Agriculture

23 pages, 1102 KB

Open AccessArticle

A RAG-Augmented LLM for Yunnan Arabica Coffee Cultivation

by Zheng Chen, Zihao Jiang and Jianping Yang

Agriculture 2025, 15(22), 2381; https://doi.org/10.3390/agriculture15222381 - 18 Nov 2025

Foundation models for agriculture often suffer from fragmented and stale knowledge, making it difficult to deliver stable, traceable answers. We present an evidence-grounded retrieval-augmented generation (RAG) system for Yunnan Arabica coffee cultivation. First, we curate a lightweight knowledge base (approximately 250k Chinese characters) [...] Read more.

Foundation models for agriculture often suffer from fragmented and stale knowledge, making it difficult to deliver stable, traceable answers. We present an evidence-grounded retrieval-augmented generation (RAG) system for Yunnan Arabica coffee cultivation. First, we curate a lightweight knowledge base (approximately 250k Chinese characters) from cultivation textbooks, technical guidelines, and reports. Second, we adopt a retrieve–rerank–generate workflow: semantic-aware chunking with stable identifiers [docid#cid]; hybrid retrieval fused by reciprocal rank fusion (RRF); cross-encoder reranking on top; and final answer generation by DeepSeek v3.1 with mandatory inline evidence tags. In addition, we use GPT-5 Thinking to synthesize 346 gold QA items on the corpus with document-/chunk-level citations, and we evaluate with citation-level per-sample macro precision/recall/F1. On this gold set, our optimized system attains a citation-level per-sample macro F1 of 0.813 (81.3%), significantly outperforming a Simple RAG baseline that reads only a vector store (0.573; 57.3%). Error analysis shows that residual errors are dominated by fragment mismatch and missing evidence; latency analysis indicates that end-to-end delay is primarily driven by generation, whereas retrieval, fusion, and reranking incur sub-0.1 s overhead. The workflow preserves traceability and verifiability, supports hot updates via index rebuilding rather than model fine-tuning, and we release scripts for corpus construction, ablation, and citation-based evaluation to facilitate reproducibility. Full article

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

24 pages, 5039 KB

Open AccessArticle

Study of the Formation Mechanism of Velocity Differences Among Paddy Grains Within Centrifugal Hullers Using CFD-DEM Coupling

by Hao Li, Haonan Gao, Dan Zhao, Ze Sun, Xinlei Wang, Xianle Li and Hanlin Yu

Agriculture 2025, 15(22), 2380; https://doi.org/10.3390/agriculture15222380 - 18 Nov 2025

Abstract

The impact velocity of the grains is a critical factor affecting the hulling efficiency in centrifugal hullers. However, significant differences in velocity are observed among paddy grains following acceleration by the impeller. Therefore, elucidating the mechanism responsible for these velocity differences is essential [...] Read more.

The impact velocity of the grains is a critical factor affecting the hulling efficiency in centrifugal hullers. However, significant differences in velocity are observed among paddy grains following acceleration by the impeller. Therefore, elucidating the mechanism responsible for these velocity differences is essential for improving hulling performance. This study employed coupled CFD-DEM simulations to analyse the kinematic behaviour of paddy grains. The results demonstrate that velocity differences among grains are prevalent within centrifugal hullers and adversely affect hulling efficiency. These differences primarily arise from tangential collisions between grains and blades prior to acceleration, as well as axial collisions during the acceleration phase. The jumping degree (S_v) quantifies the relative motion between paddy grains and blades in the normal direction. Velocity differences decrease significantly as the jumping degree approaches unity. Furthermore, a tilted curvature blade was developed to mitigate velocity differences. Computational analysis and simulation determined that a blade curvature of 300 mm combined with a 20° tilt angle achieved the most substantial reduction in velocity differences. This optimised configuration improves hulling efficiency by 4.5% compared to the original blade design. This modification is expected to substantially facilitate the optimisation of centrifugal huller designs. Full article

(This article belongs to the Special Issue Mathematical Modeling for Technological Processes of Agricultural Products)

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43 pages, 28745 KB

Open AccessReview

A Review on the Chassis Configurations and Key Technologies of Agricultural Robots

by Renkai Ding, Xiangyuan Qi, Xiangpeng Meng, Xuwen Chen, Le Zhang, Yixin Mei, Anze Li and Qing Ye

Agriculture 2025, 15(22), 2379; https://doi.org/10.3390/agriculture15222379 - 18 Nov 2025

Abstract

The chassis configuration serves as the mobility foundation of agricultural robots, directly determining their trafficability, stability, and intelligent operation in complex fields. Existing research lacks a systematic analysis of the evolution and adaptation principles of mainstream chassis technologies. This review addresses this gap [...] Read more.

