Agriculture

34 pages, 2571 KB

Open AccessReview

Nondestructive Quality Detection of Characteristic Fruits Based on Vis/NIR Spectroscopy: Principles, Systems, and Applications

by Chen Wang, Xiaonan Li, Zijuan Zhang, Xuan Luo, Jianrong Cai and Aichen Wang

Agriculture 2025, 15(20), 2167; https://doi.org/10.3390/agriculture15202167 (registering DOI) - 18 Oct 2025

Abstract

Nondestructive quality detection of characteristic fruits is essential for ensuring nutritional value, economic viability, and consumer safety in global supply chains, yet traditional destructive methods compromise sample integrity and scalability. Visible and near-infrared (Vis/NIR) spectroscopy offers a transformative solution by enabling rapid, non-invasive [...] Read more.

Nondestructive quality detection of characteristic fruits is essential for ensuring nutritional value, economic viability, and consumer safety in global supply chains, yet traditional destructive methods compromise sample integrity and scalability. Visible and near-infrared (Vis/NIR) spectroscopy offers a transformative solution by enabling rapid, non-invasive multi-attribute quantification through molecular overtone vibrations. This review examines recent advancements in Vis/NIR-based fruit quality detection, encompassing fundamental principles, system configurations, and detection strategies calibrated to fruit biophysical properties. Firstly, optical mechanisms and system architectures (portable, online, vehicle-mounted) are compared, emphasizing their compatibility with fruit structural complexity. Then, critical challenges arising from fruit-specific characteristics—such as rind thickness, pit interference, and spatial heterogeneity—are analyzed, highlighting their impact on spectral accuracy. Applications across diverse fruit categories (pitted, thin-rinded, and thick-rinded) are systematically reviewed, with case studies demonstrating the robust prediction of key quality indices. Subsequently, considerations in model development and validation are presented. Finally, persistent limitations in model transferability and environmental adaptability are discussed, proposing future research directions centered on integrating hyperspectral imaging, AI-driven calibration transfer, standardized spectral databases, and miniaturized, field-deployable sensors. Collectively, these methodological breakthroughs will pave the way for autonomous, next-generation quality assessment platforms, revolutionizing postharvest management for characteristic fruits. Full article

(This article belongs to the Special Issue Advances in High-Quality or Value-Added Processing of Fruits and Vegetables—Second Edition)

18 pages, 14076 KB

Open AccessArticle

Transcriptomic Analysis Identifies GhSACPD-Mediated Fatty Acid Regulation in the Cotton Boll Abscission

by Guangling Shui, Zewei Chang, Peng Han, Qi Zhang, Zhibo Li, Hairong Lin, Xin Wang, Yuanlong Wu and Xinhui Nie

Agriculture 2025, 15(20), 2166; https://doi.org/10.3390/agriculture15202166 (registering DOI) - 18 Oct 2025

Abstract

Boll abscission in cotton (Gossypium spp.) is a key factor that limits yield; however, the molecular mechanisms underlying this process remain poorly understood. In this study, boll abscission characteristics were uncovered in four cotton varieties that exhibited extreme differences in boll abscission [...] Read more.

Boll abscission in cotton (Gossypium spp.) is a key factor that limits yield; however, the molecular mechanisms underlying this process remain poorly understood. In this study, boll abscission characteristics were uncovered in four cotton varieties that exhibited extreme differences in boll abscission rates via tissue sectioning. Transcriptome analysis was performed on the four cotton varieties. Using weighted gene co-expression network analysis (WGCNA) of the transcriptome data, we identified a stearoyl-(acyl-carrier-protein) desaturase (SACPD) as a potential key regulator of boll abscission. We also performed evolutionary analyses on the SACPD gene family across five cotton species and identified 63 members that were classified into four evolutionary clades, with duplication-polyploidization events being a major driver of gene expansion. Tissue-specific expression profiling revealed that Gossypium hirsutum GhSACPD19 is highly expressed in the abscission zone. Our findings suggest a role of GhSACPD19 in regulating boll abscission, likely through metabolism of jasmonate, a well-known positive regulator of abscission. Our work offers new insights into the regulation of organ abscission at cellular and molecular levels and presents a valuable resource for cotton yield improvement. Full article

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

► Show Figures

Figure 1

26 pages, 5918 KB

Open AccessArticle

Cotton Picker Fire Risk Analysis and Dynamic Threshold Setting Using Multi-Point Sensing and Seed Cotton Moisture

by Zhai Shi, Dongdong Song, Changjie Han, Fangwei Wu and Yi Wu

Agriculture 2025, 15(20), 2165; https://doi.org/10.3390/agriculture15202165 (registering DOI) - 18 Oct 2025

Abstract

Fire hazards during cotton picker operations pose a significant safety concern, primarily caused by cotton blockages and friction-induced heat generation between the picking spindle and seed cotton under high-load conditions. Existing fire monitoring systems typically employ a uniform temperature threshold across multiple sensors. [...] Read more.

