Agriculture

24 pages, 3326 KB

Open AccessArticle

Development of a DEM-Based Flexible Plant Model for Mature Peanut Plants

by Dongjie Li, Zengcun Chang, Dongwei Wang, Xu Li, Jiayou Zhang, Haipeng Yan, Baiqiang Zuo and Jialin Hou

Agriculture 2026, 16(13), 1390; https://doi.org/10.3390/agriculture16131390 (registering DOI) - 25 Jun 2026

Abstract

Accurate discrete element method (DEM) modelling of mature peanut plants is essential for simulating peanut harvesting, pod detachment, and harvest-loss formation. However, existing peanut DEM models are usually simplified as isolated pods, rigid cylindrical particles, or partial stem–pod structures, which limits their ability [...] Read more.

Accurate discrete element method (DEM) modelling of mature peanut plants is essential for simulating peanut harvesting, pod detachment, and harvest-loss formation. However, existing peanut DEM models are usually simplified as isolated pods, rigid cylindrical particles, or partial stem–pod structures, which limits their ability to represent the flexible deformation of vines and pod stalks and the fracture behaviors at the pod–pod stalk junction. In this study, a DEM-based flexible plant model was developed for mature peanut plants. The geometric dimensions, contact parameters, and mechanical properties of peanut pods, pod stalks, and stems were measured through physical experiments. The Hertz–Mindlin model was used for non-bonded contacts, whereas the Hertz–Mindlin with Bonding model was adopted to represent the flexible connections among plant organs and the fracture behaviors of the pod–pod stalk junction. The main DEM parameters were calibrated using Plackett–Burman screening, steepest ascent experiments, and central composite design. The results showed that the tangential stiffness per unit area and tangential critical stress at the pod–pod stalk junction were the dominant factors affecting pod detachment force. The optimized parameter combination was a tangential stiffness per unit area of 4.738 × 10⁵ N/m³ and a tangential critical stress of 9.350 × 10⁵ Pa, corresponding to a simulated tensile force of 6.73 N. Model validation was performed by comparing peanut harvesting simulations with field trials. The relative error of pod loss rate between simulation and field measurement was less than 7.55%, and the t-test result indicated no significant difference between the two datasets (p > 0.05). These results demonstrate that the proposed flexible peanut plant model can effectively characterize pod–pod stalk separation and can provide a reliable DEM modelling basis for peanut harvesting process analysis and equipment optimization. Full article

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

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20 pages, 2202 KB

Open AccessArticle

Early Detection of Muskmelon Powdery Mildew Using Time-Series 3D Multispectral Point Clouds

by Zhiqi Hong, Qinghui Guo, Li Fang, Haiyan Cen and Yong He

Agriculture 2026, 16(13), 1389; https://doi.org/10.3390/agriculture16131389 (registering DOI) - 25 Jun 2026

Abstract

Melon (Cucumis melo L.) is a globally significant horticultural crop, characterized by high nutritional value and substantial commercial status. However, frequent outbreaks of powdery mildew severely threaten its yield and fruit quality. Current early detection methods primarily focus on detached leaf assays, [...] Read more.

Melon (Cucumis melo L.) is a globally significant horticultural crop, characterized by high nutritional value and substantial commercial status. However, frequent outbreaks of powdery mildew severely threaten its yield and fruit quality. Current early detection methods primarily focus on detached leaf assays, which often lack sufficient model generalization. This study proposes a temporal 3D multispectral point cloud reconstruction method for melon plants by integrating multispectral imaging with 3D reconstruction technology. An Artificial Neural Network (ANN) model for 3D spatial light field distribution was developed based on a hemispherical white reference to achieve precise reflectance calibration of the multispectral point clouds. Post-calibration, the coefficient of variation (CV) for the spectral reflectance of the hemispherical reference in 3D space was reduced to less than 2.4%. On this basis, an early classification model for melon powdery mildew was constructed using Partial Least Squares Discriminant Analysis (PLS-DA) based on the mean reflectance spectra of individual plant point clouds. The results demonstrate that the average recognition accuracy reaches 85.94% from 4 days post-inoculation onwards, enabling disease early warning three days in advance. This research provides critical theoretical support and technical reference for the non-destructive early monitoring and precision smart plant protection of crops in facility agriculture. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

17 pages, 2849 KB

Open AccessArticle

Contrasting Rhizosphere Soil Stoichiometric Traits and Microbial Nitrogen Limitation Between Maize and Peanut Under Intercropping and Straw Retention

by Qila Sa, Wei Qi, Jie Liang, Yujun Cao, Fanyun Yao and Yongjun Wang

Agriculture 2026, 16(13), 1388; https://doi.org/10.3390/agriculture16131388 (registering DOI) - 25 Jun 2026

Abstract

Extracellular enzyme stoichiometry is a key indicator for assessing nutrient limitation experienced by soil microorganisms. Yet, the characteristics of enzyme-inferred microbial nutrient limitation in rhizosphere soil under the combined agricultural practices of intercropping and straw retention remain unclear. Here, we conducted a field [...] Read more.