The chassis configuration serves as the mobility foundation of agricultural robots, directly determining their trafficability, stability, and intelligent operation in complex fields. Existing research lacks a systematic analysis of the evolution and adaptation principles of mainstream chassis technologies. This review addresses this gap by proposing a dual-dimensional framework—“structural design principles and dynamic adaptive control”—to evaluate wheeled, tracked, and wheel-legged hybrid chassis. Our analysis reveals that (1) wheeled chassis achieve refinement through efficiency-driven operation in structured environments but are limited by rigid wheel–ground contact; (2) tracked chassis enhance performance on soft or sloped terrain via technologies like contour-adaptive tracks, albeit with increased energy consumption; and (3) wheel-legged hybrid chassis represent a shift towards active terrain overcoming, offering superior adaptability at the cost of high control complexity. Finally, we synthesize persistent challenges and identify future breakthroughs in terrain–vehicle coupled modeling and multi-modal control, which will drive the evolution towards intelligent, mechatronic–hydraulic integrated platforms. Full article

(This article belongs to the Section Agricultural Technology)

25 pages, 9499 KB

Open AccessArticle

Integration of Machine Learning and Remote Sensing to Evaluate the Effects of Soil Salinity, Nitrate, and Moisture on Crop Yields and Economic Returns in the Semi-Arid Region of Ethiopia

by Gezimu Gelu Otoro and Katsuaki Komai

Agriculture 2025, 15(22), 2378; https://doi.org/10.3390/agriculture15222378 - 18 Nov 2025

Abstract

Soil salinity, soil moisture, and nutrient loss significantly reduce agricultural productivity and economic benefits in the semi-arid regions of Ethiopia. However, knowledge of how to mitigate these risks remains limited. This study examined the combined effects of salinity (EC), soil moisture (Sm), and [...] Read more.

Soil salinity, soil moisture, and nutrient loss significantly reduce agricultural productivity and economic benefits in the semi-arid regions of Ethiopia. However, knowledge of how to mitigate these risks remains limited. This study examined the combined effects of salinity (EC), soil moisture (Sm), and nitrate (N) on the yield and profitability of banana, cotton, and maize using field-based and satellite data with seven machine learning algorithms. Our results showed that a higher EC level reduced crop yields, whereas sufficient Sm and N improved productivity and income. Among the models, Random Forest (RF) performed the best, achieving high accuracy (e.g., R² = 0.998 for cotton, 0.869 for banana, and 0.793 for maize). SHapley Additive exPlanations (SHAP) analysis further identified EC as the most critical determinant, highlighting the priority of salinity mitigation, alongside water and nutrient management. These findings provide farmers and decision-makers with practical insights into how to sustain crop productivity, improve livelihoods, and strengthen food security in semi-arid regions. Full article

(This article belongs to the Special Issue Smart Sensor-Based Systems for Crop Monitoring)

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Graphical abstract

14 pages, 1921 KB

Open AccessArticle

Predictive Modeling of Honey Yield in Rural Apiaries: Insight from Chachapoyas, Amazonas, Peru

by Yander M. Briceño-Mendoza, José Américo Saucedo-Uriarte, Lenin Quiñones Huatangari, Jhoyd B. Gaslac-Gomez, Hurley A. Quispe-Ccasa and I. S. Cayo-Colca

Agriculture 2025, 15(22), 2377; https://doi.org/10.3390/agriculture15222377 - 18 Nov 2025

Abstract

Honey production is influenced by multiple factors, including climatic conditions, hive management practices, and harvest scheduling. This study evaluated the predictive capacity of statistical modeling techniques using data mining algorithms (MARS, CHAID, CART, and Exhaustive) and artificial neural network algorithms (Multilayer Perceptron, MLP) [...] Read more.

Honey production is influenced by multiple factors, including climatic conditions, hive management practices, and harvest scheduling. This study evaluated the predictive capacity of statistical modeling techniques using data mining algorithms (MARS, CHAID, CART, and Exhaustive) and artificial neural network algorithms (Multilayer Perceptron, MLP) to estimate honey yields in apiaries located in northeastern Peru. A structured survey was conducted with sixty-nine beekeepers across nineteen districts in the Chachapoyas province. Variables included beekeeper experience, instruction, hive count, visit frequency, harvest frequency, additional income-generating activities, and geographic location. Descriptive statistics, non-parametric tests, Spearman correlations, and exploratory factor analysis were applied to identify latent structures. A linear mixed-effects model was used to assess the combined influence of predictors on honey production, with district included as a random effect. Results indicated that hive number, beekeeping experience, harvest frequency, and exclusive engagement in apiculture were statistically associated with increased honey yields. The model explained a substantial proportion of variance, supporting the integration of technical and socio-demographic variables in production forecasting. These findings demonstrate the utility of predictive modeling for informing hive management strategies and improving the operational efficiency of small-scale beekeeping systems in Andean regions. Full article

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

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18 pages, 4152 KB

Open AccessArticle

Microbial Divergence and Degradative Capacity During Straw Enrichment

by Hui Zhang, Chenqiang Lin, Longjun Chen, Yu Fang and Xianbo Jia

Agriculture 2025, 15(22), 2376; https://doi.org/10.3390/agriculture15222376 - 18 Nov 2025

Abstract

Whether consecutive annual incorporation of rice straw can enrich straw-decomposing microorganisms, and what common and distinct dominant straw-degrading microbial populations exist in soils under long-term rice straw incorporation across different regions of Fujian Province, remain relatively unexplored. To address this, soil samples were [...] Read more.

