Agriculture

14 pages, 2334 KB

Open AccessArticle

Pressure Drop Across Animal Occupied Zone of Dairy Barns Under Multiple Scenarios

by Qianying Yi, El Hadj Moustapha Doumbia, Ali Alaei, David Janke, Thomas Amon and Sabrina Hempel

Agriculture 2026, 16(1), 79; https://doi.org/10.3390/agriculture16010079 (registering DOI) - 29 Dec 2025

Abstract

In naturally ventilated dairy barns, many questions regarding airflow, indoor air quality, and emissions are still unanswered, often resulting in inaccurate environmental control of the housing. Particularly, limited understanding of the implications of the constantly changing outdoor weather conditions in interaction with the [...] Read more.

In naturally ventilated dairy barns, many questions regarding airflow, indoor air quality, and emissions are still unanswered, often resulting in inaccurate environmental control of the housing. Particularly, limited understanding of the implications of the constantly changing outdoor weather conditions in interaction with the building design and the role of the characteristics of the animals’ movement inside the building enhances uncertainties in the estimation of airflows within and across the barns. Computational fluid dynamics (CFD) have been used in the past to better understand the dynamics of barn climate, but the models are typically too slow to be used for real-time prediction and control. We investigated the effect of animal characteristics (i.e., animal location, orientation, body posture, and dimensions) on the pressure drop in the animal occupied zone considering inlet wind speed from 0.1 m s⁻¹ to 5 m s⁻¹ and wind direction of 0° and 90° in a CFD model. The cow position in general had little impact on the pressure drop at low wind speeds, but became relevant at higher wind speeds. Cows distributed in a more organized alignment showed less airflow resistance, and, therefore, a lower pressure drop and higher air velocities. Moreover, the cow breed affected the pressure drop, with higher withers resulting in a higher pressure drop and air resistance. In contrast, the effects of cow lying–standing ratio on the pressure drop and airflow resistance coefficients were negligible for both investigated wind directions. Our study aims to provide guidance for optimizing parametrizations of the animal occupied zone in order to enhance the speed of simulations without significant loss in model accuracy. In addition, the conclusions drawn from our study may support the adaption of building design and herd management to improve the effectiveness of ventilation concepts of naturally ventilated dairy barns. Full article

(This article belongs to the Special Issue Application of Intelligent Technologies in Farm Animal Disease, Feeding and Building Environmental Control)

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18 pages, 3593 KB

Open AccessArticle

Environmental Enrichment Attenuates Acute Noise-Induced Bursal Injury in Broilers via Suppressing NF-κB and Mitochondrial Apoptotic Pathways

by Min Li, Haowen Wang, Chunye He, Runxiang Zhang and Chaochao Luo

Agriculture 2026, 16(1), 78; https://doi.org/10.3390/agriculture16010078 (registering DOI) - 29 Dec 2025

Abstract

Noise pollution represents a significant environmental stressor that compromises the health and welfare of farm animals. While music enrichment has been suggested to mitigate stress, the specific mechanisms by which it protects against noise-induced immune damage remain poorly understood. This study investigated whether [...] Read more.

Noise pollution represents a significant environmental stressor that compromises the health and welfare of farm animals. While music enrichment has been suggested to mitigate stress, the specific mechanisms by which it protects against noise-induced immune damage remain poorly understood. This study investigated whether music can mitigate acute noise-induced injury to the bursa of Fabricius in broilers. A total of 175 male Arbor Acres broilers were randomly allocated into four groups: Control (C), Noise (N), Noise plus Music (NM), and Music (M). Starting on day 14, groups N and NM were exposed to daily acute noise exposure (115–120 dB for10 min), while groups NM and M received daily 6-h Mozart’s K.448 music enrichment. We evaluated the effects of short-term (by day 21) and long-term (by day 42) music intervention. Results showed that acute noise induced significant histopathological damage, oxidative stress, and apoptosis in the bursa. While short-term music intervention showed limited efficacy, prolonged music exposure significantly attenuated these injuries. Mechanistically, music suppressed the noise-activated NF-κB signaling pathway and reduced inflammatory cytokines (IL-1β, IL-6, and TNF-α). Concurrently, it inhibited mitochondrial-dependent apoptosis by modulating Bcl-2, Bax, Cyt-C, and Caspase-3. These findings provide experimental evidence that long-term music enrichment effectively alleviates noise-induced immune injury, suggesting a practical strategy for improving poultry welfare. Full article

(This article belongs to the Section Farm Animal Production)

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22 pages, 5145 KB

Open AccessArticle

Detection of External Defects in Seed Potatoes Using Spectral–Spatial Fusion of Hyperspectral Images and Deep Learning

by Min Hao, Xingtai Cao, Jianying Sun, Yupeng Sun, Jiaxuan Wang and Hao Zhang

Agriculture 2026, 16(1), 77; https://doi.org/10.3390/agriculture16010077 (registering DOI) - 29 Dec 2025

Abstract

To improve the accuracy of detecting external defects in seed potatoes and address the reliance of current hyperspectral imaging methods on single-dimensional data, this study proposes a multi-dimensional spectral–spatial information fusion approach via concatenation based on a one-dimensional convolutional neural network (1DCNN) within [...] Read more.