Extracellular enzyme stoichiometry is a key indicator for assessing nutrient limitation experienced by soil microorganisms. Yet, the characteristics of enzyme-inferred microbial nutrient limitation in rhizosphere soil under the combined agricultural practices of intercropping and straw retention remain unclear. Here, we conducted a field experiment in the black soil region of Northeast China to quantify the effects of intercropping and straw retention on soil nutrients, microbial biomass, extracellular enzyme activities, and their C:N:P stoichiometry in the rhizosphere of maize and peanut. Our results showed that compared with sole cropping, intercropping increased soil organic carbon (SOC) by 6.21–13.57%, total nitrogen (TN) by 8.57–12.49%, and total phosphorus (TP) by 12.01–40.29% in the rhizosphere. The vector analysis revealed an average vector length (VL) of 1.68 and 1.57 for extracellular enzymes in the rhizosphere soil of maize and peanut, with a vector angle (VA) of 37.80° and 34.67°, respectively. These values suggest that soil microorganisms in the rhizosphere of both crops experienced C limitation, and that the degree of enzyme-inferred N limitation was modulated by microbial C acquisition strategies, with a dynamic trade-off between the two. This N limitation was more pronounced in the peanut rhizosphere. Notably, the combined treatment of intercropping and full straw retention increased the VA of peanut by 5.38%, corresponding to a partial alleviation of enzyme-inferred N limitation in the rhizosphere soil. The extracellular enzyme C:N:P stoichiometry in the rhizosphere soil of maize and peanut was 1.33:1.29:1.00 and 0.89:1.29:1.00, respectively. Microbial biomass nitrogen (MBN) was the primary factor affecting enzyme-inferred microbial nutrient limitation (explaining 54.6% of variation). The extracellular enzyme stoichiometric characteristics of rhizosphere soil differed significantly between the two crops. Intercropping had a stronger impact on rhizosphere microbial nutrient limitation than straw retention, and their synergistic effect was associated with a partial alleviation of rhizosphere enzyme-inferred N limitation by enhancing extracellular enzyme activity. These findings demonstrate that integrated intercropping and straw retention can support sustainable soil management in black soil agroecosystems. Full article

(This article belongs to the Topic Plant-Soil Interactions, 3rd Edition)

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23 pages, 641 KB

Open AccessArticle

Optimized Nutrition as a Driver of Cultivar-Specific Metabolic Plasticity in Sweet Basil

by Silvia Farkasová, Lucia Urbanová, Jana Lakatošová, Ivona Jančo, Eva Ivanišová, Ivana Mezeyová and Miroslav Šlosár

Agriculture 2026, 16(13), 1387; https://doi.org/10.3390/agriculture16131387 (registering DOI) - 25 Jun 2026

Abstract

Sweet basil is a medicinal herb valued for its culinary and therapeutic applications, primarily due to its secondary metabolite content. Therefore, optimizing its cultivation is essential for growers seeking to improve both the quality and nutritional value of the plants. Two cultivars of [...] Read more.

Sweet basil is a medicinal herb valued for its culinary and therapeutic applications, primarily due to its secondary metabolite content. Therefore, optimizing its cultivation is essential for growers seeking to improve both the quality and nutritional value of the plants. Two cultivars of Ocimum basilicum L., ‘Lettuce Leaf’ (LL) and ‘Purple Opal’ (PO), were evaluated under various nutritional regimes (mineral, organic, and organo-mineral). The assessment included measurements of total protein, fat, and ash content, as well as total polyphenol levels, phenolic acid content, and antioxidant activity. HPLC analysis was performed to evaluate the composition of selected phenolic and chlorogenic acids, flavonoids, and catechins. Additionally, mineral content was analyzed using OES-ICP. Gene expression of key genes involved in the phenylpropanoid pathway (PAL, C4H, 4Cl, CAD, and CVOMT) and the transcription factor OscWRKY1 was analyzed through RT-qPCR. The key findings indicated that the nutritional variants significantly impacted both primary and secondary metabolism in the assessed plants. Additionally, there was a significant (p < 0.05) cultivar-specific response to the different nutritional variants. The results suggest that the optimal nutritional strategy may vary depending on the target metabolite. Variant 4 was associated with the most favorable overall response in basil, including increased protein levels, higher total polyphenol content, and a balanced mineral composition. However, variant 5 showed the highest antioxidant activity for both cultivars. Rutin and protocatechuic acid were detected only in PO, and cryptochlorogenic acid was detected only in LL. A marked varietal difference was observed in gallocatechin content, with the LL variety containing more than fourfold higher levels than the PP variety. The results of RT-qPCR were fluctuating and strongly dependent on the cultivar. Full article

(This article belongs to the Special Issue Medicinal and Aromatic Crops: Cultivation, Quality, Processing, Application)

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20 pages, 2444 KB

Open AccessArticle

A Geometry-Aware Road-Constrained Framework for Weed Quantification and Operational Workload Assessment in Vineyard Roads

by Yunfei Wang, Weidong Jia, Ronghua Gao, Mingxiong Ou, Xiang Dong and Shuhui Fan

Agriculture 2026, 16(13), 1386; https://doi.org/10.3390/agriculture16131386 (registering DOI) - 25 Jun 2026

Abstract

To address the difficulty of road-constrained weed extraction and operational assessment in orchard road regions under weed encroachment, background interference, and complex illumination, this study developed a vision-based framework integrating road segmentation, in-road weed extraction, spatial quantification, and workload evaluation. A joint image [...] Read more.

