Agriculture

27 pages, 56134 KB

Open AccessArticle

Organic Fertilizer and Deep Tillage Synergistically Regulate Soil Physicochemical Properties and Aggregate-Associated Distribution of Carbon and Nitrogen in Dryland Foxtail Millet Fields

by Zhihong Wang, Zhigang Wang, Tingyue Huo, Jing Xu, Fan Xia, Lei Hou, Chao Wang, Wude Yang and Meichen Feng

Agriculture 2025, 15(23), 2419; https://doi.org/10.3390/agriculture15232419 (registering DOI) - 24 Nov 2025

Abstract

Foxtail millet (Setaria italica L.), a typical dryland crop, has a high nutrient uptake capacity, which can lead to rapid soil nutrient depletion. Establishing soil conservation strategies compatible with the high yield traits of hybrid millet is crucial. Although organic fertilization and [...] Read more.

Foxtail millet (Setaria italica L.), a typical dryland crop, has a high nutrient uptake capacity, which can lead to rapid soil nutrient depletion. Establishing soil conservation strategies compatible with the high yield traits of hybrid millet is crucial. Although organic fertilization and deep tillage are proven measures for maintaining soil productivity, their effects on dryland crops like millet remain understudied. This study investigated Zhangzagu 10 under five treatments: rotary tillage without fertilization (RT), rotary tillage with compound fertilizer (RTC), rotary tillage with organic fertilizer (RTO), deep tillage with organic fertilizer at 20–30 cm (DT1O), and deep tillage with organic fertilizer at 30–40 cm (DT2O). Soil physicochemical properties were measured at seven sampling periods and four tillage layer depths in a two-year field experiment. Compared to RT, RTO increased organic carbon and total nitrogen in mechanically stable macro-aggregates (0–20 cm) by up to 141.2% and 135.14%, respectively. Corresponding increases in water-stable aggregates reached 105.9% for organic carbon and 193.33% for total nitrogen. RTO also enhanced the pH buffering capacity of the topsoil while reducing soil bulk density and solid volume fraction in the surface layer during the late growth stages of foxtail millet. Combining organic fertilization with deep tillage (DT1O and DT2O) further optimized subsoil (20–40 cm) structure, increasing macro-aggregate content and stability, with effects intensifying at greater tillage depths. The integration of organic fertilization and deep tillage synergistically improved soil structure and nutrient distribution, offering a sustainable approach for dryland foxtail millet production. Full article

(This article belongs to the Section Agricultural Soils)

19 pages, 5763 KB

Open AccessArticle

Prediction Model for the Oscillation Trajectory of Trellised Tomatoes Based on ARIMA-EEMD-LSTM

by Yun Wu, Yongnian Zhang, Peilong Zhao, Xiaolei Zhang, Xiaochan Wang, Maohua Xiao and Yinlong Zhu

Agriculture 2025, 15(23), 2418; https://doi.org/10.3390/agriculture15232418 (registering DOI) - 24 Nov 2025

Abstract

Second-order damping oscillation models are incapable of precisely predicting superimposed and multi-fruit collision-induced oscillations. In view of this problem, an ARIMA-EEMD-LSTM hybrid model for predicting the oscillation trajectories of trellised tomatoes was proposed in this study. First, the oscillation trajectories of trellised tomatoes [...] Read more.

Second-order damping oscillation models are incapable of precisely predicting superimposed and multi-fruit collision-induced oscillations. In view of this problem, an ARIMA-EEMD-LSTM hybrid model for predicting the oscillation trajectories of trellised tomatoes was proposed in this study. First, the oscillation trajectories of trellised tomatoes under different picking forces were captured with the aid of the Nokov motion capture system, and then the collected oscillation trajectory datasets were then divided into training and test subsets. Afterwards, the ensemble empirical mode decomposition (EEMD) method was employed to decompose oscillation signals into multiple intrinsic mode function (IMF) components, of which different components were predicted by different models. Specifically, high-frequency components were predicted by the long short-term memory (LSTM) model while low-frequency components were predicted by the autoregressive integrated moving average (ARIMA) model. The final oscillation trajectory prediction model for trellised tomatoes was constructed by integrating these components. Finally, the constructed model was experimentally validated and applied to an analysis of single-fruit oscillations and multi-fruit oscillations (including collision oscillations and superposition oscillations). The following experimental results were yielded: Under single-fruit oscillation conditions, the prediction accuracy reached an RMSE of 0.1008–0.2429 mm, an MAE of 0.0751–0.1840 mm, and an MAPE of 0.01–0.06%. Under multi-fruit oscillation conditions, the prediction accuracy yielded an RMSE of 0.1521–0.6740 mm, an MAE of 0.1084–0.5323 mm, and an MAPE of 0.01–0.27%. The research results serve as a reference for the dynamic harvesting prediction of tomato-picking robots and contribute to improvement of harvesting efficiency and success rates. Full article

(This article belongs to the Special Issue Key Technology Research and Applications of Agricultural Inspection Robots Based on Machine Vision and Artificial Intelligence)

17 pages, 1454 KB

Open AccessArticle

QTL Mapping and Fine Mapping of a Major Quantitative Trait Locus (qBS11) Conferring Resistance to Rice Brown Spot

by Qiuyun Lin, Yujie Zhou, Yuehui Lin, Zhenyu Xie and Wei Hu

Agriculture 2025, 15(23), 2417; https://doi.org/10.3390/agriculture15232417 - 24 Nov 2025

Abstract

Rice brown spot (BS) disease, caused by Bipolaris oryzae, is a significant threat to rice production worldwide. In this study, a major quantitative trait locus (QTL), qBS11, associated with resistance to BS in rice, was identified and fine-mapped. A recombinant inbred [...] Read more.