Fire hazards during cotton picker operations pose a significant safety concern, primarily caused by cotton blockages and friction-induced heat generation between the picking spindle and seed cotton under high-load conditions. Existing fire monitoring systems typically employ a uniform temperature threshold across multiple sensors. However, this approach overlooks the distinct characteristics of different cotton picker mechanisms and the influence of seed cotton moisture content, resulting in frequent false alarms and missed detections. To address these issues, this study pioneers and tests a dynamic, tiered temperature threshold warning strategy. This approach accounts for key cotton picker components and varying seed cotton moisture content (MC), specifically MC 9–12% and MC 12–15%. Additionally, based on the operational characteristics of the cotton conveying tube, this study proposes monitoring the wall surface temperature of the conveying tube and investigates the threshold for this temperature. Results indicate that during seed cotton open burning, the average temperature is 324 °C for MC < 9%, 261.9 °C for MC 9–12%, and 178.4 °C for MC 12–15%. After transitioning to smoldering, the temperatures were 226.6 °C, 191.5 °C, and 163.5 °C, respectively, with 163.5 °C being the lowest threshold for seed cotton open burning in the cotton bin. For smoldering seed cotton, the temperature thresholds were 240 °C for MC < 9% and MC 9–12%, and 280 °C for MC 12–15%. The temperature threshold for the cotton conveyor pipe wall surface was 49 °C. The friction-induced heat generation temperature threshold at the picking head, determined through combined testing and simulation, is set at 289 °C for MC < 9%, 306 °C for MC 9–12%, and 319 °C for MC 12–15%. The aforementioned tiered early warning strategy, developed through multi-source experiments and simulations, can be directly configured into controllers. It enables dynamic threshold alarms based on harvester location, seed cotton moisture content, and temperature zones, providing quantitative support for cotton harvester fire monitoring and risk management. Full article

(This article belongs to the Section Agricultural Technology)

22 pages, 9118 KB

Open AccessArticle

Alteration of Nitrogen Fertilizer Forms Optimizes Nitrogen Balance in Drip-Irrigated Winter Wheat Systems of Northern China by Reducing Gaseous Nitrogen Losses

by Ruixuan Hao, Junyi Mu, Xiaoting Xie, Qiqi Ha, Yuanyuan Wang, Wenbo Zhai, Peng Wu, Aixia Ren, Zhiqiang Gao, Ru Guo and Min Sun

Agriculture 2025, 15(20), 2164; https://doi.org/10.3390/agriculture15202164 (registering DOI) - 18 Oct 2025

Abstract

Winter wheat covers approximately 2.21 × 10⁸ ha globally, making it the most widely cultivated cereal crop in the world. In recent years, integrated water and fertilizer management has significantly improved winter wheat yield and nitrogen use efficiency; however, quantitative assessments of [...] Read more.

Winter wheat covers approximately 2.21 × 10⁸ ha globally, making it the most widely cultivated cereal crop in the world. In recent years, integrated water and fertilizer management has significantly improved winter wheat yield and nitrogen use efficiency; however, quantitative assessments of nitrogen cycling under different fertilizer forms in such high-yield systems remain limited. From 2022 to 2024, a two-year field experiment was conducted in drip-irrigated winter wheat fields in northern China. Four nitrogen fertilizer forms were applied: nitrate nitrogen fertilizer (NON), ammonium nitrogen fertilizer (NHN), amide nitrogen fertilizer (CON), and urea ammonium nitrate fertilizer (UAN), along with an unfertilized control (CK). Compared with NON, NHN, and CON, UAN reduced cumulative N₂O emissions by 10.40–15.64% and NH₃ volatilization by 2.04–9.33% (p < 0.05). It also increased the leaf area index and biomass accumulation at maturity, as well as grain yield (3.70–10.28%), nitrogen harvest index (4.58–12.88%), and nitrogen use efficiency (12.14–39.25%) (p < 0.05). Furthermore, UAN significantly decreased the net nitrogen surplus (24.18–45.70%) and nitrogen balance values (25.64–55.82%) (p < 0.05). Correlation analysis indicated that the reduction in nitrogen balance was primarily attributed to lower N₂O emissions and improved nitrogen use efficiency (p < 0.05). In conclusion, the application of urea ammonium nitrate under integrated water–fertilizer management achieved higher yield, greater efficiency, and environmentally sustainable production in drip-irrigated winter wheat systems in northern China. Full article

(This article belongs to the Section Agricultural Water Management)

20 pages, 5398 KB

Open AccessArticle

Research on Driving Forces of Spatiotemporal Patterns in Cotton Cultivation Considering Spatial Heterogeneity

by Meng Du, Deyu Shen, Xun Yang, Fenfang Lin, Chunfa Wu and Dongyan Zhang

Agriculture 2025, 15(20), 2163; https://doi.org/10.3390/agriculture15202163 (registering DOI) - 18 Oct 2025

Abstract

Cotton is increasingly important in global development. The exploration of drivers of spatiotemporal patterns for cotton planting, considering spatial heterogeneity, is essential for optimizing its distribution and supporting sustainable production. This study combined the locally explained stratified heterogeneity (LESH) model with geographically weighted [...] Read more.