To address the difficulty of road-constrained weed extraction and operational assessment in orchard road regions under weed encroachment, background interference, and complex illumination, this study developed a vision-based framework integrating road segmentation, in-road weed extraction, spatial quantification, and workload evaluation. A joint image enhancement strategy combining LAB-based luminance correction, HSV-based color gain adjustment, ExG enhancement, and morphological refinement was first applied to improve the separability of green vegetation targets. An improved YOLOv11 with an SE attention mechanism was then used for robust orchard road segmentation. On this basis, road-region constraints and a dual-threshold HSV–ExG strategy were combined to extract in-road weeds and calculate global weed coverage. Furthermore, a geometry-adaptive grid based on actual road boundaries was constructed to quantify grid-cell coverage, aggregation, spatial heterogeneity, and workload index. Results showed that the proposed enhancement method increased the mean and standard deviation of ExG by 21.30% and 19.22%, respectively. The improved YOLOv11 achieved 91.28% precision, 87.52% recall, 93.37% AP₅₀, 68.31% mAP_@_0.5:0.95, and 89.36% F1-score. Across five sample groups, global weed coverage ranged from 0.6123 to 0.6471, and the workload index ranged from 0.6403 to 0.6859. Overall, the proposed method establishes an integrated image-based analytical pipeline that may support future variable-rate weeding and decision-making after further operational validation. Full article

(This article belongs to the Section Agricultural Technology)

27 pages, 1857 KB

Open AccessArticle

Assessment of Seed Quality and Kernel Morphological Trait Stability in Two Maize Hybrids Across Four Growing Seasons

by Vasileios Greveniotis, Elisavet Bouloumpasi, Stylianos Zotis, Adriana Skendi, Athanasios Korkovelos, Dimitrios Kantas and Constantinos G. Ipsilandis

Agriculture 2026, 16(13), 1385; https://doi.org/10.3390/agriculture16131385 (registering DOI) - 25 Jun 2026

Abstract

Maize seed quality and kernel morphological traits are important determinants of grain utilization and are influenced by both genetic factors and growing-season conditions. This study evaluated the stability of seed quality and kernel morphological traits in two commercial maize hybrids (Costanza and LG [...] Read more.

Maize seed quality and kernel morphological traits are important determinants of grain utilization and are influenced by both genetic factors and growing-season conditions. This study evaluated the stability of seed quality and kernel morphological traits in two commercial maize hybrids (Costanza and LG 3535) across four growing seasons, three row spacing systems, and two plant density levels. Seed quality traits (protein, fat, ash, starch, crude fiber, and moisture content) and kernel morphological traits (length, width, and thickness) were evaluated using univariate and multivariate statistical analyses. Significant effects of hybrid, growing season, row spacing, and their interactions were detected for most evaluated traits. Growing-season variability influenced seed composition and kernel morphology, while row spacing and plant density further contributed to trait expression. Costanza exhibited greater stability for most traits, particularly starch content and kernel morphology, whereas LG 3535 showed more variable responses across growing seasons and row spacing combinations. Correlation and multivariate analyses revealed strong associations among starch content, kernel width, and kernel thickness, whereas protein, ash, and crude fiber were less closely associated with kernel size traits. These findings demonstrate the importance of hybrid × growing-season interactions in shaping maize kernel characteristics and highlight the value of multi-environment evaluation for identifying hybrids with stable kernel quality traits under Mediterranean production conditions. Full article

(This article belongs to the Section Seed Science and Technology)

32 pages, 2871 KB

Open AccessArticle

How Does Artificial Intelligence Industry Agglomeration Affect Agricultural Pollution–Carbon Reduction Synergy in China? Evidence from a Marginal Cost Perspective

by Shuang Gao, Dan Li, Masaaki Yamada and Haisong Nie

Agriculture 2026, 16(13), 1384; https://doi.org/10.3390/agriculture16131384 (registering DOI) - 25 Jun 2026

Abstract

Examining how artificial intelligence industry agglomeration (AIIA) affects carbon and pollution reduction is crucial for China’s agricultural sustainability. Existing research mainly examines the effect of artificial intelligence (AI) on the reduction of single pollutants while overlooking how industry agglomeration influences the marginal cost [...] Read more.

Examining how artificial intelligence industry agglomeration (AIIA) affects carbon and pollution reduction is crucial for China’s agricultural sustainability. Existing research mainly examines the effect of artificial intelligence (AI) on the reduction of single pollutants while overlooking how industry agglomeration influences the marginal cost of coordinated abatement, a key issue for the agricultural resource–environment–economy system. Using panel data for 30 Chinese provinces from 2016 to 2024, this study constructs a marginal cost-based indicator of agricultural pollution–carbon reduction synergy (APCRS) and examines the effect of AIIA. The full-sample results reveal that AIIA has a U-shaped relationship with APCRS. Technological progress partially mediates this relationship. Agricultural socialized services and rural industrial integration buffer the initial negative association, whereas agricultural labor productivity strengthens the curvature of the estimated nonlinear pattern. The effect of AIIA also varies with external conditions and is more pronounced in regions with higher levels of marketization and industrialization while remaining significantly U-shaped across grain strategic zones. This dynamic process is more likely to emerge when public innovation investment and rural household income exceed critical thresholds. These findings provide new evidence for understanding how AI-driven agglomeration can support green agricultural transformation. Full article

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

24 pages, 43718 KB

Open AccessArticle

Lightweight Visual Detection Framework for Real-Time Rice Leaf Disease Identification on Edge Mobile Robots

by Yan Xu, Yinan Liu, Xiangchen Meng, Qing Yuan, Dazhong Wang, Liyan Wu, Xiang Yue, Longlong Feng and Cuihong Liu

Agriculture 2026, 16(13), 1383; https://doi.org/10.3390/agriculture16131383 (registering DOI) - 25 Jun 2026

Abstract

Rice leaf diseases severely threaten global food security, and efficient on-site detection remains challenging for resource-constrained field inspection robots. This work introduces a lightweight visual detection framework designed for the real-time and accurate identification of rice leaf diseases on agricultural edge mobile platforms. [...] Read more.