Whether consecutive annual incorporation of rice straw can enrich straw-decomposing microorganisms, and what common and distinct dominant straw-degrading microbial populations exist in soils under long-term rice straw incorporation across different regions of Fujian Province, remain relatively unexplored. To address this, soil samples were collected from rice cultivation areas with consecutive straw incorporation located in different geographical directions within Fujian Province. A straw burial pot experiment was conducted, and high-throughput sequencing was employed to analyze the bacterial and fungal community compositions in these soils. Furthermore, the degradation potential of the soil microbial communities towards rice straw was determined. The results revealed that the dominant bacterial phyla associated with straw degradation across the four treatments were Proteobacteria, Actinobacteriota, Firmicutes, and Chloroflexi, while the dominant fungal phyla were Ascomycota and Basidiomycota. At the genus level, the relative abundance of the dominant bacterial genus, Bacillus, showed a positive correlation with the straw degradation rate but a negative correlation with soil pH. In contrast, the dominant fungal genera, Zopfiella and Chaetomium, were positively correlated with both the straw degradation rate and soil pH. Furthermore, a strain designated PC1 was isolated and screened from the PC treatment samples. Sequencing of the rDNA-ITS region identified PC1 as Chaetomium sp. The degradation rate of rice straw by strain PC1 reached 49.13%, which was higher than the degradation rate observed in the PC treatment in the pot burial experiment. This finding provides a theoretical foundation for the potential application of efficient lignin-degrading fungi in field-scale straw degradation. Full article

(This article belongs to the Section Agricultural Soils)

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26 pages, 690 KB

Open AccessReview

Italian Ancient Wheats: Historical, Agronomic, and Market Characteristics: A Comprehensive Review

by Marco Ruggeri, Giuliana Vinci, Sabrina Antonia Prencipe, Simone Vieri and Lucia Maddaloni

Agriculture 2025, 15(22), 2375; https://doi.org/10.3390/agriculture15222375 - 17 Nov 2025

Abstract

Ancient wheats can be understood as dynamic populations of historically cultivated wheat, which, unlike modern varieties, have not been developed through organised genetic improvement programmes, but rather through traditional farmer selection and local adaptation over centuries. Recently, ancient wheats have enjoyed renewed popularity, [...] Read more.

Ancient wheats can be understood as dynamic populations of historically cultivated wheat, which, unlike modern varieties, have not been developed through organised genetic improvement programmes, but rather through traditional farmer selection and local adaptation over centuries. Recently, ancient wheats have enjoyed renewed popularity, particularly in Italy, due to their wide genetic diversity and the significant role of wheat and its derivatives (e.g., bread, pasta, and baked goods) in the country’s culinary and cultural heritage. However, information on the characteristics of Italian ancient wheats remains limited and fragmented. Therefore, this review aims to collect, organise and compare the available evidence on the historical, agronomic, economic and sustainability parameters of ancient wheats, in order to provide an overall assessment of these varieties. The results showed that 34 Italian ancient wheats were studied, mainly from Tuscany and Sicily. With plant heights of up to 180 cm and yields of 1.4–4.8 t/ha, ancient wheats are characterised by greater height but lower productivity compared to modern wheats. They demonstrate good adaptability to poor soils and climatic stress, natural competitiveness with weeds and potential resistance to pathogens, rendering them suitable for sustainable, low-input agricultural systems. Furthermore, ancient wheat flours cost more than twice as much as commercial flours, with average prices of €3.00–5.10/kg, mainly due to artisanal production methods and belonging to short or niche supply chains. Finally, considerable variability in test weight (TW) and thousand kernel weight (TKW) could negatively affect flour or semolina yields. In conclusion, despite their low productivity, ancient wheats could offer significant opportunities in terms of environmental sustainability and biodiversity conservation, proving to be a strategic resource for more resilient and sustainable agriculture. Full article

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

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21 pages, 2487 KB

Open AccessArticle

The Effect of Rice–Frog Co-Cropping Systems on Heavy Metal Availability and Accumulation in Rice in Reclaimed Fields

by Xinni Xia, Zhigang Wang, Zhangyan Zhu, Han Li, Yunshuang Ma and Rongquan Zheng

Agriculture 2025, 15(22), 2374; https://doi.org/10.3390/agriculture15222374 - 17 Nov 2025

Abstract

The accumulation of heavy metals in rice (Oryza sativa L.) compromises food safety and endangers public health. Previous studies have postulated that ecological co-cultivation systems can potentially improve soil quality and reduce crop absorption of heavy metals. Herein, three treatment groups, rice [...] Read more.

