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Sustainable Practices for Enhancing Soil Health and Crop Quality in Modern Agriculture: A Review
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Only Detect Broilers Once (ODBO): A Method for Monitoring and Tracking Individual Behavior of Cage-Free Broilers
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Soil Fertility and Plant Growth Enhancement Through Compost Treatments Under Varied Irrigation Conditions
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Biochar as a Feedstock for Sustainable Fertilizers: Recent Advances and Perspectives
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Potential Use of Microalgae Isolated from the Natural Environment as Biofertilizers for the Growth and Development of Pak Choi (Brassica rapa subsp. chinensis)
Journal Description
Agriculture
Agriculture
is an international, scientific peer-reviewed open access journal published semimonthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubAg, AGRIS, RePEc, and other databases.
- Journal Rank: JCR - Q1 (Agronomy) / CiteScore - Q1 (Plant Science)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18 days after submission; acceptance to publication is undertaken in 1.9 days (median values for papers published in this journal in the first half of 2025).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journals for Agriculture include: Poultry, Grasses and Crops.
Impact Factor:
3.6 (2024);
5-Year Impact Factor:
3.8 (2024)
Latest Articles
Efficiency Analysis of Sheep Farms in Cyprus
Agriculture 2025, 15(14), 1555; https://doi.org/10.3390/agriculture15141555 (registering DOI) - 20 Jul 2025
Abstract
In this study, an empirical analysis was applied to measure the efficiency level of dairy farms in Cyprus and estimate the capacity of sheep farmers to support the increasing demand for halloumi cheese. Data Envelopment Analysis was used on data from 50 dairy
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In this study, an empirical analysis was applied to measure the efficiency level of dairy farms in Cyprus and estimate the capacity of sheep farmers to support the increasing demand for halloumi cheese. Data Envelopment Analysis was used on data from 50 dairy sheep farms in Cyprus, which operate under extensive, semi-intensive, and intensive systems. The main features of the most efficient farms are presented, and a comparative financial analysis is implemented between the efficient and less efficient farms. The results indicate room for improvement in extensive and semi-intensive dairy sheep farming and verify that the transition that takes place in sheep farming towards more intensive systems constitutes the optimal approach. The most efficient farms operate under semi-intensive and intensive dairy sheep farming and achieve higher milk yields than the farms operating under extensive systems. Feeding constitutes the main cost driver, exceeding 60% in both efficient and inefficient farms, while labor wages and fixed capital cost varies between 25% and 30% of the total production cost for both efficiency groups. The findings indicate that the farms should utilize economies of scale to reduce production costs and utilize fixed capital endowments at full capacity.
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(This article belongs to the Special Issue Productivity and Efficiency of Agricultural and Livestock Systems)
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Open AccessArticle
Straw Returning Combined with Application of Sulfur-Coated Urea Improved Rice Yield and Nitrogen Use Efficiency Through Enhancing Carbon and Nitrogen Metabolism
by
Guangxin Zhao, Kaiyu Gao, Ming Gao, Xiaotian Xu, Zeming Li, Xianzhi Yang, Ping Tian, Xiaoshuang Wei, Zhihai Wu and Meiying Yang
Agriculture 2025, 15(14), 1554; https://doi.org/10.3390/agriculture15141554 (registering DOI) - 19 Jul 2025
Abstract
Straw returning inhibits tillering at the early stage of rice growth and thus affects grain yield. Sulfur-coated urea (SCU) has been expected to increase nitrogen use efficiency (NUE) and yield, save labor input, and reduce environmental pollution in crop production. Nevertheless, the sulfur
[...] Read more.
Straw returning inhibits tillering at the early stage of rice growth and thus affects grain yield. Sulfur-coated urea (SCU) has been expected to increase nitrogen use efficiency (NUE) and yield, save labor input, and reduce environmental pollution in crop production. Nevertheless, the sulfur coatings of SCU are easy to break and then shorten the nutrient release cycle. Whether there was a complementary effect between straw returning and SCU in NUE and grain yield had remained elusive. To investigate the effects of straw returning combined with the application of SCU on NUE and rice yield, a two-year field experiment was conducted from 2022 to 2023 with three treatments (straw returning combined with conventional urea (SRU), no straw returning combined with SCU (NRS), straw returning combined with SCU (SRS)). We found that straw returning combined with the application of SCU increased rice yield and NUE significantly. Compared with SRU and NRS, SRS treatments significantly increased grain yield by 14.61–16.22%, and 4.14–7.35%, respectively. Higher effective panicle numbers per m2 and grain numbers per panicle were recorded in NRS and SRS treatments than SRU. SRS treatment increased nitrogen recovery efficiency by 79.53% and 22.97%, nitrogen agronomic efficiency by 18.68% and 17.37%, and nitrogen partial factor productivity by 10.51% and 9.81% compared with SRU and NRS treatment, respectively. The enhanced NUE in SRS was driven by higher leaf area index, SPAD value, net photosynthetic rate, carbon metabolic enzyme (RuBP and SPS) activity, nitrogen metabolic enzyme (NR, GS, and GOGAT) activity, sucrose and nitrogen content in leaves, and nitrogen accumulation in plant during grain filling. Moreover, the improved yield in SRS was closely related to superior NUE. In conclusion, straw returning combined with application of SCU boosted grain yield and NUE via enhanced carbon–nitrogen metabolism during the late growth period in rice.