Rice leaf diseases severely threaten global food security, and efficient on-site detection remains challenging for resource-constrained field inspection robots. This work introduces a lightweight visual detection framework designed for the real-time and accurate identification of rice leaf diseases on agricultural edge mobile platforms. A dataset of 4622 annotated images compiled from mobile-device acquisition and publicly available online sources, covering three representative disease categories, together with an independent public benchmark, was used for evaluation. The framework integrates three complementary modules: adaptive multi-scale feature extraction via a dynamic hybrid convolution backbone (C3k2-DICN), cross-scale parameter sharing in the detection head (CSDH) to reduce redundancy, and dual-path downsampling (ADown) to preserve disease-discriminative information during resolution compression. Compared to the YOLO11n baseline, the proposed approach reduced GFLOPs by 36.5% and parameter count by 34.6%, while achieving 88.42% mAP@0.5 and 45.82% mAP@0.5:0.95 on the compiled dataset and 91.71% mAP@0.5 on the public benchmark, indicating accuracy competitive with or superior to all evaluated comparison models. Deployed on an NVIDIA Jetson TX2 with TensorRT FP16 acceleration, the model ran in real time on-device, reaching 32.2 FPS for the TensorRT inference stage and 19.8 FPS for the full end-to-end pipeline including image pre- and post-processing. The framework offers a practical basis for lightweight on-device rice disease detection; closed-loop validation on a moving field robot is left to future work. Full article

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

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17 pages, 10817 KB

Open AccessArticle

Validation of a Low-Cost Digital Apiculture System Under Variable Colony Dynamics: A Southern European Case Study

by Simone Bergonzoli, Marko M. Kostić, Zoran Stamenković, Krstan Kešelj, Alex Filisetti, Elio Romano, Simone Figorilli, Simone Vasta, Roberta Cacciatore and Antonio Scarfone

Agriculture 2026, 16(13), 1382; https://doi.org/10.3390/agriculture16131382 (registering DOI) - 25 Jun 2026

Abstract

Beekeeping is highly affected by climate change, which alters environmental conditions and challenges colony stability. In this context, digital monitoring technologies can enhance apiary resilience. This study presents the development and field validation of a low-cost hive monitoring system based on a customizable [...] Read more.

Beekeeping is highly affected by climate change, which alters environmental conditions and challenges colony stability. In this context, digital monitoring technologies can enhance apiary resilience. This study presents the development and field validation of a low-cost hive monitoring system based on a customizable Raspberry Pi architecture, integrating temperature and weight sensors with robust data continuity features. The system was evaluated over one year in Southern Europe (Serbia) against a commercial reference. Results show that correlation between systems depends on both the monitored parameter and the biological state of the colony. For weight, strong agreement was observed only during winter, when reduced biological activity allows reliable comparison, whereas correlations were weak in more active periods. Conversely, temperature monitoring exhibited the highest correlation over long-term datasets, indicating that extended time scales are required for reliable sensor validation. These findings highlight the importance of a context-aware validation approach in apiculture. The proposed system provides a cost-effective and reliable solution for continuous hive monitoring, supporting data-driven management and improved resilience under climate variability. Full article

(This article belongs to the Special Issue Sustainable Beekeeping: Strategies for Enhancing Bee Stress Resistance)

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13 pages, 10203 KB

Open AccessArticle

Effects of In Situ Tomato Straw and Green Manure Returning on Greenhouse Soil Properties and Production in Coastal Saline–Alkali Areas

by Ruiping Ma, Guoxin Zhang, Yeshuo Sun, Xiaoqing Yang, Ding Ding and Hongjiu Liu

Agriculture 2026, 16(13), 1381; https://doi.org/10.3390/agriculture16131381 (registering DOI) - 24 Jun 2026

Abstract

To clarify the effects of in situ tomato straw and green manure returning on soil quality and vegetable production in coastal saline–alkali greenhouse soils, this study employed a split-plot design to evaluate three green manure treatments (sweet corn, sorghum–sudangrass, and sesban) under two [...] Read more.

To clarify the effects of in situ tomato straw and green manure returning on soil quality and vegetable production in coastal saline–alkali greenhouse soils, this study employed a split-plot design to evaluate three green manure treatments (sweet corn, sorghum–sudangrass, and sesban) under two main treatments (tomato straw return or no straw return). The impacts on tomato and celery yield and quality, as well as soil physicochemical and biological properties, were assessed over a two-year rotation cycle. The results showed that: (1) compared to the control, green manure returning could significantly reduce soil bulk density, salinity, and fungal abundance; (2) different green manures specifically enriched functional microbes: Sesban enriched Nitrospira and Gemmatimonas; Sorghum–sudangrass enriched Streptomyces and Acidibacter; sweet corn enriched Pseudomonas; green manure reduced the relative abundance of Fusarium, whereas tomato straw showed the opposite trend; and (3) green manure, especially sorghum–sudangrass, significantly increased yields of both tomato and celery, while reducing celery cellulose content. Therefore, in situ sorghum–sudangrass returning is recommended as an effective strategy for maintaining soil health and achieving sustainable production in greenhouse systems within coastal saline–alkali regions. Full article

(This article belongs to the Section Agricultural Soils)

19 pages, 1742 KB

Open AccessArticle

Regional Genetic Signatures in Underrepresented Mediterranean Grapevine Germplasm: Comparative SSR Analysis Reveals Distinct Diversity Patterns in Greek, Moroccan, and Slovenian Landraces

by Barbara Pipan, Mohamed Neji, Georgios Merkouropoulos, Mohammed Ater, Lovro Sinkovič, Dimitrios Taskos, Salama El Fatehi, Nouhaila Dihaz, Theodora Pitsoli, Vladimir Meglič, Younes Hmimsa and Aliki Kapazoglou

Agriculture 2026, 16(13), 1380; https://doi.org/10.3390/agriculture16131380 (registering DOI) - 24 Jun 2026

Abstract

Traditional Mediterranean grapevine landraces represent irreplaceable reservoirs of adaptive diversity, yet many regional germplasm pools remain poorly characterized, limiting conservation strategies and climate-resilient breeding. This study presents the first comparative genetic assessment of 154 local Vitis accessions from three historically interconnected but genomically [...] Read more.

