Agriculture

15 pages, 1080 KB

Open AccessArticle

Predicting the Potential Distribution of the Medicinal Plant Gelsemium elegans in China Under Climate Change

by Yaping Li, Tianai Hu, Bingbing Huang and Danping Xu

Agriculture 2026, 16(11), 1169; https://doi.org/10.3390/agriculture16111169 - 26 May 2026

Gelsemium elegans is a traditionally utilized medicinal plant in China, renowned for its well-documented therapeutic properties and substantial economic potential. The primary bioactive components in this plant are indole alkaloids. It is used clinically to treat conditions including rheumatoid arthritis, neuropathic pain, and [...] Read more.

Gelsemium elegans is a traditionally utilized medicinal plant in China, renowned for its well-documented therapeutic properties and substantial economic potential. The primary bioactive components in this plant are indole alkaloids. It is used clinically to treat conditions including rheumatoid arthritis, neuropathic pain, and some cancers. Additionally, the whole plant can be processed into livestock feed. Climate change is anticipated to substantially impact the future suitable habitat of this species. Utilizing the Biomod2 ensemble model and 18 environmental variables (bio01, bio03, bio04, bio05, bio06, bio09, bio11, bio17, hf, elev, aspect, slope, gm_lc, gm_ve, ph_water, usda, d1_swr, annual_mean_uv-b) this study projected the geographical distribution of G. elegans under current and future climate scenarios; the periods of the 2050s, 2070s, and 2090s were analyzed using SSP1-2.6, SSP2-4.5, and SSP5-8.5. Current ecological niche modeling predicts that G. elegans is predominantly distributed in southern China, with its climatically and edaphically most suitable habitats concentrated in Guangxi, Guangdong, Fujian, and Hainan provinces. Across the three future time periods under various scenarios, the overall extent of suitable habitat is projected to increase, with a northward expansion of the suitable distribution range. Key environmental factors shaping the distribution of G. elegans include Isothermality (bio03), Max Temperature of Warmest Month (bio05), Min Temperature of Coldest Month (bio06), Precipitation of Driest Quarter (bio17), and Annual Average UV Radiation. The study aims to develop a scientifically grounded theoretical framework to support the conservation-oriented management and climate-resilient utilization of G. elegans resources under ongoing climate change. Full article

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

15 pages, 850 KB

Open AccessArticle

Mobile Sensing and Life-Cycle-Assessment-Based Quantitative Model for Synergistic Pesticide–Carbon Reduction and Income Growth in Mulberry Orchard Protection: A Pilot Study

by Kai Huang, Wei Song, Biyu Guo, Jianlin Qiu, Ka Po Wong, Jin Yau Tsou and Yuanzhi Zhang

Agriculture 2026, 16(11), 1168; https://doi.org/10.3390/agriculture16111168 - 26 May 2026

Abstract

Addressing the dual challenges of green agricultural transformation and the national carbon neutrality targets, the traditional pest control systems in the mulberry plantations of Nantong, Jiangsu Province, face concurrent problems, including excessive pesticide use, high direct carbon emissions, and low economic returns. This [...] Read more.

Addressing the dual challenges of green agricultural transformation and the national carbon neutrality targets, the traditional pest control systems in the mulberry plantations of Nantong, Jiangsu Province, face concurrent problems, including excessive pesticide use, high direct carbon emissions, and low economic returns. This study establishes a comprehensive evaluation framework integrating technical, environmental, and economic dimensions. Utilizing a lightweight mobile sensing system, this research enables the early identification of white powdery mildew on mulberry trees and facilitates precise spatial pesticide management. Unlike traditional life cycle assessment (LCA) studies that rely on static data, this case study uses real-time field monitoring data as dynamic input to drive the standardized life cycle assessment model. In this pilot-scale validation (n = 3 pairs, one growing season), the proposed model reduced pesticide usage by an average of 28.7% (±3.1%), achieved a carbon emission reduction of 23.1 (±2.7) g/m², and increased net income by 0.199 (±0.018) yuan/m². Precision pest control driven by mobile sensing effectively enhances the synergy between ecological and economic benefits in specialty crop systems. Consequently, the study proposes a data-driven framework that shows promise for pesticide–carbon–income synergy, pending further validation across more sites and seasons. Full article

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

43 pages, 7210 KB

Open AccessArticle

Economic Resilience and Pesticide Use Practices Among GAP Certified and Non-Certified Mango Farmers in Northern Thailand

by Yuichiro Amekawa, Surat Hongsibsong, Panamas Treewannakul, Udomsap Jaitham, Pichamon Yana, Kanlayanee Boonthawee, Phannika Tongchai, Sumed Yadoung, Peerapong Jeeno and Nid Lungmala

Agriculture 2026, 16(11), 1167; https://doi.org/10.3390/agriculture16111167 - 26 May 2026

Abstract

This multi-level study investigates the economic resilience of mango farmers during the COVID-19 pandemic and their pesticide management practices under Thailand’s Q-GAP (Quality Good Agricultural Practices) certification standard. Field surveys compared the economic outcomes of 104 certified and 151 non-certified farmers from 2019 [...] Read more.