Cotton is increasingly important in global development. The exploration of drivers of spatiotemporal patterns for cotton planting, considering spatial heterogeneity, is essential for optimizing its distribution and supporting sustainable production. This study combined the locally explained stratified heterogeneity (LESH) model with geographically weighted regression (GWR) to investigate the factors shaping cotton-planting patterns in the northern slope of the Tianshan Mountains (NSTM), China, from 2000 to 2020. Cotton distribution was derived from long-term Landsat image series, and its expansion showed an average annual growth rate of 2.10 × 10³ km², with intensive cultivation primarily distributed across the central and western counties. The dominant drivers of cotton distribution were elevation (ELE), sunshine duration (SD), slope (SLO), temperature (TEM), runoff (RO), and gross domestic product (GDP). ELE explained about 40% of the spatial heterogeneity. SD showed a declining influence, SLO remained stable, TEM increased in importance, and GDP exhibited a progressive upward trend, although weaker. Moreover, nonlinear weakening interactions, especially between ELE and other factors, as well as between socio-economic and climatic variables, substantially enhanced explanatory power. These findings highlight the significance of accounting for spatial heterogeneity and factor interactions in guiding the spatial optimization and sustainable management of cotton cultivation. Full article

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

26 pages, 3281 KB

Open AccessArticle

Canonical Analysis of the Impact of Climate Predictors on Sugarcane Yield in the Eastern Region of Pernambuco, Brazil

by Rodrigo Rogério da Silva, Geber Barbosa de Albuquerque Moura, Pabrício Marcos Oliveira Lopes, Cristina Rodrigues Nascimento and Pedro Rogério Giongo

Agriculture 2025, 15(20), 2162; https://doi.org/10.3390/agriculture15202162 (registering DOI) - 18 Oct 2025

Abstract

Sugarcane yield plays a crucial role in food safety and biofuel production, and it is strongly influenced by climatic variations. In this context, this study applies canonical correlation analysis (CCA) to identify the climatic predictors, such as sea surface temperature, atmospheric pressure, and [...] Read more.

Sugarcane yield plays a crucial role in food safety and biofuel production, and it is strongly influenced by climatic variations. In this context, this study applies canonical correlation analysis (CCA) to identify the climatic predictors, such as sea surface temperature, atmospheric pressure, and wind speed, that affect sugarcane yield from 1990 to 2019. Hierarchical cluster analysis applied to the performance of 58 municipalities in the eastern region of Pernambuco identified three distinct and homogeneous groups. An analysis of the CCA for the three sugarcane yield groups and climatic variables revealed that the first canonical function was significant with R = 0.82 and precision of 0.67 (p ≤ 0.05 at 95% confidence level), and that the sugarcane yield groups and climatic variables were different (Wilks’ lambda = 0.14), but they were associated. Climatic variables affected the three sugarcane productivity groups, with redundancy indices of 29.7%, 52.2%, and 59.9%. Climatic variables with positive canonical weights enhance performance, while those with negative weights decrease yields. The structural canonical loads and cross-loadings reveal that sea surface temperature plays a crucial role in determining sugarcane yield, potentially influencing precipitation and temperature patterns in the region. The sensitivity analysis confirms the stability of the canonical loads and the robustness of the results, demonstrating that this research can support yield forecasting, regional agricultural policy, and drought management. Identifying climate predictors, such as sea surface temperature, wind speed, and atmospheric pressure, enables the creation of accurate models to predict sugarcane productivity, assisting farmers in planning input management, irrigation during dry periods, and harvesting. Furthermore, climate data can inform policies that encourage sustainable agricultural practices and adaptation to climate conditions, strengthening food security and guiding the selection of more resilient sugarcane varieties, increasing production resilience. Full article

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

► Show Figures

Figure 1

21 pages, 1537 KB

Open AccessArticle

Evolution and Reduction in Sulfonamide Resistance Genes in Aerobic Compost of Pig Manure

by Yihao Huang, Pengyan Wang, Shenao Liu, Shengguo Zhang, Zhuqing Ren and Jian Wu

Agriculture 2025, 15(20), 2161; https://doi.org/10.3390/agriculture15202161 - 17 Oct 2025

Abstract

This study identified that the absolute abundance of 15 types of antibiotic resistance genes (ARGs) across 21 organic fertilizer samples ranged between 1.15 × 10⁴ and 6.74 × 10¹⁰ copies/g, with sulfonamide ARGs and the intI1 gene exhibiting relatively higher residuals. [...] Read more.

This study identified that the absolute abundance of 15 types of antibiotic resistance genes (ARGs) across 21 organic fertilizer samples ranged between 1.15 × 10⁴ and 6.74 × 10¹⁰ copies/g, with sulfonamide ARGs and the intI1 gene exhibiting relatively higher residuals. Subsequent analyses delved into the evolutionary patterns and reduction mechanisms pertinent to sulfonamide ARGs throughout aerobic composting processes. Three bacteria, Bacillus amyloliquefaciens, Bacillus subtilis, and Bacillus velezensis, capable of significantly reducing sulfonamide-resistant bacteria and their sul1 gene were identified. The study revealed that adding composite microbial agent, lowering the pH, or increasing the temperature could inhibit the growth of sulfonamide-resistant bacteria and decrease the abundance of the sul1 gene. Additionally, it was ascertained that the optimization of initial compost pH levels or the incorporation of a compound microbial inoculant effectively reduced the abundance of intracellular and extracellular sulfonamide ARGs and the intI1 gene. The proliferation of Actinobacteria and certain genera during the maturation phase was closely associated with the enrichment of sulfonamide ARGs. This research provides references for the multi-pathway comprehensive control of sulfonamide ARG pollution in composting. Full article

(This article belongs to the Special Issue Impacts of Emerging Agricultural Pollutants on Environmental Health)

27 pages, 1371 KB

Open AccessArticle

Linking Yield, Baking Quality, and Rheological Properties to Guide Sustainable Improvement of Rwandan Wheat Varieties

by Yves Theoneste Murindangabo, Trong Nghia Hoang, Innocent Habarurema, Petr Konvalina, Marguerite Niyibituronsa, Protegene Byukusenge, Protogene Mbasabire, Josine Uwihanganye, Roger Bwimba, Marie Grace Ntezimana and Dang Khoa Tran

Agriculture 2025, 15(20), 2160; https://doi.org/10.3390/agriculture15202160 - 17 Oct 2025

Abstract

Wheat is an important crop in Rwanda; however, rapid population growth, urbanization, and shifting dietary preferences have driven demand far beyond domestic production capacity, resulting in a steady increase in imports. Closing this gap requires a variety of management strategies that jointly optimise [...] Read more.