Full article
(This article belongs to the Special Issue Effects of Crop Management on Yields)
Open AccessArticle
A Seed Vigor Test Through a Biospeckle Laser: A Comparison of Local and Global Analyses
by
Bruno Vicentini, Roberto Alves Braga, José Luís Contado, José Eduardo da Silva Gomes and Rolando de Jesus Gonzalez-Peña
Agriculture 2025, 15(14), 1553; https://doi.org/10.3390/agriculture15141553 (registering DOI) - 19 Jul 2025
Abstract
Seed vigor testing traditionally requires large sample sizes and extended durations. The biospeckle laser (BSL) technique offers a faster, image-based alternative for seed analysis though the standardization of set protocols. This study evaluated the efficiency of local and global BSL analyses in bean
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Seed vigor testing traditionally requires large sample sizes and extended durations. The biospeckle laser (BSL) technique offers a faster, image-based alternative for seed analysis though the standardization of set protocols. This study evaluated the efficiency of local and global BSL analyses in bean seeds (Phaseolus vulgaris L.). Two groups of seeds (872 in total) were classified into high- and low-vigor seeds using the emergence test over 800 samples. The BSL test was then applied to 72 seeds (36 per group), analyzing biological activity locally (vascular and embryonic areas) and globally (whole image). BSL analysis detected significant differences between the groups (p < 0.05). Among the methods, the local analysis of the embryonic axis was most effective (F = 44.252, p = 0.000), showing a clearer distinction than the global analysis (F = 19.484, p = 0.000). The vascular area analysis did not yield significant results. These findings highlight the efficiency of the local BSL analysis at the embryonic axis for vigor tests compared to the global analysis. However, it was observed that the selected point in the local analysis affects the reliability of the vigor test. It was a relevant step toward standardization demanding additional tests in other species and varieties.
Full article
(This article belongs to the Special Issue Optics and Image Analysis in Modern Agriculture: Transforming Practices and Unveiling Opportunities)
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Drought Evolution in the Yangtze and Yellow River Basins and Its Dual Impact on Ecosystem Carbon Sequestration
by
Yuanhe Yu, Huan Deng, Shupeng Gao and Jinliang Wang
Agriculture 2025, 15(14), 1552; https://doi.org/10.3390/agriculture15141552 (registering DOI) - 19 Jul 2025
Abstract
As an extreme event driven by global climate change, drought poses a severe threat to terrestrial ecosystems. The Yangtze River Basin (YZRB) and Yellow River Basin (YRB) are key ecological barriers and economic zones in China, holding strategic importance for exploring the evolution
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As an extreme event driven by global climate change, drought poses a severe threat to terrestrial ecosystems. The Yangtze River Basin (YZRB) and Yellow River Basin (YRB) are key ecological barriers and economic zones in China, holding strategic importance for exploring the evolution of drought patterns and their ecological impacts. Using meteorological station data and Climatic Research Unit Gridded Time Series (CRU TS) data, this study analyzed the spatiotemporal characteristics of drought evolution in the YZRB and YRB from 1961 to 2021 using the standardized precipitation evapotranspiration index (SPEI) and run theory. Additionally, this study examined drought effects on ecosystem carbon sequestration (CS) at the city, county, and pixel scales. The results revealed the following: (1) the CRU data effectively captured precipitation (annual r = 0.94) and temperature (annual r = 0.95) trends in both basins, despite significantly underestimating winter temperatures, with the optimal SPEI calculation accuracy found at the monthly scale; (2) both basins experienced frequent autumn–winter droughts, with the YRB facing stronger droughts, including nine events which exceeded 10 months (the longest lasting 25 months), while the mild droughts increased in frequency and extreme intensity; and (3) the drought impacts on CS demonstrated a significant threshold effect, where the intensified drought unexpectedly enhanced CS in western regions, such as the Garzê Autonomous Prefecture in Sichuan Province and Changdu City in the Xizang Autonomous Region, but suppressed CS in the midstream and downstream plains. The CS responded positively under weak drought conditions but declined once the drought intensity surpassed the threshold. This study revealed a nonlinear relationship between drought and CS across climatic zones, thereby providing a scientific foundation for enhancing ecological resilience.