This multi-level study investigates the economic resilience of mango farmers during the COVID-19 pandemic and their pesticide management practices under Thailand’s Q-GAP (Quality Good Agricultural Practices) certification standard. Field surveys compared the economic outcomes of 104 certified and 151 non-certified farmers from 2019 to 2023, together with pesticide use practices during the year preceding the 2024 survey. The sample was drawn from three provinces in northern Thailand: Phitsanulok, Phetchabun, and Phichit. The statistical analysis of the collected information produced several key findings. Certified farms achieved significantly higher production and sales than non-certified farms over the five-year period, mainly due to larger farm size and higher prices obtained from premium export market sales. Certified farmers also adopted a wider range of coping strategies during the pandemic, whereas non-certified farmers mainly reduced mango investments related to mango cultivation. Certified farmers reported significantly higher rates of insecticide and fungicide adoption, as well as significantly higher annual pesticide application frequencies across all three pesticide categories. Residue analysis showed no significant difference in organophosphate (OP) residues between the two groups; however, pyrethroid (PY) residues were significantly higher among certified farms. This pattern suggests that certified farmers may apply pesticides more intensively to satisfy the aesthetic requirements of premium export markets. Regression results further showed that herbicide application frequency was the only factor marginally associated with PY-type residue levels among certified farmers, although this finding should be interpreted cautiously because of the weak model fit. Full article

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

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16 pages, 2472 KB

Open AccessArticle

Detection of Wheat Scab Spores Using Terahertz Metamaterial Sensor

by Yafei Wang, Tianhua Chen and Mohamed Farag Taha

Agriculture 2026, 16(11), 1166; https://doi.org/10.3390/agriculture16111166 - 26 May 2026

Abstract

To achieve label-free, highly sensitive, and rapid quantitative detection of spores of wheat scab pathogens, this study developed a flexible terahertz metamaterial perfect absorber based on a composite unit consisting of dual-U-shaped resonators and a central metal rod. The results showed that the [...] Read more.

To achieve label-free, highly sensitive, and rapid quantitative detection of spores of wheat scab pathogens, this study developed a flexible terahertz metamaterial perfect absorber based on a composite unit consisting of dual-U-shaped resonators and a central metal rod. The results showed that the metamaterial exhibited near-perfect absorption at two frequencies, 0.53 THz and 2.30 THz, with absorption rates of 99.2% and 99.5%, respectively. A sharp phase shift occurred at the resonance points, enabling significant amplification of weak sensing signals. The refractive index sensitivity was 110 GHz/RIU at low frequencies and 440 GHz/RIU at high frequencies, indicating superior sensing performance in high-frequency modes. Gradient concentration measurements of Fusarium graminearum conidia revealed a good linear relationship between spore concentration and resonance frequency shift (R² = 0.996). The detection limit was 10 spores/μL, with a detection range covering 0–1000 spores/μL. This approach meets the needs for early detection of trace amounts of pathogens and quantitative analysis throughout the disease cycle. As this technique requires no labeling, is non-invasive, and operates rapidly, it provides an efficient new method for real-time monitoring and intelligent control of wheat scab in fields. It also holds great potential for applying terahertz metamaterials in agricultural biosafety applications. Full article

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

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18 pages, 1265 KB

Open AccessArticle

Bioactivity Screening of Alkyl Sulfonamide Compounds Against Xanthomonas oryzae pv. oryzae and Molecular Docking of a High-Activity Compound with a Potential Ribosomal Target

by Lina Li, Xianxin Wu, Qiujun Lin, Tianshu Peng, Chunjing Guo, Jianzhong Wang and Xinghai Li

Agriculture 2026, 16(11), 1165; https://doi.org/10.3390/agriculture16111165 - 26 May 2026

Abstract

As a devastating disease worldwide, rice bacterial leaf blight—caused by Xanthomonas oryzae pv. oryzae (Xoo)—leads to substantial reductions in grain yield. The increasing resistance to conventional bactericides necessitates the development of novel and sustainable control agents. This study evaluated 58 novel [...] Read more.

As a devastating disease worldwide, rice bacterial leaf blight—caused by Xanthomonas oryzae pv. oryzae (Xoo)—leads to substantial reductions in grain yield. The increasing resistance to conventional bactericides necessitates the development of novel and sustainable control agents. This study evaluated 58 novel alkyl sulfonamide compounds against Xoo. In the turbidimetric assay at 100 mg/L, several compounds showed potent antibacterial activity. Among them, SYAUP-116 and SYAUP-212 exhibited in vitro inhibition comparable to that of streptomycin sulfate at the same concentration. Furthermore, in EC₅₀ determination assays, both compounds yielded lower EC₅₀ values than zinc thiazole. Among the 58 compounds tested, SYAUP-491 exhibited an in vitro EC₅₀ of 6.96 mg/L and achieved 74.1% in vivo therapeutic efficacy at 200 mg/L, representing the most promising lead for further characterization. Molecular docking, based on prior proteomic data, indicates potential stable binding to ribosomal proteins (50S L33/L34 and 30S S5), with the strongest interaction observed for L33 (binding free energy: −5.73 kcal/mol). This suggests a putative mechanism involving ribosome targeting and protein synthesis inhibition, which may be facilitated by hydrophobic interactions and halogen bonds derived from its trifluoromethyl and sulfonamide groups. SYAUP-491 demonstrates significant potential as a novel bactericide for rice bacterial leaf blight, warranting further research on structure-activity optimization, target validation, and field performance. Full article

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

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14 pages, 2484 KB

Open AccessArticle

Analysis of the Sublethal Effects of Spinetoram on Megalurothrips usitatus Across Multiple Generations Using the Age-Stage, Two-Sex Life Table Method

by Rui Gong, Lifei Huang, Wenjie Huang, Enhai Chen, Hongquan Liu and Lang Yang

Agriculture 2026, 16(11), 1164; https://doi.org/10.3390/agriculture16111164 - 26 May 2026

Abstract

Megalurothrips usitatus (Bagnall) is a major pest of cowpeas that severely affects their yield and quality. Spinetoram (a semi-synthetic derivative of natural spinosyns, modified to improve potency, residual activity, and stability) is currently one of the primary insecticides used for its control; however, [...] Read more.

