Agriculture doi: 10.3390/agriculture16111170

Authors: Xiushuang Li Leimeng Wang Jingru Ruan Dongdong Hou Yi Wang

In the dry–hot valley region of Southwest China, water infiltration exhibits temporal variations due to the combined effects of land use type and the dramatic seasonal dry–wet cycle. To accurately compare and predict the infiltration characteristics, soil water infiltration processes and cumulative infiltration were quantified for five typical land uses—traditional corn (TC), plum orchard (PO), pine forest (PF), grassland (GL), and abandoned cropland (AC)—in a dry–hot valley region during both the rainy (July) and dry (November) seasons using a Mini Disk Infiltrometer (MDI). These data were then statistically analyzed using the Kostiakov, Philip, and Horton models. The results showed that the mean infiltration rate and cumulative infiltration during the rainy season were 1.34 times and 1.31 times higher than in the dry season, respectively. The water infiltration rate and cumulative infiltration for the five land uses generally followed the order of PF > GL/TC > PO/AC during both rainy and dry seasons. The model parameters related to the initial infiltration capability (Kostiakov parameter, a) and the steady infiltration capability (Philip parameter, A; and the Horton parameter, fc) during the rainy season were all greater than those in the dry season. Compared to the Kostiakov and Horton models, the Philip model achieved the highest mean Nash–Sutcliffe efficiency (NSE) values in fitting soil water infiltration processes, the lowest mean relative error (MRE) values, and the highest determination coefficient values (R2) in predicting the cumulative infiltration, with relatively little difference between the two seasons. These results indicate that PF, GL, and TC exhibit superior soil water infiltration capabilities compared to other land uses during both the rainy and dry seasons. The Philip model is more suitable for estimating soil infiltration capacity in the dry–hot valley region during both seasons. Identification of the superior land use types and accuracy determination of the water infiltration model can help guide effective water conservation and vegetation restoration initiatives in the dry–hot valley region of Southwest China.

Agriculture doi: 10.3390/agriculture16111169

Authors: Yaping Li Tianai Hu Bingbing Huang Danping Xu

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.

Agriculture doi: 10.3390/agriculture16111168

Authors: Kai Huang Wei Song Biyu Guo Jianlin Qiu Ka Po Wong Jin Yau Tsou Yuanzhi Zhang

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.

Agriculture doi: 10.3390/agriculture16111167

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

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.

Agriculture doi: 10.3390/agriculture16111166

Authors: Yafei Wang Tianhua Chen Mohamed Farag Taha

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.

Agriculture doi: 10.3390/agriculture16111165

Authors: Lina Li Xianxin Wu Qiujun Lin Tianshu Peng Chunjing Guo Jianzhong Wang Xinghai Li

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.

Agriculture doi: 10.3390/agriculture16111164

Authors: Rui Gong Lifei Huang Wenjie Huang Enhai Chen Hongquan Liu Lang Yang

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 (exj) was significantly reduced across all developmental stages, indicating a shorter survival time. The peak age stage-specific reproductive value (vxj) was significantly lower and occurred earlier. The peak values of the age-specific survival rate (lx) and fecundity (fx, mx) 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.

Agriculture doi: 10.3390/agriculture16111163

Authors: Aylin Erdogdu Faruk Dayi Ferah Yildiz Yusuf Esmer Farshad Ganji

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.

Agriculture doi: 10.3390/agriculture16111162

Authors: Pasquale Garofalo Maria Riccardi Itzel Inti Maria Donati Anna Rita Bernadette Cammerino

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.

Agriculture doi: 10.3390/agriculture16111161

Authors: Ireneusz Sosna

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’.

Agriculture doi: 10.3390/agriculture16111160

Authors: Yizheng Yao Zishun Huang Jiaqi Li Xueyu Sun Ying Zang

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.

Agriculture doi: 10.3390/agriculture16111159

Authors: Chenjiao Tan Nolan Gao Ye Chu Changying Li

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.

Agriculture doi: 10.3390/agriculture16111158

Authors: Tieqiang Wang Hanbo Wang Yiteng Wang Daozhi Gong Xiyun Jiao

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.

Agriculture doi: 10.3390/agriculture16111157

Authors: Maria Teresa Frangipane Lara Costantini Stefania Garzoli Nicolò Merendino Riccardo Massantini

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.

Agriculture doi: 10.3390/agriculture16111156

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

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.

Agriculture doi: 10.3390/agriculture16111155

Authors: 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 Jafar AlWidyan

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.

Agriculture doi: 10.3390/agriculture16111154

Authors: Ruomeng Zhou Yunsheng Zhang Ruyu Yang

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.

Agriculture doi: 10.3390/agriculture16111153

Authors: Sunai Ma Xiaodong Na Yizhe Wang Xubin Li Zeyu Zhang

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.

Agriculture doi: 10.3390/agriculture16111152

Authors: Qiming Ding Shuaishan Cao Changchang Yu Bingbing Cai Yechao Yuan He Li

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.

Agriculture doi: 10.3390/agriculture16111151

Authors: 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 Enrique Arévalo-Gardini

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.

Agriculture doi: 10.3390/agriculture16111150

Authors: Jizhou Zhang Xianli Xia Zhe Chen

In the context of revitalizing rural development, farmer entrepreneurship has emerged as a significant driver of rural economic growth. However, existing research has not sufficiently examined the specific mechanisms or heterogeneous effects through which digital skills influence farm household entrepreneurial behavior. This gap is the focus of the present study. Utilizing micro-level survey data collected from 936 farm households across Shandong, Shaanxi, and Henan provinces in 2021, we construct a digital skills index using factor analysis. We then employ a Probit model and an Interaction term model to examine the impact of digital skills on entrepreneurial behavior among Chinese rural households and its underlying mechanisms. Additionally, we explore heterogeneity across different household types. The results show that digital skills are positively associated with entrepreneurial decision-making. Further analysis provides suggestive evidence that this relationship may operate through three channels: shaping risk preferences, expanding relational networks, and improving access to credit. Heterogeneity tests reveal that the promoting effect of digital skills is stronger among disadvantaged households, households with a head younger than 45, and those engaged in opportunity-driven or online entrepreneurship. Theoretically, this study contributes by empirically validating a multi-pathway mechanism framework and identifying relevant boundary conditions. Practically, it offers targeted insights for policymakers to design skill-based interventions and foster inclusive entrepreneurial ecosystems in rural areas.

Agriculture doi: 10.3390/agriculture16111149

Authors: Joonhee Lee Heekwon Ahn

The temporal dynamics of nitrogen (N) fractions and ammonia (NH3) volatilization were investigated over a 56-day storage period using a laboratory-scale pig slurry pit simulator. A detailed N mass balance, encompassing total N (TN), total ammonium N (TAN), organic N, and nitrate N (NO3−-N) fractions, yielded a N mass recovery of 96.5%, despite uncertainties associated with discrete emission measurements, with a TN reduction of 28.3 g vessel−1 closely matched by cumulative NH3-N emissions of 27.3 g. The NH3 emission profile exhibited a distinct two-phase pattern. During Phase I (days 1–28), emissions remained stable at 16.7–19.5 g m−2 d−1, accounting for approximately 58% of total cumulative NH3-N loss (518.6 g m−2), consistent with zero-order kinetics. Phase II (days 29–56) was characterized by first-order exponential decay (k = 0.0293 d−1, R2 = 0.982), coinciding with progressive TAN depletion. Measured emission rates were strongly correlated with theoretical free ammonia N (FAN) concentrations derived from pH and temperature (R2 = 0.74), confirming that theoretical FAN provides a useful upper bound for emission potential, although the actual gaseous flux is restricted by mass-transfer limitations at the slurry–air interface. These results demonstrate that continuous pH and temperature monitoring provides a practical basis for tracking emission dynamics and informing the timing of mitigation interventions, particularly during the high-flux initial storage phase.

Agriculture doi: 10.3390/agriculture16111148

Authors: Nkanyiso Majola Mbusiseni Vusumuzi Mkwanazi Sithembile Zenith Ndlela Michael Chimonyo

Gastrointestinal nematodes (GIN) limit village chicken productivity, yet smallholder farmers often rely on indigenous knowledge (IK) to manage, and the factors influencing their adoption are poorly understood. A cross-sectional survey of 300 households was conducted to assess the use of IK for GIN management. Predictors of IK use were evaluated with binary logistic regression. Logistic regression model revealed that households with larger flocks (>24 birds) and households with chicken ownership were more likely to use IK (p < 0.05). Whereas age, education, religion, and access to herbalists were not significant predictors. Commonly used plants included Aloe ferox, Aloe marlothii, and Elephantorrhiza elephantina, with leaves, bark, and stems being the most frequently used plant parts. Females used leaves, roots, and stems, while males used bark and seeds. Farmers with more than 10 years of experience reported higher use of leaves and seeds, and very poor households used more leaves and soft stems compared to less poor households. The adoption of IK for GIN management in village chickens is influenced by specific socio-demographic factors, including flock size and chicken ownership.

Agriculture doi: 10.3390/agriculture16111147

Authors: Jéssica da Luz Costa José Ferreira Lustosa Rhaila da Silva Rodrigues Viana Jhon Kenedy Moura Chagas Cícero Célio de Figueiredo

Biochar-based fertilizers (BBFs), including formulations enriched with additives, are sustainable alternatives to conventional fertilizers, promoting waste reuse and controlled nutrient release. This study performed a global meta-analysis to evaluate nutrient dynamics (release and leaching in water and soil) and the agronomic performance of additive-enhanced BBFs compared with unfertilized and/or conventionally fertilized controls. Thirty studies were selected, with 264 experimental pairs extracted from the Web of Science and Scopus databases, and analyzed using a random-effects model. The results indicated that BBFs enriched with natural mineral additives promoted an average increase of 204.3% in nutrient release in water (p < 0.001), whereas in soil biotechnological additives showed the greatest increase, with 109.8% (p < 0.001). Leaching was reduced by up to 74.4% with BBFs enhanced with agricultural residue additives and by 46.9% with industrial additives, indicating greater nutrient retention and greater nutrient-use efficiency. In terms of agronomic performance, additive-enhanced BBFs resulted in average increases of 49.3% in plant height, 232.3% in aboveground biomass, 60.8% in root biomass, and 11.2% in grain yield, compared to unfertilized soil. Overall, the effectiveness of BBFs depends on both the type of additive and the application method, with industrial and mineral additives being the most promising for controlled nutrient release and increased crop productivity.

Agriculture doi: 10.3390/agriculture16111146

Authors: Ahmed A. A. Abdel-Wareth Ahmed A. Ahmed Mohamed O. Taqi Md Salahudin Jayant Lohakare

The agricultural sector, particularly animal production, faces numerous unprecedented challenges driven by climate change, resource depletion, and an ever-growing global demand for quality food. These challenges are further compounded by the increasing environmental impact of livestock farming, including greenhouse gas emissions, overuse of water and land resources, and the destruction of vital ecosystems. Ensuring the sustainability of animal production systems while mitigating the negative environmental impacts of these factors is essential for future global food security. As the demand for animal-derived products continues to rise, there is a pressing need for innovations that can enhance productivity without compromising environmental integrity or animal welfare. Artificial intelligence (AI) has emerged as a transformative technology with the potential to revolutionize the animal production industry. AI-driven solutions offer promising avenues for optimizing production efficiency, enhancing animal health and welfare, and reducing the environmental footprint of livestock farming. Machine learning, sensor technologies, and advanced data analytics are being increasingly utilized to monitor and predict various aspects of animal farming, such as feed efficiency, disease prevention, and climate resilience. These technologies enable farmers to make data-driven decisions, fostering more sustainable and environmentally responsible practices. This review examines the integration of AI into animal production systems, emphasizing its applications in climate change mitigation, resource management, and advancing sustainability. The discussion addresses how AI technologies can be utilized to improve productivity while minimizing environmental impact and enhancing animal welfare. Additionally, the paper outlines future opportunities, challenges, and potential barriers to integrating AI technologies into livestock farming, thereby ensuring long-term sustainability amid global challenges.

Agriculture doi: 10.3390/agriculture16111145

Authors: Zhenwei Liang Shengjie Yu Baihao Yu

Protected horticulture moves fragile pots, plug trays, seedlings, harvested products, and carriers through narrow, humid, and crowded spaces. Transport robots must therefore integrate locomotion, perception, localization, handling, placement, scheduling, and human–robot interaction rather than operate as simple carts. This structured narrative review reorganizes evidence from seedling transplanting, nursery operations, harvest support, manipulation, perception, and autonomous navigation around the complete transport chain: target recognition, pickup, loading, loaded navigation, docking, unloading or placement, payload protection, and workflow feedback. The synthesis covers mobile platforms, payload support, perception and localization, motion control, gentle handling, digital support, and fleet coordination. Three barriers remain: short laboratory tests rarely provide season-long evidence; many prototypes are too specialized for variable workflows; and benchmarks seldom combine motion accuracy, handling reliability, payload quality, and resilience. Progress will require modular platforms, robust sensing, payload-safe control, standardized interfaces, and closer co-design between robotics and horticultural operations.

Agriculture doi: 10.3390/agriculture16111144

Authors: José Ramón Arévalo Atteneri Luis Elena Rocafull Francisco Maroto-Molina Eseró Padrón-Tejera Luis Alberto Bermejo

Grazing is widely used to manage grasslands, but its effects on plant diversity and community composition are context-dependent. In the Canary Islands, pastures are limited and fragmented but represent some of the most species-rich plant communities. This study evaluates the effects of grazing abandonment and grazing intensity on plant diversity and composition in the mountain pastures of Gran Canaria under wildfire prevention management. Vegetation was surveyed in 11 paired grazed and ungrazed plots across an environmental gradient over two years. Grazing intensity was quantified using livestock GNSS (Global Navigation Satellite System) tracking, distinguishing low- and high-intensity regimes. A total of 112 plant species were recorded, mostly typical of pasture communities. Species richness remained stable across treatments, grazing intensities, and years (2023–2024), indicating strong short-term resistance. However, species composition varied along the grazing intensity gradient: high-intensity grazing produced more homogeneous communities dominated by grazing-tolerant species, while low-intensity grazing maintained greater variability. Grazing abandonment showed no clear compositional shifts, suggesting delayed responses. Environmental factors such as soil, moisture, temperature, and coastal influence also structured species distributions. Overall, grazing intensity is the main driver of plant community structure, highlighting its importance for biodiversity conservation, wildfire risk reduction, and maintaining traditional pastoral practices.

Agriculture doi: 10.3390/agriculture16111143

Authors: Mingzhuo Guo Jiale Zhao

Agricultural production is undergoing a rapid transition from mechanized field operations to data-informed, perception-supported, and adaptive equipment systems [...]

Agriculture doi: 10.3390/agriculture16111142

Authors: Yajie Liu Jiangchun Chen Jian Bao Longpeng Ding Hongfei Yang Yuyang Liu Yufeng Li Haiyang Lu Guangshang Ge

The semi-structured vineyard environments contain numerous irregular obstacles, posing stringent requirements on the navigational safety and trajectory tracking accuracy of mobile robots. To address this challenge, this study first optimizes the A* algorithm at the global planning layer by incorporating a composite turning-cost evaluation model and a heuristic dynamic weighting strategy, thereby effectively enhancing search efficiency and path smoothness. Building upon this, a local planning method is further developed by integrating an adaptive sampling mechanism with high-order interpolation-based kinematic continuity constraints and a heading-rate-driven velocity smoothing strategy. This enables the robot to maintain a safe clearance from obstacles in dynamic environments, thereby significantly enhancing the smoothness of obstacle avoidance maneuvers. Both simulation and field experiment results demonstrate that the improved global planning algorithm reduces the number of critical turning points and the total turning angle by up to 18.0%. Across three typical path scenarios, the proposed fusion method reduces the robot’s positional deviation by up to 21.8% and the heading angle deviation by up to 29.6%, while concurrently increasing the safe clearance from obstacles by 42.0%. These findings suggest that the proposed framework establishes a viable algorithmic foundation for improving the navigation accuracy, obstacle avoidance stability, and operational safety.