The accumulation of heavy metals in rice (Oryza sativa L.) compromises food safety and endangers public health. Previous studies have postulated that ecological co-cultivation systems can potentially improve soil quality and reduce crop absorption of heavy metals. Herein, three treatment groups, rice mono-culture (CG), low-density rice–frog co-culture (LRF), and high-density rice–frog co-culture (HRF), were employed to evaluate the effects of rice–frog co-culture on the physicochemical properties of soils in reclaimed rice fields and heavy metal accumulation in rice. Notably, the rice–frog co-culture markedly increased levels of soil organic matter (SOM), dissolved organic carbon (DOC), cation exchange capacity (CEC), pH, and redox potential (Eh) (p < 0.05), particularly under high-density conditions, compared to the mono-culture system. These changes significantly reduced the bioavailable fractions of Cd, As, and Hg in the soil and substantially diminished their uptake in the roots, stems, leaves, and grains of rice. Conversely, the co-cultivation systems increased the bioavailable content and plant uptake of Pb, particularly under high-density conditions. These findings highlight the feasibility of the rice–frog co-cropping systems in improving soil conditions and reducing the accumulation of specific toxic metals within rice, thereby enhancing the safety of rice grown in reclaimed fields. However, increased Pb accumulation warrants further investigation. Full article

(This article belongs to the Section Agricultural Soils)

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27 pages, 2833 KB

Open AccessArticle

From Molecules to Fields: Mapping the Thematic Evolution of Intelligent Crop Breeding via BERTopic Text Mining

by Xiaohe Liang, Yu Wu, Jiayu Zhuang, Jiajia Liu, Jie Lei, Qi Wang and Ailian Zhou

Agriculture 2025, 15(22), 2373; https://doi.org/10.3390/agriculture15222373 - 16 Nov 2025

Abstract

The convergence of agricultural biotechnology and artificial intelligence is reshaping modern crop improvement. Despite a surge of studies integrating artificial intelligence and biotechnology, the rapidly expanding literature on intelligent crop breeding remains fragmented across molecular, phenotypic, and computational dimensions. Existing reviews often rely [...] Read more.

The convergence of agricultural biotechnology and artificial intelligence is reshaping modern crop improvement. Despite a surge of studies integrating artificial intelligence and biotechnology, the rapidly expanding literature on intelligent crop breeding remains fragmented across molecular, phenotypic, and computational dimensions. Existing reviews often rely on traditional bibliometric or narrative approaches that fail to capture the deep semantic evolution of research themes. To address this gap, this study employs the BERTopic model to systematically analyze 1867 articles (1995–2025, WoS Core Collection), mapping the thematic landscape and temporal evolution of intelligent crop breeding and revealing how methodological and application-oriented domains have co-evolved over time. Eight core topics emerge, i.e., (T0) genomic prediction and genotype–environment modeling; (T1) UAV remote sensing and multimodal phenotyping; (T2) stress-tolerant breeding and root phenotypes; (T3) ear/pod counting with deep learning; (T4) grain trait representation and evaluation; (T5) CRISPR and genome editing; (T6) spike structure recognition and 3D modeling; and (T7) maize tassel detection and developmental staging. Topic-evolution analyses indicate a co-development pattern, where genomic prediction provides a stable methodological backbone, while phenomics (UAV/multimodal imaging, organ-level detection, and 3D reconstruction) propels application-oriented advances. Attention dynamics reveal increasing momentum in image-based counting (T3), grain quality traits (T4), and CRISPR-enabled editing (T5), alongside a plateau in traditional mainstays (T0, T1) and mild cooling in root phenotyping under abiotic stress (T2). Quality stratification (citation quartiles, Q1–Q4) shows high-impact concentration in T0/T1 and a growing tail of application-driven work across T3–T7. Journal analysis reveals a complementary publication ecosystem: Frontiers in Plant Science and Plant Methods anchor cross-disciplinary dissemination; Remote Sensing and Computers and Electronics in Agriculture host engineering-centric phenomics; genetics/breeding journals sustain T0/T2; and molecular journals curate T5. These findings provide an integrated overview of methods, applications, and publication venues, offering practical guidance for research planning, cross-field collaboration, and translational innovation in intelligent crop breeding. Full article

(This article belongs to the Topic Emerging Agricultural Engineering Sciences, Technologies, and Applications—2nd Edition)

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28 pages, 4518 KB

Open AccessArticle

Study on the Detection Model of Tea Red Scab Severity Class Using Hyperspectral Imaging Technology

by Weibin Wu, Ting Tang, Yuxin Duan, Wenlong Qiu, Linhui Duan, Jinhong Lv, Yunfang Zeng, Jiacheng Guo and Yuanqiang Luo

Agriculture 2025, 15(22), 2372; https://doi.org/10.3390/agriculture15222372 - 16 Nov 2025

Abstract

Tea red scab, a contagious disease affecting tea plants, can infect both buds and mature leaves. This study developed discrimination models to assess the severity of this disease using RGB and hyperspectral images. The models were constructed from a total of 1188 [...] Read more.