Full article
(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)
Open AccessArticle
Design and Parameter Optimization of Drum Pick-Up Machine Based on Archimedean Curve
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Caichao Liu, Feng Wu, Fengwei Gu, Man Gu, Jingzhan Ni, Weiweng Luo, Jiayong Pei, Mingzhu Cao and Bing Wang
Agriculture 2025, 15(14), 1551; https://doi.org/10.3390/agriculture15141551 (registering DOI) - 19 Jul 2025
Abstract
Stones in farmland soil affect the efficiency of agricultural mechanization and the efficient growth of crops. In order to solve the problems of traditional stone pickers, such as large soil disturbance, high soil content and low picking rate, this paper introduces the Archimedean
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Stones in farmland soil affect the efficiency of agricultural mechanization and the efficient growth of crops. In order to solve the problems of traditional stone pickers, such as large soil disturbance, high soil content and low picking rate, this paper introduces the Archimedean curve with constant radial expansion characteristics into the design of the core working parts of the drum picker and designs a new type of drum stone picker. The key components such as spiral blades, rollers, and scrapers were theoretically analyzed, the structural parameters of the main components were determined, and the reliability of the spiral blades was checked using ANSYS Workbench software. Through the preliminary stone-picking performance test, the forward speed of the stone picker, the rotation speed of the drum, and the starting sliding angle of the spiral blade were determined as the test influencing factors. The picking rate and soil content of the stone picker were determined as the test indicators. The response surface test was carried out in the Design-Expert13.0 software. The results show that, when the forward speed of the stone picker is 0.726 m/s, the drum speed is 30 rpm, and the initial sliding angle of the spiral blade is 26.214°, the picking rate is 91.458% and the soil content is 3.513%. Field tests were carried out with the same parameters, and the picking rate was 91.42% and the soil content was 3.567%, with errors of 0.038% and 0.054% compared with the predicted values, indicating that the stone picker meets the field operation requirements. These research results can provide new ideas and technical paths for improving the performance of pickers and are of great value in promoting the development of advanced harvesting equipment and the efficient use of agricultural resources.
Full article
(This article belongs to the Section Agricultural Technology)
Open AccessArticle
Identification of Optimal Areas for the Cultivation of Genetically Modified Cotton in Mexico: Compatibility with the Center of Origin and Centers of Genetic Diversity
by
Antonia Macedo-Cruz
Agriculture 2025, 15(14), 1550; https://doi.org/10.3390/agriculture15141550 (registering DOI) - 19 Jul 2025
Abstract
The agricultural sector faces significant sustainability, productivity, and environmental impact challenges. In this context, geographic information systems (GISs) have become a key tool to optimize resource management and make informed decisions based on spatial data. These data support planning the best cotton planting
[...] Read more.
The agricultural sector faces significant sustainability, productivity, and environmental impact challenges. In this context, geographic information systems (GISs) have become a key tool to optimize resource management and make informed decisions based on spatial data. These data support planning the best cotton planting and harvest dates based on agroclimatic conditions, such as temperature, precipitation, and soil type, as well as identifying areas with a lower risk of water or thermal stress. As a result, cotton productivity is optimized, and costs associated with supplementary irrigation or losses due to adverse conditions are reduced. However, data from automatic weather stations in Mexico are scarce and incomplete. Instead, grid meteorological databases (DMM, in Spanish) were used with daily temperature and precipitation data from 1983 to 2020 to determine the heat units (HUs) for each cotton crop development stage; daily and accumulated HU; minimum, mean, and maximum temperatures; and mean annual precipitation. This information was used to determine areas that comply with environmental, geographic, and regulatory conditions (NOM-059-SEMARNAT-2010, NOM-026-SAG/FITO-2014) to delimit areas with agricultural potential for planting genetically modified (GM) cotton. The methodology made it possible to produce thirty-four maps at a 1:250,000 scale and a digital GIS with 95% accuracy. These maps indicate whether a given agricultural parcel is optimal for cultivating GM cotton.
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(This article belongs to the Section Digital Agriculture)
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Design and Experiment of Autonomous Shield-Cutting End-Effector for Dual-Zone Maize Field Weeding
by
Yunxiang Li, Yinsong Qu, Yuan Fang, Jie Yang and Yanfeng Lu
Agriculture 2025, 15(14), 1549; https://doi.org/10.3390/agriculture15141549 - 18 Jul 2025
Abstract
This study presented an autonomous shield-cutting end-effector for maize surrounding weeding (SEMSW), addressing the challenges of the low weed removal rate (WRR) and high seedling damage rate (SDR) in northern China’s 3–5 leaf stage maize. The SEMSW integrated seedling positioning, robotic arm control,
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This study presented an autonomous shield-cutting end-effector for maize surrounding weeding (SEMSW), addressing the challenges of the low weed removal rate (WRR) and high seedling damage rate (SDR) in northern China’s 3–5 leaf stage maize. The SEMSW integrated seedling positioning, robotic arm control, and precision weeding functionalities: a seedling positioning sensor identified maize seedlings and weeds, guiding XYZ translational motions to align the robotic arm. The seedling-shielding anti-cutting mechanism (SAM) enclosed crop stems, while the contour-adaptive weeding mechanism (CWM) activated two-stage retractable blades (TRWBs) for inter/intra-row weeding operations. The following key design parameters were determined: 150 mm inner diameter for the seedling-shielding disc; 30 mm minimum inscribed-circle for retractable clamping units (RCUs); 40 mm ground clearance for SAM; 170 mm shielding height; and 100 mm minimum inscribed-circle diameter for the TRWB. Mathematical optimization defined the shape-following weeding cam (SWC) contour and TRWB dimensional chain. Kinematic/dynamic models were introduced alongside an adaptive sliding mode controller, ensuring lateral translation error convergence. A YOLOv8 model achieved 0.951 precision, 0.95 mAP50, and 0.819 mAP50-95, striking a balance between detection accuracy and localization precision. Field trials of the prototype showed 88.3% WRR and 2.2% SDR, meeting northern China’s agronomic standards.