Traditional Mediterranean grapevine landraces represent irreplaceable reservoirs of adaptive diversity, yet many regional germplasm pools remain poorly characterized, limiting conservation strategies and climate-resilient breeding. This study presents the first comparative genetic assessment of 154 local Vitis accessions from three historically interconnected but genomically underrepresented Mediterranean regions: Greece, Morocco, and Slovenia. Using 12 highly polymorphic nuclear SSR markers, we detected substantial genetic diversity (168 alleles; mean heterozygosity He = 0.881) with distinct regional signatures. Moroccan accessions exhibited the highest allelic richness and 11 private alleles, reflecting diverse agroecological adaptation. Slovenian germplasm formed a cohesive, genetically stable cluster with high effective allele numbers. Greek accessions exhibited the highest observed heterozygosity and 14 private alleles, consistent with the Aegean’s role as a major diversification hotspot. Despite >90% of variance occurring within individuals, AMOVA and pairwise FST (0.050–0.061) revealed low to moderate but significant geographic differentiation. Multivariate analyses (PCA, UPGMA) and Bayesian clustering (sNMF, K = 3) consistently resolved three regional genetic groups with varying admixture levels, consistent with a mosaic domestication model, as previously proposed for the Mediterranean basin, shaped by recurrent introductions, wild introgression, and region-specific selection. Our results show that peripheral Mediterranean germplasm harbors meaningful, regionally distinctive, substantial, non-redundant diversity not fully represented in surveys focused on climate adaptation, disease resistance breeding, and long-term genetic resource conservation. These findings challenge simplistic diffusion models and emphasize the strategic importance of geographically comprehensive sampling in grapevine conservation programs. Full article

(This article belongs to the Special Issue Genetic Diversity in Vitis sp.)

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21 pages, 5583 KB

Open AccessReview

Nutrition as the Intelligent Nexus: Integrating Precision Farming into Sustainable Ruminant Systems

by Luis O. Tedeschi, Egleu D. M. Mendes and Marcia H. M. R. Fernandes

Agriculture 2026, 16(13), 1379; https://doi.org/10.3390/agriculture16131379 (registering DOI) - 24 Jun 2026

Abstract

Global agriculture faces a dual imperative: increase food production to meet rising demand while simultaneously reducing environmental impacts and resource inefficiencies. Addressing this challenge requires repositioning ruminant nutrition as the intelligent nexus linking crop and livestock production within Integrated Crop–Livestock Systems (ICLS). In [...] Read more.

Global agriculture faces a dual imperative: increase food production to meet rising demand while simultaneously reducing environmental impacts and resource inefficiencies. Addressing this challenge requires repositioning ruminant nutrition as the intelligent nexus linking crop and livestock production within Integrated Crop–Livestock Systems (ICLS). In this role, nutrition becomes central to restoring ecological, nutritional, and economic synergies that have been fragmented by decades of agricultural specialization. While ICLS provides the ecological foundation, Precision Livestock Farming delivers the technological and analytical infrastructure necessary to operationalize integration at the individual-animal level. Real-time sensing, Internet of Things platforms, and Artificial Intelligence (AI) enable dynamic monitoring of animal physiology, behavior, and environmental interactions across scales. A key advancement in this evolution is the development of Hybrid Intelligent Mechanistic Models (HIMM), which integrate biologically grounded mechanistic models with data-driven AI approaches. By combining interpretability with adaptive learning, HIMM enhances predictive accuracy, extrapolative capacity, and decision transparency, enabling the creation of digital twins that simulate biological responses before management interventions are implemented. Such architectures extend precision nutrition beyond feed efficiency and methane mitigation to include nutrient density and product quality, thereby linking different ecosystem processes directly to human dietary needs. Integrating nutrition with advanced modeling and monitoring tools can help livestock systems move beyond static “net-zero” benchmarks toward sustainable strategies that are responsive to local production contexts. In this reframed paradigm, nutrition is not merely a production input but the central analytical framework that computationally links biological mechanisms, environmental stewardship, technological innovation, and human health within sustainable ruminant systems. Full article

(This article belongs to the Section Farm Animal Production)

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18 pages, 2294 KB

Open AccessArticle

Optimizing Vegetative Growth and Yield in Apple Trees Through Split Applications of Prohexadione–Calcium, Ethephon, and NAA

by Renaldo Borges de Andrade Júnior, Arthur Zanrosso, Sabrina Baldissera, Alex Felix Dias, Joel de Castro Ribeiro, Adrielen Tamiris Canossa, Tainara Gris, Raquel Holtrup Wolff, Daiana Petry Rufato, Bruno Dalazen Machado and Leo Rufato

Agriculture 2026, 16(13), 1378; https://doi.org/10.3390/agriculture16131378 (registering DOI) - 24 Jun 2026

Abstract

Managing vegetative vigor is a critical challenge for apple production in subtropical regions, where high water availability often promotes excessive canopy growth. This study evaluated the effects of split applications of prohexadione–calcium (ProCa) combined with naphthaleneacetic acid (NAA) and ethephon on the vegetative [...] Read more.