Agriculture doi: 10.3390/agriculture16111141

Authors: Uğur Çakaloğulları Deniz İştipliler

Understanding diurnal canopy orientation in crops is important for interpreting plant responses to light and environmental conditions, yet field-based quantification remains limited. In this study, we present Heliocot, a field RGB imaging approach that converts time-resolved images into reference-area standardized projected leaf area (PLA) time series to quantify within-day canopy orientation dynamics in early-season cotton. Leaf instance segmentation was performed using YOLOv8m-seg and refined through a 144-combination post-processing optimization. On the held-out early-stage validation/tuning set, the selected workflow showed strong agreement with manual ground truth (R2 = 0.948; NRMSE = 0.082) and destructive leaf area measurements (R2 = 0.836). Derived diurnal metrics, including Daily Orientation Amplitude (DOA) and Peak Orientation Index (POI), consistently revealed a midday maximum (13:15) in canopy projection. Exploratory genotype-level analysis suggested negative associations between orientation indices and selected plant traits, including specific leaf area (SLA) versus DOA (r = −0.71, p = 0.021, R2 = 0.508), destructive leaf area (LA) versus DOA (r = −0.69, p = 0.028, R2 = 0.471), and stem dry weight (SDW) versus POI (r = −0.74, p = 0.014, R2 = 0.554), while plant height was not significantly associated with POI and DOA (p > 0.05). Although currently limited to early-season conditions and two field-imaging dates, this approach provides a practical workflow for field-based monitoring of canopy projection dynamics in cotton, while broader temporal and environmental validation remains necessary.

Agriculture doi: 10.3390/agriculture16111139

Authors: Chao Xu Qian Chen Siyu Wang Huihui Tao Meng Zhang Xiaofei Li

Low temperature and low irradiance (LTLI) stress severely limits strawberry growth and productivity during winter protected cultivation. This study investigated the physiological responses of the short-day strawberry cultivar ‘Benihoppe’ to individual and combined LTLI stress and developed a quantitative damage evaluation index. Seedlings were exposed to four treatments for 20 d: control (25/15 °C, 600 μmol m−2 s−1), single low temperature (LT: 15/5 °C), single low irradiance (LI: 100 μmol m−2 s−1), and combined stress (LTLI: 15/5 °C, 100 μmol m−2 s−1). Compared to isolated stress factors, combined LTLI treatment exhibited a statistically verified synergistic damaging effect (two-factor ANOVA, LT × LI p < 0.01) on leaf structure and function. LTLI-treated plants showed severe reductions in leaf area, palisade tissue thickness, chlorophyll content, and net photosynthetic rate (Pn), alongside elevated malondialdehyde (MDA) accumulation. Chlorophyll a fluorescence (OJIP) analysis revealed that LTLI stress strongly blocked the electron transport chain at the PSII acceptor side, increasing the J-step relative variable fluorescence (Vj) and suppressing the performance index (PI). To quantify these impacts, a Low Temperature and Low Irradiance Damage Index (LTLDI) was derived from 12 core physiological and morphological variables. The LTLDI scores demonstrated that LTLI induced severe damage by day 10 (score: 0.69) and extremely severe damage by day 20 (0.87), which were substantially higher than the damage caused by LT (0.58 at 20 d) and LI (0.63 at 20 d) alone. The index reliability was confirmed by its strong correlation (r > 0.9) with key stress markers (Fv/Fm, Pn, and MDA). Overall, combined LTLI stress exacerbates structural degradation and PSII photoinhibition in strawberry leaves. The proposed LTLDI offers a practical, standardized tool for evaluating stress severity, facilitating timely environmental management in greenhouse strawberry production.

Agriculture doi: 10.3390/agriculture16111140

Authors: Ryusuke Hatano Shinya Iwasaki

Soil degradation driven by inappropriate soil management is a serious global challenge, while climate change-induced yield declines are increasing the conversion of natural ecosystems to agricultural land. This review examines how soil structure influences soil health, focusing on organo-mineral complexes derived from microbial biomass and soil organic carbon-to-clay (SOC/Clay) ratio as an indicator of structural quality. Regenerative agriculture based on conservation farming practices helps mitigate SOC depletion and aligns with the nature-based solutions framework. In Hokkaido, Japan, 10 years of clean agricultural applications (cover crops and organic matter application) increased SOC storage in farmland affected by volcanic eruption. This was associated with improved bulk density, porosity, cation exchange capacity, and phosphate absorption capacity, indicating improved soil health. The increased SOC rose SOC/Clay ratio to levels comparable with unaffected farmland (≥1/13). When the SOC/Clay ratio exceeded 1/13 (soil carbon storage level of 30 t C/ha/15 cm), carbon sequestration rate became negative. This suggests that improved soil health and structural quality may promote carbon saturation and stimulate microbial decomposition of existing SOC. While the threshold for SOC/Clay ratio varies depending on soil type, vegetation type, climatic conditions, and land use, changes in the SOC/Clay ratio can provide insights into changes in soil health and structural quality.

Agriculture doi: 10.3390/agriculture16111138

Authors: Hanyu Zheng Jie Zhang Guangmei Wang Tingting Chang Shihong Yang Haonan Qiu Mir Moazzam Ali Talpur Yujie Gao

Winter wheat (Triticum aestivum L.) production in the Yellow River Delta is limited by saline–alkali soils and freshwater scarcity, while the responses of different straw-returning modes under contrasting irrigation regimes remain unclear. A field experiment was conducted with two irrigation regimes, normal irrigation (W1) and deficit irrigation (W2), and four straw-returning modes, direct straw return (RS), straw-derived cattle manure return (RM), straw biochar return (RB), and straw pellet return (RG). The experiment followed a split-plot randomized block design with three replicates. Soil properties, leaf physiology, photosynthetic performance, grain yield, and irrigation water use efficiency (IWUE) were evaluated. Compared with W2, W1 increased mean grain yield by 9.4%, whereas W2 increased mean IWUE by 36.7%. Among the straw-returning modes, RS showed the most consistent performance. Under W1, W1RS produced the highest grain yield (3509.72 kg ha−1). The stable performance of RS was characterized by relatively favorable soil moisture status, lower MDA content, higher antioxidant enzyme activity, and better maintenance of Pn. Pearson correlation analysis showed that grain yield was positively correlated with Pn and CAT activity, whereas MDA was negatively correlated with Pn. These results suggest that RS may be a feasible straw-returning mode for winter wheat production in saline–alkali soil.

Agriculture doi: 10.3390/agriculture16111137

Authors: Qihe Tang Kechun Liu Yunwei Cui Yuansong Wei Peihong Shen Junya Zhang

Microbial inoculation is widely used to improve composting performance, yet its effectiveness hinges on inoculum composition, substrate characteristics, and composting technology, which remain poorly understood. This study compared single versus mixed inoculants across different substrates and assessed their interactions with biochar amendment and nanomembrane covering, focusing on organic matter transformation, inorganic nutrient dynamics, and biological pollution control. Mixed inoculation significantly improved heating performance in cattle manure compost compared to single strains (p < 0.05) and sustained thermophilic conditions in sludge-sawdust compost, but showed limited impact in chicken manure-sludge compost. It reduced humic acid (HA) accumulation in chicken manure-sludge compost (14.29% to −39.28%) while increasing HA content in sludge-sawdust compost (3.55–5.41 g/kg, p < 0.05). Inorganic nitrogen retention was enhanced; specifically NO3−-N concentrations rose by 175.1–222.6% in the chicken manure-sludge and by 6.7–17.9% in the sludge-sawdust compost. Microbial community analysis indicated enrichment of inoculant strains during the thermophilic phase, supporting nitrogen conservation and humification. However, inoculation increased potential pathogenic bacteria by over 51.2% across all composts and enriched predicted antibiotic resistance genes (ARGs) by 9.9–22.96% in chicken manure-sludge compost, while reducing the membrane covering’s inhibitory effect on predicted ARGs (rebound by 29.5%). Moreover, we found that the predicted ARG profiles, derived from 16S-based PICRUSt2 functional inference, covaried strongly with microbial community structure, with environmental factors such as organic carbon shaping predicted ARG dynamics mainly through indirect effects on microbial communities. These findings highlight that while mixed inoculation boosts composting efficiency, it also raises biosafety concerns. Thus, a comprehensive evaluation integrating organic, inorganic, and biological perspectives is essential before promoting thermophilic inoculants.

Agriculture doi: 10.3390/agriculture16111136

Authors: Guohao Zou Xiuyi Shi Chufeng Yang

Increasing external uncertainty, supply disruptions, and market volatility have made resilience enhancement increasingly important for sustainable agricultural supply chains. While existing studies mainly examine agricultural supply chain resilience from macro or operational perspectives, limited attention has been paid to how firms’ strategic AI investment reshapes organizational resilience under external shocks. Using panel data on Chinese agricultural-related listed firms from 2010 to 2024, this study examines whether and how strategic AI investment enhances supply chain resilience. Empirical results show that strategic AI investment significantly improves both dimensions of supply chain resilience, namely resistance capacity and recovery capacity. Mechanism analyses indicate that this effect mainly operates through supply diversification, technological innovation, and information transparency. Further analyses reveal heterogeneous effects across supply chain positions, ownership structures, and regional digital development environments. In addition, compatibility analyses show that strategic AI investment not only strengthens supply chain resilience but also improves operational efficiency, R&D investment intensity, and financial stability. Overall, this study highlights strategic AI investment as an important organizational capability for strengthening agricultural supply chain resilience under increasing external uncertainty.

Agriculture doi: 10.3390/agriculture16111135

Authors: Chunlin Xiong Ren Fan Duo Jiang

The coupling coordination among digital village development, agricultural modernization, and agricultural carbon emission efficiency is critical for achieving green and high-quality agricultural development. Using panel data of 30 Chinese provinces (excluding Hong Kong, Macao, Taiwan, and Tibet) from 2011 to 2024, this study measures agricultural carbon emission efficiency via the super-efficiency SBM model, evaluates the levels of digital village development and agricultural modernization using the entropy method, constructs a coupling coordination degree model to analyze the spatiotemporal evolution characteristics of the three systems, and employs the Geographically and Temporally Weighted Regression (GTWR) model to reveal the spatiotemporally heterogeneous effects of governmental, market, and social factors on the coupling coordination degree. The results show that: (1) The three systems exhibit unbalanced development. The digital village development index increased from 0.430 to 0.634; agricultural modernization grew slowly from 0.308 to 0.411; and agricultural carbon emission efficiency surged from 0.146 to 0.655. (2) The coupling coordination degree of the three systems rose continuously from 0.382 to 0.661, transitioning from near disorder to primary coordination. Spatially, the eastern and northeastern regions led while the western region lagged, though Xinjiang reached good coordination (0.786) in 2024. (3) The GTWR model reveals that the marketization index (ranging from −0.0362 to 0.0559), agricultural land transfer rate (ranging from −0.1630 to 1.7952), fiscal support for agriculture (ranging from −0.0003 to 0.0232), and agricultural socialized services (ranging from −0.0019 to 0.0012) have positive effects with significant spatial heterogeneity. Rural infrastructure exhibits a “positive in the south, negative in the north” pattern (ranging from 0.0540 to 1.0460), while the overall social consumption level (ranging from −0.9680 to 0.6548) exerts a negative inhibiting effect. These findings provide a theoretical basis for understanding the spatial heterogeneity of the coupling coordination among the three systems and emphasize that differentiated, regionally tailored strategies are key to promoting green and high-quality agricultural development.

Agriculture doi: 10.3390/agriculture16111132

Authors: Vasileios Greveniotis Elisavet Bouloumpasi Adriana Skendi Dimitrios Kantas Constantinos G. Ipsilandis

Dactylis glomerata, a perennial forage grass widely distributed in Mediterranean areas, is recognized for its adaptability and nutritional quality. This study aimed to assess the chemical composition and fiber components of ten natural populations of Dactylis glomerata in order to characterize genetic variability in nutritional and fiber traits among populations. Seeds of all populations were established in a randomized complete block design with four replicates and cultivated for two consecutive years. Forage was collected at the boot stage, and analyses were conducted for crude protein, ash, crude fiber, neutral and acid detergent fibers, acid detergent lignin, hemicellulose, cellulose, digestible dry matter, dry matter intake, and relative feed value. Combined ANOVA indicated that genotypic effects were highly significant for all traits (p ≤ 0.001), with additional significant contributions from environmental and genotype × environment interactions. Crude protein ranged from 11.74% to 14.98%, neutral detergent fiber from 56.31% to 58.43%, and relative feed value from 100.1 to 106.4 among populations. Stability index analysis identified Kefalopotamos and Filyra as the most environmentally stable populations, whereas Kori and Xyloparoiko exhibited relatively higher values in selected forage quality traits. Broad-sense heritability values were high for the majority of traits (H2 between 93.3% and 99.9%, except for hemicellulose), suggesting a strong genetic influence. Correlation analysis also revealed inverse relationships between protein content and fiber fractions and positive relationships with digestibility-related indices. Multivariate analyses revealed a clear separation between nutritional quality traits and structural fiber components, indicating consistent differentiation among populations. Overall, these results highlight the potential of local Dactylis glomerata populations as genetic resources for further evaluation in breeding and conservation programs under Mediterranean conditions.

Agriculture doi: 10.3390/agriculture16111134

Authors: Wei Yan Xiaoyan Wu Hongliang Xin Qianqin Li Saisai Wang Ming Hou Xuyang Cheng Ming Tang Ruina Liu Jianbo Zhu

Improving cotton yield without sacrificing fiber quality remains a major breeding challenge. In this study, a carpel-specific RNA interference construct targeting GhCKX3b was introduced into the elite upland cotton cultivar ‘Yuanmian 8’, which has high fiber quality but relatively low lint percentage. We evaluated the effects of this construct on cytokinin accumulation, yield-related traits, and fiber quality across T0, T1, and T2 generations. Carpel-specific suppression of GhCKX3b increased cytokinin content in T2 positive lines by 50.3% to 102.0% relative to wild-type. Transgenic lines consistently showed increased lint percentage, boll weight, and seeds per boll, while seed index decreased moderately. In the best-performing line, lint percentage increased from 36.4% to 45.8%, and boll weight from 6.30 g to 7.31 g. Multi-year field evaluations confirmed stable inheritance of these improvements across generations. Importantly, major fiber quality parameters—including length, strength, and micronaire—remained within high-quality cotton standards. These results indicate that carpel-specific GhCKX3b suppression effectively improves key yield components in a high-quality cotton background without compromising fiber quality. This study provides breeding-oriented evidence supporting the application of tissue-specific cytokinin regulation in cotton improvement.

Agriculture doi: 10.3390/agriculture16111133

Authors: Yuanqiao Li Qi Feng Xiaoqing Zhu Bo Bo Ting Yu Hui Qu

Photoperiod-sensitive sweet sorghum (Sorghum bicolor L. Moench) accumulates biomass and sugars during vegetative growth, making it a silage candidate where water limits maize production. This study examined how harvest date and nitrogen (N) rate affect its forage quality and in vitro rumen gas production under rain-fed conditions. In a randomized complete block design with three replications, we evaluated dry matter (DM) yield, morphology, and chemical composition of sweet sorghum harvested at 80 and 110 days after planting (DAP) under five N rates (0, 75, 150, 225, and 300 kg N/ha). Each treatment was ensiled in laboratory-scale bag silos for 90 days. Silage was analyzed for silage quality and 48-h in vitro rumen gas production and fermentation parameters. Delaying harvest from 80 to 110 DAP increased DM yield and fiber fractions (NDF, ADF, lignin), but reduced crude protein (CP), water-soluble carbohydrates (WSC), and in vitro dry matter digestibility (IVDMD) in fresh forage (p < 0.001). Increasing the N rate up to 225 kg N/ha enhanced DM yield, CP, and WSC at both harvest dates. A harvest date × N rate interaction occurred for WSC (p < 0.05). After ensiling, CP and IVDMD were higher in 80-DAP silage. Butyric acid (BA) and ammonia-N (NH3-N) increased with N rate, but at ≥225 kg N/ha both were lower in 80 DAP silage. The highest 48-h gas production (71.2 and 61.0 mL/200 mg DM) occurred in forage and silage from 110 DAP with 150 kg N/ha. Ruminal pH remained optimal range (6.2–6.8) across treatments. Harvest date and N rate interactively influence sweet sorghum silage quality and rumen fermentability. Under rain-fed conditions, 80 DAP with 225 kg N/ha optimizes silage quality, while 110 DAP with 150 kg N/ha maximizes rumen fermentation potential. These findings support sweet sorghum as a viable silage option where maize production is constrained by water availability.