Wheat is an important crop in Rwanda; however, rapid population growth, urbanization, and shifting dietary preferences have driven demand far beyond domestic production capacity, resulting in a steady increase in imports. Closing this gap requires a variety of management strategies that jointly optimise yield, processing quality, and sustainability. This study evaluated ten widely cultivated wheat (Triticum aestivum L.) varieties in Rwanda through an integrated assessment of grain yield, quality traits, and rheological properties. Yields ranged from 4.3 to 6.3 t ha⁻¹, with Nyaruka and Gihundo achieving the highest productivity. Quality attributes, including protein content (PC), wet gluten (WG), gluten index (GI), falling number (FN), and Zeleny sedimentation value (ZSV), varied significantly, with Cyumba and Reberaho showing superior protein levels. Mixolab-based rheological analyses revealed marked diversity in dough development time, torque, and water absorption, with Keza and Nyangufi exhibiting favorable baking profiles. Statistical analyses highlighted trade-offs between yield and quality, as high-yielding varieties such as Nyaruka showed weaker baking characteristics. These findings demonstrate that linking agronomic performance with grain and dough quality traits provides a pathway towards targeted breeding, sustainable intensification, and enhanced food security. Integrating genetic selection with tailored management and processing strategies can improve both productivity and product value, strengthening the resilience and economic viability of Rwanda’s wheat sector. Full article

(This article belongs to the Section Agricultural Systems and Management)

20 pages, 10807 KB

Open AccessArticle

Evaluating the Seedling Emergence Quality of Peanut Seedlings via UAV Imagery

by Guanchu Zhang, Qi Wang, Guowei Li, Dunwei Ci, Chen Zhang and Fangyan Ma

Agriculture 2025, 15(20), 2159; https://doi.org/10.3390/agriculture15202159 - 17 Oct 2025

Abstract

Accurate evaluation of peanut seedling emergence is critical for ensuring agronomic research accuracy and planting benefit efficiency, but traditional manual methods are limited by strong subjectivity and inconsistent batch inspection standards. In order to quickly and accurately evaluate the emergence rate and quality [...] Read more.

Accurate evaluation of peanut seedling emergence is critical for ensuring agronomic research accuracy and planting benefit efficiency, but traditional manual methods are limited by strong subjectivity and inconsistent batch inspection standards. In order to quickly and accurately evaluate the emergence rate and quality of peanuts, this study proposes an intelligent evaluation system for peanut seedling conditions, which is constructed based on an improved YOLOv11 combined with the Segment Anything Model (SAM) for peanut seedling emergence evaluation, using high-resolution images collected by Unmanned Aerial Vehicles as the data foundation. Experimental results show that the improved YOLOv11 model achieves a detection precision of 96.36%, a recall rate of 96.76%, and an mAP@0.5 of 99.03%. The segmentation performance of SAM is outstanding in terms of integrity. In practical applications, the detection time for a single image by the system is as low as 83.4 ms, and the efficiency of video counting is 6–10 times higher than that of manual counting. Without extensive data annotation, this method performs excellently in peanut seedling emergence quantity statistics and growth status classification, providing efficient, accurate technical support for refined peanut cultivation management and mechanical sowing quality evaluation. Full article

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

24 pages, 731 KB

Open AccessArticle

Eco-Efficiency of Crop Production in the European Union and Serbia

by Tihomir Novaković, Dragan Milić, Dragana Novaković, Mirela Tomaš Simin and Vladislav Zekić

Agriculture 2025, 15(20), 2158; https://doi.org/10.3390/agriculture15202158 - 17 Oct 2025

Abstract

This paper evaluates the eco-efficiency of crop production in the European Union (EU) and the Republic of Serbia for the period 2015–2023, using a stochastic frontier analysis (SFA) model based on panel data. Eco-efficiency was assessed as the ratio of agricultural output to [...] Read more.