Tea red scab, a contagious disease affecting tea plants, can infect both buds and mature leaves. This study developed discrimination models to assess the severity of this disease using RGB and hyperspectral images. The models were constructed from a total of 1188 samples collected in May 2024. The results demonstrated that the model based on hyperspectral Imaging (HSI) data significantly outperformed the RGB-based model. Four spectral preprocessing methods were applied, among which the combination of SNV, SG, and FD (SNV-SG-FD) proved to be the most effective. To better capture long-range dependencies among spectral bands, a hybrid architecture integrating a Gated Recurrent Unit (GRU) with a one-dimensional convolutional neural network (1D-CNN), termed CNN-GRU, was proposed. This hybrid model was compared against standalone CNN and GRU benchmarks. The hyperparameters of the CNN-GRU model were optimized using the Newton-Raphson-based optimizer (NRBO) algorithm. The proposed NRBO-optimized SNV-SG-FD-CNN-GRU model achieved superior performance, with accuracy, precision, recall, and F₁-score reaching 92.94%, 92.54%, 92.42%, and 92.43%, respectively. Significant improvements were observed across all evaluation metrics compared to the single-model alternatives, confirming the effectiveness of both the hybrid architecture and the optimization strategy. Full article

(This article belongs to the Special Issue Application of Smart Agricultural Technologies in Mountain Farming Systems)

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5 pages, 159 KB

Open AccessEditorial

Innovative Design and Application of Modern Agricultural Machinery Systems in Cropping Systems

by Chung-Liang Chang and Mustafa Ucgul

Agriculture 2025, 15(22), 2371; https://doi.org/10.3390/agriculture15222371 - 16 Nov 2025

Abstract

The recent increase in extreme weather events and the variety of crop types and planting patterns have shifted agricultural machinery research toward achieving consistent, high-quality performance in the field [...] Full article

(This article belongs to the Special Issue Innovative Design and Application of Modern Agricultural Machinery Systems in Cropping Systems)

4 pages, 166 KB

Open AccessEditorial

Beneficial Microbes for Sustainable Crop Production

by Vlad Stoian and Roxana Vidican

Agriculture 2025, 15(22), 2370; https://doi.org/10.3390/agriculture15222370 - 15 Nov 2025

Abstract

Microbial communities represent a major component of cultivated soils and are responsible for the successful production of crops [...] Full article

(This article belongs to the Special Issue Beneficial Microbes for Sustainable Crop Production)

17 pages, 2525 KB

Open AccessArticle

Effects of Freeze–Thaw Cycles on Soil Aggregate Stability and Organic Carbon Distribution Under Different Land Uses

by Yuting Cheng, Maolin Liu, Yi Zhang, Shuhao Hao, Xiaohu Dang and Ziyang Wang

Agriculture 2025, 15(22), 2369; https://doi.org/10.3390/agriculture15222369 - 15 Nov 2025

Abstract

Soil aggregates are critical determinants of soil erosion resistance and nutrient retention capacity, while freeze–thaw cycles (FTCs) induce the structural reorganization of soil aggregates, thereby altering soil stability and influencing soil organic carbon (SOC) sequestration. This study was located in the Minjia River [...] Read more.