Managing vegetative vigor is a critical challenge for apple production in subtropical regions, where high water availability often promotes excessive canopy growth. This study evaluated the effects of split applications of prohexadione–calcium (ProCa) combined with naphthaleneacetic acid (NAA) and ethephon on the vegetative growth and yield performance of ‘Maxi Gala’ and ‘Fuji Suprema’ apples during the 2022/23 and 2023/24 growing seasons. The experimental design consisted of six plant growth regulator (PGR) protocols: a commercial standard (Control) with two applications, and five protocols based on six split applications initiated when fruit diameter reached ~8 mm, with 10-day intervals. The treatments included ProCa; ProCa + NAA; ProCa + ethephon; ProCa + NAA + ethephon; and ethephon + NAA. The ProCa + NAA protocol demonstrated the highest efficiency in vigor control, reducing shoot growth by up to 38% in ‘Maxi Gala’ and 65% in ‘Fuji Suprema’ relative to Control. Furthermore, this treatment enhanced fruit skin coloration, increased the proportion of Category 1 fruit, and improved return bloom and overall yield. These findings suggest that split applications of ProCa associated with NAA provide a robust strategy to optimize apple orchard productivity under the edaphoclimatic conditions of southern Brazil. Full article

(This article belongs to the Topic Advances in Cultivation Techniques for Increasing Crop Yield)

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20 pages, 3246 KB

Open AccessArticle

Shelf-Life Evaluation of Stored Vermicompost Organic Fertilizer via PCA-PLS Modeling

by Kongtan Wang, Dingmei Wang, Yuqi Pang, Xiaolan Yu, Liwen Mai, Shiliang Peng, Qinfen Li and Jiacong Lin

Agriculture 2026, 16(13), 1377; https://doi.org/10.3390/agriculture16131377 (registering DOI) - 24 Jun 2026

Abstract

Vermicomposting is an eco-friendly biotechnology for organic waste valorization. As the primary product of earthworm biotransformation, vermicompost is a high-value bio-organic fertilizer abundant in diverse biologically active components. To date, most studies have focused on quality variation during the earthworm transformation process, while [...] Read more.

Vermicomposting is an eco-friendly biotechnology for organic waste valorization. As the primary product of earthworm biotransformation, vermicompost is a high-value bio-organic fertilizer abundant in diverse biologically active components. To date, most studies have focused on quality variation during the earthworm transformation process, while research on quality variations in the resulting vermicompost fertilizer during long-term storage remains scarce. To explore the shelf-life of vermicompost fertilizer and its key influencing indicators, this study investigated the changes in quality indicators in sealed-packaged vermicompost over a 180-day period using two typical vermicompost, namely cattle manure vermicompost (CM) and straw-amended cattle manure vermicompost (CMS). The temporal dynamics of physicochemical properties, nutrient contents, humification indices, enzyme activities, and microbial communities were monitored. The vermicompost quality was evaluated, and core quality drivers were identified using an integrated principal component analysis-partial least squares (PCA-PLS) approach. The results indicated that moisture content (MC), total organic carbon (TOC), and total nitrogen (TN) declined progressively, whereas available phosphorus (AP) and available potassium (AK) peaked at day 150 and day 120, respectively, and the humification rate (HR) increased by 2.6–4.0-fold. Bacterial diversity and relative abundance slightly decreased, accompanied by taxonomic differentiation, whereas fungal communities maintained stable diversity. Most enzyme activities, including urease, phosphatase, catalase, and dehydrogenase, reached their maxima at day 120. Comprehensive quality scores peaked at day 150, with a marked decline observed by day 180. The recommended shelf-life of vermicompost fertilizer is 150 days. The key quality determinants include TN, electrical conductivity (EC), pH, actinomycete abundance, TOC, TP, bacterial abundance, AP, AK, and HR. These findings provide theoretical support and references for the storage management and quality control of commercial vermicompost products in practice. Full article

(This article belongs to the Special Issue Agro-Food Waste Valorization: Sustainable Pathways for Agricultural Applications)

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22 pages, 17249 KB

Open AccessArticle

Research on Intelligent Identification Method for Nitrogen Content in Greenhouse Cucumber Leaves Integrating YOLOv11n Segmentation and Machine Learning

by Weibing Jia, Sicun Lin, Zhengying Wei, Beibei Tian, Xingchen Meng and Yubin Zhang

Agriculture 2026, 16(13), 1376; https://doi.org/10.3390/agriculture16131376 (registering DOI) - 24 Jun 2026

Abstract

Rapid and non-destructive detection of nitrogen content in greenhouse cucumber leaves is essential for precision fertilization, yet traditional chemical methods are destructive and time-consuming, and existing spectral technologies suffer from high cost and poor field adaptability. This study aims to propose a high-precision [...] Read more.