Agriculture doi: 10.3390/agriculture16111131

Authors: Hyeon-Seong Yoo Hyun-Jin Jung Na-Yun Lee Eun-Chae Bae Eun-Bin Hwang Eun-Seong Baek Se-Jin Oh Yu-Mi Lee Sang-Cheol Gwak Moon-Sub Lee Seong-Woo Cho Tae-Young Hwang

This study was conducted to evaluate regional variation in wheat traits within the same genetic background using the Korean-bred cultivar ‘Saekumkang’, thereby minimising genetic effects. Field trials were conducted across six major wheat-growing regions in Korea: Gyeongsangnam-do (GN), Gyeongsangbuk-do (GB), Jeollanam-do (JN), Jeollabuk-do (JB), Chungcheongnam-do (CN), and Chungcheongbuk-do (CB). Regional grain-filling environments were characterised using temperature, vegetation indices, and photosynthesis-related traits measured at approximately 20 days after anthesis. Differences in grain-filling environments and leaf physiological status were accompanied by variation in grain morphology, starch composition, and protein-related traits. Grain area was highest in GN (17.92 ± 0.33 mm2) and lowest in CB (13.41 ± 0.49 mm2). Total grain protein concentration was highest in GB (12.39 ± 3.70 mg/g) and lowest in JN (5.40 ± 1.93 mg/g), whereas total grain starch content was highest in GN (45.09 ± 0.33%) and lowest in CB (36.48 ± 0.22%). Principal component analysis and partial least squares discriminant analysis showed that grain size- and starch-related traits were mainly associated with GN, whereas photosystem II energy flux and protein-related traits were associated with CB or GB. These results indicate that regional grain-filling environments are closely associated with coordinated changes in leaf physiology, grain development, and starch- and protein-related quality traits within a single cultivar, providing baseline information for region-specific wheat quality management.

Agriculture doi: 10.3390/agriculture16101130

Authors: Umme Fawzia Rahim Md. Mushibur Rahman Hiroshi Mineno

Accurate tomato maturity detection is critical for optimizing key agricultural operations in precision agriculture, including harvesting, grading, and quality control. Despite advances in deep learning and machine vision, reliable detection in real-world environments remains challenging due to cluttered backgrounds, dense fruit clustering, and subtle color differences between maturity stages. In response to these challenges, we present TAA-YOLOv8, an attention-enhanced detection architecture integrating a novel Tomato-Adaptive Attention (TAA) module that performs sequential channel–spatial feature refinement using an adaptive 1D convolution for channel recalibration and a balanced 5 × 5 spatial kernel for improved localization, enhancing discriminative representation while preserving computational efficiency. The framework is evaluated on three datasets representing diverse agricultural environments: a newly introduced Cross-Regional Tomato dataset collected from open-field farms in Bangladesh and greenhouse facilities in Japan, and two public benchmarks, Laboro Tomato and Tomato Plantfactory. TAA-YOLOv8m outperforms baseline YOLOv8m, achieving mAP@50–95 improvements of +9.29%, +9.00%, and +6.65% with F1-scores of 0.968, 0.976, and 0.955, respectively. It further surpasses attention-enhanced variants and RT-DETR-L, and remains competitive with YOLOv11m. Gradient-Weighted Class Activation Mapping (Grad-CAM) shows concentrated fruit-centered activations, providing transparent decision-making evidence and supporting stakeholder confidence in practical deployment within vision-based agricultural management systems.

Agriculture doi: 10.3390/agriculture16101129

Authors: Jiahao Ye Chao Xu Biao Cao Tianyuan Feng Tengyan Feng Jun Sun Lei Zhang

Optimizing fertilizer management is crucial for increasing crop yields while reducing environmental impact. However, traditional methods rely on extensive field trials, which are costly and limit their scalability. To overcome these limitations, this study developed data-driven yield prediction models (YPM) for wheat, rice, and maize by integrating multiple feature selection and machine learning algorithms with explainable ensemble learning, namely stacking regression (SR) and voting mean (VM). The optimal YPM was subsequently combined with the hybrid optimization strategy to construct an intelligent fertilization decision model (IFDM), and the economic–environmental benefits were subsequently evaluated. The best-performing models were SHAP-SR for wheat and rice and GBM-SR for maize, achieving R2 values of 0.79, 0.69, and 0.67, and RMSEs of 681.69, 725.35, and 1091.49 kg ha−1, respectively. Based on the IFDM, the recommended application ranges for nitrogen (N), phosphorus (P2O5), and potassium (K2O) were as follows: for wheat, 122.1–256.3, 45.4–98.2, and 30.6–60.7 kg ha−1; for rice, 170.8–261.2, 55.1–91.4, and 40.6–98.5 kg ha−1; and for maize, 157.5–293.4, 84.2–156.4, and 30.1–62.7 kg ha−1. Simulation-based evaluation suggested that adopting these recommendations could potentially increase average yields by 9.2–12.4% and enhance economic–environmental benefits by 32.86–97.73% across the three crops. This study indicates that coupling interpretable ensemble learning with a hybrid optimization strategy can support efficient decision-making for field-scale fertilization and provides a data-driven and cost-effective approach for precision fertilization, with potential applicability to arid agricultural regions under similar agro-ecological conditions.

Agriculture doi: 10.3390/agriculture16101128

Authors: Zhiheng Sun Adul Supanut Jianxu Liu Polpat Kotrajaras

Digital inclusive finance has grown rapidly in China in recent years, yet its effect on agricultural economic resilience remains debated. This study investigates the effect of digital inclusive finance on agricultural economic resilience, focusing on the mediating role of rural industry integration. Using annual panel data covering 29 Chinese provinces from 2011 to 2021, we employ two-way fixed-effect panel regressions, mediation analysis, threshold analysis, instrumental variable estimation, and spatial econometric models. The results show that digital inclusive finance has a significant negative effect on agricultural economic resilience, and this finding is robust across alternative specifications and instrumental variable estimations. Rural industry integration serves as an important transmission channel, with the indirect effect accounting for approximately one-third of the total effect. The two stages of this mediation pathway are moderated by distinct threshold variables: rural digital infrastructure positively moderates the effect of digital inclusive finance on rural industry integration, while government fiscal support negatively moderates the effect of rural industry integration on agricultural economic resilience. The spatial analysis further reveals that digital inclusive finance generates negative spatial spillovers onto neighboring provinces. Based on these findings, we suggest that the government continue to invest in rural digital infrastructure, guide digital finance toward rural industry integration in underdeveloped regions, and maintain fiscal support at an appropriate level to preserve the vitality of integrated industries.

Agriculture doi: 10.3390/agriculture16101127

Authors: Shengyong Xu Honglei Yang Yu Zeng Shaodong Wang Shuo Yang Zhilong Bie Yuan Huang

Plant leaf starch content is a critical indicator of metabolic status, yet traditional enzymatic methods are destructive, labor-intensive, and costly. This study proposes a novel non-destructive detection method using watermelon–pumpkin grafted seedlings. To optimize hardware design, 12 characteristic wavelengths were identified via competitive adaptive reweighted sampling (CARS). A portable multispectral imaging system was developed, featuring narrowband LEDs and integrated human–computer interaction software for real-time visualization. We constructed a multimodal deep learning architecture that integrates a convolutional neural network (CNN) for spatial feature extraction from RGB images, a fully connected neural network (FCNN) for spectral data, and a Transformer network for high-level feature fusion. Experimental results showed that the ShuffleNet v2-Transformer model achieved an R2 of 0.956 (RMSE = 0.036) for watermelon leaves, while the EfficientNet b1-Transformer model reached an R2 of 0.967 (RMSE = 0.052) for pumpkin leaves. This multimodal approach significantly outperformed conventional PLSR and single-modal CNN models, demonstrating superior ability in processing long-range dependencies within spectral–spatial data. The system enables accurate detection with a throughput of 120 samples per hour at a hardware cost approximately 90% lower than commercial multispectral cameras. This provides an efficient, low-cost solution for large-scale monitoring of plant physiological indicators in precision breeding.

Agriculture doi: 10.3390/agriculture16101126

Authors: Beibei Gao Liejun Wang Jinkai Qiu

Accurate identification of arable land, as the foundation of the high-standard farmland construction, impacts the crop layout, accurate management of water and fertilizers, and intelligent control. Due to the 10-m resolution limitation of Sentinel-2 imagery, there is feature overlap within individual pixels of the satellite imagery. This leads to fragmented semantic features during farmland identification, and adjacent plots often appear unclear and intertwined. To address these issues, a Hierarchical Agricultural Segmentation Network (HASNet) was proposed. Built upon the classic encoder-decoder structure, this HASNet model incorporates an expanded feature enhancer (DFE) module to recover weak features and reconstruct cropland features (e.g., edges and shapes) that are obscured by mixed pixels. It also introduces a lightweight strip spatial attention (LSSA) mechanism to capture long-range features unique to farmland. Furthermore, it used a pyramid decoding module (PDM) to refine cropland parcel boundaries. Taking a farm in Xinjiang Uygur Autonomous Region, a semantic segmentation dataset of cultivated land was constructed based on Sentinel-2 imagery. Through accuracy comparisons, visualizations, and inferences, HASNet achieved an MIoU of 88.52% and a Kappa coefficient of 87.82%, outperforming mainstream models such as Unetformer and MPFUnet. Ablation experiments confirmed the effectiveness of the DFE, LSSA, and PDM modules in feature capture and edge refinement. The large-scale image sliding inference experiment prevented the seam effect and demonstrated its practicality. In summary, HASNet provides low-cost technical and theoretical support for the intelligent monitoring of high-standard farmland.

Agriculture doi: 10.3390/agriculture16101125

Authors: Tingting Lang Yuanqing Wang Ying Liu Xinzhe Song Yanzhao Yang

The yield of highland barley is not only related to the food security of Tibet but also to the social stability and development in the frontier region. This study revealed the spatiotemporal distribution of highland barley yield potential using the DSSAT model and GIS technology in the Yarlung Zangbo River and its two tributaries (YZTT) of Tibet from 1981 to 2020, and analyzed its response relationship to climate factors. The results show that the highland barley yield potential ranged from 4284.75 to 7341.15 kg/ha in the YZTT region during 1981 to 2020, with an average of 6719.87 kg/ha. Under the climate change, the highland barley yield potential was on a downward trend of −14.49 kg/ha·a over the past 40 years. In terms of the response of highland barley yield potential to climate change, the highland barley yield potential decreased by 2.90 kg/ha for every 1 MJ/m2 decrease in solar radiation. For every 1 °C increase in the maximum temperature, the highland barley yield potential increased by 219.68 kg/ha. Meanwhile, for every 1 °C increase in the minimum temperature, the highland barley yield potential increased by 91.40 kg/ha. These findings aim to provide reference for decision-making in agricultural policy and spatial allocation of agricultural resources.

Agriculture doi: 10.3390/agriculture16101123

Authors: Yongjun Sun Yaqin Tong Jiachen Ding Yejun Zhu Weihua Wei Maohua Xiao Guosheng Geng

This study proposes an attitude stabilization method based on an improved adaptive Kalman filter (AKF). The aim is to address attitude fluctuations and rollover risks in rail-based agricultural transport platforms on hilly terrain caused by slope changes, load shifts and vibrations. A dynamic model integrating the load distribution and center-of-mass migration was established, and an adaptive noise covariance mechanism was used to precisely estimate the roll and pitch angles in real time. A dual-channel proportional–integral–derivative controller was designed for automatic leveling, and a rollover risk index (RRI) was adopted for safety evaluation. Simulations revealed the ability of the improved AKF to decrease the roll estimation (RMSE) from 1.2684° to 0.8670° and the stabilization time from 0.6250 to 0.3830 s for the roll and from 0.6930 to 0.4110 s for the pitch. Under 10–30° slope disturbances, the average RRI decreased from 0.1861 to 0.1506. Field tests further demonstrated decreases in the peak roll and pitch angles from 4.8° and 4.1° to 3.1° and 2.7°, respectively, and a decrease in the average RRI from 0.203 to 0.169. The improvements in estimation accuracy, leveling performance, and operational safety under complex disturbances indicate the strong engineering potential of the proposed method.

Agriculture doi: 10.3390/agriculture16101124

Authors: Monika Marković Irena Jug Danijel Jug Boris Đurđević Bojana Brozović Vedran Lederer Željko Barač

Structural and functional soil degradation under conventional tillage has reached a critical point, requiring a shift towards conservation practices to mitigate the negative effects of climate change. This study evaluated the multi-year effects (2021–2024) of conventional tillage (CT), conservation deep tillage (CD), and conservation shallow tillage (CS) on soil physical properties (density, air capacity, and water content), water distribution, infiltration rate, and maize yield in a silty Gleysol. Soil water content (SWC), i.e., distribution, was monitored using PR2 profile probes at depths of 10, 20, 30, and 40 cm. CT treatment resulted in impaired soil physical properties, characterized by a significant increase in air capacity (+233.9%) and with a significant decrease in volumetric water content (qw, ≈40%). In contrast to CT (47.91 cm h−1), the CS treatment resulted in more favorable hydraulic properties, i.e., and infiltration rate of 102.29 cm h−1, by 2024. Statistical analysis (R2, RMSE) confirmed that CS provides the most reliable and consistent environment for monitoring SWC. While maize yields were significantly higher in CT during the initial year (2021; 9.5 t ha−1 vs. 8.4 t ha−1 in CS), no significant differences were observed by 2024, and all tillage systems reached yields of ≈13.0 t ha−1. The results suggest that after the four-year study period, CS tillage stabilized soil hydraulic properties and pore continuity, thereby resulting in maize yields equivalent to those of CT. Therefore, CS has proven to be a more resilient and effective strategy for sustainable water management in silty Gleysols.

Agriculture doi: 10.3390/agriculture16101122

Authors: Lening Hu Songqi Zhu Xuehui Liu Hua Deng Anyu Li Linxuan Li Limei Pan Yuan Huang

The soil of Siraitia grosvenorii (LHG) farmland often suffers from acidification, compaction, and declining organic matter content. As biochar helps improve soil quality and enhance soil carbon sequestration capacity, an increasing number of studies are utilizing biochar for soil quality improvement. To address the soil degradation problem in LHG farmland and achieve the goals of soil organic carbon (SOC) sequestration and nutrient increase, we conducted a 100-day indoor constant-temperature incubation experiment by adding different proportions of LHG vine biochar. We analyzed the changes in SOC mineralization, different carbon fractions, and soil nutrient content in LHG farmland. The main results showed that, compared with the control group, the cumulative mineralization (CumulMine) of SOC increased by 3% to 51%, and organic carbon content increased by 52.43% to 193.87%. As the LHG vine biochar application rate increased, the metabolic entropy (qCO2) rose, whereas the microbial entropy (qMBC) showed an opposite trend. Similarly, compared with the control group, the addition of 1.0%, 2.0%, and 4.0% LC increased water-soluble organic carbon by 45.87 mg·kg−1, 67.00 mg·kg−1, and 81.73 mg·kg−1, respectively, and soil nutrients also increased, but microbial biomass carbon (MBC) and readily oxidizable organic carbon (ROC) contents decreased. The main conclusions indicate that adding LHG vine biochar increases SOC content, which is associated with reduced microbial activity. Biochar-derived DOC may serve as a substrate for microbial respiration, thereby contributing to increased CO2 release and accelerated nutrient release. The application of LHG vine biochar enhanced the carbon sequestration capacity of LHG farmland soil while improving soil nutrient content, with the 4% application rate treatment performing the best.