Soil aggregates are critical determinants of soil erosion resistance and nutrient retention capacity, while freeze–thaw cycles (FTCs) induce the structural reorganization of soil aggregates, thereby altering soil stability and influencing soil organic carbon (SOC) sequestration. This study was located in the Minjia River Basin in the typical seasonal freeze–thaw areas of the Loess Plateau and aimed to quantify the effects of FTCs on soil aggregate stability and SOC content under different land use types. Farmland, grassland, and forestland with more than 20 years of usage in the region were selected, and a 0–20 cm soil layer was subjected to seven FTCs (−8 °C to 20 °C), followed by wet and dry sieving classification, focusing on soil aggregate distribution, aggregate stability, mean weight diameter (MWD), geometric mean diameter (GMD), aggregate particle fractal dimension (APD), and SOC content of the aggregate. The results showed that soil aggregates in all land use types were dominated by macroaggregates (>2 mm), with the proportion in forestland (61–63%) > grassland (54–58%) > farmland (38–51%). FTCs enhanced aggregate stability across all land use types, especially in farmland. Concurrently, FTCs reduced the SOC content in all aggregate size fractions, with reduction rates ranging from farmland (9.00–21%) to grassland (4–26%) to forestland (5–31%). Notably, FTCs significantly increased the contribution of 2–5 mm water-stable (WS) aggregates to SOC sequestration, with increment rates of 86% (farmland), 80% (grassland), and 86% (forestland). Furthermore, FTCs altered the correlation between SOC content and aggregate stability. Specifically, the positive correlations of SOC with MWD and GMD were strengthened in aggregates < 0.5 mm but weakened in aggregates >0.5 mm. These findings advance our understanding of the coupled mechanisms underlying soil erosion and carbon cycling across land uses under freeze–thaw, providing a theoretical basis for ecosystem restoration and optimized soil carbon management in cold regions. Full article

(This article belongs to the Topic The Role of Plant-Soil Interactions on Crop Yields and Carbon Sequestration)

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21 pages, 3744 KB

Open AccessArticle

Impact-Induced Breakage Behavior During Grain Discharge and Modeling Framework for Discharge Impact Prediction

by Yawen Xiao, Minyue Sun, Anqi Li, Yanlong Han, Yanqin Zhao, Xiaobo Xi and Ruihong Zhang

Agriculture 2025, 15(22), 2368; https://doi.org/10.3390/agriculture15222368 - 14 Nov 2025

Abstract

Grain breakage serves as a primary causative factor for microbial infestation and oxidative deterioration, significantly diminishing product value and resulting in substantial grain waste and economic losses. The grain discharging process represents the most extensively involved and primary breakage-inducing stage throughout harvest handling [...] Read more.

Grain breakage serves as a primary causative factor for microbial infestation and oxidative deterioration, significantly diminishing product value and resulting in substantial grain waste and economic losses. The grain discharging process represents the most extensively involved and primary breakage-inducing stage throughout harvest handling and processing operations. However, impact and impact-induced breakage behavior during grain discharge are still poorly understood. To elucidate the impact-induced breakage behavior during grain discharge, this study first employed the discrete element method (DEM) to numerically simulate the discharging process, thereby quantifying the variation patterns of grain kinematic characteristics (e.g., velocity and attitude). Building upon the simulated kinematic data, a dedicated impact testing platform was constructed to investigate single-grain breakage. This enabled the determination of critical unit mass impact energy (along 90°: 106.4 J kg⁻¹; along 0°: 57.28 J kg⁻¹) and critical breakage velocity (along 90°: 14.59 m s⁻¹; along 0°: 10.70 m s⁻¹) under two extreme impact attitude conditions. By integrating the DEM-derived kinematics with the experimentally obtained breakage thresholds, a breakage probability zoning diagram for both large-scale and small-scale discharge processes was developed. Finally, leveraging this comprehensive understanding of the flow and breakage mechanics, theoretical models were successfully established to predict key engineering design parameters, including mass flow rate, impact force, and impact pressure. All models were validated and demonstrated excellent predictive capabilities. The research result is of guiding significance for the design of relevant parameters of discharge systems to minimize grain breakage loss to the greatest extent possible. Full article

(This article belongs to the Section Agricultural Technology)

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56 pages, 25039 KB

Open AccessReview

Advances and Future Trends in Electrified Agricultural Machinery for Sustainable Agriculture

by Yue Shen, Feng Yang, Jianbang Wu, Shuai Luo, Zohaib Khan, Lanke Zhang and Hui Liu

Agriculture 2025, 15(22), 2367; https://doi.org/10.3390/agriculture15222367 - 14 Nov 2025

Abstract

The global transition toward sustainable and intelligent farming has positioned Electrified Agricultural Machinery (EAM) as a central focus in modern equipment development. By integrating advanced electrical subsystems, high-efficiency powertrains, and intelligent Energy Management Strategies (EMSs), EAM offers considerable potential to enhance operational efficiency, [...] Read more.

The global transition toward sustainable and intelligent farming has positioned Electrified Agricultural Machinery (EAM) as a central focus in modern equipment development. By integrating advanced electrical subsystems, high-efficiency powertrains, and intelligent Energy Management Strategies (EMSs), EAM offers considerable potential to enhance operational efficiency, reduce greenhouse-gas emissions, and improve adaptability across diverse agricultural environments. Nevertheless, widespread deployment remains constrained by harsh operating conditions, complex duty cycles, and limitations in maintenance capacity and economic feasibility. This review provides a comprehensive synthesis of enabling technologies and application trends in EAM. Performance requirements of electrical subsystems are examined with emphasis on advances in power supply, electric drive, and control systems. The technical characteristics and application scenarios of battery, series hybrid, parallel hybrid, and power-split powertrains are compared. Common EMS approaches (rule-based, optimization-based, and learning-based) are evaluated in terms of design complexity, energy efficiency, adaptability, and computational demand. Representative applications across tillage, seeding, crop management, and harvesting are discussed, underscoring the transformative role of electrification in agricultural production. This review identifies the series hybrid electronic powertrain system and rule-based EMSs as the most mature technologies for practical application in EAM. However, challenges remain concerning operational reliability in harsh agricultural environments and the integration of intelligent control systems for adaptive, real-time operations. The review also highlights key technical bottlenecks and emerging development trends, offering insights to guide future research and support the wider adoption of EAM. Full article