Rapid and non-destructive detection of nitrogen content in greenhouse cucumber leaves is essential for precision fertilization, yet traditional chemical methods are destructive and time-consuming, and existing spectral technologies suffer from high cost and poor field adaptability. This study aims to propose a high-precision detection scheme for cucumber leaf nitrogen content based on a lightweight model, suitable for complex scenarios. A total of 698 cucumber leaf images covering three growth stages were collected to build a segmentation dataset. Four categories and eight types of deep learning segmentation models were optimized and compared, and the optimal one was selected to extract leaf regions. Nine color features were extracted and combined with Kjeldahl-measured nitrogen content to construct and optimize three machine learning models, forming a deep learning segmentation–color feature extraction–machine learning prediction process. The results showed that YOLOv11n achieved the best segmentation accuracy, with an IoU of 0.9212 and AP of 0.9998 for high-resolution images. The optimized XGBoost had the highest prediction accuracy, with an MAE of 0.469, MSE of 0.461, and RMSE of 0.679, which are 10.15%, 8.71%, and 4.36% lower than Support Vector Regression with Radial Basis Function kernel (SVR_RBF) respectively, and its predicted nitrogen content aligned well with true values. The proposed scheme integrating YOLOv11n and XGBoost offers a lightweight technical solution for nitrogen nutrition diagnosis and precise fertilization of greenhouse cucumbers. Full article

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

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20 pages, 8158 KB

Open AccessArticle

IIR-PoinTr: A Framework for Enhancing Pig Body Structure in Pose Point Cloud Completion

by Faming Chang, Mengting Zhou, Zhenwei Yu, Haobo Hu, Benhai Xiong, Fuyang Tian and Xiangfang Tang

Agriculture 2026, 16(13), 1375; https://doi.org/10.3390/agriculture16131375 (registering DOI) - 24 Jun 2026

Abstract

In precision livestock farming, 3D point clouds provide important data support for analyzing pig behavior and monitoring their health. However, due to environmental occlusions, limited sensor viewpoints, and mutual shielding between pigs, the acquired point clouds are often severely partial, which affects the [...] Read more.

In precision livestock farming, 3D point clouds provide important data support for analyzing pig behavior and monitoring their health. However, due to environmental occlusions, limited sensor viewpoints, and mutual shielding between pigs, the acquired point clouds are often severely partial, which affects the accuracy of body shape modeling and behavior recognition. To address these challenges, this study constructed a pig pose point cloud dataset using multi-view depth camera acquisition and point cloud registration techniques. Based on this dataset, an improved point cloud completion model, IIR-PoinTr, is proposed to enhance the reconstruction of geometric and topological structures in pig bodies. By strengthening local geometric perception and high-dimensional feature representation, the model improves the reconstruction quality of partial pig point clouds and produces more structurally consistent pig body shapes. Experimental results show that, on the self-constructed pig posture dataset, the proposed method reduces Chamfer Distance (CD-L1) by 3.6%, CD-L2 by 6.9%, and Earth Mover’s Distance (EMD) by 2.0%, while improving the F-score by 5.4% compared with the baseline model. In single-view point cloud completion tasks, the method is capable of reconstructing geometrically consistent pig body structures and increases downstream classification accuracy by 34.9%. These results indicate that the proposed method can improve the reconstruction quality of partial pig point clouds and provide preliminary technical support for posture analysis under occlusion. Full article

(This article belongs to the Special Issue Machine Learning in Precision Livestock Farming: From Animal Activity Forecasting to Environmental Control)

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23 pages, 5421 KB

Open AccessArticle

Simulation and Experimental Investigation of the Effects of Process Parameters on the Thermal Characteristics of Alfalfa Open-Die Densification at Ambient Temperature

by Ting Lei, Hongfeng Chu, Yanhua Ma, He Su, Chunmao Fan and Wentao Xu

Agriculture 2026, 16(13), 1374; https://doi.org/10.3390/agriculture16131374 (registering DOI) - 24 Jun 2026

Abstract

Alfalfa densification is a critical step in feed utilization and biomass energy conversion because it directly affects the transport efficiency, storage stability, and energy consumption of biomass processing systems. However, the thermodynamic behavior of the densification process remains poorly understood, especially under open-die [...] Read more.

Alfalfa densification is a critical step in feed utilization and biomass energy conversion because it directly affects the transport efficiency, storage stability, and energy consumption of biomass processing systems. However, the thermodynamic behavior of the densification process remains poorly understood, especially under open-die conditions without external heating. This study investigated the thermo-mechanical characteristics of alfalfa pellet open-die densification without external heating by combining experimental measurements with ANSYS macro-continuum simulation. Stress transmission and temperature field distributions were analyzed. The results showed that the pellet quality index under different process conditions remained above 800, meeting the requirements for pelleted feed. Moisture content had a more significant effect on forming pressure than other factors; as moisture content increased, the forming pressure decreased. At an aspect ratio of 5.0, the forming pressure was below 45 kN. Simulation results further indicated that aspect ratio had a stronger influence on frictional behavior during densification. Under an aspect ratio of 5.0, the energy consumption was 888.53 J, and the heat flux reached 0.0062 W/mm². These results indicate that frictional dissipation driven by radial force is the dominant mechanism governing thermo-mechanical coupling. Moisture content and aspect ratio significantly affected both peak compression force and coupling intensity. Although reducing moisture content or increasing aspect ratio improved pellet quality, it also increased die load due to enhanced radial force. The coupling intensity followed the order: peak pressure stage > moving stage > compression stage. These findings reveal the evolution of stress and temperature fields during alfalfa densification, offering critical theoretical guidance for optimizing densification process parameters. Full article

(This article belongs to the Section Agricultural Technology)

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16 pages, 4348 KB

Open AccessArticle

Varying Corn Flour Inclusion Levels Modulate Fiber Fraction Degradation and Nutritional Value of Rice Straw via Co-Extrusion

by Wenjie Zhang, Siran Wang, Nengxiang Xu, Chenglong Ding and Beiyi Liu

Agriculture 2026, 16(13), 1373; https://doi.org/10.3390/agriculture16131373 (registering DOI) - 24 Jun 2026

Abstract

Rice straw, one of the most abundant agricultural residues worldwide, remains significantly underutilized as a ruminant feed source owing to its intrinsic lignocellulosic recalcitrance. This study investigated the effects of co-extruding rice straw with varying proportions of corn flour on nutritional composition and [...] Read more.