Full article
(This article belongs to the Special Issue Innovative Design and Application of Modern Agricultural Machinery Systems in Cropping Systems)
Open AccessArticle
Calibration of DEM Parameters and Microscopic Deformation Characteristics During Compression Process of Lateritic Soil with Different Moisture Contents
by
Chao Ji, Wanru Liu, Yiguo Deng, Yeqin Wang, Peimin Chen and Bo Yan
Agriculture 2025, 15(14), 1548; https://doi.org/10.3390/agriculture15141548 - 18 Jul 2025
Abstract
Lateritic soils in tropical regions feature cohesive textures and high specific resistance, driving up energy demands for tillage and harvesting machinery. However, current equipment designs lack discrete element models that account for soil moisture variations, and the microscopic effects of water content on
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Lateritic soils in tropical regions feature cohesive textures and high specific resistance, driving up energy demands for tillage and harvesting machinery. However, current equipment designs lack discrete element models that account for soil moisture variations, and the microscopic effects of water content on lateritic soil deformation remain poorly understood. This study aims to calibrate and validate discrete element method (DEM) models of lateritic soil at varying moisture contents of 20.51%, 22.39%, 24.53%, 26.28%, and 28.04% by integrating the Hertz–Mindlin contact mechanics with bonding and JKR cohesion models. Key parameters in the simulations were calibrated through systematic experimentation. Using Plackett–Burman design, critical factors significantly affecting axial compressive force—including surface energy, normal bond stiffness, and tangential bond stiffness—were identified. Subsequently, Box–Behnken response surface methodology was employed to optimize these parameters by minimizing deviations between simulated and experimental maximum axial compressive forces under each moisture condition. The calibrated models demonstrated high fidelity, with average relative errors of 4.53%, 3.36%, 3.05%, 3.32%, and 7.60% for uniaxial compression simulations across the five moisture levels. Stress–strain analysis under axial loading revealed that at a given surface displacement, both fracture dimensions and stress transfer rates decreased progressively with increasing moisture content. These findings elucidate the moisture-dependent micromechanical behavior of lateritic soil and provide critical data support for DEM-based design optimization of soil-engaging agricultural implements in tropical environments.
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(This article belongs to the Section Agricultural Technology)
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Exploring the Impact of Species Participation Levels on the Performance of Dominant Plant Identification Models in the Sericite–Artemisia Desert Grassland by Using Deep Learning
by
Wenhao Liu, Guili Jin, Wanqiang Han, Mengtian Chen, Wenxiong Li, Chao Li and Wenlin Du
Agriculture 2025, 15(14), 1547; https://doi.org/10.3390/agriculture15141547 - 18 Jul 2025
Abstract
Accurate plant species identification in desert grasslands using hyperspectral data is a critical prerequisite for large-scale, high-precision grassland monitoring and management. However, due to prolonged overgrazing and the inherent ecological vulnerability of the environment, sericite–Artemisia desert grassland has experienced significant ecological degradation.
[...] Read more.
Accurate plant species identification in desert grasslands using hyperspectral data is a critical prerequisite for large-scale, high-precision grassland monitoring and management. However, due to prolonged overgrazing and the inherent ecological vulnerability of the environment, sericite–Artemisia desert grassland has experienced significant ecological degradation. Therefore, in this study, we obtained spectral images of the grassland in April 2022 using a Soc710 VP imaging spectrometer (Surface Optics Corporation, San Diego, CA, USA), which were classified into three levels (low, medium, and high) based on the level of participation of Seriphidium transiliense (Poljakov) Poljakov and Ceratocarpus arenarius L. in the community. The optimal index factor (OIF) was employed to synthesize feature band images, which were subsequently used as input for the DeepLabv3p, PSPNet, and UNet deep learning models in order to assess the influence of species participation on classification accuracy. The results indicated that species participation significantly impacted spectral information extraction and model classification performance. Higher participation enhanced the scattering of reflectivity in the canopy structure of S. transiliense, while the light saturation effect of C. arenarius was induced by its short stature. Band combinations—such as Blue, Red Edge, and NIR (BREN) and Red, Red Edge, and NIR (RREN)—exhibited strong capabilities in capturing structural vegetation information. The identification model performances were optimal, with a high level of S. transiliense participation and with DeepLabv3p, PSPNet, and UNet achieving an overall accuracy (OA) of 97.86%, 96.51%, and 98.20%. Among the tested models, UNet exhibited the highest classification accuracy and robustness with small sample datasets, effectively differentiating between S. transiliense, C. arenarius, and bare ground. However, when C. arenarius was the primary target species, the model’s performance declined as its participation levels increased, exhibiting significant omission errors for S. transiliense, whose producer’s accuracy (PA) decreased by 45.91%. The findings of this study provide effective technical means and theoretical support for the identification of plant species and ecological monitoring in sericite–Artemisia desert grasslands.