(This article belongs to the Section Agricultural Technology)

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20 pages, 5309 KB

Open AccessArticle

Seed Priming as a Tool for Optimizing Sugar Beet Canopy Traits, Root Yield and Technological Sugar Yield

by Beata Michalska-Klimczak, Zdzisław Wyszyński, Vladimír Pačuta, Marek Rašovský, Jan Buczek and Chrystian Chomontowski

Agriculture 2025, 15(22), 2366; https://doi.org/10.3390/agriculture15222366 - 14 Nov 2025

Abstract

Seed priming is a proven method for enhancing early plant development and stress resilience, yet its field-level effects on sugar beet performance remain underexplored. This study evaluated the impact of seed priming on emergence dynamics, canopy traits, root yield, and sugar productivity over [...] Read more.

Seed priming is a proven method for enhancing early plant development and stress resilience, yet its field-level effects on sugar beet performance remain underexplored. This study evaluated the impact of seed priming on emergence dynamics, canopy traits, root yield, and sugar productivity over three growing seasons with variable weather conditions in central Poland. We found that primed seeds consistently improved emergence uniformity, plant spacing, and early growth, resulting in a more regular canopy structure and greater biomass accumulation. Sugar beet root yield increased by 6.2–7.7%, primarily due to higher average root mass, while final plant density remained unaffected. Although sucrose content was not significantly altered, sugar beet roots from primed seeds exhibited lower concentrations of molasses-forming substances (Na⁺, K⁺, and α-amino nitrogen). As a result, biological and technological sugar yields increased by 5.9% and 6.1%, respectively. Our results illustrate how seed priming enhances both agronomic performance and processing quality of sugar beet under field conditions, offering a low-cost strategy for stabilizing yield in temperate environments. Full article

(This article belongs to the Section Crop Production)

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29 pages, 6539 KB

Open AccessArticle

Optimization of a Low-Loss Peanut Mechanized Shelling Technology Based on Moisture Content, Flexible Materials, and Key Operating Parameters

by Xuan Liao, Tao Liu, Jiannan Wang, Minji Liu, Chenyang Sun, Jiyou An, Huanxiong Xie, Zhichao Hu, Yi Shen and Hai Wei

Agriculture 2025, 15(22), 2365; https://doi.org/10.3390/agriculture15222365 - 14 Nov 2025

Abstract

In order to address the problems of high mechanical damage rate (MDR) and poor variety adaptability in mechanical peanut shelling, this paper improves a small, flexible arc-plates drum–circular grid bar concave screen-type peanut-shelling device. Firstly, by combining the Hertz theory and [...] Read more.

In order to address the problems of high mechanical damage rate (MDR) and poor variety adaptability in mechanical peanut shelling, this paper improves a small, flexible arc-plates drum–circular grid bar concave screen-type peanut-shelling device. Firstly, by combining the Hertz theory and the Weibull distribution model, the shelling and separation models of drums of rigid rods and flexible arc-plates were established. Through comparative analysis, it was verified that the latter has a lower MDR and energy consumption and has excellent shelling performance. Then, through single-factor experiments and an Analysis of Variance (ANOVA), the influence laws of peanut moisture content, drum speed, shelling spacing, and hardness of flexible material (silicone) on the MDR and shelling efficiency (SE) were explored. Subsequently, Box–Behnken’s four-factor three-level regression experiments were carried out, and the optimal shelling operation parameters were obtained by using the response surface multi-objective optimization method (RSM) and verified experiments. The results show that when moisture content is 11%, drum speed is 227 rpm, shelling spacing is 24 mm, and silicone hardness is 40 HA, the kernel’s MDR after shelling is 4.73%, which is reduced by 5.51% and the SE is 95.21%, which is increased by 3%. The R² and the Root Mean Square Error (RMSE) of the actual value versus the predicted value of the model were 0.9921, 0.9624, 7.99 × 10⁻², and 3.1 × 10⁻³, respectively. The relevant research provides references for reducing losses, improving quality, and applying new materials for components in mechanical peanut shelling. Full article

(This article belongs to the Section Agricultural Technology)