This paper evaluates the eco-efficiency of crop production in the European Union (EU) and the Republic of Serbia for the period 2015–2023, using a stochastic frontier analysis (SFA) model based on panel data. Eco-efficiency was assessed as the ratio of agricultural output to key environmental pressures, with expenditures on fertilizers, plant protection products, and energy serving as proxies for ecological burden. The analysis shows that the average eco-efficiency score (Total EE) across the sample is 59.26%, implying that nearly 41% of inputs could be reduced without decreasing output. Decomposition reveals high residual eco-efficiency (93.62%) and lower persistent eco-efficiency (63.30%), suggesting that systematic inefficiencies dominate and are primarily linked to internal farm-level factors such as management practices, organizational structures, and technology adoption. Serbia’s total eco-efficiency score of 63.0% places it close to the EU average, confirming structural similarities with Southern and Eastern European countries. Eco-efficiency scores exhibit notable cross-country variation, ranging from approximately 35% to 96%. About 59% of countries fall within the 50–75% interval, while roughly 11% exceed 75%, indicating considerable scope for further improvement. Cluster analysis further indicates that while Serbia belongs to the lower-intensity group, it has significant potential to converge toward EU frontrunners through farm-level improvements. The findings highlight the importance of targeting internal determinants of efficiency, while recognizing that policy measures can provide enabling conditions and long-term incentives for the green transition. A coherent policy for the green transition should prioritize farm-level structural upgrades, such as technology adoption, advisory and knowledge transfer, and sustainable nutrient and soil management, supported by enabling CAP instruments (eco-schemes and GAEC) and IPARD measures to accelerate improvements in resource efficiency and environmental performance. Full article

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

23 pages, 5077 KB

Open AccessArticle

Spatiotemporal Variation in Water–Energy–Food Synergy Capacity Based on Projection Pursuit Model in the Central Area of Yangtze River Delta, China

by Zhengwei Ye, Zonghua Li, Qilong Ren, Jingtao Wu, Manman Fan and Hongwen Xu

Agriculture 2025, 15(20), 2157; https://doi.org/10.3390/agriculture15202157 - 17 Oct 2025

Abstract

Water, energy, and food (WEF) constitute the core strategic resources essential for regional sustainable development, and the governance of the WEF system holds critical significance for the Central Area of the Yangtze River Delta (caYRD)—one of China’s most economically dynamic regions. In this [...] Read more.

Water, energy, and food (WEF) constitute the core strategic resources essential for regional sustainable development, and the governance of the WEF system holds critical significance for the Central Area of the Yangtze River Delta (caYRD)—one of China’s most economically dynamic regions. In this area, however, the potential risks associated with insufficient WEF synergy capacity have become increasingly prominent amid continuous population growth and rapid urbanization. Against this backdrop, this study aimed to evaluate the WEF synergy capacity of 27 prefecture-level cities (PLCs) in the caYRD over the period 2005–2023 using the Projection Pursuit Model (PPM), based on an evaluation framework encompassing 12 indicators. Our results revealed that (1) the WEF system exhibits significant spatiotemporal heterogeneity, which is evident not only in the water resource, energy resource, and food resource subsystems but also in the overall WEF synergy capacity. In the water subsystem, Wenzhou and Ma’anshan achieved the highest and lowest PPM evaluation scores, respectively; in the energy subsystem, Zhoushan and Shanghai recorded the highest and lowest scores, respectively; and in the food subsystem, Yancheng and Zhoushan ranked first and last in terms of PPM scores, respectively. (2) For the integrated WEF synergy capacity evaluation, Yancheng obtained the highest score, whereas Shanghai ranked the lowest; additionally, Chuzhou exhibited the largest fluctuation range in scores, while Taizhou (Jiangsu) exhibited the smallest fluctuation range. (3) Subsequently, based on the PPM evaluation values of WEF synergy capacity, the 27 PLCs were clustered into three groups: the High WEF synergy capacity value cluster, which includes Yancheng and Chuzhou; the Low WEF synergy capacity value cluster, which consists of Shanghai and Suzhou; and the Mid-level WEF synergy capacity value cluster, which comprises the remaining 22 PLCs and is further subdivided into three sub-clusters. The cluster results of WEF synergy capacity imply that special attention to the consumption control of WEF resources is required for different PLCs. The variations in WEF synergy capacity and its spatial distribution patterns provide critical insights for formulating region-specific strategies to optimize the WEF system, which is of great significance for supporting sustainable development decision-making in the caYRD. Full article

(This article belongs to the Topic Remote Sensing and GIS for Monitoring Land Use Change and Its Ecological Effects)

► Show Figures

Figure 1

16 pages, 2060 KB

Open AccessArticle

Characteristics of the Spatiotemporal Evolution and Driving Mechanisms of Soil Organic Matter in the Songnen Plain in China

by Yao Wang, Yimin Chen, Xinyuan Wang, Baiting Zhang, Yining Sun, Yuhan Zhang, Yuxuan Li, Yueyu Sui and Yingjie Dai

Agriculture 2025, 15(20), 2156; https://doi.org/10.3390/agriculture15202156 - 17 Oct 2025

Abstract

Soil organic matter (SOM) is a key component of nutrient cycling and soil fertility in terrestrial ecosystems. SOM is of great significance to the stability of terrestrial ecosystems and the improvement of soil productivity; to further exert its role, it is first necessary [...] Read more.