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(This article belongs to the Section Digital Agriculture)
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The Fusion of Focused Spectral and Image Texture Features: A New Exploration of the Nondestructive Detection of Degeneration Degree in Pleurotus geesteranus
by
Yifan Jiang, Jin Shang, Yueyue Cai, Shiyang Liu, Ziqin Liao, Jie Pang, Yong He and Xuan Wei
Agriculture 2025, 15(14), 1546; https://doi.org/10.3390/agriculture15141546 - 18 Jul 2025
Abstract
The degradation of edible fungi can lead to a decrease in cultivation yield and economic losses. In this study, a nondestructive detection method for strain degradation based on the fusion of hyperspectral technology and image texture features is presented. Hyperspectral and microscopic image
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The degradation of edible fungi can lead to a decrease in cultivation yield and economic losses. In this study, a nondestructive detection method for strain degradation based on the fusion of hyperspectral technology and image texture features is presented. Hyperspectral and microscopic image data were acquired from Pleurotus geesteranus strains exhibiting varying degrees of degradation, followed by preprocessing using Savitzky–Golay smoothing (SG), multivariate scattering correction (MSC), and standard normal variate transformation (SNV). Spectral features were extracted by the successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and principal component analysis (PCA), while the texture features were derived using gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) models. The spectral and texture features were then fused and used to construct a classification model based on convolutional neural networks (CNN). The results showed that combining hyperspectral and image texture features significantly improved the classification accuracy. Among the tested models, the CARS + LBP-CNN configuration achieved the best performance, with an overall accuracy of 95.6% and a kappa coefficient of 0.96. This approach provides a new technical solution for the nondestructive detection of strain degradation in Pleurotus geesteranus.
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(This article belongs to the Section Agricultural Product Quality and Safety)
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Open AccessArticle
A Single-Cell Assessment of Intramuscular and Subcutaneous Adipose Tissue in Beef Cattle
by
Mollie M. Green, Hunter R. Ford, Alexandra P. Tegeler, Oscar J. Benitez, Bradley J. Johnson and Clarissa Strieder-Barboza
Agriculture 2025, 15(14), 1545; https://doi.org/10.3390/agriculture15141545 - 18 Jul 2025
Abstract
Deposition of intramuscular fat (IM), also known as marbling, is the deciding factor of beef quality grade in the U.S. Defining molecular mechanisms underlying the differential deposition of adipose tissue in distinct anatomical areas in beef cattle is key to the development of
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Deposition of intramuscular fat (IM), also known as marbling, is the deciding factor of beef quality grade in the U.S. Defining molecular mechanisms underlying the differential deposition of adipose tissue in distinct anatomical areas in beef cattle is key to the development of strategies for marbling enhancement while limiting the accumulation of excessive subcutaneous adipose tissue (SAT). The objective of this exploratory study was to define the IM and SAT transcriptional heterogeneity at the whole tissue and single-nuclei levels in beef steers. Longissimus dorsi muscle samples (9–11th rib) were collected from two finished beef steers at harvest to dissect matched IM and adjacent SAT (backfat). Total RNA from IM and SAT was isolated and sequenced in an Illumina NovaSeq 6000. Nuclei from the same samples were isolated by dounce homogenization, libraries generated with 10× Genomics, and sequenced in an Illumina NovaSeq 6000, followed by analysis via Cell Ranger pipeline and Seurat in RStudio (v4.3.2) By the expression of signature marker genes, single-nuclei RNA sequencing (snRNAseq) analysis identified mature adipocytes (AD; ADIPOQ, LEP), adipose stromal and progenitor cells (ASPC; PDGFRA), endothelial cells (EC; VWF, PECAM1), smooth muscle cells (SMC; NOTCH3, MYL9) and immune cells (IMC; CD163, MRC1). We detected six cell clusters in SAT and nine in IM. Across IM and SAT, AD was the most abundant cell type, followed by ASPC, SMC, and IMC. In SAT, AD made up 50% of the cellular population, followed by ASPC (31%), EC (14%), IMC (1%), and SMC (4%). In IM depot, AD made up 23% of the cellular population, followed by ASPC at 19% of the population, EC at 28%, IMC at 7% and SMC at 12%. The abundance of ASPC and AD was lower in IM vs. SAT, while IMC was increased, suggesting a potential involvement of immune cells on IM deposition. Accordingly, both bulk RNAseq and snRNAseq analyses identified activated pathways of inflammation and metabolic function in IM. These results demonstrate distinct transcriptional cellular heterogeneity between SAT and IM depots in beef steers, which may underly the mechanisms by which fat deposits in each depot. The identification of depot-specific cell populations in IM and SAT via snRNAseq analysis has the potential to reveal target genes for the modulation of fat deposition in beef cattle.