Rice brown spot (BS) disease, caused by Bipolaris oryzae, is a significant threat to rice production worldwide. In this study, a major quantitative trait locus (QTL), qBS11, associated with resistance to BS in rice, was identified and fine-mapped. A recombinant inbred line (RIL) population from a cross between the susceptible variety Zhenshan97 and the resistant variety C309 was used for QTL mapping. Using composite interval mapping (CIM) and bulked segregant analysis sequencing (BSA-seq), qBS11 was narrowed to a 244.6 kb interval on chromosome 11, explaining up to 47.7% of the phenotypic variance. Fine mapping identified several potential candidate genes, including LOC_Os11g41170 and LOC_Os11g41210, encoding disease resistance proteins. The resistance exhibited by qBS11 was found to be partially dominant, with heterozygotes showing medium resistance. High broad-sense heritability (89.2%) confirmed the dominance of genetic factors in BS resistance. Additionally, regulatory region variations in the candidate genes suggest a gene dosage effect, which may explain the partial dominance observed for qBS11. This study provides valuable insights into the genetic basis of BS resistance and offers a foundation for breeding BS-resistant rice varieties through molecular marker-assisted selection (MAS). The findings also pave the way for future functional studies of the identified genes. Full article

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

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16 pages, 4333 KB

Open AccessArticle

Integrated Transcriptomic and Metabolomic Analyses Implicate Key Genes and Metabolic Pathways in Maize Lodging Resistance

by Chunlei Xue, Haiyan Wu, Xuting Zhang, Fengcheng Sun, Sainan Zhang, Zhonghao Yu, Qi Dong, Yanan Liu, Hailong Zhang, Qing Ma and Liming Wang

Agriculture 2025, 15(23), 2416; https://doi.org/10.3390/agriculture15232416 - 24 Nov 2025

Abstract

Maize stalk lodging causes substantial yield losses worldwide. Although stalk strength is a genetically determined trait, its molecular mechanisms—particularly the dynamic changes during key developmental stages—remain inadequately characterized due to limitations of single-omics approaches. This study employed an integrated transcriptomic and metabolomic analysis [...] Read more.

Maize stalk lodging causes substantial yield losses worldwide. Although stalk strength is a genetically determined trait, its molecular mechanisms—particularly the dynamic changes during key developmental stages—remain inadequately characterized due to limitations of single-omics approaches. This study employed an integrated transcriptomic and metabolomic analysis strategy to compare stalk tissues from three maize genotypes with contrasting lodging resistance: the highly resistant inbred line PHB1M, the susceptible inbred line Chang 7-2, and their recombinant inbred line 23NWZ561 (abbreviated as P, C, and Z, respectively). Dynamic sampling of all three genotypes was conducted at both grain-filling and maturity stages, with simultaneous measurement of physiological traits related to stalk strength. Phenotypic analysis revealed that the resistant genotype PHB1M exhibited superior rind penetration strength, cell wall composition (cellulose, hemicellulose, and lignin) content, and vascular bundle development. Multi-omics analysis indicated that the molecular basis of lodging resistance is primarily established during the maturity stage. The transcriptomic and metabolomic profiles of the recombinant inbred line Z shifted from clustering with the susceptible parent C at the grain-filling stage to grouping with the resistant parent P at maturity. Key pathways including phenylpropanoid biosynthesis were significantly enriched specifically at maturity, accompanied by upregulation of related genes (PAL, HCT, CCR) and accumulation of metabolites such as lignin precursors in PHB1M. Integrated analysis identified a core co-expression network within the phenylpropanoid pathway comprising three genes and three metabolites. This study systematically demonstrates that lodging resistance in maize is regulated by transcriptional and metabolic reprogramming during late stalk developmental stages, particularly at maturity, where enhanced activation of the phenylpropanoid biosynthesis pathway plays a central role. These findings provide valuable candidate genes and metabolic markers for breeding lodging-resistant maize varieties. Full article

(This article belongs to the Special Issue Crop Yield Improvement in Genetic and Biology Breeding)

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21 pages, 2744 KB

Open AccessArticle

Time-Dependent Stress Response to Force-Feeding Is Associated with Dynamic Gut Microbiota Changes in Mule Ducks

by Ziyuan Du, Zhihao Zhu, Yuhang Chen, Xuanci Yu, Hongyu Jia, Ang Li, Xinzhu Chen and Caiyun Huang

Agriculture 2025, 15(23), 2415; https://doi.org/10.3390/agriculture15232415 - 24 Nov 2025

Abstract

This study aimed to investigate the temporal dynamics of physiological and gut microbial responses in Mule ducks (M-D) during force-feeding (F-F), with the goal of identifying potential regulatory targets to reduce feeding stress. Male M-Ds were subjected to either F-F or ad libitum [...] Read more.