Megalurothrips usitatus (Bagnall) is a major pest of cowpeas that severely affects their yield and quality. Spinetoram (a semi-synthetic derivative of natural spinosyns, modified to improve potency, residual activity, and stability) is currently one of the primary insecticides used for its control; however, prolonged or repeated exposure to this insecticide may lead to sublethal effects and the development of resistance. This study aimed to clarify the transgenerational effects of sublethal spinetoram stress on the development, reproduction, and population parameters of M. usitatus, with F₄ offspring reared on untreated pods to assess maternal effects. The LC₂₅ of spinetoram against M. usitatus was determined using an improved leaf-tube residual film method, and the thrips were successively selected for three generations (F₁–F₃) at this concentration. An age-stage, two-sex life table was constructed to systematically analyze the developmental duration, adult longevity, fecundity, and population life table parameters of the F₄ generation. The results showed that after three consecutive generations of LC₂₅ stress, the resistance ratio of M. usitatus to spinetoram reached 2.7. Compared with the water control, the F₄ generation from the treated group exhibited significantly shortened 1st and 2nd instar nymphal durations, as well as the total egg-to-adult period, while the prepupal duration was significantly prolonged. Adult longevity in females decreased from 23.65 ± 1.05 days to 16.07 ± 1.40 days (32.1% reduction), and male longevity decreased from 18.78 ± 0.96 days to 15.40 ± 0.82 days (18.0% reduction). Mean fecundity per female decreased from 247.15 ± 30.47 to 34.53 ± 6.02 eggs (86.0% decrease). Regarding population parameters, the net reproductive rate (R₀) decreased from 98.80 ± 0.07 to 10.36 ± 0.01 (89.5% decrease), the intrinsic rate of increase (r) decreased from 0.2506 ± 0.0001 to 0.1452 ± 0.0001 (40.0% decrease), the finite rate of increase (λ) decreased from 1.2849 ± 0.0001 to 1.1564 ± 0.0001 (10.1% decrease), and the mean generation time (T) was shortened from 18.24 ± 0.001 days to 15.84 ± 0.001 days (13.2% reduction). Age-stage-specific life expectancy (e_xj) was significantly reduced across all developmental stages, indicating a shorter survival time. The peak age stage-specific reproductive value (v_xj) was significantly lower and occurred earlier. The peak values of the age-specific survival rate (l_x) and fecundity (f_x, m_x) curves were significantly lower in the treated group. These findings indicate that multigenerational sublethal exposure to spinetoram can induce low-level resistance in M. usitatus and suppress the population growth potential by shortening developmental duration, reducing life expectancy, and reproductive contribution, and significantly inhibiting fecundity and survival. These results reveal the transgenerational sublethal effects of spinetoram and provide a theoretical basis for the integrated pest management (IPM) and resistance control of M. usitatus. Full article

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

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27 pages, 2329 KB

Open AccessArticle

A Hybrid Deep Learning–Fuzzy–Genetic Framework for Climate-Resilient Agricultural Investment and Resource Allocation Under Carbon Market Uncertainty

by Aylin Erdogdu, Faruk Dayi, Ferah Yildiz, Yusuf Esmer and Farshad Ganji

Agriculture 2026, 16(11), 1163; https://doi.org/10.3390/agriculture16111163 - 26 May 2026

Abstract

Climate variability, environmental uncertainty, and carbon-market dynamics increasingly challenge agricultural investment and resource allocation decisions worldwide. This study proposes an integrated hybrid decision-support framework combining Long Short-Term Memory (LSTM) deep learning, Interval Type-2 Fuzzy Logic Systems, and Genetic Algorithms to support climate-resilient agricultural [...] Read more.

Climate variability, environmental uncertainty, and carbon-market dynamics increasingly challenge agricultural investment and resource allocation decisions worldwide. This study proposes an integrated hybrid decision-support framework combining Long Short-Term Memory (LSTM) deep learning, Interval Type-2 Fuzzy Logic Systems, and Genetic Algorithms to support climate-resilient agricultural investment analysis under uncertainty. The framework integrates predictive modeling, uncertainty representation, and multi-objective optimization within a unified computational architecture. The empirical analysis was conducted using agricultural, climate, and carbon-market datasets covering Europe, Asia, and Africa over the 2010–2025 period. Agricultural productivity indicators, commodity price variables, climate-risk parameters, and carbon-market data were integrated into the modeling process. LSTM models were employed to analyze temporal agricultural and climate-related dynamics, while Interval Type-2 fuzzy systems were used to represent ambiguity associated with environmental and market uncertainty. Genetic Algorithms were subsequently applied to optimize investment allocation under conflicting objectives related to profitability, sustainability, and risk. The findings suggest that the proposed hybrid framework may improve adaptive investment evaluation and optimization performance under uncertain climate conditions relative to standalone computational approaches within the scope of the analyzed datasets. The results further highlight the importance of integrating predictive analytics, uncertainty modeling, and sustainability-oriented optimization within agricultural decision-support systems. However, the framework should be interpreted as a climate-resilient decision-support architecture rather than a universally deterministic forecasting mechanism. Overall, the study contributes to the emerging literature on agricultural sustainability and climate-resilient investment by presenting a transparent and uncertainty-aware computational framework under evolving environmental and carbon-market conditions. Full article

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

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22 pages, 1845 KB

Open AccessArticle

AHP-Based Ranking of Durum Wheat Management Scenarios in a Mediterranean Environment

by Pasquale Garofalo, Maria Riccardi, Itzel Inti Maria Donati and Anna Rita Bernadette Cammerino

Agriculture 2026, 16(11), 1162; https://doi.org/10.3390/agriculture16111162 - 26 May 2026

Abstract

The analytic hierarchy process (AHP) was applied to six agronomic scenarios for durum wheat (Triticum durum Desf.) in the Capitanata plain (Apulia, southern Italy), combining three sowing dates (15 October, 1 November, and 15 November) with two water regimes (rainfed; supplemental irrigation [...] Read more.