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(This article belongs to the Special Issue Current Research and Strategies for Improving Farm Animal Meat Quality)
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Improving Winter Wheat Yield Estimation Under Saline Stress by Integrating Sentinel-2 and Soil Salt Content Using Random Forest
by
Chuang Lu, Maowei Yang, Shiwei Dong, Yu Liu, Yinkun Li and Yuchun Pan
Agriculture 2025, 15(14), 1544; https://doi.org/10.3390/agriculture15141544 - 18 Jul 2025
Abstract
Accurate estimation of winter wheat yield under saline stress is crucial for addressing food security challenges and optimizing agricultural management in regional soils. This study proposed a method integrating Sentinel-2 data and field-measured soil salt content (SC) using a random forest (RF) method
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Accurate estimation of winter wheat yield under saline stress is crucial for addressing food security challenges and optimizing agricultural management in regional soils. This study proposed a method integrating Sentinel-2 data and field-measured soil salt content (SC) using a random forest (RF) method to improve yield estimation of winter wheat in Kenli County, a typical saline area in China’s Yellow River Delta. First, feature importance analysis of a temporal vegetation index (VI) and salinity index (SI) across all growth periods were achieved to select main parameters. Second, yield models of winter wheat were developed in VI-, SI-, VI + SI-, and VI + SI + SC-based groups. Furthermore, error assessment and spatial yield mapping were analyzed in detail. The results demonstrated that feature importance varied by growth periods. SI dominated in pre-jointing periods, while VI was better in the post-jointing phase. The VI + SI + SC-based model achieved better accuracy (R2 = 0.78, RMSE = 720.16 kg/ha) than VI-based (R2 = 0.71), SI-based (R2 = 0.69), and VI + SI-based (R2 = 0.77) models. Error analysis results suggested that the residuals were reduced as the input parameters increased, and the VI + SI + SC-based model showed a good consistency with the field-measured yields. The spatial distribution of winter wheat yield using the VI + SI + SC-based model showed significant differences, and average yields in no, slight, moderate, and severe salinity areas were 7945, 7258, 5217, and 4707 kg/ha, respectively. This study can provide a reference for winter wheat yield estimation and crop production improvement in saline regions.
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(This article belongs to the Special Issue How Optical Sensors and Deep Learning Enhance the Production Management in Smart Agriculture)
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Open AccessArticle
Evaluation of Olive Mill Waste Compost as a Sustainable Alternative to Conventional Fertilizers in Wheat Cultivation
by
Ana García-Rández, Silvia Sánchez Méndez, Luciano Orden, Francisco Javier Andreu-Rodríguez, Miguel Ángel Mira-Urios, José A. Sáez-Tovar, Encarnación Martínez-Sabater, María Ángeles Bustamante, María Dolores Pérez-Murcia and Raúl Moral
Agriculture 2025, 15(14), 1543; https://doi.org/10.3390/agriculture15141543 - 17 Jul 2025
Abstract
This study evaluates the agronomic and environmental performance of pelletized compost derived from olive mill waste as a sustainable alternative to mineral fertilizers for cultivating wheat (Triticum turgidum L.) under conventional tillage methods. A field experiment was conducted in semi-arid Spain, employing
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This study evaluates the agronomic and environmental performance of pelletized compost derived from olive mill waste as a sustainable alternative to mineral fertilizers for cultivating wheat (Triticum turgidum L.) under conventional tillage methods. A field experiment was conducted in semi-arid Spain, employing three fertilization strategies: inorganic (MAP + Urea), sewage sludge (SS), and organic compost pellets (OCP), each providing 150 kg N ha−1. The parameters analyzed included wheat yield, grain quality, soil properties, and greenhouse gas (GHG) emissions. Inorganic fertilization yielded the highest productivity and nutrient uptake. However, the OCP treatment reduced grain yield by only 15%, while improving soil microbial activity and enzymatic responses. The SS and OCP treatments showed increased CO2 and N2O emissions compared to the control and inorganic plots. However, the OCP treatment also acted as a CH4 sink. Nutrient use efficiency was greatest under mineral fertilization, though the OCP treatment outperformed the SS treatment. These results highlight the potential of OCP as a circular bio-based fertilizer that can enhance soil function and partially replace mineral inputs. Optimizing application timing is critical to aligning nutrient release with crop demand. Further long-term trials are necessary to evaluate their impact on the soil and improve environmental outcomes.