This study aimed to investigate the temporal dynamics of physiological and gut microbial responses in Mule ducks (M-D) during force-feeding (F-F), with the goal of identifying potential regulatory targets to reduce feeding stress. Male M-Ds were subjected to either F-F or ad libitum feeding. We conducted longitudinal analysis at 72, 78, and 84 days of age to assess growth performance, serum biochemical profiles, and intestinal inflammatory markers, while assessing gut microbiota composition through 16S rDNA sequencing. The F-F group exhibited superior growth performance. Initial physiological responses at day 72 included significantly reduced serum corticotropin-releasing hormone (CRH) and jejunal tumor necrosis factor-alpha (TNF−α). Conversely, F-F induced a persistent and profound alteration in the gut microbiome by day 84, characterized by reduced alpha diversity and a significant enrichment of the genus Limosilactobacillus. Correlation analysis identified Limosilactobacillus as a keystone taxon, strongly associated with intestinal metabolites. Our findings demonstrate that M-Ds undergo time-dependent metabolic and immunological adaptations in response to F-F stress, which correlates with distinct alterations in gut microbiota composition, particularly the enrichment of Limosilactobacillus. These findings provide a theoretical basis for developing microbiota-targeted strategies to alleviate F-F stress in foie gras production. Full article

(This article belongs to the Section Farm Animal Production)

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22 pages, 348 KB

Open AccessArticle

Agroecological Adoption Pathways in Europe: Drivers, Barriers, and Policy Implication Opportunities in the Czech Republic, Hungary, and Portugal

by Apolka Ujj, Kinga Nagyné Pércsi, Fernanda Ramos-Diaz, Jana Budimir-Marjanović, Lanka Horstink, Rita Queiroga-Bento, Chisenga Emmanuel Mukosha, Jan Moudrý, Koponicsné Györke Diána and Paulina Jancsovszka

Agriculture 2025, 15(23), 2414; https://doi.org/10.3390/agriculture15232414 - 24 Nov 2025

Abstract

Agroecology offers a transformative pathway toward sustainable food systems by integrating ecological, economic, and social dimensions of farming. While its conceptual and policy foundations are increasingly recognized in European Union (EU) strategies, the practical adoption of agroecological principles at the farm level remains [...] Read more.

Agroecology offers a transformative pathway toward sustainable food systems by integrating ecological, economic, and social dimensions of farming. While its conceptual and policy foundations are increasingly recognized in European Union (EU) strategies, the practical adoption of agroecological principles at the farm level remains uneven, particularly in socio-economically peripheral Member States. This article investigates the enabling and constraining factors of agroecological uptake in three EU countries—Czech Republic, Hungary, and Portugal, using a mixed qualitative approach that combined literature review, policy mapping, and 42 in-depth farmer interviews conducted in 2020–2021. Data were analyzed through a shared coding framework, iterative team discussions, and a standardized comparative matrix to ensure cross-country validity. The results reveal shared barriers, including limited institutional coordination, subsidy dependency, and structural land inequalities, alongside country-specific dynamics such as farmer-to-farmer learning in Portugal, family-farm identity in Czechia, and trust-based advisory relations in Hungary. The findings underscore that systemic constraints, rather than conceptual gaps, impede agroecological transitions, and highlight the need for context-sensitive policy instruments, advisory reforms, and training programs aligned with agroecological principles. The paper contributes to the literature by providing empirical insight into farmer attitudes and practices in Central and Southern Europe and by offering actionable recommendations for designing policies and training. Full article

(This article belongs to the Special Issue Agroecological Transition in Sustainable Food Systems)

42 pages, 22675 KB

Open AccessArticle

Study on the Impact of Grazing Density on Seasonal Pasture NPP in the Northern Slope of the Tianshan Mountains in Xinjiang: A Case Study of Hutubi County

by Qun Luo, Hang Zhou, Chenhui Zhu, Xiaolin Wang, Tianyu Jiao, Changhui Ma, Fei Zhang and Xu Ma

Agriculture 2025, 15(23), 2413; https://doi.org/10.3390/agriculture15232413 - 23 Nov 2025

Abstract

Grazing pressure (GP) was a key factor influencing net primary productivity (NPP) in pasturelands and was characterized by two indicators: grazing intensity (GI) and grazing density (GD). However, current research has not yet clarified whether the mechanisms linking GP to NPP varied by [...] Read more.

Grazing pressure (GP) was a key factor influencing net primary productivity (NPP) in pasturelands and was characterized by two indicators: grazing intensity (GI) and grazing density (GD). However, current research has not yet clarified whether the mechanisms linking GP to NPP varied by season, or whether seasonal thresholds of grazing pressure existed. This study employed the Carnegie–Ames–Stanford Approach (CASA) model to estimate NPP over eight time periods between 2010 and 2024 for three seasonal pastures (spring–autumn, summer, and winter) in the study area. Estimation accuracy was evaluated by comparing our NPP estimates with existing NPP products. Trends in NPP and their significance were analyzed using the Sen–MK method, followed by further examination of spatiotemporal variations in NPP across the three seasonal pastures. Subsequently, by comparing two grazing pressure indicators (GI and GD), we identified the optimal metric to represent GP and, on this basis, analyzed the spatiotemporal variations and threshold dynamics of pasture NPP across three seasons under the influence of GP. Results indicated that the CASA model achieved R² > 0.90 for multi-year NPP estimation, with RMSE ranging from 27 to 45 g C m⁻² y⁻¹. Spring–autumn and winter pastures exhibited pronounced slope changes and intense spatiotemporal NPP variations, whereas summer pastures showed insignificant slope changes and stable spatiotemporal NPP patterns. Of the two GP indicators, the GD metric developed herein more effectively characterized grazing pressure across the study area. Across the three seasonal pastures, a consistent negative feedback between GD and NPP was evident; however, its strength differed markedly, with spring–autumn and winter pastures exhibiting greater NPP sensitivity to GD. The GD thresholds for spring–autumn, summer, and winter pastures in the study area were approximately 900, 700, and 5000 sheep km⁻², respectively. Exceeding these thresholds led to degradation, while falling below them promoted recovery. The study revealed a threshold-mediated negative feedback between GD and NPP across seasonal pastures, quantified season-specific upper bounds of carrying capacity, and provided an evidence base for zoned rest/rotational grazing and GD regulation along the northern slope of the Tianshan Mountains. Full article