The analytic hierarchy process (AHP) was applied to six agronomic scenarios for durum wheat (Triticum durum Desf.) in the Capitanata plain (Apulia, southern Italy), combining three sowing dates (15 October, 1 November, and 15 November) with two water regimes (rainfed; supplemental irrigation at flowering). Five performance indicators were derived from AquaCrop-GIS simulations coupled with cradle-to-gate life-cycle assessment: grain yield, CO₂-equivalent emissions (CO₂_eq), carbon footprint (CFP), total water consumption (TotW), and water footprint (WFP). Six theoretical decision profiles were defined through a symmetric weight scheme (w = 0.60 for the dominant criterion, w = 0.10 for each of the remaining four; balanced profile with equal weights). The rankings revealed a systematic inversion between absolute and ratio indicators: under absolute-metric profiles, the lowest-yielding scenario paradoxically ranked first because reduced productivity mechanically lowered per-hectare resource consumption, whereas under ratio-metric and balanced profiles, early-November rainfed sowing consistently led the rankings. Switching point analyses quantified the weight thresholds at which leadership transitions occurred, providing a continuous sensitivity assessment of the dominant weight, and the AHP procedure was also applied to the 72 simulation replicates spanning the soil × climatic-cell variability of the 2013–2023 dataset to obtain empirical rank distributions for each scenario under each profile. The results highlight that the choice between absolute and ratio environmental indicators is a substantive methodological decision that directly affects the ranking of agronomic alternatives in multi-criteria evaluation. Full article

(This article belongs to the Section Crop Production)

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12 pages, 880 KB

Open AccessArticle

Growth, Yield and Fruit Biological Value of Several Less Known Pear Cultivars on the Lower Silesia (Poland)

by Ireneusz Sosna

Agriculture 2026, 16(11), 1161; https://doi.org/10.3390/agriculture16111161 - 26 May 2026

Abstract

The aim of this study was to evaluate the biological value of several less known pear cultivars growing in the climatic conditions of Lower Silesia. The experiment was carried out at the Wrocław University of Environmental and Life Sciences. In the spring of [...] Read more.

The aim of this study was to evaluate the biological value of several less known pear cultivars growing in the climatic conditions of Lower Silesia. The experiment was carried out at the Wrocław University of Environmental and Life Sciences. In the spring of 2006, annual trees of 9 cultivars were planted. All the cultivars were grafted onto strongly growing Caucasian pear seedlings (Pyrus caucasica Fed.). The highest total yield in the years 2007–2016 was recorded for the ‘Fertilia Delbard’ and ‘Noiabrska’ (169.7 and 152.0 kg per tree, respectively). The ‘Blanka’ produced the largest fruit (467 g), while fruit of the ‘Isolda’ were the smallest (163 g). In terms of biological value, the fruit of the tested cultivars showed great diversity. Based on the averages from 2011–2012, the maximum vitamin C was found in the fruit of ‘Morava’ and ‘Wyżnica’ (12.08 and 11.13 mg 100 g⁻¹, respectively), and in ‘Uta’ dry matter and extract. The highest content of total polyphenols was recorded in the fruit of the ‘Isolda’ (54.23 mg 100 g⁻¹), and of carotenoids in the fruit of the ‘Noiabrska’, ‘Morava’ and ‘Fertilia Delbard’. The highest antioxidant activity, using the DPPH, ABTS and FRAP methods, was demonstrated by ‘Isolda’ and ‘Noiabrska’. Full article

(This article belongs to the Special Issue Adapting Horticultural Plant Cultivation Technology and Storage to Changing Conditions)

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32 pages, 9709 KB

Open AccessArticle

HSSD-YOLO: A Motion-Blur-Robust Object Detection Framework for Real-Time Seed Detection in High-Speed Pneumatic Seeders

by Yizheng Yao, Zishun Huang, Jiaqi Li, Xueyu Sun and Ying Zang

Agriculture 2026, 16(11), 1160; https://doi.org/10.3390/agriculture16111160 - 25 May 2026

Abstract

For high-speed pneumatic seeders, accurate real-time seed detection underpins downstream quality assessments including seed counting, seeding-rate estimation, and uniformity evaluation. Under high-speed operating conditions, seeds exhibit rapid motion, dense distribution, frequent occlusion, and severe motion-blur-induced edge degradation, posing substantial challenges for vision-based detection. [...] Read more.