Agriculture doi: 10.3390/agriculture16101121

Authors: Al-Noor Abdullah Sanzidur Rahman Rita Goyal

Climate change, depleting fossil fuel reserves, and instability in petroleum prices are driving developing economies to explore cost-effective, efficient, and sustainable energy sources such as hydropower. However, there is an ongoing debate regarding the relevance, suitability, and impact of mega-dams. Much of the existing research on mega-dams examines this debate through the lens of development theories. However, mega-dams impact a wide range of stakeholders at local, national, regional, and global levels, necessitating exploration of their role from a socioeconomic perspective. This interdisciplinary case study draws knowledge from management, sociology, and economics and provides a comprehensive account of multi-stakeholder perspectives on the impact of a mega-dam and addresses the research question: How do stakeholders perceive the impact of the Merowe Dam on agricultural livelihoods, and how do they interpret the role of governance processes? Participants included farmers, a focus group with 10 members from the affected communities, and 32 key informant interviews from non-governmental organizations, political actors, academics, businessmen and leaders in the catchment areas of the Merowe Dam, Sudan. The findings suggest that despite some concerns about motivations and processes of mega-dam commissioning, these projects are perceived as beneficial for long-term and sustainable socioeconomic growth and gaining support for renewable energy use in developing economies. The participants reported that modernization of agriculture, following the establishment of the dam, increased crop yields, e.g., wheat production has increased per hectare. Farmers’ income and irrigated land have increased substantially per family due to an increase in land sizes allocated to relocated communities, leading to an overall increase in land size. Therefore, with improved processes in both pre- and post-commissioning stages, transparency, accountability, and deeper stakeholder engagement, mega-dams can facilitate a smoother transition from fossil fuels to large-scale hydropower on one hand and livelihood enhancement through agriculture and other income generating activities on the other.

Agriculture doi: 10.3390/agriculture16101120

Authors: Dimitrios Koulouris Nikolaos Malamos

Soil moisture content (SMC) is a key variable in hydrology, irrigation, and land-atmosphere interactions, yet continuous monitoring remains constrained by sensor limitations and site heterogeneity. This study evaluated Bayesian Additive Regression Trees (BART) for estimating daily SMC at 10, 30, and 50 cm depths in the Arta plain, northwestern Greece, using combinations of in situ soil moisture observations from other depths together with Sentinel-2-derived NDVI and NDMI. BART was trained with 2020–2021 data and evaluated using 2022 observations. Model performance was generally high, with Nash–Sutcliffe efficiency often exceeding 0.90 and RMSE remaining below nominal sensor uncertainty. The best results were obtained when soil moisture from two additional depths was used as predictor information, confirming the strong vertical dependence of profile moisture dynamics. NDVI and NDMI did not systematically improve point prediction accuracy but provided complementary information by improving the estimation of predictive uncertainty and generating more reliable credible intervals within the probabilistic formulation. Residuals were normally distributed and showed no evident systematic bias. Preliminary external validation at an independent site showed moderate skill, with most cases still producing errors below nominal sensor accuracy. Finally, a comparison between BART and Multiple Linear Regression (MLR) showed that BART outperformed MLR, particularly in cases where both machine learning models performed weakly. Overall, BART proved to be a robust framework for multi-depth soil moisture estimation.

Agriculture doi: 10.3390/agriculture16101119

Authors: Magdalena Szymańska Bożena Smreczak Pavel Čermák Tomasz Sosulski

Portable X-ray fluorescence (pXRF) is increasingly used as a rapid and cost-effective technique for soil analysis; however, its comparability with laboratory-based methods remains uncertain. This study aimed to evaluate the applicability of pXRF for determining the concentrations of six elements (K, Ca, Fe, Pb, Mn, and Zn) in agricultural soils classified as Albic Luvisols with a loamy sand texture. A total of 96 dried, ground soil samples from a long-term fertilization experiment were analyzed using pXRF and compared with inductively coupled plasma mass spectrometry (ICP-MS) following aqua regia digestion. Association and agreement between methods were assessed using correlation analysis, Deming regression, Lin’s concordance correlation coefficient (CCC), and Bland–Altman analysis. Substantial differences were observed between the two methods. The mean pXRF/ICP-MS ratios were approximately 25 for K, 4.0 for Ca, 1.43 for Fe, 1.41 for Mn, 1.21 for Pb, and 1.06 for Zn. The observed discrepancies are attributed to methodological factors. In particular, ICP-MS after aqua regia digestion represents pseudo-total concentrations, whereas pXRF measures total solid-phase content. Bland–Altman analysis revealed substantial systematic differences between methods. The largest biases were observed for K (−13,110 mg kg−1) and Ca (−2904 mg kg−1), indicating differences spanning several orders of magnitude. Smaller biases were found for Fe (−1179 mg kg−1), Mn (−50.0 mg kg−1), Pb (−2.37 mg kg−1), and Zn (−1.30 mg kg−1). The limits of agreement were particularly wide for K and Ca, whereas Zn exhibited the narrowest range. CCC values confirmed poor agreement for most elements (0.00049–0.36), with Zn showing the highest concordance (0.89). Overall, in the study condition, Zn demonstrated the best agreement between methods. Moreover, the results highlight that correlation-based metrics alone are insufficient for comparing methods and should be complemented by agreement-based approaches.

Agriculture doi: 10.3390/agriculture16101118

Authors: Qinjie Huang Linchao Wang Yong Chen Qiqi Zhang Shumin Li Yuchen Lin Jing Ye Shuisheng Fan

As Agricultural Heritage Systems (AHS) shift from recognition toward dynamic conservation and revitalization, understanding how visitors perceive heritage values is essential for improving interpretation and management. Guided by landsenses ecology, this study provides one of the first comparative assessments of visitor perceptions across different types of Tea Agricultural Heritage Systems (TAHS), using three representative cases in Fujian, China. A visitor-oriented framework integrating physical, psychological, and cultural perceptions was developed, and 600 questionnaire responses were analyzed through entropy-weighted fuzzy comprehensive evaluation. The results show that visitors generally perceived the three TAHS positively, but perception levels differed significantly across dimensions and heritage types (p < 0.01). Psychological perceptions, especially sense of safety, sense of space, and sense of belonging, were more readily formed, whereas deeper cultural perceptions, such as understanding of heritage cultural content and community cultural connections, remained weaker. These findings reveal a hierarchical pattern in which immediate sensory and psychological experiences precede deeper cultural cognition. Practically, the study suggests that TAHS conservation should move beyond resource protection by translating heritage values into identifiable, contextualized, and participatory visitor experiences through interpretation systems, community-based participation, and experiential presentation.

Agriculture doi: 10.3390/agriculture16101117

Authors: María Luisa Lara Ruiz Rosa Gallardo-Cobos

The rapid digitalisation of the agri-food sector has generated unprecedented volumes of farm and value chain data, but also highly fragmented data ecosystems and asymmetric power relations between farmers, technology providers, and public authorities. In response, the European Union has developed a comprehensive data governance architecture—including the Data Governance Act, the Data Act, the GDPR and the EU Code of Conduct on Agricultural Data Sharing—and is building a Common European Agricultural Data Space (CEADS). This article examines that governance framework and explores its implications for the agri-food sector in Spain. Through a qualitative legal policy review, we map the regulatory landscape, analyse five major European and Spanish initiatives (CEADS/AgriDataSpace, AgData, Agdatahub, RegenAg-X, and DADS), and use Spain as a national case study. A multi-level actor model (meta-governance, data originators, transformation intermediaries, and data users) structures the comparative analysis. On this basis, six design principles for responsible agri-food data spaces are identified: clarity of use cases, inclusive multi-stakeholder governance, data life cycle mapping, privacy and sovereignty by design, a fair economic model, and regulatory compliance as a trust factor. The article identifies open research questions on anonymisation of georeferenced data, data sovereignty, and equitable value distribution, and outlines an agenda for future empirical and legal research.

Agriculture doi: 10.3390/agriculture16101116

Authors: Kevan W. Lamm Haoming Fan Alexa J. Lamm Masoud Yazdanpanah

Ensuring global food security is one of the greatest challenges facing humanity and meat production is a critical source for protein; however, there are many critical issues facing the industry. This study focused on consumer perceptions of four key issues facing the meat industry: (1) the public perception of the animal industry, (2) environmental sustainability, (3) animal health and well-being, and (4) ensuring human health and well-being (e.g., food safety, nutrition). Analyzing the data from an importance and urgency perspective, the results indicated most respondents tended to perceive ensuring human health and well-being as most important and urgent relative to the other items. However, after calculating the criticality index (a measure of within-person concordance), environmental sustainability had the highest observed mean criticality score, followed by public perception. Lastly, a cluster analysis was undertaken. Four distinct clusters emerged: (1) Health-Focused/Environment-Skeptic, (2) High Engagement, (3) Low Engagement, and (4) Important But Not Urgent. Overall, results indicate a range of consumer perspectives regarding critical issues facing the meat industry; however, human health and well-being was consistently identified as the most important and urgent issue from a consumer perspective which can help inform more targeted communication strategies and effective policy development.

Agriculture doi: 10.3390/agriculture16101115

Authors: Jeong-Eun Sim Yun-Ha Lee Min-Seok Gang Ju-Yeon Ahn Han-Kyeol Park Jae-Kyung Kim Won-Kyung Lee Si-Hong Kim Ho-Min Kang

Tomato yellow leaf curl virus (TYLCV) is one of the most destructive viral diseases causing severe yield losses in tomato production worldwide. This study investigated the effects of TYLCV infection on plant growth, photosynthetic physiological responses, and yield formation in greenhouse-grown tomatoes and evaluated the applicability of physiological trait-based yield prediction models. Two large-fruited tomato cultivars widely cultivated in Korean protected horticulture systems, ‘Daphnis’ and ‘Pink Star’, were inoculated with TYLCV under greenhouse conditions, and their growth, physiological responses, and yield characteristics were compared under high- and low-temperature growing seasons. TYLCV infection significantly reduced leaf length, leaf width, and leaf area index (LAI), and decreased both flowering truss number and fruit-setting truss number, resulting in reduced total yield. Physiological analyses showed that infected plants exhibited decreases in the OJIP fluorescence rise curve and Fv/Fm values, indicating a reduced photochemical efficiency in photosystem II. In addition, A–Ci response curve analysis revealed a reduction in net photosynthetic rate, suggesting limited carbon assimilation capacity. Total yield showed significant positive correlations with maximum net photosynthetic rate (Amax), Fv/Fm, and Ci300. GGE and GT biplot analyses further indicated that yield was closely associated with photosynthetic performance and canopy development traits. A multiple regression model based on physiological traits and virus infection status explained a significant proportion of the variation in tomato yield (R2 = 0.367), indicating that TYLCV infection acts as a key limiting factor for yield reduction. These findings demonstrate that TYLCV infection restricts tomato productivity through reduced photosynthetic efficiency and altered canopy structure. Moreover, physiological trait-based yield prediction approaches may provide a useful framework for evaluating productivity under viral infection conditions and for developing data-driven crop management strategies in greenhouse tomato production systems.

Agriculture doi: 10.3390/agriculture16101114

Authors: Xilun Zhang Jing Wang Yalong Liu Ping Wang Bin Ma Qiuju Wang Jingkuan Wang

Albic soils are a typical problematic soil type distributed worldwide. These soils are characterized by a thin humus layer, low organic matter content, nutrient insufficiency, and weak microbial activity. Therefore, microbial-based approaches hold great potential for the amelioration of Albic soils. This review synthesizes microbial characteristics, influencing factors, amelioration mechanisms, and related technical efficacy of Albic soils. Microbial communities of Albic soils exhibit distinct regional characteristics, with Acidobacteriota and Proteobacteria dominating the bacterial community. Reasonable agricultural management practices—including deep plowing and subsoil mixing, combined organic fertilization and straw return—can increase microbial biomass by 62–248% and enhance enzyme activities by 12–303%, ultimately increasing crop yield by 1.5–13%. Such practices drive fertility enhancement and ecological functional improvement in Albic soils. Inoculation with functional microbes (e.g., Arbuscular Mycorrhizal Fungi, Trichoderma) alleviates Albic soil acidification by 1.1–3.8%, activates recalcitrant nutrients, and accelerates Soil Organic Matter (SOM) decomposition. Through extracellular polymeric substance secretion, such inoculation promotes aggregate formation, improving soil permeability and structural stability. However, challenges remain for current research, including difficult microbial agent colonization, unstable amelioration effects, and a lack of long-term field studies. Future research should utilize bio-omics technologies, artificial intelligence, and big data technologies to analyze microbial functions and regulate soil quality for cultivated land improvement and sustainable agriculture development.

Agriculture doi: 10.3390/agriculture16101113

Authors: Wenbin Zhang Yue Lu Yong Lin Zikang Cao Haijian Wu Liju Liu Yingyin Chen Ding Hu Quan Lu

Late spring frost poses a serious threat to orchard production, especially in mountainous orchards where timely frost monitoring and adaptive protection are difficult to implement. To address this problem, this study developed an intelligent smoke-based anti-frost machine integrating a LoRa-based wireless temperature monitoring system, a smoke actuation unit, and a closed-loop control terminal. To overcome the slow response and large overshoot of conventional PID control under nonlinear field conditions, a fuzzy PID control strategy optimized by the Hippopotamus Optimization Algorithm (HOA) was proposed to regulate smoke release in real time. Comparative simulations were conducted using conventional PID, fuzzy PID, and HOA-fuzzy PID controllers, and field experiments were performed in an apple orchard. The results showed that the HOA-fuzzy PID controller achieved the best dynamic performance. Compared with conventional PID, the overshoot, rise time, and settling time were reduced by 60.12%, 33.21%, and 61.94%, respectively; compared with fuzzy PID, they were reduced by 22.85%, 50.45%, and 60.11%, respectively. Disturbance simulation further indicated improved control robustness. Field experiments showed that the prototype increased the orchard canopy temperature by 0.9–3.0 K, and the PM2.5 distribution in the operational area indicated improved smoke coverage. The adaptive regulation strategy also avoided continuous fixed-output operation, suggesting its potential to improve energy-use efficiency. Overall, the proposed system provides a feasible field-operable approach for improving canopy thermal conditions and reducing frost-risk exposure in mountainous orchards, although further biological validation is still required.

Agriculture doi: 10.3390/agriculture16101112

Authors: Teresa Ana Lía Ocante Fernando Gabriel Martinez Federico Zannier Angeles Prieto-Fernandez Juliana María Saez Analía Álvarez

Glyphosate [N-(phosphonomethyl)glycine] is the most widely used herbicide worldwide, and its environmental persistence has prompted increasing interest in microbial processes that may contribute to its dissipation. This study evaluated a collection of 15 soil-derived actinobacterial strains for plant growth-promoting traits, extracellular enzymatic activities, glyphosate tolerance, and glyphosate removal under nutrient-sufficient and phosphate-starved conditions. Herbicide tolerance evaluated on agar plates was widespread across the collection, with all strains sustaining growth at 10 and 50 g L−1 of glyphosate. Under nutrient-sufficient conditions glyphosate removal remained limited, with maximum values of 16.15 ± 2.08% (Streptomyces sp. Con7.16) and 15.34 ± 2.89% (Streptomyces sp. Z38). In contrast, prior phosphate starvation markedly enhanced removal efficiency, reaching 42.21 ± 3.59% in Streptomyces sp. Z38 and 39.46 ± 1.94% in Streptomyces sp. Con7.16. Transmission electron microscopy coupled with X-ray microanalysis in the selected Streptomyces sp. Z38 revealed starvation-associated depletion of intracellular polyphosphate granules, followed by partial replenishment when glyphosate was supplied as the sole phosphorus source, consistent with indirect evidence of glyphosate-derived phosphorus acquisition. Genome mining of Streptomyces sp. Z38 identified candidate genes potentially consistent with a non-canonical, C-P lyase-independent phosphonate utilization route; however, these assignments are based exclusively on bioinformatic evidence and require experimental validation. Collectively, these findings indicate that phosphate limitation enhances glyphosate removal in the selected actinobacteria, and the physiological and genomic data are consistent with a starvation-triggered shift toward alternative phosphorus scavenging strategies. Because this strain is intended for future phytoremediation applications in glyphosate-contaminated agricultural soils, elucidating the underlying phosphorus dynamics is essential for anticipating its functional behavior and environmental relevance.