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(This article belongs to the Special Issue Fertilizer Management Strategies for Enhancing the Growth, Yield and Quality in Crops)
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Open AccessEditorial
Leaning on Smart Agricultural Systems for Crop Monitoring
by
Adrian Gracia-Romero, Karen Marti-Jerez and Fabio Fania
Agriculture 2025, 15(14), 1542; https://doi.org/10.3390/agriculture15141542 - 17 Jul 2025
Abstract
Contemporary precision agriculture and breeding programs are heavily dependent on the evaluation of a high number of experimental plots, usually involving different genotypes, irrigation methods, and managing systems [...]
Full article
(This article belongs to the Special Issue Smart Agriculture Sensors and Monitoring Systems for Field Detection)
Open AccessArticle
Relative Growth Rate and Specific Absorption Rate of Nutrients in Lactuca sativa L. Under Secondary Paper Sludge Application and Soil Contamination with Lead
by
Elena Ikkonen and Marija Yurkevich
Agriculture 2025, 15(14), 1541; https://doi.org/10.3390/agriculture15141541 - 17 Jul 2025
Abstract
Cost-effective methods for improving soil fertility and mitigating the negative impact of heavy metal contamination in agricultural soils are currently under investigation. This study aimed to evaluate the impact of soil lead (Pb) contamination and the application of secondary pulp and paper mill
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Cost-effective methods for improving soil fertility and mitigating the negative impact of heavy metal contamination in agricultural soils are currently under investigation. This study aimed to evaluate the impact of soil lead (Pb) contamination and the application of secondary pulp and paper mill sludge on the relative growth rate (RGR) and its determinants, as well as the specific absorption rate (SAR) of nutrients of Lactuca sativa L. For the 46-day pot experiment, which was carried out in 2022 under controlled conditions at the Karelian Research Centre of RAS, sandy loam soil was used, to which Pb was added at rates of 0, 50, and 250 mg Pb(NO3)2 kg−1. Secondary sludge was applied with each watering at concentrations of 0%, 20%, and 40%. RGR values varied significantly, primarily due to changes in net assimilation rate (NAR) rather than specific leaf area. Positive relationships were found between RGR and NAR, and RGR and SAR of nitrogen and phosphorus, but not potassium. Sludge applications can stimulate NAR at early stages of plant growth. For plants grown on soil with the highest Pb concentration studied, secondary sludge reduced root lead content by an average of 35%. Soil contamination with lead increased nutrient SAR by 79 and 39% when applied as 20 and 40% sludge, respectively, while 40% sludge increased nitrogen SAR by 51% but did not change phosphorus and potassium SAR. A sludge-mediated reduction in root Pb content and an increase in NAR suggest that secondary paper sludge may contribute to the remediation of Pb-contaminated soils and reduce the toxicity of heavy metals to plants. The results may help in finding new ways to manage soil fertility, especially for contaminated soils.
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(This article belongs to the Section Agricultural Soils)
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Open AccessArticle
Acoustic Wave Propagation Characteristics of Maize Seed and Surrounding Region with the Double Media of Seed–Soil
by
Yadong Li, Caiyun Lu, Hongwen Li, Jin He, Zhinan Wang and Chengkun Zhai
Agriculture 2025, 15(14), 1540; https://doi.org/10.3390/agriculture15141540 - 17 Jul 2025
Abstract
When monitoring seed positions in soil using ultrasonic waves, the main challenge is obtaining acoustic wave characteristics at the seed locations. This study developed a three-dimensional ultrasonic model with the double media of seed–soil using the discrete element method to visualize signal variations
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When monitoring seed positions in soil using ultrasonic waves, the main challenge is obtaining acoustic wave characteristics at the seed locations. This study developed a three-dimensional ultrasonic model with the double media of seed–soil using the discrete element method to visualize signal variations and analyze propagation characteristics. The effects of the compression ratio (0/6/12%), excitation frequency (20/40/60 kHz), and amplitude (5/10/15 μm) on signal variation and attenuation were analyzed. The results show consistent trends: time/frequency domain signal intensity increased with a higher compression ratio and amplitude but decreased with frequency. Comparing ultrasonic signals at soil particles before and after the seed along the propagation path shows that the seed significantly absorbs and attenuates ultrasonic waves. Time domain intensity drops 93.99%, and first and residual wave frequency peaks decrease by 88.06% and 96.39%, respectively. Additionally, comparing ultrasonic propagation velocities in the double media of seed–soil and the single soil medium reveals that the velocity in the seed is significantly higher than that in the soil. At compression ratios of 0%, 6%, and 12%, the sound velocity in the seed is 990.47%, 562.72%, and 431.34% of that in the soil, respectively. These findings help distinguish seed presence and provide a basis for ultrasonic seed position monitoring after sowing.