(This article belongs to the Topic Advances in Smart Agriculture with Remote Sensing as the Core and Its Applications in Crops Field)

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21 pages, 2438 KB

Open AccessArticle

Organic Fertilization Enhances Microbial-Mediated Dissolved Organic Matter Composition and Transformation in Paddy Soil

by Long Chen, Huajun Fang, Shulan Cheng, Hui Wang, Yifan Guo, Fangying Shi, Bingqian Liu and Haiguang Pu

Agriculture 2025, 15(23), 2412; https://doi.org/10.3390/agriculture15232412 - 22 Nov 2025

Abstract

Dissolved organic matter (DOM) is a crucial carbon source for soil microorganisms and plays a vital role in nutrient cycling and carbon (C) sequestration in soils. However, the extent to which soil microbes mediate DOM transformation at the molecular level, and whether this [...] Read more.

Dissolved organic matter (DOM) is a crucial carbon source for soil microorganisms and plays a vital role in nutrient cycling and carbon (C) sequestration in soils. However, the extent to which soil microbes mediate DOM transformation at the molecular level, and whether this is regulated by different organic fertilization, remains unclear. Here, we designed a field experiment to investigate the transformations of DOM under three types of organic fertilization (straw, biochar, and manure) using Fourier transform ion cyclotron resonance mass spectrometry and metagenomic analysis. Compared to the control, manure fertilization increased the molecular chemodiversity of DOM by 33.2%, with recalcitrant compounds (e.g., highly unsaturated phenolic compounds and lignins) increasing by 47.2%. In contrast, labile compounds (e.g., aliphatics) decreased by 73.5%. Compared to straw treatment, manure application significantly increased the average conversion rate of dissolved organic matter (DOM). This process was accompanied by a significant increase in the Shannon index of the soil microbial community (p < 0.05) and upregulation of ABC transporter-encoding genes (e.g., livK, livM). DOM composition directly governed transformation potential (p < 0.01), whereas functional genes enhanced transformation indirectly by modulating DOM composition. This study elucidates microbial-mediated DOM transformation mechanisms under varying organic fertilization practices, providing a scientific basis for optimizing soil organic matter management in paddy ecosystems. Full article

(This article belongs to the Topic Ammonium Biology: From Molecular Response to Fertilization)

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26 pages, 2106 KB

Open AccessArticle

Comprehensive Quality Analysis of Common Vetch (Vicia sativa L.) Varieties Using Image Processing Techniques and Artificial Intelligence

by Necati Çetin, Onur Okumuş, Satı Uzun, Mahmut Kaplan, Ahmad Jahanbakhshi and Gniewko Niedbała

Agriculture 2025, 15(23), 2411; https://doi.org/10.3390/agriculture15232411 - 22 Nov 2025

Abstract

Common vetch (Vicia sativa L.) is a cool-season annual legume cultivated for grain and forage, valued for its high nutrient content, broad edaphoclimatic adaptability, and suitability for crop rotations. Physical seed attributes are critical for variety classification, quality evaluation, and breeding selection. [...] Read more.

Common vetch (Vicia sativa L.) is a cool-season annual legume cultivated for grain and forage, valued for its high nutrient content, broad edaphoclimatic adaptability, and suitability for crop rotations. Physical seed attributes are critical for variety classification, quality evaluation, and breeding selection. This study aimed to characterize the nutritional composition, mineral contents, and physical attributes of nine common vetch varieties and to assess the feasibility of binary variety classification using supervised machine learning (ML). Proximate analyses (e.g., crude protein, tannin), macro/micro minerals, and morpho-physical seed descriptors were determined. Multivariate and discriminant analyses were conducted. Binary classifiers were developed with a multilayer perceptron (MLP) and random forest (RF) under stratified 10-fold cross-validation. The highest values were observed for crude protein (22.66%, Alper), ADF (11.36%, Alınoğlu), NDF (16.47%, Alperen), and tannin (3.12%, Alınoğlu). For mineral profiles, Alper, Ankara Moru, and Doruk emerged as prominent varieties. In pairwise discrimination, Ankara Moru vs. Ayaz achieved 89% (MLP) and 90% (RF) accuracy, followed by Ankara Moru vs. Özveren with 88% (MLP) and 90.50% (RF). These results demonstrate that MLP and RF can classify common vetch varieties from physical attributes with high reliability. Integrating compositional, mineral, and morpho-physical data with supervised learning provides an objective, low-cost pathway for variety identification. The approach has direct implications for quality assessment, planting system design, and breeding. Future work should expand datasets, incorporate color-rich/hyperspectral cues, and compare feature-based models with domain-adapted deep learning on larger, multi-site collections. Full article

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

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21 pages, 8079 KB

Open AccessArticle

How Do Spectral Scales and Machine Learning Affect SPAD Monitoring at Different Growth Stages of Winter Wheat?

by Xueqing Zhu, Jun Li, Yali Sheng, Weiqiang Wang, Haoran Wang, Hui Yang, Ying Nian, Jikai Liu and Xinwei Li

Agriculture 2025, 15(23), 2410; https://doi.org/10.3390/agriculture15232410 - 22 Nov 2025

Abstract

Chlorophyll serves as a crucial indicator for crop growth monitoring and reflects the health status of crops. Hyperspectral remote sensing technology, leveraging its advantages of repeated observations and high-throughput analysis, provides an effective approach for non-destructive chlorophyll monitoring. However, determining the optimal spectral [...] Read more.