Agriculture doi: 10.3390/agriculture16101111

Authors: Xuejun Yang Jinbiao Zhong Kaiyan Lin Junhui Wu Jie Chen Huajun Zhu

High-precision plant phenotyping requires efficient 3D reconstruction methods with high geometric quality. 3D Gaussian Splatting (3DGS) has recently emerged as a promising approach for real-time 3D reconstruction, achieving impressive visual quality. However, in crop environments dominated by monochromatic and low-texture regions, existing 3DGS methods often produce ambiguous geometries and fail to recover geometry-consistent 3D surfaces. To address these limitations, we propose LV-3DGS (Leafy Vegetables-3DGS), an optimized 3DGS-based framework tailored for the reconstruction of leafy vegetable scenes. First, a blurred reconstruction module is introduced to mitigate reconstruction artifacts caused by camera motion blur during multi-view image acquisition. Second, we propose a planar optimization strategy and design both local and global geometric consistency regularizations to optimize the model, thereby improving the surface reconstruction quality and geometric accuracy. Third, based on an analysis of individual Gaussian contributions, a contribution-based pruning strategy is developed to selectively remove inaccurate geometric components, achieving accurate scene geometry while reducing memory consumption and improving rendering efficiency. In addition, a quantitative geometric evaluation method is proposed for assessing reconstruction quality. Experimental results demonstrate that the proposed method achieves the highest accuracy among the tested baselines, with SSIM, PSNR, and LPIPS reaching 0.94, 34.53 dB, and 0.11, respectively. Moreover, the geometric consistency (GC) metric attains 0.317 cm. Finally, phenotypic parameters are measured from the reconstructed leafy vegetable point clouds. Compared with ground truth measurements, the proposed approach yields coefficients of determination (R2) of 0.9959, 0.9651, and 0.9895 for plant height, leaf number, and leaf area, respectively. These results are significantly outperform to some existing phenotyping methods, providing a new methodology and technical solution for high-precision, low-cost, and high-throughput crop phenotyping.

Agriculture doi: 10.3390/agriculture16101110

Authors: Yifei Sun Xinying Miao Yi Zhang Zhipeng He Xinyue Tao Zhenghan Wang Tianwen Hou Ping Ren Wei Wang

Sweet cherry cracking severely impairs its commercial value and causes huge economic losses, and the accurate real-time detection of fine cracking defects remains a challenging small-target detection task. Traditional manual sorting and conventional machine vision methods suffer from low efficiency and poor robustness, while existing YOLO-based models have limitations in multi-scale feature fusion, local feature discrimination and spatial information retention for cherry cracking detection, and their effectiveness in natural production environments has not been statistically validated. To address these issues, this study proposes YOLO-CY for cherry cracking defect detection. Three key modules were optimized: the C3k2_AdditiveBlock was designed to enhance multi-scale feature extraction, the C2PSA_CGLU module improved the discriminability of local crack features via refined channel attention, and the Efficient Up-Convolution Block replaced traditional upsampling to reduce spatial information loss. Experiments were conducted on a self-constructed dataset of 3662 cherry images acquired on a real sorting line under natural ambient light. The results showed that YOLO-CY achieved an mAP50 of 94.88% and an mAP50-95 of 64.92%, with precision and recall reaching 93.90% and 90.81%, respectively, significantly outperforming mainstream lightweight YOLO models and two-stage detectors. Ablation experiments verified the synergistic effect of the three improved modules, and the model only had a marginal increase in parameters (2.62 M) and GFLOPs (6.60), maintaining lightweight characteristics. YOLO-CY can accurately detect fine, low-contrast and pedicel-overlapping cracks and is suitable for real-time detection on automated cherry-sorting lines, providing a technical solution for intelligent cherry quality inspection.

Agriculture doi: 10.3390/agriculture16101109

Authors: Yang Liu Yushan Xie Xue Wu Qi Wang

Accurate and actionable crop disease diagnosis requires not only visual recognition of disease symptoms but also the ability to generate grounded reports that integrate symptom interpretation with agronomic knowledge. Existing image-based plant disease diagnosis methods mainly focus on disease classification and often lack fine-grained symptom description, evidence retrieval, and decision-oriented report generation. To address these limitations, we propose CornCare, a multimodal framework for corn disease diagnosis and diagnostic report generation that combines visual recognition, phenotype captioning, document retrieval, and knowledge-graph-based recommendation support. Given a field corn image, CornCare first localizes disease-relevant leaf regions to reduce background interference. The localized leaf image is then used for disease classification and phenotype caption generation, producing both a disease category and a fine-grained symptom description. These outputs jointly support hierarchical knowledge retrieval, where the disease category narrows the search to relevant expert documents and the phenotype caption retrieves symptom-consistent evidence. The retrieved evidence is further combined with a structured agricultural knowledge graph to generate diagnostic reports with symptom interpretation, likely causes, and management suggestions. Experiments show that CornCare achieves competitive performance in disease identification and phenotype description generation while improving the groundedness, completeness, and practical usefulness of generated diagnostic reports. These results suggest that combining multimodal perception with symptom-grounded knowledge retrieval provides a promising path toward more practical and explainable crop disease diagnosis.

Agriculture doi: 10.3390/agriculture16101108

Authors: Haoran Yang Xinjun Wang Qingfu Liang Shuhan Huang Panfeng Wang Jiandong Sheng

Accurate parcel-level crop field information is essential for precision agriculture, field management, and crop monitoring based on Unmanned Aerial Vehicle (UAV) imagery. However, it remains difficult to achieve both reliable crop-type recognition and fine boundary delineation from UAV RGB imagery. Although deep learning-based semantic segmentation models can effectively identify crop types, they often produce coarse or incomplete boundaries. The Segment Anything Model (SAM) can produce high-quality boundaries, but it depends on manual prompts and lacks semantic recognition ability, which limits its use in large-scale automatic mapping. To address this issue, this study proposes a parcel-level crop field identification framework based on Semantic–SAM fusion, enabling automatic semantic recognition and fine boundary extraction without manual prompts. Based on UAV RGB remote sensing imagery, this study developed a two-stage Semantic–SAM framework. Semantic segmentation models, including DeepLabv3+, U-Net, HRNet, and PSPNet, were first used to generate initial results. Then, bounding boxes or internal high-confidence points were extracted from the initial field regions as prompts for SAM to refine the segmentation. The final results preserved crop category information while producing finer boundaries. To evaluate the framework, this study compared four semantic segmentation models and their Semantic–SAM versions on the same-region test set, and further tested their spatial generalization ability on the different-region test set. The results showed that the Semantic–SAM framework provided more consistent gains in boundary quality, with regional recognition accuracy improving in several models and test scenarios. On the same-region test set, the PSPNet-based framework showed clear improvement, with mean Intersection over Union (mIoU) increasing from 78.99% to 83.13% under point-box prompts. The U-Net-based framework achieved the best mIoU of 87.09% with box prompts. On the different-region test set, the DeepLabv3+-based framework showed the largest gain in spatial generalization, with mIoU increasing from 67.22% to 73.45% under point-box prompts. Overall, the PSPNet-based fusion framework showed a better balance in accuracy, boundary quality, and robustness under different-region conditions. These results demonstrate that Semantic–SAM fusion supports automatic multi-class crop field mapping and boundary refinement from UAV RGB imagery without manual prompts or SAM fine-tuning, providing a practical approach for parcel-level crop monitoring and precision agriculture applications.

Agriculture doi: 10.3390/agriculture16101107

Authors: Chao Ma Ling Zhao Junjie Ma Fenglei Wang Jun Qian Xinjun Li

This paper conducts numerical simulations of nozzles with different structural parameters based on fluid mechanics, computational fluid dynamics and jet theory. The structural parameters of the nozzles were optimised by analysing flow field characteristics such as the pressure distribution within the nozzle chamber, velocity distribution, curves of the outlet cross-sectional area and external axial velocity, and velocity uniformity. Combining the results of orthogonal experiments, the optimal combination of factors was determined, and the impurity removal efficiency of the optimised nozzle was tested in the field, providing a reference for subsequent optimisation design. The results indicate that adding a fillet transition to the nozzle can mitigate sudden pressure drops and suppress the generation of vortices; when the fillet transition radius is 80 mm, the flow performance approaches the optimum; the optimal combination of the three factors was determined to be a contraction angle of 13°, λ of 0.65 (corresponding to an outlet height of 27 mm and an inlet diameter of 41 mm), and a nozzle length of 15 mm; this configuration yields the best external flow field characteristics and velocity uniformity; Analysis of the orthogonal test results indicates that the contribution of each structural parameter to velocity uniformity, in descending order, is: contraction angle (77.16%), λ (outlet height/inlet diameter) (18.25%), and nozzle length (0.73%); Field tests confirmed that the removal efficiency of foreign fibres using the optimal parameter combination remained consistently above 95%, with an overall average removal rate of 96.31%. This represents an improvement of approximately 7.5 percentage points compared to the original nozzle (88.83%). The optimised nozzle reduced the number of false rejections of cotton by 57%, demonstrating excellent and highly stable overall removal performance. The influence of the nozzle’s vertical height and its angle relative to the cotton on the removal efficiency requires further investigation.

Agriculture doi: 10.3390/agriculture16101106

Authors: Andrzej Parzonko Anna Justyna Parzonko Tomasz Wojewodzic Marta Czekaj

The primary objective of this study was to present and assess the effects of direct payments and other subsidies targeted at dairy farms under the EU’s Common Agricultural Policy (CAP) guidelines implemented in 2014–2023 on their financial performance and changes in equity. To narrow the focus on the research problem, the scope of the analysis was limited to dairy farms from the five EU countries with the highest milk production. To achieve this objective, the study employed economic measures and indicators used to evaluate the resources and outcomes of agricultural activity. The empirical material used in the analysis consisted of farm-level accounting data collected within the European Farm Accountancy Data Network (FADN). The results indicate that direct payments and other subsidies had a very substantial impact on farm income in the analysed countries. The average share of direct payments in dairy farm income in 2014–2023 in the five analysed EU countries ranged from 19.7% in Italy to 88.4% in France. Without direct payments, the average dairy farm would have incurred financial losses from its activity during periods of unfavourable economic conditions on the milk market. The new model for distributing direct payments and other subsidies introduced in 2023, whose main modification compared with the previous system was a stronger alignment of direct payments with environmental objectives, did not result in substantial changes in either the level of payments or their impact on dairy farms’ financial performance. In 2023, the average payment per hectare of agricultural land in the analysed farms amounted to EUR 461.34, which was EUR 19.88 less than in 2022.

Agriculture doi: 10.3390/agriculture16101105

Authors: Hongsheng Zhou Jiangping Liu Rigeng Wu Baoping Zhao

Weed segmentation is a fundamental task in precision agriculture, essential for targeted intervention and sustainable farming. However, achieving accurate segmentation remains challenging due to the high visual similarity between weeds and crops, as well as the ambiguous, fine-grained boundaries often present in complex field environments. To address this, we present WS-DINO, a novel weed segmentation network built upon the DINOv2 vision foundation model. Our framework introduces two key innovations: (1) a Feature Prior Module that leverages a Canny-guided refinement process to extract and inject fine-grained cues related to weed texture, morphology, and boundaries into specific blocks of the Vision Transformer; and (2) a Spatial Feature Fusion Module that leverages convolutional layers to generate multi-scale spatial features, which are then fused with the semantically rich token features from DINOv2, effectively compensating for the Transformer’s limitations in capturing local spatial details. Comprehensive evaluation on the public PhenoBench dataset shows that WS-DINO achieves an mIoU of 88.67% and outperforms the evaluated benchmark methods. Moreover, on the challenging MotionBlurred dataset, WS-DINO reaches 88.75% mIoU, showing stable performance under motion blur and degraded visual conditions.

Agriculture doi: 10.3390/agriculture16101104

Authors: Andrzej Madej Adam Kleofas Berbeć

Agriculture, as a production sector, is exposed to external shocks. The instability of agricultural markets, changes in prices of inputs, dropping crop prices, or changes in climate patterns put their economic resilience to the test. Agroecological diversification of production is widely cited as a key adaptive strategy to increase farms’ resilience to these shocks. At the same time, empirical evidence linking crop diversity to economic stability across different production systems remains limited. The aim of the study was to assess whether the integration of more complex crop rotations and livestock production increases the economic resilience of organic farms compared to stockless organic farms and conventional farms. The analysis utilized data from the Polish FADN covering the multi-crisis period of 2020–2022, which included the COVID-19 pandemic, Russia’s war against Ukraine, and the sharp rise in fertilizer and energy prices. Farms were grouped by production type. Crop diversity was assessed using the Shannon–Wiener index (H′) and the Pielou evenness index (J′). The economic resilience of tested farms was determined based on their income, income variability during the study period, and the ability to maintain income above the parity threshold. The results indicated the existence of different pathways for building resilience. Organic farms with permanent crops and field crops were characterized by the highest crop diversity on arable land, while organic farms with dairy cows had the highest overall economic resilience, despite relatively low crop diversity on arable land. This phenomenon can be explained by the high proportion of permanent grasslands, which promoted feed self-sufficiency and the internal circulation of nutrients. The results indicate that in organic systems, the integration of crop and livestock production, based on permanent grassland, may be a more effective way to strengthen economic resilience than crop diversification on arable land alone.

Agriculture doi: 10.3390/agriculture16101103

Authors: Qi Luo Fangxin Wan Xiaobin Mou Guojun Ma Xiaoping Yang Fengwei Zhang Zepeng Zang Xiaopeng Huang

Seed melon pulping is a critical process in the full utilization of seed melon. However, controlling the performance during the pulping process presents several challenges, particularly the unclear relationship between pulping performance and process parameters. This study proposes an optimization of seed melon pulping process parameters based on the optimal Latin hypercube sampling (OLHS) method. The seed melon pulping rate and the large-particle ratio after pulping were selected as performance indicators, with process parameters including the feeding rate of rind–flesh, the rotational speed of first-channel pulping knife roller, and the rotational speed of second-channel pulping knife roller. The OLHS method was combined with the discrete element method (DEM) of pulping to derive the input parameters required for training the radial basis function neural network (RBFNN). Subsequently, the non-dominated sorting genetic algorithm II (NSGA-II) was employed to find the optimal solution for the pulping performance approximation model, followed by validation through comparison experiments. The multi-objective optimization results showed that the optimal process parameters were rind–flesh feeding rate of 175.69 kg/min−1, first-channel pulping knife roller rotational speed of 797.71 r/min−1, and second-channel pulping knife roller rotational speed of 708.34 r/min−1. Under these parameters, the seed melon pulping rate reached 92.81%, and the large-particle ratio after pulping was 2.19%. Furthermore, the RBFNN-trained approximation model demonstrated a high degree of model fit for the process parameters and performance indicators, as well as strong predictive ability for the macroscopic behavior of the pulping process parameters. Further verification through seed melon pulping experiments showed consistent results with the simulation outcomes, indicating that the optimization results can effectively improve seed melon pulping performance and further confirm the reliability of the method.

Agriculture doi: 10.3390/agriculture16101102

Authors: Alessandro Cammerata Viviana Del Frate Angela Iori Francesco Gallucci

Early and non-destructive identification of fungal contamination in cereals is essential to support post-harvest management, reduce economic losses, and mitigate food safety risks along the wheat supply chain. Among filamentous fungi, Alternaria spp. are widespread contaminants of durum wheat and producers of toxic secondary metabolites such as alternariol (AOH), whose early detection remains analytically challenging. The aim of this study was to evaluate the potential of near-infrared transmittance (NIT) spectroscopy as a rapid, non-destructive pre-screening tool for the early identification of Alternaria-contaminated durum wheat lots and associated AOH risk. Samples from three durum wheat cultivars were artificially inoculated with Alternaria spp. and monitored over time. NIT spectra (570–1100 nm) were acquired in transmittance mode and analyzed using partial least squares (PLS) regression, focusing on the 870–1100 nm spectral region. Clear and time-dependent spectral differences were observed between inoculated and control samples, with the strongest discriminative features at 834 and 966 nm. Classification performance was high, with area under the curve (AUC) values between 0.96 and 0.97. ELISA analysis confirmed progressive AOH accumulation in inoculated kernels, consistent with the observed spectral changes, while control experiments excluded autoclaving and visual grain damage as confounding factors. From an applied perspective, the results indicate that NIT spectroscopy can support post-harvest decision-making as a rapid pre-screening approach, enabling the prioritization of suspect wheat lots for confirmatory analytical testing. Multivariate analysis further confirmed the consistency of spectral differences across datasets.