For high-speed pneumatic seeders, accurate real-time seed detection underpins downstream quality assessments including seed counting, seeding-rate estimation, and uniformity evaluation. Under high-speed operating conditions, seeds exhibit rapid motion, dense distribution, frequent occlusion, and severe motion-blur-induced edge degradation, posing substantial challenges for vision-based detection. This study proposes HSSD-YOLO, an improved detection algorithm built upon YOLOv11, incorporating three modules: a Motion Blur Enhanced Stem module (MBE-Stem) employing learnable Sobel gradient operators for edge feature extraction under motion blur; an Attention-enhanced Deformable Convolutional Network (ADCN) with a Residual Spatial-Channel Attention (RSCA) mechanism for adaptive sampling of irregularly shaped seeds; and an Edge-Guided Adaptive Recalibration Feature Pyramid Network (EGAR-FPN) injecting edge prior information into multi-scale feature fusion. On a self-constructed dataset of indica rice, japonica rice, and wheat seeds, HSSD-YOLO achieves 96.6% mAP@0.5 and 77.4% mAP@0.5–0.95, surpassing YOLOv11n by 2.5 and 5.4 percentage points, respectively, with only 5.2 M parameters. Ablation studies confirm synergistic gains exceeding linear superposition. Under the conditions evaluated, HSSD-YOLO outperformed all compared algorithms, providing the per-frame detection foundation for downstream seeding-quality tasks; empirical validation of those tasks on continuous video and embedded hardware remains outside the present scope. Full article

(This article belongs to the Special Issue Intelligent Agricultural Seeding Equipment)

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24 pages, 3470 KB

Open AccessArticle

BerryFlowerNet: A Customized Convolutional Neural Network for Blueberry Flower Cluster Detection and Flowering Stage Prediction with a Field Phenotyping Robot

by Chenjiao Tan, Nolan Gao, Ye Chu and Changying Li

Agriculture 2026, 16(11), 1159; https://doi.org/10.3390/agriculture16111159 - 25 May 2026

Abstract

Blueberry production has rapidly expanded over the past decade, accompanied by growing demand for efficient and accurate methods to monitor the flowering and fruiting phases of blueberry development, which has a direct impact on yield potential. Accurate determination of blueberry phenology enables growers [...] Read more.

Blueberry production has rapidly expanded over the past decade, accompanied by growing demand for efficient and accurate methods to monitor the flowering and fruiting phases of blueberry development, which has a direct impact on yield potential. Accurate determination of blueberry phenology enables growers to make data-driven decisions on freeze protection applications and harvest windows. In addition, objective phenology data of blueberry mapping populations will provide high-quality phenotype data for the discovery of genetic mechanisms regulating blueberry flowering and fruiting times. Traditional approaches, such as manual counting and visual ratings, are labor-intensive and subjective in capturing variation across genotypes. Recent progress in computer vision and deep learning has enabled automated flower detection, but most existing studies on blueberries remain restricted to narrow flowering windows or close-up images, limiting their application at the bush level and across the seasonal development. In this study, we developed BerryFlowerNet, a customized YOLO-based model to detect and count blueberry flower clusters from bud to green fruit stages. A comprehensive dataset was collected on three dates using a field phenotyping robot, covering five flowering stages. The integration of CFNet, a custom module fusing shallow spatial features, and PIoU loss improved the detection performance. Additionally, the Slicing Aided Hyper Inference algorithm was employed to address small-object detection in bush-level images. Experimental results demonstrated that BerryFlowerNet outperformed the baseline YOLO model and three additional detectors, achieving an average mAP0.5 of 0.644 across five independent training runs. The model achieved an accuracy of 0.88 when predicting blueberry flowering stages, indicating its effectiveness and accuracy. Additionally, the results of the bush-level image analysis showed the capability of the model to capture genotype-level differences in flowering dynamics. Overall, this approach offers new opportunities for growers and breeders to determine blueberry phenological development that is critical for optimizing on-farm management strategies and advancing precision phenotyping to facilitate the development of climate-resilient blueberries. Full article

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

14 pages, 2683 KB

Open AccessArticle

Drip Irrigation Depth and Water Salinity Synergistically Drive the Rhizosphere Soil Eukaryotic Community and Key Microbial Groups of Wheat

by Tieqiang Wang, Hanbo Wang, Yiteng Wang, Daozhi Gong and Xiyun Jiao

Agriculture 2026, 16(11), 1158; https://doi.org/10.3390/agriculture16111158 - 25 May 2026

Abstract

Eukaryotic organisms play a critical role in maintaining agricultural ecosystem functions and crop health. Irrigation practices and water salinity significantly affect eukaryotic communities, yet the interactive effects of drip irrigation depth and water salinity on these communities remain unclear. This study aimed to [...] Read more.

Eukaryotic organisms play a critical role in maintaining agricultural ecosystem functions and crop health. Irrigation practices and water salinity significantly affect eukaryotic communities, yet the interactive effects of drip irrigation depth and water salinity on these communities remain unclear. This study aimed to investigate the interactive effects of drip irrigation depth and water salinity on the diversity, community structure, and functional groups of winter wheat rhizosphere eukaryotes, and to examine their relationships with soil environmental factors. A two-year field experiment was conducted in Cangzhou, Hebei Province, with two drip irrigation depths (5 cm shallow, 25 cm deep) and two irrigation water salinity levels (2 g·L⁻¹, 3 g·L⁻¹). High-throughput sequencing was used to analyze rhizosphere microbial communities, and α/β diversity, species composition, LEfSe differential analysis, and redundancy analysis (RDA) were performed to assess the effects of environmental factors. Results showed that both irrigation depth and water salinity significantly influenced α/β diversity and community structure of soil eukaryotes. The 5 cm shallow + 2 g·L⁻¹ salinity treatment favored species richness, while the 25 cm deep + 3 g·L⁻¹ treatment promoted community evenness. Dominant taxa responded selectively, with Annelida markedly suppressed and groups such as Streptophyta and Chytridiomycota enriched under different treatments. Network analysis revealed that key microbial taxa occupied central positions in interspecies interactions. RDA indicated that soil pH, nitrogen, potassium, and organic matter were important drivers of community structure. In conclusion, drip irrigation depth and water salinity synergistically shape soil eukaryotic community structure. These findings provide a scientific basis for optimizing drip irrigation depth, utilizing brackish water, and enhancing agricultural ecosystem functions. Full article