Chlorophyll serves as a crucial indicator for crop growth monitoring and reflects the health status of crops. Hyperspectral remote sensing technology, leveraging its advantages of repeated observations and high-throughput analysis, provides an effective approach for non-destructive chlorophyll monitoring. However, determining the optimal spectral scale remains the primary bottleneck constraining the widespread application of hyperspectral remote sensing in crop chlorophyll estimation: excessively fine spectral scale readily introduces redundant information, leading to dramatically increased data dimensions and reduced computational efficiency; conversely, overly coarse spectral scale risks losing critical spectral features such as absorption peaks and reflection troughs, thereby compromising model accuracy. Therefore, establishing an appropriate spectral scale that effectively preserves spectral feature information while maintaining computational efficiency is crucial for enhancing the accuracy and practicality of chlorophyll remote sensing estimation. To address this, this study proposes a three-dimensional analytical framework integrating “spectral scale—machine learning algorithm—crop growth stage” to systematically solve the scale optimization problem. Ground-truth measurements and hyperspectral data from five growth stages of winter wheat in Fengyang County, Anhui Province, were collected. Spectral bands sensitive to chlorophyll were analyzed, and four modeling methods—Ridge Regression (RR), K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Regression (SVR)—were employed to integrate data from different spectral scales with respective bandwidths of 2, 3, 5, 7, 10, 20, and 50 nanometers (nm). The results evaluated the response characteristics of raw band reflectance to chlorophyll values and its impact on machine learning-based chlorophyll estimation across different spectral scales. Results indicate: (1) Canopy spectra significantly correlated with winter wheat chlorophyll primarily reside in the red and red-edge bands; (2) For single-scale analysis, larger spectral scales (10, 20 nm) enhance monitoring accuracy compared to 1 nm high-resolution data, while medium and small scales (5, 7 nm) may degrade accuracy due to redundant noise introduction. (3) Integrating growth stages, spectral scales, and machine learning revealed optimal monitoring accuracy during the jointing and heading stages using 1–5 nm spectral scales combined with the KNN algorithm. For the booting, flowering, and grain filling stages, the highest accuracy was achieved using 20–50 nm spectral scales combined with either the KNN or RF algorithm. The results indicate that high-precision chlorophyll inversion for winter wheat does not rely on a single fixed model or scale, but rather on the dynamic adaptation of the “scale-model-growth stage” triad. The proposed systematic framework not only provides a theoretical basis for chlorophyll monitoring using multi-platform remote sensing data, but also offers methodological support for future crop-sensing sensor design and data processing strategy optimization. Full article

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

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20 pages, 2167 KB

Open AccessArticle

Removal of Neonicotinoid Residues from Beeswax Using an Eco-Friendly Oxalic Acid Treatment: A Sustainable Solution for Apicultural Decontamination

by Karen Yáñez, Ramón Arias, Daniel Ramírez, Fabián Guerrero and Mario Toledo

Agriculture 2025, 15(23), 2409; https://doi.org/10.3390/agriculture15232409 - 22 Nov 2025

Abstract

Beeswax is widely used in apiculture and can accumulate neonicotinoid residues due to the intensive use of systemic pesticides in agriculture. These contaminants pose potential risks to honeybee health and may indirectly affect the quality and safety of hive products such as honey, [...] Read more.

Beeswax is widely used in apiculture and can accumulate neonicotinoid residues due to the intensive use of systemic pesticides in agriculture. These contaminants pose potential risks to honeybee health and may indirectly affect the quality and safety of hive products such as honey, pollen, and royal jelly. This study evaluates several decontamination methods for neonicotinoid removal from contaminated beeswax, including modern techniques (microwaves, ultrasonic baths, and magnetic stirring with heating) and conventional approaches based on heat, agitation, and water—either pure or acidified. Among these, the traditional method that uses an aqueous oxalic acid solution proved highly effective, removing over 99% of neonicotinoid residues after two treatment cycles, even at wax quantities up to 200 g. The treatment also improved the colour and physical properties of the wax and was well tolerated by bees, according to a qualitative acceptance test. The simplicity, low cost, and absence of hazardous by-products make this method suitable for scale-up and adoption in real apicultural practices. These findings support the development of accessible and sustainable strategies for the decontamination of wax matrices that may otherwise act as long-term reservoirs of pesticide residues in the food chain. Full article

(This article belongs to the Special Issue Pesticide Ecotoxicology and Application Technology to Reduce Contamination)

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20 pages, 2800 KB

Open AccessArticle

Analysis of Causal Relationships Between Climate Perceptions and Ecological Production Among Tea Farmers in the Wuyi Mountains

by Han Zhang, Li Ma, Jiaming Liu, Jiaji Xing, Yilei Hou and Yali Wen

Agriculture 2025, 15(23), 2408; https://doi.org/10.3390/agriculture15232408 - 22 Nov 2025

Abstract

Climate change adaptation in ecologically sensitive agriculture remains underexplored, especially regarding whether farmers’ climate perceptions translate into ecological production behaviors (EPBs). Using survey data from 730 tea farmers in China’s Wuyi Mountains National Park, this study examines how general and extreme climate change [...] Read more.