Agriculture doi: 10.3390/agriculture16101101

Authors: Lintao Chen Zeyu Chen Xiangwei Mou Ying Lan Yucan Huang Xu Ma Xiangwu Deng

Accurate discrete element method (DEM) simulations are essential for elucidating the precision seeding mechanisms and collision damage characteristics of cassava seed stem (CSS); however, such simulations are often limited by a lack of precise contact parameters. In this study, “Guire No. 7” CSS was selected as the research object to calibrate discrete element (DE) parameters by integrating physical experiments with DEM simulations. A stem model was constructed in EDEM software (Altair EDEM 2022) using three-dimensional scanning technology combined with SolidWorks 2024 modeling functions to investigate the influence of the model’s mesh face count on simulation accuracy. Physical experiments measured the average repose angle (RA) of the stems (30.28° ± 1.09°), along with parameters including the restitution coefficient for stem-stem and stem-steel plate collisions, and the coefficient of static friction between the stem and steel plate. The Plackett-Burman Design experiment was employed to screen parameters affecting the RA, and the steepest ascent experiment was conducted to determine their optimal value ranges. Using the RA as the response value, a Central Composite Design experiment combined with machine learning regression models was applied to optimize the influencing parameters and compare model performance. The results indicated that the GA-BP algorithm exhibited superior predictive capability compared to Support Vector Regression (SVR) and the BP neural network. Through optimization using a genetic algorithm (GA), the calibrated parameters were obtained: a stem-steel plate static friction coefficient (SFC) of 0.488, a stem-stem SFC of 0.489, and a stem-stem rolling friction coefficient of 0.131. The resulting simulated RA was 30.73°, yielding a relative error of 1.49% compared to the physically measured value. The GA-BP-GA method demonstrated better optimization performance than the central composite design experiment, thereby validating the accuracy of the calibrated contact parameters between stems. Furthermore, parameters for the Tavares model were calibrated through physical experiments on CSS, using collision damage force and collision damage energy (CDE) as validation indicators. The results showed that the relative errors for both collision damage force and CDE were less than 3%, which is within the acceptable error range, thereby confirming the validity of the calibrated DE parameters for the cassava seed stem.

Agriculture doi: 10.3390/agriculture16101100

Authors: Qingqing Wang Bin Zhu Chunxia Jiang Juan Wang Kechuan Yi

As a key component of sugarcane harvesting machinery, the spiral sugarcane lifter (SSL) enhances harvesting quality by lifting lodged sugarcane (LSC) into a posture suitable for stalk-base cutting and feeding. To improve the SSL’s lifting performance for LSC, this study developed a rigid–flexible coupling (RFC) simulation model of the sugarcane–SSL interaction and conducted kinematic and force analyses to identify the main shortcomings of the original design. Critical structural and operational parameters affecting lifting performance–including the lifting roller pitch, roller diameter, roller inclination angle, and lifter shoe length—were redesigned using mechanism-based constraints and simulation-assisted evaluation. The optimized SSL exhibited increased lifting speed and stability under low–speed, severe–lodging conditions. Under side-forward lodging (side deflection angle = 30°), the average maximum vertical height of the centroid (VHC) increased by 40.36%, and paired comparisons across three simulated lodging-angle scenarios showed significant improvement. Field tests under severe lodging at 0.55 m/s (≈2 km/h) yielded an average absolute simulation–to–field error of 5.37%. These findings support the effectiveness of the proposed parameter redesign for the tested medium-size harvester, although further validation is required under higher forward speeds, greater biomass throughput, and more variable soil conditions.

Agriculture doi: 10.3390/agriculture16101099

Authors: Lorenzo Infascelli Raffaella Tudisco Piera Iommelli Federico Infascelli Fabian Capitanio

This study investigates consumer knowledge, perceptions, and purchasing behaviors regarding products with geographical indications and certifications in the Campania region. Traditional Agri-Food Product (PAT) is the regional label used in Italy to identify traditional products whose distribution is so limited that they do not qualify for PDO or PGI designation. In this view, this research examines the diffusion of such products, their economic and sustainability attributes, and alignment with modern objectives, including environmental impact reduction, rural development, and the European Common Agricultural Policy (CAP) 2023–2027. Using a structured questionnaire administered to a sample of 706 respondents, the study combines descriptive statistics and econometric analysis, trying to identify key factors influencing Willingness to Pay (WTP) for certified products and knowledge of certifications. Findings reveal that education, knowledge of certifications, and lifestyle factors positively affect WTP, highlighting opportunities for targeted marketing and awareness campaigns, also emphasizing critical issues in view of new trade scenarios (e.g., Mercosur agreement) and climate change.

Agriculture doi: 10.3390/agriculture16101094

Authors: Guangjie Liu Yi Xia Lu Wang Li Bao Naiming Zhang

Rapid urbanization and stringent ecological protection policies in China have reshaped spatial competition among urban, agricultural, and ecological spaces. However, existing studies often overlook how this competition evolves across different slope structures. To address this, this study establishes a fine-scale analytical framework using H3 hexagonal grids and slope spectrum analysis to investigate slope structure evolution and spatial competition patterns from 1990 to 2023. The results reveal a distinct topographic stratification: urban space dominates low-slope regions (<6°) but exhibits a pervasive “upslope expansion” trend, with its average slope increasing from 1.81° to 2.07°, equivalent to an annualized increase of approximately 0.008°yr−1; agricultural space characterizes the transition zones (6–15°), showing an “upslope migration” in the Southeastern Hills associated with urban expansion pressure in low-slope areas; and ecological space functions as a stable barrier in steep terrains (>15°) but faces encroachment in transition zones. Furthermore, cluster analysis identifies significant regional heterogeneity aligned with China’s macro-topography, including “low-slope agglomeration” in the Eastern Plains, “interwoven upslope” patterns in the Southern Hilly Regions, and ecological dominance in the Western Highlands. Association analysis using GeoDetector and Multiscale Geographically Weighted Regression (MGWR) indicates that competition intensity is most strongly associated with human activity factors, especially human footprint and nighttime lights (q>0.29), which show the highest explanatory power among the examined factor groups. The interaction between human activity and elevation further shows relatively high explanatory power (q=0.41), suggesting that spatial competition is more pronounced where intensive human activities overlap with topographic constraints. Crucially, this study challenges the traditional flat-projection planning model. We propose a transition to “three-dimensional topographic regulation,” advocating differentiated management strategies—such as strict “slope redlines” for urban-agricultural transition zones—to mitigate intensifying spatial conflicts in complex terrains and safeguard agricultural sustainability.

Agriculture doi: 10.3390/agriculture16101096

Authors: Xiaolong Tian Xinnan Du Ming Dai Jianzhao Zhou Yuchen Lei Zihui Lian Boyan Huang

Accurate state of charge (SOC) estimation is essential for the reliable operation and energy management of electric agricultural machinery, particularly electric tractors operating under complex field conditions. This study aims to improve SOC estimation accuracy and robustness by proposing a hybrid method that integrates a gated recurrent unit (GRU) neural network with a fractional-order extended Kalman filter (FOEKF). The GRU model is employed to capture the nonlinear behavior of lithium-ion batteries, while the FOEKF is used to mitigate noise and compensate for model uncertainties, forming a coupled data-driven and model-based framework. Experiments were conducted on lithium-ion batteries for electric tractors under hybrid pulse power characterization (HPPC) conditions at 15 °C, 25 °C, and 35 °C. These experiments can effectively simulate the dynamic power fluctuation characteristics of the battery caused by variations in electric tractor operating conditions during agricultural operations in different seasons. Experimental results demonstrate that, compared with conventional GRU and FOEKF methods, the proposed GRU-FOEKF method achieves lower estimation errors and improved robustness. In particular, at 25 °C, the proposed method achieves an MAE of 0.9% and an RMSE of 1.1%, outperforming the compared algorithms. These findings indicate that the proposed strategy is a feasible and effective solution for battery management systems in electric agricultural machinery, contributing to the development of smart and sustainable agriculture.

Agriculture doi: 10.3390/agriculture16101098

Authors: Bin Xu Xu Tan

In recent years, the frequency, damage and impact scope of extreme weather events have increased and expanded significantly. Based on the official secondary panel data of 26 provinces in China from 2006 to 2022, this paper explores the impact of extreme weather on the urban–rural income gap. Employing benchmark regression, mediating effect and moderating effect models, this study empirically analyzed the transmission mechanism by which extreme weather affects the urban–rural income gap through crop damage caused by disasters and the mitigating role of agricultural insurance. The key findings reveal that extreme weather significantly widens the urban–rural income gap, with the severity of disaster losses serving as the primary transmission path. Furthermore, agricultural insurance effectively mitigates this shock by hedging against the loss of rural residents’ disposable income. Heterogeneity analysis shows that extreme precipitation and droughts exert the most pronounced effects, and the widening of the income gap is particularly significant in the western region of China. Consequently, it is imperative to promote the integration of meteorological services and agricultural insurance risk reduction services, improve the core infrastructure of rural disaster resistance, and build a differentiated agricultural insurance policy system for risk zones to narrow the income gap between urban and rural areas.

Agriculture doi: 10.3390/agriculture16101097

Authors: Pelin Sarıkaya Hakan Fidan

Tomato brown rugose fruit virus (ToBRFV) poses a major threat to global tomato (Solanum lycopersicum) production, as it can overcome conventional resistance genes that are effective against tobamoviruses. In this study, a multiplex CRISPR/Cas9 system was developed to target the SlTOM1 susceptibility gene family (SlTOM1a–d), which encodes host factors essential for tobamovirus replication. Six guide RNAs (gRNAs), designed following 12 off-target analyses, were assembled into a multiplex CRISPR/Cas9 construct using a Golden Gate cloning strategy and introduced into tomato genotypes through an Agrobacterium-based tissue culture transformation procedure. Although primary T0 transformants exhibited chimeric mutation patterns, stable inheritance and segregation of edited alleles were confirmed in the T1 generation. Sequence analyses identified diverse indel mutations across target loci, with SlTOM1d exhibiting the highest editing efficiency. Multiplex genome editing successfully generated single-, double-, and triple-mutant combinations, with higher-order mutants displaying the strongest tolerance phenotypes. Following mechanical ToBRFV inoculation, edited T1 plants exhibited markedly reduced symptom severity, low viral accumulation, and improved fruit health compared to wild-type controls. RT-qPCR analysis further confirmed significantly reduced viral RNA levels, supporting a host-factor-mediated tolerance mechanism. Importantly, edited lines maintained normal growth and agronomic performance. Collectively, these findings demonstrate that multiplex CRISPR/Cas9-mediated targeting of SlTOM1 homologs represents a promising and practical strategy for improving ToBRFV tolerance in tomato breeding programs.

Agriculture doi: 10.3390/agriculture16101095

Authors: Simona Roxana Pătărlăgeanu Mihai Dinu Luxița Rîșnoveanu Alina Florentina Gheorghe (Gavrilă) Andreea Pătărlăgeanu

Intensive agricultural production contributes significantly to greenhouse gas (GHG) emissions, accounting for between 10 and 12% of global anthropogenic emissions, at a time when the agricultural sector is facing increasing pressure to adapt to ever-stricter environmental regulations. This study develops and applies a multi-objective Goal Programming model to identify the optimal mix of crops and management practices that simultaneously minimize the carbon footprint and maximize productivity, at the level of a 300-hectare (ha) model agricultural system in Romania. The life cycle assessment (LCA) methodology, in accordance with ISO 14040/14044 standards and Ecoinvent 3.8 emission factors, was applied to nine crops distributed across three soil types, within four management scenarios, over an annual planning horizon. The unit of measurement used is a ton of CO2 equivalent per agricultural system. The results show that the optimized configuration achieves near-zero total carbon emissions (0.33 t CO2eq for the entire farm), reduces synthetic nitrogen inputs to 35.7% of the limit set by the EU Nitrates Directive, and generates water savings of 48%. However, these environmental gains entail a 52.9% production trade-off relative to the maximum target of 3000 tons, highlighting a Pareto-optimal structural conflict between climate and food security objectives. The sensitivity analysis identifies the nitrogen emission factor and crop yield as the most influential parameters. The results confirm the technical feasibility of the European Green Deal targets through systematic mathematical optimization, while also demonstrating that achieving economic parity requires policy support of 110–165 EUR/ha/year.

Agriculture doi: 10.3390/agriculture16101093

Authors: Hui Xu Zhihang Hu Ming Xu Juanjuan Ding Shijun Chen Zhulin Li Tianlai Li

The temperature management models for solar greenhouses exhibit strong regional dependency. Their application in non-target environments often faces significant limitations, frequently resulting in severe temperature control deviations. To address this challenge, seven solar greenhouses located in Lingyuan (Liaoning Province) and Yinan (Shandong Province) were utilized as experimental platforms. Using real-time environmental data collected by the NEUT-80S IoT monitoring system, backpropagation (BP) neural network models were trained and validated. Multiple stepwise regression analysis identified total solar radiation and sunshine duration as the primary determinants of cucumber yield. Based on these findings, a dynamic weight matrix was constructed using a solar radiation clustering algorithm. By integrating similarity distance and similarity coefficient, a microclimate similarity determination logic was established, leading to the proposal of an automatic model selection strategy with an 11-day update cycle. Quantitative validation demonstrated that when the threshold conditions—a similarity coefficient (R) ≥ 0.6 and a similarity distance (D) ≤ 0.85—are met, triggering the optimally matched model significantly improves the simulation goodness-of-fit (R2) from 0.6716 in the unmatched state to 0.9851. This strategy effectively achieves the cross-regional adaptation of high-yield temperature management models, providing robust technical support for the advancement of precision protected agriculture.

Agriculture doi: 10.3390/agriculture16101091

Authors: Kun Yang Sheng Li Feilong Jie Yanyan Ge Yinggang Jia

Layered saline soils containing weakly permeable interlayers exhibit restricted infiltration, surface salt accumulation, and limited deep salt discharge. This study investigated how weakly permeable interlayer thickness, hydraulic-parameter scenario, hole diameter, hole spacing, and irrigation salinity affect soil water redistribution, salt leaching, and profile desalination under vertical-hole treatment. Pilot-scale soil-box experiments were used for model calibration and validation, and HYDRUS-3D simulations were then used for controlled-condition scenario analysis and preliminary layout screening. The weakly permeable interlayer reduced hydraulic connectivity, increased water retention above the interface, and intensified surface salt enrichment, with stronger effects at greater thickness. Vertical holes improved hydraulic continuity and promoted downward percolation and salt leaching, but their effectiveness depended on layout. At a spacing of 30 cm, increasing hole diameter from 5 to 10 cm increased the mean desalination rate from 7.07% to 13.44% in the surface layer and from 4.06% to 18.61% in the deep layer. Irrigation salinity had little effect on water content but increased soil salt accumulation. Under the assumed conceptual cost–performance framework, the 10 cm diameter and 30 cm spacing combination showed the highest composite performance within the tested parameter range. These findings provide a mechanistic basis and preliminary layout-screening reference for vertical-hole treatment in layered saline soils with weakly permeable interlayers.

Agriculture doi: 10.3390/agriculture16101092

Authors: Petru Alexandru Vlaicu Mihaela Dumitru Alexandra Gabriela Oprea-Oancea

The poultry sector is one of the most dynamic segments of global livestock production, being essential for ensuring food security due to its high feed conversion efficiency and the accessibility of the resulting products [...]