(This article belongs to the Section Agricultural Water Management)

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14 pages, 3468 KB

Open AccessArticle

Sensory Profiles, Volatile Compounds and Antioxidant Activity of Organically Grown Almonds (Prunus dulcis Mill. DA Webb)

by Maria Teresa Frangipane, Lara Costantini, Stefania Garzoli, Nicolò Merendino and Riccardo Massantini

Agriculture 2026, 16(11), 1157; https://doi.org/10.3390/agriculture16111157 - 25 May 2026

Abstract

Consumers currently consider organic foods superior to conventional ones. They regard them as more environmentally friendly and healthier. The sensory and volatile properties, as well as the antioxidant content of the Italian organic almond of the “Tuono” cultivar, were evaluated in this study. [...] Read more.

Consumers currently consider organic foods superior to conventional ones. They regard them as more environmentally friendly and healthier. The sensory and volatile properties, as well as the antioxidant content of the Italian organic almond of the “Tuono” cultivar, were evaluated in this study. The following methods were used: sensory analysis, determination of total antioxidant capacity and the HS-SPME sampling technique followed by GC/MS analysis for the analysis of volatile compounds. Our findings highlighted the enhanced sensory quality of the organic sample in comparison to the conventional one. The presence of almond aroma, marzipan/benzaldehyde, tobacco, floral notes, sweetness, and crunchiness was exhibited by the analysed organic samples. The floral attribute is especially prominent, with its concentration being roughly four times higher in organic almonds than in conventional ones (4.96 vs. 1.25). There was no statistically significant difference in total phenolic content and antioxidant capacity between organic and conventional almonds. Significant differences were found between the organic and conventional systems for the volatile profile. Organic almonds were characterised by a higher presence of butanol in comparison to conventional (5.2 vs. 1.3, respectively) and limonene (3 vs. 1.5, respectively), both of which are associated with fruity aromas. Higher levels (expressed as %) of 2-methylbutanal, 3-methylbutanal, isobutyric acid, 2-heptanone, 3-heptanone, octanoic acid, and pinacol were also found in organic almonds. The possibility of producing almonds of superior sensory quality through organic systems could be considered a key factor in the potential contribution to maintaining the sustainability of agroecosystems. Full article

(This article belongs to the Section Agricultural Product Quality and Safety)

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17 pages, 653 KB

Open AccessArticle

Carcass and Meat Quality Traits in Fast-Growing, Local, and Crossbred Chickens Under Standard and Low-Input Diets

by Almudena Huerta, Anton Pascual, Alice Cartoni Mancinelli, Cesare Castellini, Cecilia Mugnai, Edoardo Fiorilla, Gerolamo Xiccato, Angela Trocino, Francesco Bordignon and Marco Birolo

Agriculture 2026, 16(11), 1156; https://doi.org/10.3390/agriculture16111156 - 25 May 2026

Abstract

The integration of alternative feeding strategies and diversified genetic resources represents a key approach to improving the sustainability of poultry production systems. This study evaluated the effects of genotype and diet on carcass traits, meat quality, fatty acid profile, and sensory characteristics in [...] Read more.

The integration of alternative feeding strategies and diversified genetic resources represents a key approach to improving the sustainability of poultry production systems. This study evaluated the effects of genotype and diet on carcass traits, meat quality, fatty acid profile, and sensory characteristics in a fast-growing genotype (Ross 308), two Italian local breeds (Bionda piemontese and Robusta maculata), and their crosses with a medium-growing strain (Sasso). A total of 441 chickens were allocated according to a factorial design including genotype, diet (standard vs. low-input), and sex. At genotype-specific commercial endpoints, 240 carcasses were analyzed for carcass traits and meat quality, and a subset (n = 120) was used for chemical composition, fatty acid profile, and sensory evaluation. Ross 308 showed the highest carcass weight and breast yield, but also the highest cooking losses and lipid oxidation. Compared with Ross 308, local breeds and crossbred chickens exhibited lower carcass performance but also lower “wet feathers” scores and higher polyunsaturated fatty acid (PUFA) and n-3 proportions. The low-input diet reduced carcass weight and breast yield, impaired some sensory attributes, and shifted fatty acid composition towards lower PUFA and n-3 contents and a higher n-6/n-3 ratio. Overall, crossbred genotypes showed intermediate carcass performance and some meat quality traits comparable to those of local breeds. Full article

(This article belongs to the Special Issue Sustainable Production of Poultry: Feeds, Eggs and Meat Quality)

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13 pages, 338 KB

Open AccessArticle

Potassium Fertigation Enhances Yield and Berry Development in Table Grapevines Under Semi-Arid Mediterranean Conditions

by Hamzeh M. Rawashdeh, Mazen A. Al-Kilani, Mohammad Al Kadiri, Asem Abu Alloush, Ali Mahasneh, Osama Migdadi, Manal Alhiari, Jaffar Y. M. AlKassasbeh, Isra Al Kharabsheh, Ahmad Abu-Dalo and Jafar AlWidyan

Agriculture 2026, 16(11), 1155; https://doi.org/10.3390/agriculture16111155 - 25 May 2026

Abstract

Efficient nutrient management through fertigation is essential for sustaining table grape production under water-limited Mediterranean environments. This study evaluated the effects of graded potassium (K) fertigation rates on yield and berry quality of grapevines under semi-arid conditions in northern Jordan. Field experiments were [...] Read more.