To improve the accuracy of detecting external defects in seed potatoes and address the reliance of current hyperspectral imaging methods on single-dimensional data, this study proposes a multi-dimensional spectral–spatial information fusion approach via concatenation based on a one-dimensional convolutional neural network (1DCNN) within the framework of deep learning. Hyperspectral three-dimensional data were acquired for normal seed potatoes and for samples presenting six types of external defects—decay, mechanical damage, wormhole, common scab, black scurf, and frostbite—across a wavelength range of 935–1721 nm. From the hyperspectral images, one-dimensional spectral data and two-dimensional spatial data were extracted. The one-dimensional spectral data were preprocessed using six methods: Savitzky–Golay smoothing (SG), standard normal variate (SNV), multiplicative scatter correction (MSC), first derivative (FD), second derivative (SD), and orthogonal signal correction (OSC). Feature wavelengths were subsequently selected through the successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS), serving as inputs for traditional machine learning models. Two-dimensional spatial data were first subjected to dimensionality reduction via principal component analysis (PCA). Texture features were then extracted from each principal component using the gray-level co-occurrence matrix (GLCM). Following normalization, all spatial texture data were fused with the preprocessed spectral data to form the inputs for the deep learning models Basic1DCNN and Stacked1DCNN. The results demonstrate that the fusion data with the Stacked1DCNN model yielded the best performance in identifying normal seed potatoes and six types of external defects. The overall accuracy, precision, recall, F1 score, and mean average precision reached 98.77%, 98.77%, 98.93%, 98.73%, and 99.66%, respectively, outperforming traditional machine learning approaches. Compared with the Stacked1DCNN model trained using spectral data alone, these metrics improved by 2.81%, 2.78%, 3.20%, 3.01%, and 1.11%. This study offers theoretical and technical insights into the development of automated sorting and non-destructive detection systems for seed potatoes. Full article

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

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18 pages, 5020 KB

Open AccessArticle

Siloxane and Nano-SiO₂ Dual-Modified Bio-Polymer Coatings Based on Recyclable Spent Mushroom Substrate: Excellent Performance, Controlled-Release Mechanism, and Effect on Plant Growth

by Jianrong Zhao, Yuanhao Zhang, Fuxin Liu, Songling Chen, Hongbao Wu and Ruilin Huang

Agriculture 2026, 16(1), 76; https://doi.org/10.3390/agriculture16010076 (registering DOI) - 29 Dec 2025

Abstract

Spent mushroom substrate (SMS)-derived bio-based polyurethane coatings typically exhibit poor hydrophobicity and short nutrient release durations, limiting their ability to satisfy long-term crop requirements. This study developed improved controlled-release urea by preparing water-repellent and compact bio-polymer coatings from recyclable SMS using non-toxic siloxane [...] Read more.

Spent mushroom substrate (SMS)-derived bio-based polyurethane coatings typically exhibit poor hydrophobicity and short nutrient release durations, limiting their ability to satisfy long-term crop requirements. This study developed improved controlled-release urea by preparing water-repellent and compact bio-polymer coatings from recyclable SMS using non-toxic siloxane and nano-SiO₂ modifiers through simple processes. The dual modification markedly reduced water absorption (from 6.60% to 4.43%) and porosity (from 6.32% to 3.92%), creating a dense coating with lotus-leaf-like nanoscale surface protrusions and fewer intermembrane pores. As a result, the nitrogen (N) release period of the dual-modified bio-polymer-polyurethane-coated urea (SBPCU) with a 7% coating thickness was extended from 23 days to 42 days. Phytotoxicity assessments confirmed the excellent biosafety of the bio-polymer coating, revealing no adverse effects on maize growth and even promotional effects at low concentrations. This approach offers a sustainable, eco-friendly, and scalable strategy for producing bio-polymer-coated urea from agricultural waste, serving as a viable alternative to petrochemical coatings while improving nutrient use efficiency and biosafety. Full article

(This article belongs to the Section Agricultural Technology)

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33 pages, 1059 KB

Open AccessArticle

Physiological and Agronomic Responses of Adult Citrus Trees to Oxyfertigation Under Semi-Arid Drip-Irrigated Conditions

by Juan M. Robles, Francisco Miguel Hernández-Ballester, Josefa M. Navarro, Elisa I. Morote, Pablo Botía and Juan G. Pérez-Pérez

Agriculture 2026, 16(1), 75; https://doi.org/10.3390/agriculture16010075 (registering DOI) - 29 Dec 2025

Abstract

Oxyfertigation with hydrogen peroxide (H₂O₂) has been successfully applied in several crops and production systems, but its use in mature citrus orchards under no-tillage conditions and semi-arid Mediterranean environments remains scarcely studied. This study aimed to evaluate the physiological [...] Read more.