Agriculture doi: 10.3390/agriculture16101090

Authors: Cátia F. Martins Mariana Palma Ivan Viegas John G. Jones João P. B. Freire André M. Almeida José A. M. Prates

Microalgae-based feeds are increasingly considered sustainable ingredients for animal nutrition, although their impact on skeletal muscle metabolism remains poorly understood. This study investigated the metabolic changes in piglet muscle in response to dietary Chlorella vulgaris, with or without supplementation with carbohydrate-active enzymes (CAZymes), using an untargeted metabolomics approach. Forty-two weaned piglets were assigned to four dietary treatments: a control diet; 5% C. vulgaris (CH); CH supplemented with 0.005% Rovabio® Excel AP (CH+R); and CH supplemented with 0.01% of a four-CAZyme mixture (CH+M). Muscle metabolomes were analysed using 1H-NMR spectroscopy. Multivariate analysis showed a largely conserved muscle metabolomic profile across treatments, indicating that dietary treatment did not cause distinct metabolic shifts. However, univariate analysis identified significant differences in specific metabolites (p < 0.05). Piglets fed C. vulgaris-supplemented diets had higher concentrations of methionine, succinate, β-alanine, and betaine than the control group, whereas tyramine levels were lower (p < 0.05). Generally, dietary interventions resulted in minor metabolic changes in muscle tissue, affecting particular metabolites. There was no evidence of changes in overall muscle metabolism.

Agriculture doi: 10.3390/agriculture16101089

Authors: Simas Sokas Sidona Buragienė Marius Kazlauskas Indrė Bručienė Vilma Naujokienė Tomas Mickevičius Egidijus Šarauskis

This study conducted a comprehensive comparative assessment of the effects of site-specific tillage (SST) and uniform-depth tillage (UDT) on soil physical properties, fuel consumption and CO2 emissions. The aim was to determine whether using different tillage depths based on variability in soil properties associated with apparent electrical conductivity (ECa) could improve the efficiency of soil management, which would be beneficial for the soil and the environment. Field experiments were conducted using a multifunctional cultivator with three SST depths (10, 14 and 18 cm), which were distributed over variable soil management zones. UDT was applied at a constant depth of 15 cm. The results of the experimental studies showed that SST affected the physical properties of the soil in different management zones with different tillage depths. Reduced tillage depths ensured adequate soil physical properties in areas of lower soil resistance, while deeper tillage was only effective in areas of higher soil resistance. Soil density in the top 0–10 cm soil layer varied within the plant-friendly range of 1.2–1.3 g cm−1 in the region and 1.4–1.5 g cm−1 in the deeper 10–20 cm layer, while total soil porosity responses differed in different management zones. UDT reduced total soil porosity by 3.17% and 3.5% in the top and deeper soil layers, respectively. Changes in total soil porosity due to SST in the 0–10 cm layer depended on tillage depth: it decreased slightly at 10 cm, remained unchanged at 14 cm and increased slightly at 18 cm. In addition, SST reduced fuel consumption and associated CO2 emissions compared with UDT, with environmental impact related to fuel combustion decreasing by approximately 14%. These findings demonstrate that site-specific tillage, when guided by soil variability, can improve the efficiency and environmental sustainability of tillage operations without compromising soil physical properties.

Agriculture doi: 10.3390/agriculture16101088

Authors: Renyi Zhang Kexin Liang Zimo Qiu Dexi Shi Shuang He Guangqi Zhu Bingjie Xu Iqbal Hussain Jiabao Huang Rana Muhammad Amir Gulzar

Brassica napus L. is a globally important crop and its productivity is constrained by multiple abiotic stresses (salinity, drought, and heat). Calcineurin B-like proteins (CBLs) act as calcium sensors and play key roles in regulating ion homeostasis and stress-responsive signaling pathways, thereby contributing to plant adaptation under unfavorable environmental conditions. Here, through detailed bioinformatics analyses, the BnCBL gene family has been identified along with its role in tolerance to multiple abiotic stresses. The identified 17 BnCBLs comprised four groups, as in Arabidopsis thaliana. The predicted molecular weights of the CBL proteins ranged from approximately 24.35 kDa (BnCBL3 and -9) to 29.7 kDa (BnCBL5), with protein lengths spanning 213 (BnCBL3, -9, -10, -12 and -15) to 260 amino acids (BnCBL5). Sequence, promoter, and structural analyses showed that BnCBL proteins harbor palmitoylation and myristoylation motifs in their EF-hand domains, contain hormone- and stress-responsive cis-elements, and exhibit characteristic post-translational modification sites and tertiary structures. RNA-seq and RT-qPCR expression analyses showed that several BnCBL genes (BnCBL2, -6, -9, -10, and -15) exhibit differential expression (3~6-fold) under NaCl, drought, and heat stresses, as well as in response to phytohormones (IAA, GA3, ABA, and JA). In addition, BnCBL2, -3, -6, -8, -9, -11, -12 and -16 showed significant expression (around 7-fold) against biotic stresses (Sclerotinia sclerotiorum (Lib.) de Bary and Plasmodiophora brassicae (Woronin, 1877), indicating their roles in both biotic and abiotic stress tolerance and potential utility in biotechnological breeding of stress-enduring B. napus cultivars.

Agriculture doi: 10.3390/agriculture16101087

Authors: Zishuo Wang Chong Zheng Kai Shi Leyi Wang Yanqun Dou Hua Shao

Biological invasions and environmental pollution are the two primary threats facing contemporary agricultural ecosystems, and their interaction exacerbates agroecological risks and undermines agricultural sustainability. This study was conducted to systematically elucidate how heavy metals (HMs) and microplastics (MPs) alter the relative advantages of invasive plants in ecosystems, clarify the ecological processes involved, and propose recommendations for the protection of farmland ecosystems. The main conclusions are as follows: (1) Pollution acts as an environmental filter that negatively affects native species, including crops, while creating relative advantages for invasive plants with high tolerance and adaptive physiological mechanisms. (2) Pollution stress enables invasive plants to gain a competitive advantage over native plants through highly plastic resource allocation strategies, prioritization of growth, and more powerful allelopathic effects. (3) Pollution systematically amplifies the advantage of invasive plants by altering the strength of plant–soil feedback (PSF) and driving the restructuring of rhizosphere microbial communities. (4) Invasive plants can be used to produce biochar, which can then be applied in farmland ecosystems for the control of invasive plants and remediation of soil pollution. The framework constructed in this study indicates that heavy metal and microplastic pollution may enhance the invasion of alien plants, posing a serious threat to agroecosystem health and food security. However, using invasive plants as feedstock to produce biochar may offer a solution to the intertwined challenges of plant invasion and environmental pollution.

Agriculture doi: 10.3390/agriculture16101081

Authors: Andreea Butnariu Geta-Mirela Ispas Levente Fehér Alexandru-Emil Bejenaru Oana Coca Gavril Ștefan

Emerging technologies offer significant opportunities for sustainable rural development; however, their applications have not been systematically mapped across all dimensions of sustainability. This scoping review aims to identify, classify, and synthesize the literature on emerging technologies in rural development, structured around four pillars: economic, social, environmental, and governance. Eligible studies included English-language scientific articles published between 2015 and 2025 that propose solutions based on emerging technologies in rural contexts, identified in the Web of Science Core Collection database, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Data extracted from the 129 eligible articles were synthesized in thematic tables and comparatively analyzed for each pillar. Results indicate an accelerated growth in publications after 2020, with machine learning, deep learning, and the Internet of Things dominating applications such as precision agriculture, telemedicine, and water management. Critical gaps persist in biodiversity monitoring, climate adaptation, elderly care services, and rural circular economy, with the governance pillar remaining the least represented. This study proposes an integrated framework and a knowledge map to guide future research and public policies toward balanced and sustainable rural transformation.

Agriculture doi: 10.3390/agriculture16101086

Authors: Yunjie Li Yuqian Huang Yuchen Lu Minqiu Kuang Yuhang Wu Dafang Guo Zhengqiang Fan Li Yang Yuxuan Zhang

With the continuous growth of global population and increasing pressure on food security, the transformation toward precise and intelligent agricultural production has become an inevitable trend. In this context, accurate identification of field weeds is crucial for improving crop yields and reducing agricultural inputs. However, agricultural Internet of Things (IoT) edge devices are generally subject to strict constraints in terms of power consumption, storage, and real-time performance. Existing lightweight convolutional neural networks often struggle to simultaneously achieve high accuracy and low resource consumption for fine-grained weed identification tasks. To address this challenge, this paper proposes a hardware aware lightweight convolutional neural network named Weed-Aware Ghost Enhanced Network (WAGENet) for microcontroller deployment. The network synergistically integrates Ghost low-cost feature generation, Mobile Inverted Bottleneck Convolution (MBConv) for deep semantic extraction, Squeeze and Excitation (SE) and Coordinate Attention (CA) dual attention mechanisms for channel space joint calibration, and Atrous Spatial Pyramid Pooling (ASPP) for multi-scale context fusion. It constructs a progressive feature abstraction system from shallow textures to high-level semantics. On the public DeepWeeds dataset, WAGENet achieves 95.71% classification accuracy and 93.80% F1 score with only 0.163 M parameters and 2.43 × 108 multiply accumulate operations (MACC), attaining a parameter efficiency of 587.19%/M and significantly outperforming existing mainstream lightweight models. The model has been successfully deployed on the STM32H7B3I microcontroller development board, achieving a single inference latency of 94.63 ms, an internal Flash footprint of only 686.95 KiB, and a single inference energy consumption of 41.45 mJ. Experimental results demonstrate that WAGENet achieves a trade off among accuracy, latency, and energy consumption under strict resource constraints, providing a reproducible microcontroller deployment paradigm for battery powered field robots, drones, and other agricultural IoT edge devices.

Agriculture doi: 10.3390/agriculture16101084

Authors: Bin Yang Kaijing Yang Fengxin Wang Clinton C. Shock Yujie Zhang

Moisture, salinity and aeration in saline–alkali soil are three critical factors affecting the biotic and abiotic environment. A three-factorial experiment including two tillage measures (ridge and flat tillage, denoted as R and F, respectively), three drip irrigation levels (soil wetting proportion of 40, 55 and 70%, denoted as P1, P2 and P3) and the presence or absence of air injection (AI) were investigated to determine their effects on soil moisture, salinity, aeration and sunflower growth and yield. Field trials were conducted in the Hetao irrigation district of Inner Mongolia in 2021 and 2022. Results showed that R increased daily average topsoil (0–20 cm depth) temperature by 0~4.7 °C, water-filled pore space (WFPS) by 4.3~9.1% and redox potential (Eh) by 16.7~31.6% compared to F. P2 reduced the Eh of topsoil by 50.4% and 55.1% respectively under R and F. Under the same P, the effect of different tillage methods (R and F) on salt accumulation was not notable. AI increased topsoil temperature under R (0.1~2.7 °C) and F (0~2.2 °C) and increased salt accumulation in the topsoil. Compared with other treatments, the yield of sunflower increased by 10~36% and 12~37% respectively under the conditions of P3R and P2FAI. The net profit of P3R treatment was 3421–3551 USD ha−1, which was 12.0–71.5% higher than the other treatments. Furthermore, random forest analysis revealed that air injection, salinity, tillage method, and Eh were the primary determinants of sunflower yield and quality, while WFPS and temperature were of secondary importance. These findings also suggest that, in heavily saline–alkali soils, sunflower yield can be effectively enhanced either by adopting flat tillage with air injection or by using ridge tillage without air injection.

Agriculture doi: 10.3390/agriculture16101085

Authors: Marzia Ingrassia Stefania Chironi Pietro Chinnici Amparo Baviera-Puig Simona Bacarella

Although AI systems are increasingly being used as strategic tools in the agri-food sector, empirical evidence regarding their use and integration into wine marketing by wineries has been limited to date. The reasons for this delay may lie in various factors, both internal and external to the companies. This study aims to help fill this gap by examining some possible causes that could influence the propensity to use this technology and by attempting to analyze them. In-depth semi-structured interviews with marketing managers of 17 selected wineries in Italy and Spain were carried out. Process flows and Social Network Analysis (SNA) were developed to investigate marketing structures, levels of digitalization, and suitability for technological innovation. Findings show that wine marketing processes are data-driven systems integrating strategic and operational dimensions, but their implementation remains partial and fragmented. The observed wineries exhibit a moderate level of digitalization, characterized by the potential availability of data but limited capabilities in data collection and integration. SNA reveals a dense and homogeneous relational network, which could support shared data management systems; however, inter-firm collaboration is largely absent. Overall, the study identifies a latent potential for AI-driven marketing transformation, which is hindered by limited internal capabilities, and cultural resistance.

Agriculture doi: 10.3390/agriculture16101083

Authors: Natalia Białecka Paweł Migdał Krzysztof Latarowski Beata Madras-Majewska Beniamin Stępień

Honey bees are constantly exposed to various environmental threats, among which pesticide pollution, including fungicides, is one of the most serious. The bees were 3 days old when they received the experimental solution. This study aimed to evaluate the behavior and mortality of honey bee workers exposed to a commercial formulation of the fungicide tebuconazole (Tebu® EW, a.i. 25.8%; HELM, Hamburg, Germany). The experiment was conducted under laboratory conditions and lasted 7 days for all experimental groups. The fungicide solution was prepared by adding 6.25 mL of Tebu® EW per 1 L of water, corresponding to 156.25 mg of tebuconazole (active ingredient) in the prepared solution of sugar syrup. The solution was served in 5 mL dispensers (=group feeding) placed in the cages. This concentration was used for the acute-exposure group (24 h). After 24 h bees were supplied with untreated sugar syrup for the remainder of the experiment. For the chronic-exposure group (168 h), the solution was a 1000-fold dilution of the acute solution, containing 0.15625 mg tebuconazole, dissolved in sugar syrup, provided continuously for 7 days with daily replacement. After 7 days, bee behavior was recorded using a camera and analyzed with Noldus Observer XT software (12.5: Windows 7 64-bit (SP1) version) Five basic honey bee behaviors were examined: walking, flight, self-grooming, contact between individuals and stillness. The results showed statistically significant differences between the experimental groups and the control group (α = 0.05) in the duration of walking, contact between individuals and self-grooming, and the frequency of walking and flight. This was particularly evident for self-grooming; the longer the group was exposed to tebuconazole, the less time the bees spent on this behavior (the acute group spent 47% less time self-grooming and the chronic group spent 88.8% less time self-grooming compared to the control group). Meanwhile, the frequency of walking and flying increased significantly with increasing exposure. No significant differences were observed in the survival between the groups. Based on these findings, it can be concluded that the fungicide containing tebuconazole significantly affects the behavior of honey bee workers.

Agriculture doi: 10.3390/agriculture16101082

Authors: Siwen Hao Donglin Zhang Yafeng Wen Jie Dai

Climate change and intensifying extreme weather events challenge plant adaptability, making the evaluation of adaptive potential imperative. This study aims to identify climatically stable habitats for Rhododendron dauricum, a nationally protected (Class II) shrub species in China. Species occurrence records were integrated with multiple environmental datasets, and habitat suitability was inferred using a maximum entropy model under current and future climate scenarios. The model outputs indicate that habitat suitability is primarily driven by temperature and moisture, vegetation plays a secondary role, and topographic and soil factors are less influential. Projections show a consistent contraction of suitable habitats, particularly in highly suitable areas, with stronger declines under higher emission scenarios and longer time horizons. Spatial patterns shift from continuous to fragmented distributions, with suitable habitats increasingly concentrated in the northeastern regions and northern mountain ranges. Core areas that remain suitable across scenarios are identified through multi-scenario consistency analysis, representing climatically stable regions. These areas should be prioritized for in situ conservation, while populations maintaining high suitability across scenarios may serve as candidate provenances for ex situ conservation and future landscape deployment. This study elucidates the adaptive potential of R. dauricum under future climate scenarios and identifies key environmental drivers, informing conservation, breeding, and climate-adaptive management.