Efficient nutrient management through fertigation is essential for sustaining table grape production under water-limited Mediterranean environments. This study evaluated the effects of graded potassium (K) fertigation rates on yield and berry quality of grapevines under semi-arid conditions in northern Jordan. Field experiments were conducted over three consecutive seasons at three locations using four potassium application rates (0, 100, 200, and 300 kg K₂O ha⁻¹) applied through drip fertigation and synchronized with key vine phenological stages. Yield and fruit-quality parameters were analyzed using linear mixed-effects models accounting for treatment, year, location, and their interactions. Potassium fertigation significantly increased total yield, cluster weight, and berry physical attributes, including firmness, volume, weight, and diameter, whereas total soluble solids (TSS) and juice pH were largely unaffected. Relative to the control, potassium fertigation progressively increased total yield per vine by approximately 21%, 47%, and 72% under the 100, 200, and 300 kg K₂O ha⁻¹ treatments, respectively, although the magnitude of response differed among locations and growing seasons. Significant treatment × location interactions indicated that site-specific soil conditions influenced potassium response. These results demonstrate that synchronizing potassium supply with vine phenological demand through fertigation enhances productivity and berry physical quality without compromising fruit chemical composition. The observed improvements are consistent with the established physiological roles of potassium in osmotic regulation, assimilate transport, and berry development, supporting optimized potassium fertigation as a key component of precision nutrient management for sustainable viticulture in semi-arid Mediterranean regions. Full article

(This article belongs to the Special Issue Advances in Sustainable Viticulture)

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25 pages, 1696 KB

Open AccessArticle

Rural Income Growth Through Digital Infrastructure: Evidence from China’s Yellow River Basin

by Ruomeng Zhou, Yunsheng Zhang and Ruyu Yang

Agriculture 2026, 16(11), 1154; https://doi.org/10.3390/agriculture16111154 - 24 May 2026

Abstract

The digital economy has changed the way agricultural production is organized and how rural households access markets, jobs, and information. Yet it remains unclear whether these changes translate into higher income for rural residents, especially in major agricultural regions. This study examines the [...] Read more.

The digital economy has changed the way agricultural production is organized and how rural households access markets, jobs, and information. Yet it remains unclear whether these changes translate into higher income for rural residents, especially in major agricultural regions. This study examines the income effect of digital infrastructure development by using the rollout of the Broadband China policy as a quasi-natural experiment. The analysis draws on panel data for 77 prefecture-level administrative units in the Yellow River Basin, one of China’s major agricultural regions, from 2009 to 2021. A staggered difference in differences model is used to estimate the policy effect. The results show that digital infrastructure development significantly increases rural residents’ income. Under the log income specification, the baseline coefficient indicates an average income increase of about 8.33%. The mechanism analysis shows that innovation capacity and nonfarm employment both serve as positive partial transmission channels, with innovation capacity explaining a larger share of the total effect. The heterogeneity results suggest that the income effect is stronger in regions with higher GDP and larger population size. These findings indicate that digital infrastructure can support rural income growth when it is linked with local innovation capacity, employment opportunities outside agriculture, and rural development policies suited to local conditions. Full article

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

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22 pages, 54685 KB

Open AccessArticle

Flash Drought Assessment in the Black Soil Region of Northeast China Using FDHI

by Sunai Ma, Xiaodong Na, Yizhe Wang, Xubin Li and Zeyu Zhang

Agriculture 2026, 16(11), 1153; https://doi.org/10.3390/agriculture16111153 - 24 May 2026

Abstract

Flash droughts, characterized by rapid onset and intensification, are occurring more frequently under global warming. Accurately identifying the frequency and hazard severity of flash droughts remains challenging, as they are influenced by multiple hydroclimatic drivers, including precipitation deficits, temperature increases, and soil moisture [...] Read more.

Flash droughts, characterized by rapid onset and intensification, are occurring more frequently under global warming. Accurately identifying the frequency and hazard severity of flash droughts remains challenging, as they are influenced by multiple hydroclimatic drivers, including precipitation deficits, temperature increases, and soil moisture depletion. We developed a daily-scale Flash Drought Hazard Index (FDHI) by integrating the interactive effects of multiple driving factors, aiming to assess the spatiotemporal patterns of flash drought hazard in the Black Soil Region of Northeast China during the period 2000–2020. The FDHI employs the daily Standardized Precipitation Evapotranspiration Index, Standardized Soil Moisture Index, Standardized Soil Temperature Index, and Standardized Runoff Index to characterize short-term anomalies in multiple hydrometeorological variables. Results showed that flash droughts occurred most frequently in the southern part of the Black Soil Region of Northeast China, particularly in the Songnen Plain and the Liaohe Plain, with annual frequencies of 5.98 and 5.80 events, respectively. Flash drought severity in the Liaohe Plain exhibited a significant increasing trend during the past decade. Moreover, the dominant driving factors varied substantially among regions. Flash droughts in the Liaohe Plain were mainly associated with precipitation deficits and enhanced evapotranspiration, whereas soil moisture depletion and temperature anomalies played a more important role in the Songnen Plain. These results reveal pronounced regional heterogeneity in flash drought mechanisms across the Black Soil Region of Northeast China and demonstrate the effectiveness of the proposed FDHI for daily-scale agricultural flash drought monitoring. The study provides scientific support for agricultural drought risk management and disaster mitigation under climate change. Full article

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

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21 pages, 29085 KB

Open AccessArticle

PGi-YOLO: An Enhanced Detection Model for Maize Root–Stem Junction in Complex Field Environments

by Qiming Ding, Shuaishan Cao, Changchang Yu, Bingbing Cai, Yechao Yuan and He Li

Agriculture 2026, 16(11), 1152; https://doi.org/10.3390/agriculture16111152 - 24 May 2026

Abstract

Precise detection of maize root–stem junction is crucial for hole fertilization in maize cultivation. However, maize root–stem junction detection under field conditions is severely affected by soil clods, crop residues, and weeds, and is further complicated by variations in plant morphology, the small [...] Read more.