Oxyfertigation with hydrogen peroxide (H₂O₂) has been successfully applied in several crops and production systems, but its use in mature citrus orchards under no-tillage conditions and semi-arid Mediterranean environments remains scarcely studied. This study aimed to evaluate the physiological responses of adult citrus trees and the agronomic performance of a mature citrus orchard subjected to chemical oxyfertigation based on the application of H₂O₂ in irrigation water as an oxygen source for the root zone. The experiment was conducted over four consecutive seasons (2018–2021) on adult ‘Ortanique’ hybrid mandarin trees grown in an orchard located in Torre Pacheco (Murcia, Spain). Two treatments were established: a ‘Control’ (0 mg L⁻¹ of H₂O₂) and an ‘OXY’ treatment (50–100 mg L⁻¹ of H₂O₂ applied throughout the growing season). Oxyfertigation significantly increased the dissolved oxygen in irrigation water and soil oxygen diffusion rate, with treatment and treatment × time effects showing greater oxygenation under conditions favoring transient root-zone hypoxia. Soil CO₂ and H₂O vapor fluxes exhibited marked seasonal dynamics but no consistent treatment effect, and soil salinity and macro- and micronutrient contents were not significantly altered. At the plant level, oxyfertigation episodically enhanced leaf gas exchange and transiently improved the water status, but did not produce a sustained increase in leaf-level water use efficiency. In contrast, OXY trees showed greater pruning biomass, more fruits (+18%), higher cumulative yield (+13%), and significantly higher crop water use efficiency (YWUE) while the mean fruit weight and most quality attributes were governed by interannual climatic variability. In summary, oxyfertigation acted as a complementary and safe agronomic practice that improved rhizosphere oxygenation and supported modest gains in fruit load and YWUE in mature citrus orchards. Full article

(This article belongs to the Section Agricultural Systems and Management)

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25 pages, 728 KB

Open AccessArticle

New Theory Agriculture and Smart Agriculture as Contexts for Learning: A Structural Equation Model of Mathematical Literacy and Community Learning

by Anek Putthidech, Amnaj Sookjam, Wannaporn Suthon, Varit Kankaew, Sangtong Boonying, Suwit Somsuphaprungyos and Parinya Natho

Agriculture 2026, 16(1), 74; https://doi.org/10.3390/agriculture16010074 (registering DOI) - 29 Dec 2025

Abstract

This study explores the interconnections between new farming practices, smart agricultural technology, mathematical skills, data-driven decision-making, and community learning in areas commonly affected by drought. Using a statistical method known as Structural Equation Modeling (SEM) and data from 320 farmers, the study explores [...] Read more.

This study explores the interconnections between new farming practices, smart agricultural technology, mathematical skills, data-driven decision-making, and community learning in areas commonly affected by drought. Using a statistical method known as Structural Equation Modeling (SEM) and data from 320 farmers, the study explores how new farming ideas encourage smart practices that improve math skills. It also demonstrates how smart farming creates an environment where data helps inform decision-making, which benefits community learning. The results indicate that New Theory Agriculture (NT) encourages Smart Agriculture (SA) engagement, thereby facilitating both Mathematical Literacy (ML) and Data-Driven Decision-Making (DD). Engagement in SA is closely linked to improvements in ML, which, in turn, strengthen DD abilities. ML plays a central role by serving as a bridge between SA and DD, which, in turn, directly affects Community Learning Outcomes (CL). The findings show that NT fosters community-level outcomes by first building SA and ML, both of which shape DD and ultimately enhance CL, clarifying the sequence of concept connections. The findings reveal that implementing NT and smart technology in agriculture systematically enhances farmers’ resource management and the evolution of mathematical and data skills beyond formal education. The research demonstrates how cognitive skills, technological participation, and collective learning are linked within the community: NT leads to SA engagement, which develops ML, enables DD, and produces CL. The study discusses implications for community education, digital agriculture policy, and rural capacity development, suggesting that future longitudinal or experimental studies could clarify how these connections change over time. Full article

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

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19 pages, 913 KB

Open AccessArticle

Effect of a Long-Term Integrated Multi-Crop Rotation and Cattle Grazing on No-Till Hard Red Spring Wheat (Triticum aestivum L.) Production, Soil Health, and Economics

by Songul Senturklu, Douglas Landblom and Larry J. Cihacek

Agriculture 2026, 16(1), 73; https://doi.org/10.3390/agriculture16010073 (registering DOI) - 29 Dec 2025

Abstract

Integrated crop grazing systems can improve farm profitability due to enterprise complementarity. Utilizing the supply of N from legumes, livestock manure, and plant residues will result in improving grain yield and quality. A long-term 12-year integrated systems study evaluated continuous spring wheat (HRSW-CTRL) [...] Read more.

Integrated crop grazing systems can improve farm profitability due to enterprise complementarity. Utilizing the supply of N from legumes, livestock manure, and plant residues will result in improving grain yield and quality. A long-term 12-year integrated systems study evaluated continuous spring wheat (HRSW-CTRL) with spring wheat (HRSW-ROT) grown in a five-crop rotation: (1) spring wheat, (2) seven-species cover crop, (3) forage corn, (4) field pea/forage barley mix, and (5) sunflower. Yearling beef cattle steers grazed the field pea/forage barley mix, unharvested corn, and a seven-species cover crop. Spring wheat was marketed as a cash crop. Contrary to expectations, HRSW-ROT did not significantly increase grain yield or improve quality over HRSW-CTRL. Improved soil fertility was observed in the HRSW-ROT plots throughout the study relative to SOM, N, P, and K. However, the rotation with grazing management significantly reduced input costs but resulted in negligible gross and net returns over the 12-year period. Year-to-year weather variability was the cause of the differences between the two production management methods. Full article

(This article belongs to the Special Issue Integrated Management and Efficient Use of Nutrients in Crop Systems—2nd Edition)

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15 pages, 3294 KB

Open AccessArticle

The Influence of Nematocidal Plants on the Effectiveness of Pleurotus ostreatus Mycelium Against Caenorhabditis elegans and Heterodera schachtii

by Ewa Moliszewska, Małgorzata Nabrdalik, Robert Nelke and Mirosław Nowakowski

Agriculture 2026, 16(1), 72; https://doi.org/10.3390/agriculture16010072 (registering DOI) - 29 Dec 2025

Abstract

The vegetative mycelium of Pleurotus ostreatus (oyster mushroom) exhibits the ability to reduce nematode populations. This property may be utilized in integrated management programs targeting harmful nematodes such as Heterodera schachtii, a major pest of sugar beet crops. In addition to sugar [...] Read more.