Agriculture doi: 10.3390/agriculture16101080

Authors: Jinxiang Wang Xu Cui Panliang Liu Yaling Zhao Guohua Chang Chao Wang Liyang Xue Yabian Wang Tianpeng Gao

Strengthening the carbon sequestration function of agriculture and reducing carbon emissions during production are critical for enhancing the carbon neutrality capacity of agricultural systems. This study focuses on Lanzhou City in the arid northwest region of China, and uses the emission factor method to analyze carbon emissions and crop carbon sequestration within the local agricultural production system (2000–2023). The results indicate that plastic film and fertilizers, as agricultural production inputs, contribute substantially to the total carbon emissions of the planting industry, while the annual average carbon emissions from sheep account for approximately half of the total annual carbon emissions from animal husbandry. The annual average carbon sequestration of crops is 366,057 tons, with an average annual growth rate of 1.1%. The ratio of crop carbon sequestration to the total carbon emissions from planting and animal husbandry is approximately 2.1:1. Although the carbon sequestration of crops has increased over time, its average annual growth rate remains lower than that of carbon emissions from planting and animal husbandry, resulting in an Agricultural Sustainable Development Index of 54%. Therefore, further efforts are needed to control carbon emissions and increase the carbon sequestration capacity of crops to improve the sustainability of agriculture development in the region. Finally, the Monte Carlo algorithm is used to simulate and predict future carbon emissions from animal husbandry within the agricultural production system, thereby obtaining the relative trends in total carbon emissions from pigs, cows, and sheep over a given period. Limiting the scale and growth rate of major livestock populations can help limit the increase in carbon emissions from animal husbandry.

Agriculture doi: 10.3390/agriculture16101079

Authors: Minmin Lu Shuang Gao Ruochen Li Xiaofan Wu Yang Liu Siyuan Huangfu Baixue Zhang Haibo Li Xiuqing Yang

The amino acid composition of hazelnut seeds is a critical determinant of nutritional value and flavor. Understanding the biosynthesis mechanisms during seed development is essential for breeding functional varieties. In this study, we analyzed the seed quality characteristics of three Corylus heterophylla × Corylus avellana cultivars (DW, YZ, and B-21) using extensive targeted metabolomics and transcriptomics at the seed enrichment and fruit maturity stages. A total of 273 amino acid-related metabolites—including free proteinogenic amino acids, non-proteinogenic amino acids, small peptides, and their derivatives— were identified. While all cultivars contained a complete profile of essential amino acids, their accumulation patterns varied significantly. Notably, B-21 exhibited significantly higher total amino acid content compared with DW and YZ, although its content decreased during ripening. Integrated metabolomics and transcriptomics analysis, facilitated by Pearson correlation network analysis (PCA), identified 16 key structural genes strongly associated with amino acid synthesis, including PK, ENO, PHGDH, aroA, and trpE. Specifically, IMDH was significantly positively correlated with arginine synthesis, while ilvH, rpiA, and lysC were potential contributors to the synthesis of methionine, histidine, and tryptophan. These findings highlight a putative regulatory network of amino acid biosynthesis in hybrid hazelnuts and provide candidate genes for future functional validation and the genetic improvement of hazelnut nutritional quality.

Agriculture doi: 10.3390/agriculture16101078

Authors: Lina Zhang Meng Sun Nengde Zeng Mingzhe Zhao Mingda Liu

Cadmium (Cd) contamination in paddy soils threatens food security by accumulating in rice grains. This study aimed to elucidate Cd-accumulation mechanisms using major japonica cultivars from Liaoning Province, a key northern Chinese rice-producing region where systematic comparisons remain limited. Four Liaoning japonica varieties (low-Cd: YF47, SN9903; high-Cd: QTXT, TJ) were analyzed for Cd accumulation, physiological responses, including malondialdehyde (MDA), superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT), and expression of Cd-related transporter genes under Cd stress. Cd distribution in rice plants followed the following order: root > stems and leaves > grain. Varietal differences were attributed to root-to-shoot transport rather than root uptake, as low-Cd varieties exhibited lower transport coefficients and higher root Cd retention. Low-Cd varieties showed smaller MDA increases and significantly higher SOD and CAT activities under Cd stress. Notably, OsLCD was significantly down-regulated in low-Cd varieties but up-regulated in high-Cd varieties, an opposite regulation pattern that clearly distinguishes the two groups. The root-to-shoot translocation process and the OsLCD expression pattern are key determinants differentiating low- from high-Cd japonica varieties. These findings provide region-specific mechanistic insights and screening indicators for breeding low-Cd rice in northern China.

Agriculture doi: 10.3390/agriculture16101077

Authors: Yong Zhou Feng Zhao Jiajia Sun Xin Liu Wei Yang Jiangwen Nie Zhangyong Liu Bo Zhu

In southern China, Chinese milk vetch is used as green manure to substitute for inorganic nitrogen (N) fertilizers and improve soil fertility, but how different incorporation methods affect its decomposition and underlying microbial mechanisms is unclear. This study used four fertilization regimes (CK: no N; CF: sole chemical N; CM: sole vetch; CMCF: vetch + 40% reduced N) to evaluate bacterial diversity, community composition and life history strategies during early vetch decomposition, and the nylon bag method to compare decomposition and C/N release dynamics. The results show that vetch dry matter decomposition reached 81.9–85.2% in 34 days, slowing to 11.8–14.4% after 192 days. CMCF significantly accelerated early decomposition and N release compared with CM. While CMCF reduced the bacterial Ace and Chao indices compared to CK with similar community structure, CF/CM exhibited distinct community structures. Compared to CM, CMCF increased r-strategy bacteria (e.g., Proteobacteria, Bacteroidota) and decreased K-strategy ones (e.g., Chloroflexi). Furthermore, decomposition rate positively correlated with r-strategy and negatively with K-strategy bacteria, with soil temperature as the primary driver. Compared to CMCF, CM reduced bacterial network complexity, decreasing nodes by 63.6% and average degree by 68.5%. In conclusion, combining vetch with chemical N enhances vetch residue decomposition while preserving microbial network structure and functional diversity.

Agriculture doi: 10.3390/agriculture16101076

Authors: Yan Jiang Jiaolong Wang Lang Yi Xiaoping Chen Yuanying Peng Huiyu Luo

Climate change is intensifying the frequency and severity of extreme weather events, posing significant challenges to crop productivity and agroclimatic management in subtropical regions. However, quantitative insights into how different cropping systems respond to climate extremes remain limited. In this study, crop net primary productivity (CNPP) of two representative cropping systems, early–late rice (ER–LR) and dry rapeseed–sweet potato (DR–SP), was analyzed in Pingxiang, a typical subtropical agricultural region of China. Nineteen extreme temperature and precipitation indices were evaluated using an integrated Trend–Prediction–Sensitivity–Threshold (TPST) framework combining statistical and machine learning approaches. CNPP exhibited an upward trend (slope = 4.29 g C m−2 yr−1) from 2000 to 2023, with ER–LR showing faster growth (slope = 4.54 g C m−2 yr−1) and higher stability (high-volatility area: 1.25%) than DR–SP (slope = 4.11 g C m−2 yr−1; 4.94%). Temperature extremes were the dominant drivers, exhibiting nonlinear responses with threshold effects. DR–SP was more climate-sensitive, while ER–LR showed greater tolerance, highlighting the role of cropping systems in enhancing resilience. The TPST framework provides a transferable approach for assessing agroecosystem productivity responses to climate extremes and supports climate-resilient cropland management in subtropical regions.

Agriculture doi: 10.3390/agriculture16101072

Authors: Mingyue Li Luoyao Wen Pujie Zhao Zibo Bai Weili Zhu Kai Chen

Intensive pesticide application sustains global agriculture but poses poorly characterized risks to complex soil ecosystems. Here, we quantitatively evaluated pesticide residues and utilized high-resolution environmental DNA (eDNA) metagenomics to decode multi-trophic community responses across a typical major grain-producing region located in China. Among 39 targeted pesticides, 26 were detected with total concentrations ranging from 27.9 to 478.8 ng/g. While herbicides and fungicides dominated the residual mass, insecticides posed the most severe ecological threat. Notably, the neonicotinoid imidacloprid exhibited high-risk levels (RQ = 1.78 ± 1.49) at >61.1% of the sampling sites. eDNA profiling and Procrustes analyses revealed a clear trophic-dependent sensitivity gradient (p < 0.01). Lower-trophic microbial communities were significantly altered in composition; pesticide stress was strongly associated with profound non-target suppression on keystone plant-beneficial bacteria (e.g., Nocardioides). Concurrently, the fungal eDNA profiles indicated that the soil mycobiome harbored an alarming 34.7% of potential phytopathogenic fungi (e.g., Aspergillus and Colletotrichum), intrinsically driving the massive fungicide reliance. In contrast, higher-trophic soil metazoa (Rotifera, 40.4%) and weed communities (e.g., Digitaria sanguinalis) exhibited significant spatial stability, reflecting robust environmental buffering and herbicide-driven ecological escapes. Furthermore, co-occurrence networks decoupled target from non-target toxicities, uniquely revealing that persistent herbicide metabolites (desethylatrazine) induce prolonged legacy toxicities on specific soil fauna. Collectively, this study unveils the deep, cross-kingdom ecological disruptions caused by current pesticide regimes, underscoring the urgency of integrating eDNA biomonitoring to guide precision pest management and safeguard soil health in vital agricultural hubs.

Agriculture doi: 10.3390/agriculture16101074

Authors: Péter Jobbágy Katalin Allacherné Szépkuthy Gyöngyi Györéné Kis Dóra Drexler

Organic farming has gained increasing relevance worldwide due to its environmental benefits and its prominent role in sustainable food systems; however, the persistence of organic farms remains uneven across regions, particularly within the European Union. While the number of organic farms has grown overall in the EU, significant exits from organic production highlight the need to better understand the factors shaping farm survival, especially in newer Member States, where organic conversion and maintenance support schemes are often implemented through area-based CAP payments. This study aims to identify the structural and contextual determinants of short-term organic farm persistence in Hungary within a broader European context. Using farm-level data for the period 2020–2023, including Standard Output (SO) indicators, we applied a combined modelling framework based on Logistic Regression, Decision Trees, and Random Forest algorithms to assess the relative importance of economic, structural, and regional variables. The results show that organic farm persistence is primarily driven by structural characteristics such as farm size, economic scale, degree of conversion to organic farming and regional embeddedness, while production specialization and organizational features play a secondary, conditional role. The convergence of results across modelling approaches indicates that survival is shaped by hierarchical structural constraints rather than isolated management decisions. Our findings suggest that policy measures aiming to stabilize and expand the organic sector should move beyond uniform incentives, such as area-based payments, and should place greater emphasis on the structural conditions of farms and region-specific support mechanisms.

Agriculture doi: 10.3390/agriculture16101075

Authors: Kaiwen Su Jie Ren Yali Wen

This study investigates the impact of organic agriculture on farmers’ conservation behaviors, focusing on a sample of 816 households in the Chinese Crested Ibis habitats of Yang County, Shaanxi Province, China. Employing partial least squares structural equation modeling (PLS-SEM), we analyzed the ecological feedback mechanisms bridging agricultural practices and species protection outcomes. The results identify two primary pathways through which organic agriculture fosters conservation: (1) Enhanced perceived benefits directly drive conservation behaviors, with significant path coefficients for ecological benefits (0.105, p < 0.05) and overall benefits (0.290, p < 0.001). (2) Government regulations fortify ecological cognition and conservation efforts (0.123, p < 0.001). Notably, while ecological cognition alone exhibited no direct behavioral impact, ecological emotions emerged as a critical mediator (0.153, p < 0.001). These mechanisms align with the remarkable recovery of the Crested Ibis population—from near extinction to over 7000 individuals—since the reserve’s establishment in 1981. Ultimately, this study highlights organic agriculture’s capacity to generate a positive ecological feedback loop, wherein economic viability and emotional connections to conservation mutually reinforce sustainable behaviors. The findings underscore that personal emotional investment in environmental stewardship is a stronger behavioral catalyst than cognitive understanding alone. This research offers robust empirical evidence to inform policy designs that harmonize agricultural livelihoods with biodiversity goals through targeted organic agriculture incentives and emotionally engaging ecological education programs.

Agriculture doi: 10.3390/agriculture16101073

Authors: Tao Lin Qiurong Lv Fuchun Sun Wei Ma Xiaoxiao Li

To improve grape target perception and picking-point positioning for intelligent harvesting robots, this study develops a vision-based method for orchard grape detection and harvesting-point localization. The method is intended to address missed detections, insufficient recognition accuracy, and unsatisfactory peduncle segmentation caused by illumination variation, occlusion, and interference from branches and leaves in complex orchard scenes. For grape cluster and peduncle detection, a lightweight YOLOv7-derived model, termed YOLO-FES, was established. In this model, FasterNet and SCConv were introduced to refine the backbone and neck structures, and the EMA mechanism was incorporated to lower parameter complexity and computational cost while improving detection performance. For suspended grape structure association and peduncle extraction, the GJK algorithm was combined with nearest-neighbor rectangular discrimination, and an improved YOLACT-based peduncle segmentation network, named M-YOLACT, was constructed. With the integration of the MLCA mechanism and the Mish activation function, accurate peduncle segmentation was achieved. In addition, a stereo depth camera was employed to obtain two-dimensional picking-point information and further recover the corresponding three-dimensional spatial coordinates. Experimental results showed that the mAP@0.5 of YOLO-FES for grape clusters and peduncles reached 95.37%. For grape peduncle segmentation, the mAP@0.5 values of the bounding boxes and masks produced by M-YOLACT reached 95.73% and 94.36%, respectively. The proposed method achieved an overall harvesting success rate of 89.2%, with an average time consumption of 11 s for a single harvesting operation. By integrating deep-learning-based detection and segmentation with binocular-vision localization, this study provides a practical technical solution and useful reference for the visual system design of grape-harvesting robots.

Agriculture doi: 10.3390/agriculture16101071

Authors: Zihao Xu Mengfei Li Long Sun Zhiqiang Cheng Yingying Yu Yong Chen Fengming Li Changjiang Zang

Under grazing conditions, it is difficult for lactating Yili mares to meet their nutritional requirements and those of their suckling foals solely through the consumption of natural pasture. Furthermore, seasonal variations and rainfall significantly influence the quality and nutrient content of forage, which severely constrains the healthy breeding of Yili horses and the industrial development of mare milk resources. Therefore, this study aimed to investigate the effects of concentrate supplementation on lactation performance and milk concentrations of amino acids, fatty acids, and mineral elements in Yili horses under grazing conditions. Twenty-two healthy Yili mares in early lactation, with similar ages (3–4 years), foaling dates, and body weights (391.5 ± 13.74 kg), were randomly assigned to either a grazing group (G, n = 11) or a grazing + supplementation group (GS, n = 11). Mares in group G grazed naturally on pasture, while those in group GS received 1 kg of concentrate supplement twice daily (totaling 2 kg/day) in addition to grazing. The experimental period lasted for 100 days, including a 10-day adaptation period and a 90-day formal experimental period. The results showed that: (1) In terms of lactation performance, the GS group exhibited highly significant increases in milk yield and lactose yield (p < 0.01), as well as significant increases in milk protein and milk fat yields (p < 0.05), with an extended duration of the peak lactation period. (2) Regarding the amino acid profile, the concentrations of threonine (Thr), serine (Ser), glycine (Gly), and alanine (Ala) in the milk of the GS group were significantly higher than those in the G group (p < 0.05), whereas the proline (Pro) content was significantly lower (p < 0.01); supplementation improved the uptake of certain functional amino acids by the mammary gland. (3) Concerning the fatty acid profile, the concentrations of polyunsaturated fatty acids (PUFA) and alpha-linolenic acid in the milk of the G group were significantly or highly significantly higher than those in the GS group (p < 0.05 or p < 0.01). (4) For mineral elements, concentrate supplementation highly significantly decreased the potassium (K) content and the K/Na ratio in horse milk (p < 0.01), highly significantly increased the levels of iron (Fe) and cobalt (Co) (p < 0.01), and significantly enhanced the chromium (Cr) content (p < 0.05). In conclusion, concentrate supplementation during grazing improved lactation performance in Yili mares, primarily by increasing milk yield and extending the peak lactation period. However, grazing alone was more favorable for maintaining higher PUFA and α-linolenic acid proportions in milk. Therefore, concentrate supplementation should be regarded as a nutritional strategy that increases milk output and modifies amino acid and mineral element composition, but may involve a trade-off with some beneficial fatty acids.