Soil organic matter (SOM) is a key component of nutrient cycling and soil fertility in terrestrial ecosystems. SOM is of great significance to the stability of terrestrial ecosystems and the improvement of soil productivity; to further exert its role, it is first necessary to clarify its actual distribution and occurrence status in specific regions. Under the combined impacts of intensive agriculture, unreasonable farming practices, and climate change, the SOM content in the Songnen Plain is showing a degradation trend, posing multiple stresses on its soil ecosystem functions. This study aims to systematically track the dynamic changes of SOM in the Songnen Plain, assess its spatiotemporal evolution characteristics, and reveal its driving mechanisms. A total of 113 representative soil profiles were selected in 2023; standardized excavation and sampling procedures were employed in the Songnen Plain. Soil pH, SOM, total nitrogen (TN), total phosphorus (TP), total potassium (TK), particle size (PSD), texture, and Munsell soil colors of samples were determined. Temporal variation characteristics, as well as horizontal and vertical spatial distribution patterns, in SOM content in the Songnen Plain were assayed. Structural equation modeling (SEM), together with freeze–thaw of soil and soil color mechanism analyses, was applied to reveal the spatiotemporal dynamics and driving mechanisms of SOM. The result indicated that the distribution pattern of SOM content in horizontal space shows higher levels in the northeastern region and lower levels in the southwestern region, and decreased with increasing soil depth. SEM analysis indicated that TN and PSD were the main positive factors, whereas bulk density exerted a dominant negative effect. The ranking of contribution rates is TN > TK > TP > PSD > annual average temperature > annual precipitation > bulk density. Mechanistic analysis revealed a significant negative correlation between SOM content and R, G, B values, with soil color intensity serving as a visual indicator of SOM content. Freeze–thaw thickness of soil was positively correlated with SOM content. These findings provide a scientific basis for soil fertility management and ecological conservation in cold regions. Full article

(This article belongs to the Section Agricultural Soils)

► Show Figures

Figure 1

27 pages, 1490 KB

Open AccessReview

Application of Gene Editing Technology in Livestock: Progress, Challenges, and Future Perspectives

by Jing Wang, Lei Zhang, Chuanying Pan, Xianyong Lan, Baosong Xing and Mingxun Li

Agriculture 2025, 15(20), 2155; https://doi.org/10.3390/agriculture15202155 - 17 Oct 2025

Abstract

Gene editing technologies, particularly CRISPR/Cas9, have revolutionized livestock genetics. They enable precise, efficient, and inheritable genome modifications. This review summarizes recent advances in the application of gene editing in livestock. We focus on six key areas: enhancement of disease resistance, improvement of growth [...] Read more.

Gene editing technologies, particularly CRISPR/Cas9, have revolutionized livestock genetics. They enable precise, efficient, and inheritable genome modifications. This review summarizes recent advances in the application of gene editing in livestock. We focus on six key areas: enhancement of disease resistance, improvement of growth performance and meat production traits, modification of milk composition, regulation of reproductive traits, adaptation to environmental stress, and promotion of animal welfare. For example, they have played an important role in improving mastitis resistance in cows, enhancing meat production performance in pigs, increasing milk yield in goats, and producing polled cows. Despite rapid progress, practical implementation in animal breeding still faces challenges. These include off-target effects, low embryo editing efficiency, delivery limitations, and ethical as well as regulatory constraints. Future directions emphasize the development of advanced editing tools, multiplex trait integration, and harmonized public policy. With continued innovation and responsible oversight, gene editing holds great promise for sustainable animal agriculture and global food security. Full article

(This article belongs to the Section Farm Animal Production)

► Show Figures

Figure 1

24 pages, 6739 KB

Open AccessArticle

Linking Microstructure and Hydraulic Behavior in Cocopeat–Based Substrates Using Pore-Scale Flow Simulation and Micro-CT

by Kai Yao, Tianxiao Li, Qiang Fu, Jing Wang, Weikang Li, Xuan Zhang and Jing Li

Agriculture 2025, 15(20), 2154; https://doi.org/10.3390/agriculture15202154 - 17 Oct 2025

Abstract

The pore structure of cocopeat-based substrates critically influences their hydraulic properties, directly affecting water use efficiency in soilless cultivation systems. Previous macroscopic modeling approaches infer pore structures indirectly from water retention curves and rely on empirical parameterization of pore geometry and connectivity, overlooking [...] Read more.

The pore structure of cocopeat-based substrates critically influences their hydraulic properties, directly affecting water use efficiency in soilless cultivation systems. Previous macroscopic modeling approaches infer pore structures indirectly from water retention curves and rely on empirical parameterization of pore geometry and connectivity, overlooking microscale features that directly control fluid pathways and permeability. To address this gap, this study employed micro-CT imaging to reconstruct the three-dimensional pore structures of coarse cocopeat and a fine cocopeat–perlite mixture. Nine regions of interest (ROIs), representing three typical pore types in each substrate, were selected for quantitative pore structure analysis and pore-scale saturated flow simulations. Results show that over 90% of pore diameters in both substrates fall within the 0–400 μm range, and variations in cocopeat particle size and perlite addition significantly affect average pore diameter, porosity, fractal dimension, and tortuosity, thereby influencing permeability and local flow distribution. This study provides new insights into the microscale mechanisms governing water movement in cocopeat-based substrates and reveals key structural factors regulating hydraulic behavior in soilless cultivation systems. Full article

(This article belongs to the Section Agricultural Water Management)

► Show Figures

Figure 1

15 pages, 3498 KB

Open AccessArticle

Synergistic Effects of Phosphorus and EDDS on Enhancing Phytoremediation Efficiency of Ricinus communis L. in Cu and Cd Co-Contaminated Soils

by Wenying Liu, Rongli Tang, Xinlei Peng, Xueting Yang, Yi Wang and Hongqing Hu

Agriculture 2025, 15(20), 2153; https://doi.org/10.3390/agriculture15202153 - 16 Oct 2025

Abstract

The use of biodegradable chelating agents and fertilizer to improve phytoremediation is a cost-effective and environmental-friendly method for remediation of copper (Cu)- and cadmium (Cd)-polluted agricultural soil. A pot experiment was conducted to investigate the effects of phosphorus (P) fertilizer and the chelator [...] Read more.