The vegetative mycelium of Pleurotus ostreatus (oyster mushroom) exhibits the ability to reduce nematode populations. This property may be utilized in integrated management programs targeting harmful nematodes such as Heterodera schachtii, a major pest of sugar beet crops. In addition to sugar beet, many other plant species serve as hosts for this nematode; susceptible plants promote H. schachtii development and population growth. Current control strategies rely on integrated plant protection methods, including the use of tolerant cultivars, fallowing, and trap crops such as oilseed radish and white mustard. This study aimed to determine whether sugar beet cv. Janetka or nematocidal plants—oilseed radish cv. Romesa and white mustard cv. Bardena—affect the nematocidal activity of P. ostreatus mycelium when applied together. Specifically, the influence of root or seed secretions from these plants on the activity of ten P. ostreatus mycelial strains was assessed using the model nematode Caenorhabditis elegans and the target pest H. schachtii. Experiments were conducted under laboratory conditions on water agar media colonized by P. ostreatus mycelium. Seeds or root exudates of the tested plants were applied to the mycelial surface. Following incubation, nematode mobility (C. elegans) and cyst entwining by the mycelium (H. schachtii) were evaluated, along with the ability of the mycelium to produce toxocysts. The results indicate that trap plants did not significantly alter the nematocidal activity of the mycelium. However, certain mycelial strains were slightly stimulated by seed diffusates or root exudates. Oilseed radish moderately influenced the nematocidal activity of four mycelial strains against C. elegans, whereas in the case of H. schachtii, similar effects were observed with white mustard. The mycelial elimination of H. schachtii occurred through cyst entwining, which was generally more effective in the presence of plant exudates. Overall, the findings demonstrate that incorporating trap crops such as oilseed radish cv. Romesa or white mustard cv. Bardena, as green manure in crop rotation systems, does not interfere with the nematocidal activity of P. ostreatus mycelium and simultaneously may enrich the soil with nutrients. The study further confirms that P. ostreatus maintains its ability to effectively entwine and eliminate H. schachtii cysts even in the presence of sugar beet, supporting its potential role as a biological control agent. To our knowledge, this is the first experiment that integrates the activities of trap plants and sugar beet with the nematocidal effects of P. ostreatus mycelium. Full article

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

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26 pages, 10404 KB

Open AccessArticle

Accurate and Efficient Recognition of Mixed Diseases in Apple Leaves Using a Multi-Task Learning Approach

by Peng Luan, Nawei Guo, Libo Li, Bo Li, Zhanmin Zhao, Li Ma and Bo Liu

Agriculture 2026, 16(1), 71; https://doi.org/10.3390/agriculture16010071 - 28 Dec 2025

Abstract

The increasing complexity of plant disease manifestations, especially in cases of multiple simultaneous infections, poses significant challenges to sustainable agriculture. To address this issue, we introduce the Apple Leaf Mixed Disease Recognition (ALMDR) model, a novel multi-task learning approach specifically designed for identifying [...] Read more.

The increasing complexity of plant disease manifestations, especially in cases of multiple simultaneous infections, poses significant challenges to sustainable agriculture. To address this issue, we introduce the Apple Leaf Mixed Disease Recognition (ALMDR) model, a novel multi-task learning approach specifically designed for identifying and quantifying mixed disease infections in apple leaves. ALMDR comprises four key modules: a Group Feature Pyramid Network (GFPN) for multi-scale feature extraction, a Multi-Label Classification Head (MLCH) for disease type prediction, a Leaf Segmentation Head (LSH), and a Lesion Segmentation Head (LeSH) for precise delineation of leaf and lesion areas. The GFPN enhances the traditional Feature Pyramid Network (FPN) through differential sampling and grouping strategies, significantly improving the capture of fine-grained disease characteristics. The MLCH enables simultaneous classification of multiple diseases on a single leaf, effectively addressing the mixed infection problem. The segmentation heads (LSH and LeSH) work in tandem to accurately isolate leaf and lesion regions, facilitating detailed analysis of disease patterns. Experimental results on the Plant Pathology 2021-FGVC8 dataset demonstrate ALMDR’s effectiveness, outperforming state-of-the-art methods across multiple tasks. Our model achieves high performance in multi-label classification (F1-score of 93.74%), detection and segmentation (mean Average Precision (mAP) of 51.32% and 45.50%, respectively), and disease severity estimation (

R^{2}

= 0.9757). Additionally, the model maintains this accuracy while processing 6.25 frames per second, balancing performance with computational efficiency. ALMDR demonstrates potential for real-time disease management in apple orchards, with possible applications extending to other crops. Full article

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

19 pages, 763 KB

Open AccessArticle

Digital Dividend or Digital Divide? How the Digital Economy Shapes China’s Agri-Food Trade Dynamics: Evidence on Impacts, Mechanisms, and Heterogeneity

by Feng Ye, Mengzhuo Wu, Liang Fu and Qing Zhang

Agriculture 2026, 16(1), 70; https://doi.org/10.3390/agriculture16010070 - 28 Dec 2025

Abstract

Digital economy has profoundly reshaped the global trade landscape, yet its implications for agricultural trade, particularly in major agricultural trading countries, remain relatively underexplored. Using provincial panel data from China covering the period from 2013 to 2023, this study investigates whether digital economy [...] Read more.

