and safeguarding national food security. This study examines provincial panel data from [...] Read more.

As the digital economy becomes increasingly integrated with the real economy, agricultural production is experiencing fundamental transformation. Digital–real integration has emerged as strategically important for cultivating agricultural new quality productive forces and safeguarding national food security. This study examines provincial panel data from 13 major grain-producing regions in China between 2012 and 2023. We develop an evaluation index system to assess both digital–real integration and agricultural new quality productive forces. Using the entropy weight method, we quantify the development levels of these two dimensions. Our empirical analysis employs fixed effects models, mediation effect models, and spatial econometric approaches to investigate how digital–real integration influences agricultural new quality productive forces in major grain-producing regions. The research findings indicate the following: (1) Digital–real integration demonstrates a robust positive correlation with agricultural new quality productive forces in major grain-producing regions. (2) Both agricultural industrial structure upgrading and agricultural green total factor productivity serve as significant mediating channels through which digital–real integration enhances agricultural new quality productive forces. (3) The impact exhibits notable heterogeneity across three dimensions: regional characteristics, industrial structure levels, and fiscal decentralization levels. (4) Digital–real integration generates substantial positive spatial spillover effects on agricultural new quality productive forces, facilitating coordinated improvements in neighboring regions. (5) A significant threshold effect exists in how digital–real integration promotes agricultural new quality productive forces. Specifically, the promotional effect intensifies once innovation level and human capital level exceed certain critical thresholds. These findings offer both theoretical insights and practical guidance for advancing high-quality development in agriculture within major grain-producing regions while strengthening the national food security strategy. Full article

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

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