Rice straw, one of the most abundant agricultural residues worldwide, remains significantly underutilized as a ruminant feed source owing to its intrinsic lignocellulosic recalcitrance. This study investigated the effects of co-extruding rice straw with varying proportions of corn flour on nutritional composition and in vitro digestibility for ruminant nutrition. Extrusion was conducted using a twin-screw extruder at 180 °C barrel temperature, 5 MPa pressure, and 50% feed moisture content. Five corn levels were formulated on a dry matter basis: pure rice straw (RS100); three blends with increasing corn flour inclusion: RS75:C25 (75% straw + 25% corn flour), RS67:C33 (67% straw + 33% corn flour), and RS60:C40 (60% straw + 40% corn flour); and pure corn flour (C100) as a control. Chemical composition including neutral detergent fiber (NDF), acid detergent fiber (ADF), acid detergent lignin (ADL), cellulose, hemicellulose, water-soluble carbohydrates (WSC), and starch was analyzed. In vitro dry matter digestibility (IVDMD) was determined using a pepsin-cellulase assay. Regression analysis within the practical 0–40% corn flour inclusion range revealed a significant quadratic relationship with IVDMD (R² = 0.999, p < 0.001). The optimal corn flour proportion was calculated to be approximately 37.5%, which closely matched the RS60:C40 formulation (40% corn flour). Among the tested formulations, RS60:C40 exhibited the greatest extrusion-induced nutritional improvements. Relative to its pre-extrusion values, cellulose decreased by 55.7% (p < 0.05), followed by ADF (16.1%), NDF (12.8%), and hemicellulose (10.2%); IVDMD increased by 34.2% (p < 0.01) and WSC by 56.7% (p < 0.05). Compared with RS100 after extrusion, RS60:C40 raised IVDMD by 49.5% and lowered cellulose by 60.6%. Its IVDMD also surpassed those of RS75:C25 and RS67:C33 (p < 0.05), whereas RS75:C25 showed only marginal improvements. ADL content showed no extrusion-induced change (p > 0.05). Scanning electron microscopy (SEM) of the RS60:C40 formulation revealed that, unlike the intact fibrous structures observed prior to extrusion, post-extrusion samples exhibited extensive disruption of the fibrous matrix. Pearson correlation analysis further supported these findings, showing strong positive correlations between IVDMD and WSC (r = 0.96, p < 0.001) and strong negative correlations between IVDMD and NDF (r = −0.95, p < 0.001). In conclusion, extrusion generally increased IVDMD and WSC while reducing fiber fractions, with the effect depending on corn level. Co-extrusion with 40% corn flour effectively enhanced the nutritional value of rice straw, offering a viable strategy for producing a more digestible ruminant feed. Full article

(This article belongs to the Section Farm Animal Production)

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15 pages, 1807 KB

Open AccessArticle

Yield Stability and Grain Yield Performance of Proso Millet (Panicum miliaceum L.) Genotypes Across Contrasting Years in Northern Kazakhstan

by Yuri Dolinny, Vladimir Kobernitsky, Timur Savin, Aiman Rysbekova, Vera Volobaeva, Yevgeniya Miller, Tatyana Kobernitskaya and Irina Zhirnova

Agriculture 2026, 16(13), 1372; https://doi.org/10.3390/agriculture16131372 (registering DOI) - 23 Jun 2026

Abstract

Proso millet (Panicum miliaceum L.) is a drought-tolerant cereal crop with considerable potential for dry-steppe agriculture. This study evaluated grain yield performance and stability of 104 proso millet genotypes originating from 21 countries under climatic conditions in Northern Kazakhstan during 2022–2024. Field [...] Read more.

Proso millet (Panicum miliaceum L.) is a drought-tolerant cereal crop with considerable potential for dry-steppe agriculture. This study evaluated grain yield performance and stability of 104 proso millet genotypes originating from 21 countries under climatic conditions in Northern Kazakhstan during 2022–2024. Field experiments were conducted under rainfed conditions using a randomized complete block design with three replications. Grain yield data were analyzed using analysis of variance (ANOVA), additive main effects and multiplicative interaction (AMMI) analysis, genotype plus genotype-by-environment (GGE) biplot analysis, and the stress tolerance index (STI). The study years differed substantially in weather conditions, ranging from severe drought in 2023 (HTC = 0.495) to excessive moisture availability in 2024 (HTC = 4.245). Mean grain yield varied from 2.58 t ha⁻¹ in 2022 to 4.18 t ha⁻¹ in 2024, demonstrating the high productive potential of proso millet under Northern Kazakhstan conditions. ANOVA revealed significant effects of genotype and year on grain yield. AMMI and GGE analyses were used to visualize genotype performance patterns and identify promising germplasm. Shortandinskoe 11 and K-2754 combined relatively high grain yield with stable performance, whereas K-2804, K-2724, and K-2291 demonstrated high productivity and elevated STI values. These accessions represent valuable germplasm for breeding programs aimed at improving grain yield, stability, and drought tolerance; however, further multi-location testing is required to confirm the breeding value and stability of the identified accessions under a wider range of environmental conditions. Full article

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

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