Digital economy has profoundly reshaped the global trade landscape, yet its implications for agricultural trade, particularly in major agricultural trading countries, remain relatively underexplored. Using provincial panel data from China covering the period from 2013 to 2023, this study investigates whether digital economy development in China’s agricultural trade generates a digital dividend or instead exacerbates a digital divide. We construct a unified analytical framework and employ two-way fixed-effects models to identify the effects and underlying mechanisms. The results indicate that digital economy development significantly enhances overall agricultural trade performance. Mechanism analyses further show that this effect operates primarily through improvements in agricultural total factor productivity and the upgrading of rural human capital. Notably, the trade-enhancing effects of the digital economy exhibit pronounced regional heterogeneity. These effects are concentrated mainly in eastern and northern regions and are substantially stronger in non-grain-producing areas, while remaining statistically insignificant in central and western regions. This study contributes to the literature by providing a regionally differentiated assessment of the relationship between the digital economy and agricultural trade. It also offers policy implications for narrowing the digital divide through coordinated investments in digital infrastructure, productivity enhancement, and human capital accumulation. Full article

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

21 pages, 3834 KB

Open AccessArticle

Interoperable IoT/WSN Sensing Station with Edge AI-Enabled Multi-Sensor Integration for Precision Agriculture

by Matilde Sousa, Ana Alves, Rodrigo Antunes, Martim Lima Aguiar, Pedro Dinis Gaspar and Nuno Pereira

Agriculture 2026, 16(1), 69; https://doi.org/10.3390/agriculture16010069 - 28 Dec 2025

Abstract

This study presents an in-depth exploration of an innovative monitoring system that contributes to precision agriculture (PA) and supports sustainability and biodiversity. Amidst the challenges of global population growth and the need for sustainable, high-yield agricultural practices, PA, supported by modern technology and [...] Read more.

This study presents an in-depth exploration of an innovative monitoring system that contributes to precision agriculture (PA) and supports sustainability and biodiversity. Amidst the challenges of global population growth and the need for sustainable, high-yield agricultural practices, PA, supported by modern technology and data-driven methodologies, emerges as a pivotal approach for optimizing crop yield and resource management. The proposed monitoring system integrates Wireless sensor networks (WSNs) into PA, enabling real-time acquisition of environmental data and multimodal observations through cameras and microphones, with data transmission via LTE and/or LoRaWAN for cloud-based analysis. Its main contribution is a physically modular, pole-mounted station architecture that simplifies sensor integration and reconfiguration across use cases, while remaining solar-powered for long-term off-grid operation. The system was evaluated in two field deployments, including a year-long wild-flora monitoring campaign (three stations; 365 days; 1870 images; 63–100% image-based operational availability), during which stations remained operational through a wildfire event. In the viticulture deployment, the acoustic module supported bat monitoring as a bio-indicator of ecosystem health, achieving bat call detection performance of 0.94 (AP Det) and species classification performance of 0.85 (mAP Class). Overall, the results support the use of modular, energy-aware monitoring stations to perform sustained agricultural and ecological data collection under practical field constraints. Full article

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

12 pages, 338 KB

Open AccessArticle

Milk Performance and Blood Biochemical Indicators of Dairy Goats Fed with Black Oat Supplements

by Zvonko Antunović, Josip Novoselec, Zvonimir Steiner, Mislav Didara, Mario Ronta and Željka Klir Šalavardić

Agriculture 2026, 16(1), 68; https://doi.org/10.3390/agriculture16010068 - 28 Dec 2025

Abstract

This research determined the milk performance and milk and blood biochemical indicators of dairy goats fed with black oat supplements. The experiment was conducted on 20 French Alpine goats on the 48th day of lactation, divided into two groups of 10 goats each [...] Read more.

This research determined the milk performance and milk and blood biochemical indicators of dairy goats fed with black oat supplements. The experiment was conducted on 20 French Alpine goats on the 48th day of lactation, divided into two groups of 10 goats each (initial body weights (BW) of 53.90 and 52.15 kg). The research lasted for 30 days, and the monitoring of production properties and blood sampling were carried out on the 1st, 15th, and 30th days of the research. Goats in the BOG group were fed a diet in which yellow oats were gradually replaced with black oats, whereas goats in the COG group received a diet containing yellow oats (CP: 143.64 vs. 150.40 g/kg DM; EE: 48.60 vs. 48.80 g/kg DM; NEL: 7.18 vs. 7.19 MJ/kg DM). These values were subjected to repeated-measures analysis using the PROC MIXED procedure and were further analyzed using Tukey’s post hoc test. Compared with the COG group, no significant differences were observed in the BOG group for the production performance of the goats, except for a slightly increased milk yield (1264.94 vs. 1542.10 g/day, p = 0.098) and reduced concentrations of urea and globulin in the milk of the BOG group (7.90 vs. 7.05 mmol/L, p = 0.081; 5.16 vs. 3.96 g/L, p = 0.091). In the blood of BOG goats, a significantly lower urea concentration was detected (8.75 vs. 7.05 mmol/L, p = 0.020). However, compared with the COG group, goats in the BOG group showed a slight increase (p > 0.05) in protein fractions and a decrease in lipid-related indicators in the blood. These findings confirm the moderate benefit of black oats as a dietary supplement in feed for lactating goats. Full article