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24 pages, 2430 KB

Open AccessArticle

Effect of Irrigation with Saline Water on Germination, Physiology, Growth, and Yield of Durum Wheat Varieties on Silty Clay Soil

by Khadija Manhou, Rachid Moussadek, Houria Dakak, Abdelmjid Zouahri, Ahmed Ghanimi, Hatim Sanad, Majda Oueld Lhaj and Driss Hmouni

Agriculture 2025, 15(22), 2364; https://doi.org/10.3390/agriculture15222364 - 14 Nov 2025

Abstract

Freshwater scarcity in arid regions forces farmers to use saline water, reducing durum wheat (Triticum turgidum L. subsp. durum) productivity, particularly during early growth stages. This study evaluated two Moroccan varieties, Faraj and Nachit, on silty clay soil under five salinity [...] Read more.

Freshwater scarcity in arid regions forces farmers to use saline water, reducing durum wheat (Triticum turgidum L. subsp. durum) productivity, particularly during early growth stages. This study evaluated two Moroccan varieties, Faraj and Nachit, on silty clay soil under five salinity levels (0.2, 4, 8, 12, and 16 dS m⁻¹) in a randomized complete block design with three replications, aiming to identify tolerance thresholds and characterize physiological and agronomic responses. Key traits measured included germination percentage, germination stress index, mean germination time, root and coleoptile length, plant height, leaf number, chlorophyll fluorescence, grain yield, weight of 200 grains, and straw yield. Germination percentage declined from 8 dS m⁻¹, with delayed germination and inhibited vegetative growth at higher salinity. Both varieties maintained grain yield up to 8 dS m⁻¹ and weight of 200 grains and straw yield up to 12 dS m⁻¹, with Nachit showing higher tolerance. Multivariate analyses, including principal component analysis and heatmaps, linked soil sodium, chloride, and electrical conductivity negatively to growth and yield, whereas potassium, calcium, and magnesium supported plant growth and physiological activity. These findings provide insights for breeding and irrigation strategies to sustain durum wheat under salinity stress. Full article

(This article belongs to the Topic Plant Responses and Tolerance to Salinity Stress, 2nd Edition)

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22 pages, 629 KB

Open AccessArticle

Determinants of Postharvest Quality in ‘Gala Schniga^® SchniCo Red(s)’ Apples: The Role of Harvest Date, Storage Duration, and 1-MCP Application

by Maria Małachowska and Kazimierz Tomala

Agriculture 2025, 15(22), 2363; https://doi.org/10.3390/agriculture15222363 - 14 Nov 2025

Abstract

Poland, as a leading apple producer in the EU, must maintain high fruit quality during prolonged storage and distribution, which is crucial for exports to distant markets. Therefore, it is essential to clearly identify which factors most strongly affect quality and the magnitude [...] Read more.

Poland, as a leading apple producer in the EU, must maintain high fruit quality during prolonged storage and distribution, which is crucial for exports to distant markets. Therefore, it is essential to clearly identify which factors most strongly affect quality and the magnitude of their effects in order to make informed choices about pre- and postharvest practices, storage technology, and logistics. The objective of this study was to assess the effect of selected factors on the quality of apples of the ‘Gala Schniga^® SchniCo Red(s)’ cultivar after long-term storage. The study analyzed the effects of harvest date (optimal and delayed), three variants of 1-methylcyclopropene application (control-0 µL·L⁻¹ 1-MCP, Harvista™, SmartFresh™, and Harvista™ + SmartFresh™), storage period (5, 7, and 9 months), simulated trading period (0 or 7 days at 20 °C) and storage technology (ULO: 1.2% CO₂: 1.2% O₂; DCA: 0.6% CO₂: 0.6% O₂) in two consecutive seasons (2022/2023 and 2023/2024). Five quality parameters were evaluated: flesh firmness (F), soluble solid content (SSC), titratable acidity (TA), SSC/TA ratio, and the concentration of 1-aminocyclopropane-1-carboxylic acid (ACC). Backward-elimination stepwise regression and partial eta squared (η²) calculations were used to analyze the data to determine the factors with the greatest impact. The post-harvest application of 1-MCP had the strongest effect in terms of maintaining firmness (η² = 70.4%) and acidity (η² = 38.0%) and reducing ACC content (η² = 21.3%). Harvista™ preparation had a weaker or negligible effect on ACC content, but reduced SSC (η² = 22.7%). Harvest date, storage duration, and shelf life significantly influenced all traits, with controlled-atmosphere regime further modulating outcomes. By integrating preharvest maturity with treatment timing and CA storage, we disentangled the relative contributions of harvest timing, treatment, and storage. The results provide actionable inputs for a decision-support tool to help producers maintain target quality—firmness, SSC, TA, SSC/TA, and ACC—through optimized practice, storage technology choice, and logistics. Full article

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

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