Precise detection of maize root–stem junction is crucial for hole fertilization in maize cultivation. However, maize root–stem junction detection under field conditions is severely affected by soil clods, crop residues, and weeds, and is further complicated by variations in plant morphology, the small scale of targets, and their sparse spatial distribution. To address these issues, an improved model named PGi-YOLO is proposed in this study, based on YOLOv11n-OBB. A P2 high-resolution detection layer is introduced to improve multi-scale feature representation and enhance small-target localization. The C2PSA-iRMB module replaces the original attention module by integrating an inverted residual mobile block (iRMB) mechanism, thereby strengthening global contextual information fusion while preserving its lightweight design. In addition, the Group Shuffle Convolution (GSConv) module is adopted to replace part of the standard convolution operations, reducing computational redundancy and improving inference efficiency. Experimental results show that PGi-YOLO achieves a precision of 92.0%, a recall of 93.4%, and an mAP@0.5 of 96.9%, with parameters of 2.61 M, a model size of 6.0 MB and an inference time of 5.1 ms. Overall, PGi-YOLO achieves a favorable balance between accuracy and efficiency, demonstrating strong robustness for maize root–stem junction detection in complex field environments and providing reliable support for precision agriculture applications. Full article

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

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24 pages, 5093 KB

Open AccessArticle

Scale-Up Green Synthesis of Maghemite–Citrus reticulata Hybrid Nanoparticles with High Magnetization and Their Effects on Cd/Ni Uptake in Cacao Seedlings

by Juan A. Ramos-Guivar, Mercedes del Pilar Marcos-Carrillo, Melissa-Alisson Mejía-Barraza, Renzo Rueda-Vellasmin, Noemi-Raquel Checca-Huaman, Edson Caetano Passamani, Cesar Oswaldo Arévalo-Hernández and Enrique Arévalo-Gardini

Agriculture 2026, 16(11), 1151; https://doi.org/10.3390/agriculture16111151 - 24 May 2026

Abstract

Metal accumulation in cacao (Theobroma cacao L.) cultivation represents an important agronomic and food-safety concern, particularly in acidic tropical soils where cadmium (Cd) and other trace metals can become bioavailable and translocate to plant tissues. Green magnetic nanomaterials offer a potential strategy [...] Read more.

Metal accumulation in cacao (Theobroma cacao L.) cultivation represents an important agronomic and food-safety concern, particularly in acidic tropical soils where cadmium (Cd) and other trace metals can become bioavailable and translocate to plant tissues. Green magnetic nanomaterials offer a potential strategy for reducing metal mobility in agricultural substrates, but their performance depends on surface chemistry, dose, and plant genotype. In this study, we synthesized and evaluated MCRES, defined here as a maghemite–Citrus reticulata extract system, a biofunctionalized γ-Fe₂O₃-based nanosystem prepared by coupling iron oxide nanoparticles (NPs) with a 3% (w/v) Citrus reticulata peel extract. The objective was to determine whether citrus-mediated biofunctionalization could produce a scalable magnetic nanoamendment capable of modifying Cd and naturally occurring Ni partitioning in cacao seedlings. MCRES was recovered magnetically and dried, yielding 8.44 g of product from 10 g of precursor. Rietveld analysis performed in X ray diffractograms confirmed phase-pure cubic γ-Fe₂O₃ with a lattice parameter of 0.8332 nm, a crystallite size of 11.3(1) nm, and satisfactory refinement quality (χ² ≈ 1.34). Transmission electron microscope images showed quasi-spherical NPs with a log-normal size distribution centered at 7.5 nm. Magnetic measurements showed superparamagnetic-like behavior at 300 K, high saturation magnetization values of 62 emu g⁻¹ at 300 K and 71 emu g⁻¹ at 5 K, and elevated effective anisotropy values obtained from the Law of Approach to Saturation fitting. MCRES was applied at 0, 1, 2, 4, and 6 g pot⁻¹ to cacao seedlings containing Cd-amended Ultisol with naturally occurring Ni. Plant responses were genotype and dose dependent: TSH-1188 genotype showed limited dose sensitivity for most biometric variables, whereas ICS-95 genotype showed significant dose effects, with maximum growth at the 2 g pot⁻¹ treatment. Metal-partitioning results indicated that Cd remained comparatively mobile toward shoots, whereas Ni was preferentially retained in roots. In TSH-1188 genotype, the Ni translocation factor decreased from 3.07 in the control to 0.85–1.00 at higher MCRES doses. Compared with previous work on non-biofunctionalized nanomaghemite, these results suggest that citrus-mediated biofunctionalization produces a distinct Cd/Ni partitioning response. Overall, MCRES is recommended as a promising nursery-scale green nanoamendment for reducing metal mobility in cacao cultivation, but its agronomic use should be optimized according to genotype and dose. Future work should include side-by-side comparisons with unfunctionalized γ-Fe₂O₃, Citrus reticulata extract alone, and non-contaminated controls under field conditions to validate its long-term effectiveness and environmental safety. Full article

(This article belongs to the Special Issue From Nano to Bio-Based Solutions for Preserving Soil Health in Agroecosystems)

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