(This article belongs to the Special Issue Effects of New Feeds or Additives on Farm Animal Performance and Carcasses Composition)

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24 pages, 5813 KB

Open AccessArticle

Small Non-Coding RNAs in the Regulatory Network of Wheat Dwarf Virus-Infected Wheat

by Abdoallah Sharaf, Jiban K. Kundu, Przemysław Nuc, Emad Ibrahim and Jan Ripl

Agriculture 2026, 16(1), 67; https://doi.org/10.3390/agriculture16010067 - 28 Dec 2025

Abstract

Wheat dwarf virus (WDV) is a major constraint to global wheat production, causing severe yield losses and economic disruption. Understanding the molecular basis of wheat–WDV interactions is essential for developing resistant cultivars. Non-coding RNAs (ncRNAs), including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), [...] Read more.

Wheat dwarf virus (WDV) is a major constraint to global wheat production, causing severe yield losses and economic disruption. Understanding the molecular basis of wheat–WDV interactions is essential for developing resistant cultivars. Non-coding RNAs (ncRNAs), including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), are key regulators of gene expression and defence. This study identified ncRNAs involved in wheat responses to WDV, including host lncRNAs, miRNAs, and viral small interfering RNAs (siRNAs) targeting WDV genomic regions. High-throughput sequencing revealed extensive ncRNA reprogramming under WDV infection. A total of 437 differentially expressed lncRNAs (DElncRNAs) and 58 miRNAs (DEmiRNAs) were detected. Resistant genotypes displayed more DElncRNAs (204 in Svitava; 163 in Fengyou 3) than the susceptible Akteur (141). In Akteur, 66.7% of DElncRNAs were downregulated, whereas in Svitava, 56.9% were upregulated. Akteur also exhibited more DEmiRNAs (28) than resistant genotypes (15), with predominant downregulation. A co-expression network analysis revealed 391 significant DElncRNA–mRNA interactions mediated by 16 miRNAs. The lncRNA XLOC_058282 was linked to 298 transcripts in resistant genotypes, suggesting a central role in the host defence. Functional annotation showed enrichment in signalling, metabolic, and defence-related pathways. Small RNA profiling identified 1166 differentially expressed sRNAs targeting WDV, including conserved hotspots and 408 genotype-specific sites in Akteur versus Fengyou 3. Infected plants displayed longer sRNAs, a sense-strand bias, and a 5′ uridine preference, but lacked typical 21–24 nt phasing. These findings highlight the central roles of ncRNAs in orchestrating wheat antiviral defence and provide a molecular framework for breeding virus-resistant wheat. Full article

(This article belongs to the Special Issue Molecular Breeding for Wheat Disease Resistance)

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16 pages, 993 KB

Open AccessArticle

Perennial Common Basilisk (Prangos ferulacea (L.) Lindl.): Ecological Aspects, Forage Value, and Assessment of Its Effects on Chemical and Microbiological Properties of Raw Milk and Ricotta—A Case Study in Sicily (Italy)

by Giuseppe Di Miceli, Marialetizia Ponte, Nicoletta Lala, Davide Farruggia, Mario Licata, Adriana Bonanno, Antonino Di Grigoli, Giuliana Garofalo, Luca Settanni, Claudia Lino, David Bongiorno, Giuseppe Avellone and Gianniantonio Domina

Agriculture 2026, 16(1), 66; https://doi.org/10.3390/agriculture16010066 - 27 Dec 2025

Abstract

This paper illustrates the results of a case study conducted in the Madonie Regional Park (Sicily, Italy) focusing on Prangos ferulacea (L.) Lindl. This species spontaneously grows in the area and plays an important role as forage plant, contributing to the production of [...] Read more.

This paper illustrates the results of a case study conducted in the Madonie Regional Park (Sicily, Italy) focusing on Prangos ferulacea (L.) Lindl. This species spontaneously grows in the area and plays an important role as forage plant, contributing to the production of traditional dairy products. A multidisciplinary approach was adopted to investigate the ecological characteristics and the chemical composition of the species, and to assess its effects on chemical and microbiological properties of raw milk and ricotta from grazing animals. Indices of bioindication were used to analyse the ecological features of the study area, and a change in the landscape has been observed. Samples of P. ferulacea were collected in the wild in specific plot areas. Chemical analyses were carried out to determine the main nutritional parameters of the species. Chemical and microbiological analyses were performed on raw milk and ricotta samples to evaluate their nutritional composition and quantify the main microbial groups. Raw milk showed no significant microbial differences between samples, with low levels of lactic acid bacteria (LAB) and some Enterobacteriaceae and Escherichia coli (~10² CFU/mL), while pathogens like Listeria monocytogenes and Salmonella spp., as well as spoilage yeasts were undetectable. Ricotta cheese showed a high hygienic profile, with LAB around 10⁴ CFU/g and no spoilage or pathogenic microbes detected, including STEC-negative E. coli. Additionally, SPME-GC/MS and LC/MS analyses were carried out to identify the phenolic compounds of the species with those of dairy products and showed how the contribution of P. ferulacea to ricotta was effective for the aromatic profile and negligible for the polyphenolic component. Full article

(This article belongs to the Section Farm Animal Production)

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22 pages, 1600 KB

Open AccessArticle

Forecasting Crop Yields in Rainfed India: A Comparative Assessment of Machine Learning Baselines and Implications for Precision Agribusiness

by Amir Karbassi Yazdi, Claudia Durán, Iván Derpich and Gonzalo Valdés González

Agriculture 2026, 16(1), 65; https://doi.org/10.3390/agriculture16010065 - 27 Dec 2025

Abstract

Machine learning (ML) has emerged as a practical approach to forecasting crop yields in climate-vulnerable, rainfed agricultural systems where production uncertainty is strongly influenced by monsoon variability. In India’s semi-arid and sub-humid regions, reliable yield forecasts are critical for agribusiness planning and managing [...] Read more.