Climate change adaptation in ecologically sensitive agriculture remains underexplored, especially regarding whether farmers’ climate perceptions translate into ecological production behaviors (EPBs). Using survey data from 730 tea farmers in China’s Wuyi Mountains National Park, this study examines how general and extreme climate change perceptions relate to EPB adoption. Employing Ordered Probit models and Karlson-Holm-Breen (KHB) mediation analysis, we estimate perception–behavior associations and test indirect effects through information-seeking and policy participation, alongside moderation by ecosystem service cognition and ecological production benefit cognition. The results indicate that both general and extreme climate perceptions are positively associated with EPB adoption (β = 0.406 and 0.626, p < 0.01), with extreme perceptions showing significantly stronger effects. Climate perceptions influence EPB adoption across all dimensions (green production, ecological management, and market-based practices). Information-seeking and policy participation function as complementary mediating pathways (combined indirect effects = 0.101 and 0.117), linking climate perceptions to ecological actions. Moreover, higher ecosystem service cognition and ecological production benefit cognition strengthen the perception–behavior relationships across multiple EPB dimensions. Overall, the findings suggest that climate change perceptions are an important driver of farmers’ ecological production choices in high-ecological-value contexts. Interpreted alongside existing adaptation strategies, EPB may enhance resilience by leveraging ecosystem functions while aligning with market incentives for ecological products. These results underscore the value of policies that improve access to ecological training and market information and support demonstration programs that facilitate experiential learning. Full article

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

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17 pages, 850 KB

Open AccessReview

Strategies for Protecting Cereals and Other Utility Plants Against Cold and Freezing Conditions—A Mini-Review

by Julia Stachurska and Anna Maksymowicz

Agriculture 2025, 15(23), 2407; https://doi.org/10.3390/agriculture15232407 - 21 Nov 2025

Abstract

Low-temperature (LT) stresses (cold and frost) are major abiotic factors limiting plant growth and productivity. LT induces numerous physiological and biochemical changes in plants, changes hormonal balance and photosynthetic efficiency. Stress induced by LT often leads to yield losses in crops. While plants [...] Read more.

Low-temperature (LT) stresses (cold and frost) are major abiotic factors limiting plant growth and productivity. LT induces numerous physiological and biochemical changes in plants, changes hormonal balance and photosynthetic efficiency. Stress induced by LT often leads to yield losses in crops. While plants like maize and cucumber are highly sensitive to cold, winter cereals such as wheat and rye suffer mainly from severe frosts. Ongoing climate change and temperature fluctuations further increase the risk of LT-induced damage. To counteract the problems connected with LT stress, multiple strategies have been developed to enhance plant tolerance. Agrotechnical practices and biochemical treatments involving the application of phytohormones or osmoprotectants are designed to improve plant tolerance to LT. Beneficial plant–microbe interactions also contribute to alleviating LT stress. In addition, genetic engineering offers powerful tools for creating new cultivars that are more tolerant to LT. The CRISPR/Cas system, in particular, enables precise modifications and represents a promising tool for advancing sustainable agriculture. Integrated methods of protection are crucial for securing food supplies, especially under conditions of a changing climate. This mini-review summarises strategies for protecting plants against LT stress, with special attention paid to crop plants. Full article

(This article belongs to the Special Issue Physiological and Biochemical Responses to Abiotic Stress in Cereal and Pseudocereal Crops)

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12 pages, 668 KB

Open AccessReview

Mass Trapping as a Sustainable Approach for Scarabaeidae Pest Management in Crops and Grasslands

by Sergeja Adamič Zamljen, Tanja Bohinc and Stanislav Trdan

Agriculture 2025, 15(23), 2406; https://doi.org/10.3390/agriculture15232406 - 21 Nov 2025

Abstract

Soil-dwelling beetles, including native and invasive species such as Popilia japonica Newman (Coleoptera: Scarabaeidae), are persistent and damaging agricultural pests worldwide. Mass trapping, using pheromone-, light-, or food-based lures to attract and remove adults, is being developed as an environmentally sustainable alternative within [...] Read more.

Soil-dwelling beetles, including native and invasive species such as Popilia japonica Newman (Coleoptera: Scarabaeidae), are persistent and damaging agricultural pests worldwide. Mass trapping, using pheromone-, light-, or food-based lures to attract and remove adults, is being developed as an environmentally sustainable alternative within integrated pest management (IPM). Scarab beetles respond positively to attractant-based traps, and large-scale programs against P. japonica in North America provide valuable insights for global applications. The efficacy of mass trapping depends on species biology, trap density, environmental conditions and landscape structure. Capturing adults does not always immediately reduce larval populations, as underground stages persist in soil for multiple years. Light traps are effective but often attract many non-target insects, whereas pheromone traps are more selective but require careful optimization of lure composition, release rate and placement. To achieve reliable suppression, mass trapping should be integrated with complementary strategies such as biological control agents (Beauveria spp., Metarhizium spp.), crop rotation, tolerant crop varieties and soil management. Future research should focus on refining lure design, optimizing deployment, testing predictive models and evaluating multi-bait systems. Overall, mass trapping represents a promising and environmentally sustainable tool that, when incorporated into integrated approaches, can enhance the management of soil-dwelling scarab beetles across diverse agroecosystems worldwide. Full article

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

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18 pages, 5935 KB

Open AccessArticle

Rapid Identification and Accurate Localization of Walnut Trunks Based on TIoU-YOLOv8n-Pruned

by Chenchen Ye, Yan Xu, Jianping Zhou, Chengcheng Li, Fubao Fang and Zhengyang Jin

Agriculture 2025, 15(23), 2405; https://doi.org/10.3390/agriculture15232405 - 21 Nov 2025

Abstract

Visual perception has become a prerequisite for the operation of automated walnut vibration harvesting robots under complex orchard conditions. This study proposes an effective trunk target detection algorithm, TIoU-YOLOv8n-Pruned, based on YOLOv8n. First, to enhance the accuracy of walnut trunk prediction boxes matching [...] Read more.