The use of biodegradable chelating agents and fertilizer to improve phytoremediation is a cost-effective and environmental-friendly method for remediation of copper (Cu)- and cadmium (Cd)-polluted agricultural soil. A pot experiment was conducted to investigate the effects of phosphorus (P) fertilizer and the chelator ethylenediamine disuccinic acid (EDDS), both individually and in combination, on the phytoremediation efficiency of castor plants. The experiment included six treatments with three replicates, which were as follows: control (no P or EDDS), EDDS alone, P at 100 mg kg⁻¹, P at 300 mg kg⁻¹, P at 100 mg kg⁻¹ + EDDS, and P at 300 mg kg⁻¹ + EDDS. The results demonstrated that phosphorus significantly promoted the growth of castor plants. In the treatment in which 300 mg kg⁻¹ P₂O₅ and 5.0 mmol kg⁻¹ EDDS were added, the shoot dry weight and root dry weight increased by 42.0% and 67.6%, respectively, when compared to the treatment only applying EDDS, and this treatment significantly promoted the absorption of Cd by shoots of castor. In the absence of phosphorus application, EDDS significantly diminished the dry weight of castor roots by 27.3%. Nevertheless, it improved the concentrations of Cu in the shoots and roots of castor plants, which were 3.43 times and 3.27 times higher than those of the control, respectively. Furthermore, when combined with phosphorus fertilizers, EDDS further promoted the absorption of Cu and Cd in the shoots of castor, which significantly increased by 13.34 times and 0.47 times, respectively, with addition of 100 mg kg⁻¹ phosphorus and 5.0 mmol kg⁻¹ of EDDS compared with the control. Phosphorus and EDDS synergistically decreased the activity of POD enzymes in leaves and roots compared with those treated with only EDDS and alleviated the toxicity of EDDS and heavy metals to castor plants. These findings provide scientific evidence for the use of agronomic measures and chelators to optimize phytoremediation efficiency in Cu and Cd co-contaminated soils. Full article

(This article belongs to the Section Agricultural Soils)

► Show Figures

Figure 1

19 pages, 4056 KB

Open AccessArticle

Data-Driven Multi-Objective Optimization Design of Micro-Textured Wet Friction Pair

by Yulin Xiao, Donghui Chen, Shiqi Hao, Chong Ning, Xiaotong Ma, Bingyang Wang and Xiao Yang

Agriculture 2025, 15(20), 2152; https://doi.org/10.3390/agriculture15202152 - 16 Oct 2025

Abstract

Friction pairs in heavy-duty power-shift tractor wet clutches operate under complex conditions, making them vulnerable to damage and reducing reliability. Optimizing their tribological performance requires a trade-off between a high coefficient of friction (COF) for torque transmission and a low temperature rise ( [...] Read more.

Friction pairs in heavy-duty power-shift tractor wet clutches operate under complex conditions, making them vulnerable to damage and reducing reliability. Optimizing their tribological performance requires a trade-off between a high coefficient of friction (COF) for torque transmission and a low temperature rise (

∆ T

) to prevent thermal damage. Surface texturing is an effective method for improving the tribological performance of friction pairs. This study simulated the friction of wet clutch pairs via pin-on-disk tests and designed micro-textures on the pin surface to enhance tribological performance. Based on the experimental data, a Gaussian Process Regression (GPR) surrogate model was developed to accurately predict COF and

∆ T

as a function of the clutch’s operating and micro-texture’s geometric parameters. A Multi-Objective Particle Swarm Optimization (MOPSO) algorithm was then employed to obtain the optimal set of solutions. The obtained pareto front clearly revealed the COF–temperature rise trade-off. From the optimal solution set, optimal micro-texture parameters for two typical operating conditions of different clutches were extracted. Compared with the untextured surface, the optimal solutions increased COF by 2.6%/1.2% and reduced

∆ T

by 39.2%/12.1%. Relative to neighboring experimental points, COF further increased by 11.3%/2.7% and

∆ T

decreased by 16.6%/1.7%. This work establishes a method for balancing the frictional and thermal performance of friction pairs. Full article

(This article belongs to the Section Agricultural Technology)

► Show Figures

Figure 1

37 pages, 1690 KB

Open AccessReview

Advances in Crop Row Detection for Agricultural Robots: Methods, Performance Indicators, and Scene Adaptability

by Zhen Ma, Xinzhong Wang, Xuegeng Chen, Bin Hu and Jingbin Li

Agriculture 2025, 15(20), 2151; https://doi.org/10.3390/agriculture15202151 - 16 Oct 2025

Abstract

Crop row detection technology, as one of the key technologies for agricultural robots to achieve autonomous navigation and precise operations, is related to the precision and stability of agricultural machinery operations. Its research and development will also significantly determine the development process of [...] Read more.