Machine learning (ML) has emerged as a practical approach to forecasting crop yields in climate-vulnerable, rainfed agricultural systems where production uncertainty is strongly influenced by monsoon variability. In India’s semi-arid and sub-humid regions, reliable yield forecasts are critical for agribusiness planning and managing climate risks. This study presents a standardized evaluation of three widely used ML forecasting models—Linear Regression (LR), Random Forest (RF), and Support Vector Regression (SVR)—for rainfed cereal yields in eight Indian administrative divisions from 2000 to 2025. The study applied a unified methodological framework that included data cleaning, z-score normalization, domain-informed feature selection, strict chronological train–test splitting, and five-fold cross-validation. The dataset integrates agroclimatic and soil variables, including temperature, precipitation, relative humidity, wind speed, and soil pH, comprising approximately 1250 division-year observations. Model performance was assessed on an independent, temporally held-out test set using root mean square error (RMSE), mean absolute error (MAE), and R². The results show that RF provides the most robust predictive performance under realistic forecasting conditions. It achieved the lowest RMSE (0.268 t/ha) and the highest R² (0.271), outperforming LR and SVR. Although the explained variance is modest, it reflects strict temporal validation and the inherent uncertainty of rainfed systems. Feature importance analysis highlights temperature and precipitation as dominant yield drivers. Overall, this study establishes a conservative and reproducible baseline for operational machine learning (ML)-based yield forecasting in precision agribusiness. Full article

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

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39 pages, 3635 KB

Open AccessReview

Application of Navigation Path Planning and Trajectory Tracking Control Methods for Agricultural Robots

by Fan Ye, Feixiang Le, Longfei Cui, Shaobo Han, Jingxing Gao, Junzhe Qu and Xinyu Xue

Agriculture 2026, 16(1), 64; https://doi.org/10.3390/agriculture16010064 - 27 Dec 2025

Abstract

Autonomous navigation is a core enabler of smart agriculture, where path planning and trajectory tracking control play essential roles in achieving efficient and precise operations. Path planning determines operational efficiency and coverage completeness, while trajectory tracking directly affects task accuracy and system robustness. [...] Read more.

Autonomous navigation is a core enabler of smart agriculture, where path planning and trajectory tracking control play essential roles in achieving efficient and precise operations. Path planning determines operational efficiency and coverage completeness, while trajectory tracking directly affects task accuracy and system robustness. This paper presents a systematic review of agricultural robot navigation research published between 2020 and 2025, based on literature retrieved from major databases including Web of Science and EI Compendex (ultimately including 95 papers). Research advances in global planning (coverage and point-to-point), local planning (obstacle avoidance and replanning), multi-robot cooperative planning, and classical, advanced, and learning-based trajectory tracking control methods are comprehensively summarized. Particular attention is given to their application and limitations in typical agricultural scenarios such as open-fields, orchards, greenhouses, and hilly slopes. Despite notable progress, key challenges remain, including limited algorithm comparability, weak cross-scenario generalization, and insufficient long-term validation. To address these issues, a scenario-driven “scenario–constraint–performance” adaptive framework is proposed to systematically align navigation methods with environmental and operational conditions, providing practical guidance for developing scalable and engineering-ready agricultural robot navigation systems. Full article

(This article belongs to the Section Agricultural Technology)

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15 pages, 2515 KB

Open AccessArticle

The Utilization of Mixed Silage Composed of Pennisetum giganteum and Rice Straw as an Alternative to Maize Silage in Fattening Lambs

by Yaochang Feng, Beiyu Weng, Wenhui Xu, Shaoyan Wu, Liuyan Fang, Yuezhang Lu, Lu Lin, Wenjie Zhang and Jian Ma

Agriculture 2026, 16(1), 63; https://doi.org/10.3390/agriculture16010063 - 27 Dec 2025

Abstract

This experiment evaluated the application effects of the dietary substitution of maize silage with mixed silage prepared with Pennisetum giganteum and rice straw on fattening lambs. Forty-eight male Hu lambs with similar body weights and ages were randomly divided into four groups. The [...] Read more.