Visual perception has become a prerequisite for the operation of automated walnut vibration harvesting robots under complex orchard conditions. This study proposes an effective trunk target detection algorithm, TIoU-YOLOv8n-Pruned, based on YOLOv8n. First, to enhance the accuracy of walnut trunk prediction boxes matching true boxes at high overlap levels, a TIoU loss function is introduced. Second, to mitigate vibration effect caused by the PTO axis of the tractor, vibration trajectory fitting and coordinate correction are performed by capturing multiple images per second. To meet the frame rate requirements for coordinate correction, channel pruning removes 55% of the model’s non-essential channels. Experimental results show that the number of parameters and GFLOPs of TIoU-YOLOv8-Pruned are 950,000 and 3.6 GFLOPs, respectively, while its accuracy and mAP@0.5:0.95 reach 94.1% and 57.2%, outperforming YOLOv5n, YOLOv8n, YOLOv11n, FasterNet-YOLOv8n, Ghost-YOLOv8n, ShuffleNetv2-YOLOv8n, MobileNetV3-YOLOv8n, EfficientNet-YOLOv8n, GhostNetV3-YOLOv8n and MobileNetV4-YOLOv8n. After trajectory fitting and coordinate correction, it significantly reduces vibration-induced errors and enhances localization accuracy. Overall, the TIoU-YOLOv8n-Pruned model demonstrates applicability for trunk identification and localization in walnut orchard mechanical shaking harvesting, offering theoretical guidance for developing automated shaking harvesting equipment. Full article

(This article belongs to the Special Issue Advanced Image Collection, Processing, and Analysis in Crop and Livestock Management)

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29 pages, 7375 KB

Open AccessArticle

Design and Test of Straw Crushing and Spreading Device Based on Straw Mulching No-Tillage Planter

by Shouyin Hou, Hanfei Zhang, Yunze Shi, Bo Jin, Hao Huang, Naiyu Shi, Wenyi Ji and Cheng Zhou

Agriculture 2025, 15(23), 2404; https://doi.org/10.3390/agriculture15232404 - 21 Nov 2025

Abstract

To address issues such as slow soil temperature recovery and delayed sowing periods caused by straw mulching in the cold regions of northern Heilongjiang Province, this study designed a straw crushing and scattering device compatible with the 2BMFJ series no-till planters, aiming to [...] Read more.

To address issues such as slow soil temperature recovery and delayed sowing periods caused by straw mulching in the cold regions of northern Heilongjiang Province, this study designed a straw crushing and scattering device compatible with the 2BMFJ series no-till planters, aiming to achieve moderate straw fragmentation and uniform distribution. By establishing mathematical models for the straw pick-up, crushing, and scattering processes, key parameters affecting the device’s performance were determined. Utilizing the discrete model of EDEM 2018 software virtual simulation experiments were conducted based on response surface methodology. The test factors included the blade angle of the crushing long blade, the edge thickness of the crushing long blade, the weight of the crushing long blade, and the rotational speed of the crushing long blade. The performance evaluation indicators were the straw pick-up rate, straw crushing rate, power consumption, and inter-row straw coverage consistency. The optimal parameter combination was identified to be a blade angle of 25°, an edge thickness of 1.25 mm, a weight ranging from 0.35 to 0.41 kg, and a rotational speed between 1400 and 1750 r/min, resulting in a straw pick-up rate of 83%, a straw crushing rate of 84%, power consumption of 6.8 KW, and a straw cleaning consistency between rows of 75%. Field test results indicated that the straw pick-up rate reached 87.2%, the straw crushing rate achieved 81.5%, power consumption was 7.7 kW, and the straw cleaning consistency between rows attained 79.3%. The deviations from simulation results were within acceptable limits. This equipment can effectively complete straw crushing and scattering operations, thereby creating favorable seedbed conditions. Full article

(This article belongs to the Section Agricultural Technology)

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21 pages, 518 KB

Open AccessArticle

The Impact of Digital–Green Synergy on Agricultural New Quality Productive Forces in China

by Jingjing Zhang, Huajing Li and Hongqiong Li

Agriculture 2025, 15(23), 2403; https://doi.org/10.3390/agriculture15232403 - 21 Nov 2025

Abstract

The synergy between agricultural digitalization and greening is an inherent requirement for high-quality agricultural development and is a vital pathway for cultivating agricultural new quality productive forces (ANQPFs). Based on 2012–2023 provincial-level data from 30 Chinese provinces, this study constructs comprehensive evaluation index [...] Read more.