Crop row detection technology, as one of the key technologies for agricultural robots to achieve autonomous navigation and precise operations, is related to the precision and stability of agricultural machinery operations. Its research and development will also significantly determine the development process of intelligent agriculture. The paper first summarizes the mainstream technical methods, performance evaluation systems, and adaptability analysis of typical agricultural scenes for crop row detection. The paper also summarizes and explains the technical principles and characteristics of traditional methods based on visual sensors, point cloud preprocessing based on LiDAR, line structure extraction and 3D feature calculation methods, and multi-sensor fusion methods. Secondly, a review was conducted on performance evaluation criteria such as accuracy, efficiency, robustness, and practicality, analyzing and comparing the applicability of different methods in typical scenarios such as open fields, facility agriculture, orchards, and special terrains. Based on the multidimensional analysis above, it is concluded that a single technology has specific environmental adaptability limitations. Multi-sensor fusion can help improve robustness in complex scenarios, and the fusion advantage will gradually increase with the increase in the number of sensors. Suggestions on the development of agricultural robot navigation technology are made based on the current status of technological applications in the past five years and the needs for future development. This review systematically summarizes crop row detection technology, providing a clear technical framework and scenario adaptation reference for research in this field, and striving to promote the development of precision and efficiency in agricultural production. Full article

(This article belongs to the Section Agricultural Technology)

► Show Figures

Figure 1

18 pages, 890 KB

Open AccessArticle

Genotype × Environment Interaction and Yield Stability of “Pinto” Bean (Phaseolus vulgaris L.) Lines in a Semi-arid Region of Mexico

by Odilón Gayosso Barragán, Jorge Alberto Acosta Gallegos, Juan Samuel Guadalupe Jesús Alcalá Rico, Yanet Jiménez Hernández, Griselda Chávez Aguilar, Ismael Fernando Chávez Díaz and Ulises Aranda Lara

Agriculture 2025, 15(20), 2150; https://doi.org/10.3390/agriculture15202150 - 16 Oct 2025

Abstract

The present study aimed to determine the Genotype × Environment interaction (GEI), yield stability, and agronomic performance of 24 “Pinto” bean lines under semi-arid conditions in Central-West Mexico. All the lines possess a slow-darkening seed coat, a trait that prolongs visual quality and [...] Read more.

The present study aimed to determine the Genotype × Environment interaction (GEI), yield stability, and agronomic performance of 24 “Pinto” bean lines under semi-arid conditions in Central-West Mexico. All the lines possess a slow-darkening seed coat, a trait that prolongs visual quality and increases market value. The lines, which exhibit an indeterminate prostrate growth habit, were evaluated in three contrasting environments: irrigated, rainfed, and drought-stressed. A combined analysis of variance, Tukey’s test, and the additive main effects and multiplicative interaction (AMMI 2) model were applied to assess seed yield and agronomic traits. Average seed yield declined markedly across environments, from 2279 kg ha⁻¹ under irrigation to 593 kg ha⁻¹ under drought stress, with different lines performing best in each environment. AMMI 2 biplot analysis showed that the first two principal components explained 100% of GEI variability for seed yield, dry shoot biomass, total biomass, harvest index, pods per plant, and seeds per pod. Both genetic and environmental effects were significant, with notable GEI patterns. Despite pronounced environmental influence, several lines exhibited stable performance across environments. Line 11 consistently combined high yield and stability, positioning it as a strong candidate for cultivar registration and as a parent in breeding programs targeting semiarid regions. These results underscore the importance of multi-environment evaluation for identifying genotypes with broad or specific adaptation, contributing to genetic improvement and sustainable bean production under variable moisture regimes. Full article

(This article belongs to the Special Issue Advancements in Genotype Technology and Their Breeding Applications)

► Show Figures

Figure 1

16 pages, 12891 KB

Open AccessArticle

On Improving the Performance of Kalman Filter in Denoising Oil Palm Hyperspectral Data

by Imanurfatiehah Ibrahim, Hamzah Arof, Mohd Izzuddin Anuar and Mohamad Sofian Abu Talip

Agriculture 2025, 15(20), 2149; https://doi.org/10.3390/agriculture15202149 - 15 Oct 2025

Abstract

A common drawback of denoising methods of images is that all pixels are filtered regardless of the amount of noise affecting them individually. Since the essence of denoising is lowpass filtering, subjecting clean pixels to denoising results in blurring. In this paper, a [...] Read more.

A common drawback of denoising methods of images is that all pixels are filtered regardless of the amount of noise affecting them individually. Since the essence of denoising is lowpass filtering, subjecting clean pixels to denoising results in blurring. In this paper, a filtering framework is introduced where a fitness function is incorporated in a Kalman filter (KF) to assess the suitability of accepting the value recommended by KF or retaining the existing value of a pixel. Furthermore, a limit on the number of iterations is imposed to avoid over filtering that leads to shrinkage of pixel value ranges of the channels and loss of spectral signatures. In post processing, the means of the filtered channels are shifted to their original values prior to filtering, to spread the pixel value ranges and regain important spectral signatures. The experiments involve the implementation of KF, extended Kalman filter (EKF), Kalman smoother (KS), extended Kalman smoother (EKS) and moving average filter (MAF) in filtering noisy channels of oil palm hyperspectral data under the same framework. Their performances are compared in terms of execution time, SNR gain, NIQE and SSIM metrics. In the second set of experiments, the performance of the improved KF with a fitness function and mean restoration is compared to those of KF and MAF. The results show that the improved KF outperforms the other two filters in the spectral signature characteristics and pixel value ranges of the denoised channels. Full article

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

► Show Figures

Figure 1

Journal Description

Agriculture

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Further Information

Guidelines

MDPI Initiatives

Follow MDPI