This experiment evaluated the application effects of the dietary substitution of maize silage with mixed silage prepared with Pennisetum giganteum and rice straw on fattening lambs. Forty-eight male Hu lambs with similar body weights and ages were randomly divided into four groups. The maize silage in the diet was replaced with Pennisetum giganteum and rice straw mixed silage in proportions of 0 (CON), 25% (PR1), 50% (PR2) and 75% (PR3). The average daily gain of the PR3 group was lower (p < 0.05) than that of the other groups. The highest substitution level increased (p < 0.05) ruminal ammonia nitrogen concentration and acetate-to-propionate ratio in lambs compared with the CON and PR1 groups. Moreover, dry matter and neutral detergent fiber digestibility in PR3 lambs were lower (p < 0.05) than in PR1 lambs. Compared with the CON group, the concentrations of serum catalase and total antioxidant capacity were increased (p < 0.05) in the PR2 and PR3 groups. Overall, the dietary substitution of maize silage with Pennisetum giganteum and rice straw mixed silage at a 50% level did not show a negative influence on growth performance of fattening lambs but displayed positive effects on their fiber digestibility and antioxidative capacity. Full article

(This article belongs to the Section Farm Animal Production)

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18 pages, 891 KB

Open AccessArticle

Effect of Nitrogen Sources on the Phenological Phases of Italian Zucchini Under Salt Stress

by Gleydson de Freitas Silva, Tayd Dayvison Custódio Peixoto, Miguel Ferreira Neto, Antônio Gustavo de Luna Souto, Ricardo André Rodrigues Filho, Kariolania Fortunato de Paiva Araújo, Jussiara Sonally Jácome Cavalcante, Kleane Targino Oliveira Pereira, Rômulo Carantino Lucena Moreira, Pedro Dantas Fernandes, Nildo da Silva Dias, Josinaldo Lopes Araújo Rocha, Alberto Soares de Melo, Alex Álvares da Silva and Francisco Vanies da Silva Sá

Agriculture 2026, 16(1), 62; https://doi.org/10.3390/agriculture16010062 - 27 Dec 2025

Abstract

Salt stress is one of the most significant abiotic factors limiting plant growth and crop productivity worldwide, especially in arid and semiarid regions. We aimed to investigate nitrogen fertilization strategies using nitrate and ammoniacal sources during different phenological phases of Italian zucchini cv. [...] Read more.

Salt stress is one of the most significant abiotic factors limiting plant growth and crop productivity worldwide, especially in arid and semiarid regions. We aimed to investigate nitrogen fertilization strategies using nitrate and ammoniacal sources during different phenological phases of Italian zucchini cv. Caserta to alleviate salt stress. The experiment was conducted in a greenhouse using a randomized block design with four replications. The treatments were as follows: T1 = entire cycle with nitrate nitrogen + 0.50 dS m⁻¹ (control); T2 = entire cycle with nitrate nitrogen + 4.5 dS m⁻¹ (salt stress); T3 = 50% nitrate nitrogen + 50% ammoniacal nitrogen + 4.50 dS m⁻¹; T4 = ammoniacal nitrogen during the vegetative phase + nitrate nitrogen during the reproductive phase + 4.50 dS m⁻¹; T5 = nitrate nitrogen during the vegetative phase + ammoniacal nitrogen during the reproductive phase + 4.50 dS m⁻¹; T6 = entire cycle with ammoniacal nitrogen + 4.50 dS m⁻¹. Under salt stress conditions, Italian zucchini cv. Caserta showed a leaf area of 5783 cm² compared to an average of 4521 cm² under salt stress. Similarly, production per plant reached 1361 g in the control, while under salt stress it averaged only 442 g. However, under salt stress, T2 resulted in higher production compared with T3, T4, T5 and T6, although it was still lower than T1. The use of ammoniacal nitrogen throughout the cycle or during the reproductive phase caused flower abortion. Under salt stress, the application of ammoniacal nitrogen during the vegetative phase (T4) or a 1:1 ammonium–nitrate ratio throughout the cycle (T3) resulted in yields that were comparable to those achieved with nitrate-only fertilization (T2). Full article

(This article belongs to the Special Issue Advanced Cultivation Technologies for Horticultural Crops Production)

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19 pages, 4630 KB

Open AccessArticle

Effects of Biochar and Straw Regulation on Snowmelt Infiltration in Seasonal Frozen Soil Regions of Northeast China

by Zhaoxing Xiao, Shuang Lv, Qiang Fu, Tianxiao Li, Renjie Hou, Mo Li and Dong Liu

Agriculture 2026, 16(1), 61; https://doi.org/10.3390/agriculture16010061 - 26 Dec 2025

Abstract

In the seasonal frozen soil region of Northeast China, freeze–thaw processes destabilize soil structure and elevate the risk of spring flooding. While biochar and straw are recognized for their ability to enhance soil structure, their regulatory effects on the characteristics of frozen front [...] Read more.

In the seasonal frozen soil region of Northeast China, freeze–thaw processes destabilize soil structure and elevate the risk of spring flooding. While biochar and straw are recognized for their ability to enhance soil structure, their regulatory effects on the characteristics of frozen front migration and snowmelt infiltration in this region have not been thoroughly investigated. This study conducted indoor simulation experiments in 2024, establishing three different initial moisture contents (W1: 20%, W2: 15%, W3: 10%) and four distinct regulation measures (CK: blank control, B: 1.0% biochar, J: 0.5% straw, BJ: 1.0% biochar and 0.5% straw) to investigate the influence of various regulation modes on snowmelt water infiltration in freeze–thaw soils. The experimental results indicate that the application of biochar and straw significantly enhances soil aggregate stability, with the BJ treatment increasing small pores by 58.25–60.17% and micropores by 26.69–77.71%. The application of biochar and straw can increase both the migration depth of the soil freezing front and its average migration velocity. An appropriate amount of biochar and straw can enhance the cumulative soil infiltration amount and infiltration rate. Additionally, biochar and straw enhance the relationship between the cumulative soil infiltration amount and the migration characteristics of the freezing front. Full article

(This article belongs to the Section Agricultural Soils)

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