The synergy between agricultural digitalization and greening is an inherent requirement for high-quality agricultural development and is a vital pathway for cultivating agricultural new quality productive forces (ANQPFs). Based on 2012–2023 provincial-level data from 30 Chinese provinces, this study constructs comprehensive evaluation index systems for agricultural digitalization, greening, and ANQPFs. A coupling coordination model is applied to measure the degree of digital–green synergy, and a two-way fixed effects model is employed to test its impact on ANQPFs, along with the underlying mechanisms and regional heterogeneity. The results indicate that digital–green synergy significantly enhances ANQPFs. A 1% increase in the synergy index improves ANQPFs by 29.6%, primarily through industrial structure optimization, technological innovation stimulation, and resource allocation efficiency improvement. The positive effect is most prominent in the central region, after Digital Village Strategy implementation, and in major grain-producing areas. This study innovatively integrates digitalization and greening into the analytical framework of agricultural productivity, expanding the theoretical understanding of how synergistic transformation drives high-quality agricultural development. Regarding policy, governments should strengthen coordination between digital and green policies, promote the integration and innovation of related technologies, and foster an enabling environment that supports the formation and evolution of new quality productive forces in agriculture. Full article

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

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19 pages, 1504 KB

Open AccessArticle

A Gated Ridge Regression-Based Multimodal Spectral Fusion Approach for Predicting Soil Organic Matter

by Guofang Wang, Juanling Wang, Mingjing Huang, Jiancheng Zhang, Xuefang Huang, Xiuquan Zhang and Wuping Zhang

Agriculture 2025, 15(23), 2402; https://doi.org/10.3390/agriculture15232402 - 21 Nov 2025

Abstract

The fusion of Raman and visible–near-infrared (VIS–NIR) spectroscopy provides a promising pathway for rapid and non-destructive soil analysis. However, conventional fusion strategies often fail to properly balance modality discrepancies and complementary information. To address this limitation, this study develops an adaptive Gated Ridge [...] Read more.

The fusion of Raman and visible–near-infrared (VIS–NIR) spectroscopy provides a promising pathway for rapid and non-destructive soil analysis. However, conventional fusion strategies often fail to properly balance modality discrepancies and complementary information. To address this limitation, this study develops an adaptive Gated Ridge Regression fusion model (Fusion_GatedRidge) for predicting soil organic matter (SOM). A total of 246 soil samples collected from a dryland agricultural region in Shanxi Province were analyzed using laboratory Raman and VIS–NIR spectroscopy. After standard preprocessing, three baseline fusion frameworks—EarlyFusion_Ridge, AE_LatentFusion, and WeightedLate_Fusion—were implemented for comparison with the proposed gated fusion method. Under fivefold cross-validation, Fusion_GatedRidge achieved the best performance, with an R² of 0.83, RMSE of 2.01 g·kg⁻¹, and RPD of 2.39. Compared with single-modality models, R² increased by up to 18.6% and RMSE decreased by up to 23.0%. The gating mechanism dynamically adjusted the contributions of Raman and VIS–NIR features, leading to more stable predictions with residuals largely within −2 to 2 g·kg⁻¹. Overall, the proposed model demonstrates that adaptive modality weighting enhances the exploitation of complementary spectral information and significantly improves SOM prediction accuracy. These findings offer a concise and effective multimodal fusion framework for laboratory-based soil nutrient assessment. Full article

(This article belongs to the Section Agricultural Soils)

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18 pages, 2297 KB

Open AccessArticle

Drought-Induced Antioxidant and Biochemical Responses in Castanea sativa Cultivars: A Mediterranean Case Study

by Tiago Marques, Andrea Ferreira-Pinto, Pedro Fevereiro, Teresa Pinto and José Laranjo

Agriculture 2025, 15(22), 2401; https://doi.org/10.3390/agriculture15222401 - 20 Nov 2025

Abstract

Chestnut (Castanea sativa Mill.) is a key crop in Mediterranean regions increasingly threatened by recurrent drought stress. To investigate cultivar-specific tolerance mechanisms, we evaluated four Portuguese cultivars (Longal, Judia, Martaínha, and ColUTAD^®) across four orchards with contrasting water regimes. Physiological [...] Read more.

Chestnut (Castanea sativa Mill.) is a key crop in Mediterranean regions increasingly threatened by recurrent drought stress. To investigate cultivar-specific tolerance mechanisms, we evaluated four Portuguese cultivars (Longal, Judia, Martaínha, and ColUTAD^®) across four orchards with contrasting water regimes. Physiological (midday stem water potential—Ψ_wmid, soluble sugars, electrolyte leakage and proline) and biochemical traits (phenolics, flavonoids, catalase, peroxidase, ascorbate peroxidase and ferric reducing antioxidant power) were quantified under a natural drought gradient. Results revealed that environmental factors had a stronger influence than genetic background. Longal showed robust osmotic adjustment with high proline and soluble sugar levels, alongside stable starch reserves; Judia relied on inducible antioxidant activity, particularly peroxidase and ascorbate peroxidase; and Martaínha exhibited intermediate plasticity, whereas ColUTAD^® was consistently stress-sensitive, with weaker defences and greater membrane damage. Clustering analysis confirmed that location effects outweighed cultivar differences, separating orchards into conservative strategies (better water balance, higher starch, stronger peroxidase activity) and stress-adaptive strategies (enhanced enzymatic antioxidants). Overall, resilience in chestnut is not determined by a single trait but by a synergistic network of osmotic regulation, membrane protection, and antioxidant activity. Traits such as proline accumulation, starch stability, and inducible enzyme activation emerged as reliable biochemical indicators of tolerance. These findings provide a physiological basis for selecting climate-resilient cultivars and designing site-specific management strategies, thereby supporting the sustainability of chestnut production under Mediterranean climate change scenarios. Full article

(This article belongs to the Section Crop Production)

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