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(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)
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Open AccessArticle
Physico-Chemical and Resistance Characteristics of Rosehip Seeds
by
Alina-Daiana Ionescu, Gheorghe Voicu, Elena-Madalina Stefan, Gabriel-Alexandru Constantin, Paula Tudor and Gheorghe Militaru
Agriculture 2025, 15(14), 1539; https://doi.org/10.3390/agriculture15141539 - 17 Jul 2025
Abstract
Both the pulp and the seeds of rosehip are important for human health. Rosehip seeds are rich in polyunsaturated fats, which support a healthy skin membrane and protect it from inflammatory factors. In order to be used, the seeds require initial processing, mainly
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Both the pulp and the seeds of rosehip are important for human health. Rosehip seeds are rich in polyunsaturated fats, which support a healthy skin membrane and protect it from inflammatory factors. In order to be used, the seeds require initial processing, mainly by grinding. This paper first presents a brief review of the physicochemical properties and the content of bioactive compounds in rosehip (Rosa canina) and its seeds. Original research results on the compression behavior of rosehip seeds are presented below, together with the key values of the most important parameters derived from the analysis. For seeds with a thickness ranging from 1.80 to 3.55 mm, the compressive force at the onset of fracture was recorded to be between 94.4 and 156.0 N, while the force required for complete fracture ranged from 114.0 to 495.0 N (with about 12.5% of values considered outside a normal distribution). Additionally, for these forces, the deformation of the seeds ranged between 0.142 and 0.916 mm at the onset of fracture and between 0.248 and 1.878 mm at complete fracture. For these characteristics, the energy consumed ranged between 0.012 and 0.041 J at the onset of fracture and between 0.017 and 0.322 J at complete breaking. The elasticity of the seeds also ranged between 159.9 and 789.1 N/mm, considering the forces and deformations at the onset of fracture. The results of our study contribute to expanding the database on the mechanical characteristics of rosehip seeds, knowledge of which is essential for the initial processing operations used in the pharmaceutical industry aimed at oil extraction.
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(This article belongs to the Section Seed Science and Technology)
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Open AccessArticle
Harnessing Microbial Agents to Improve Soil Health and Rice Yield Under Straw Return in Rice–Wheat Agroecosystems
by
Yangming Ma, Yanfang Wen, Ruhongji Liu, Zhenglan Peng, Guanzhou Luo, Cheng Wang, Zhonglin Wang, Zhiyuan Yang, Zongkui Chen, Jun Ma and Yongjian Sun
Agriculture 2025, 15(14), 1538; https://doi.org/10.3390/agriculture15141538 - 17 Jul 2025
Abstract
We clarified the effect of wheat straw return combined with microbial agents on rice yield and soil properties. A field experiment was conducted using hybrid indica rice ‘Chuankangyou 2115’ and five treatments: no wheat straw return (T1), wheat straw
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We clarified the effect of wheat straw return combined with microbial agents on rice yield and soil properties. A field experiment was conducted using hybrid indica rice ‘Chuankangyou 2115’ and five treatments: no wheat straw return (T1), wheat straw return alone (T2), T2+ microbial agent application (Bacillus subtilis/Trichoderma harzianum = 1:1) (T3); T2+ microbial agent application (Bacillus subtilis/Trichoderma harzianum = 3:1) (T4); T2+ microbial agent application (Bacillus subtilis/Trichoderma harzianum = 1:3) (T5). T2–T5 significantly increased dry matter accumulation, soil total N, ammonium N, nitrate N, and organic matter, improving yield by 3.81–26.63%. T3 exhibited the highest yield increases in two consecutive years. At the jointing and heading stages, Penicillium and Saitozyma dominated under T3 and positively correlated with dry matter, yield, and nitrogen levels. Straw return combined with Bacillus subtilis and Trichoderma harzianum (20 g m−2 each) enhanced soil nitrogen availability and dry matter accumulation and translocation. Our findings guide efficient straw utilization, soil microbial regulation, and sustainable high-yield rice production.
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(This article belongs to the Section Agricultural Soils)
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Design and Evaluation of a Novel Actuated End Effector for Selective Broccoli Harvesting in Dense Planting Conditions
by
Zhiyu Zuo, Yue Xue, Sheng Gao, Shenghe Zhang, Qingqing Dai, Guoxin Ma and Hanping Mao
Agriculture 2025, 15(14), 1537; https://doi.org/10.3390/agriculture15141537 - 16 Jul 2025
Abstract
The commercialization of selective broccoli harvesters, a critical response to agricultural labor shortages, is hampered by end effectors with large operational envelopes and poor adaptability to complex field conditions. To address these limitations, this study developed and evaluated a novel end-effector with an
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The commercialization of selective broccoli harvesters, a critical response to agricultural labor shortages, is hampered by end effectors with large operational envelopes and poor adaptability to complex field conditions. To address these limitations, this study developed and evaluated a novel end-effector with an integrated transverse cutting mechanism and a foldable grasping cavity. Unlike conventional fixed cylindrical cavities, our design utilizes actuated grasping arms and a mechanical linkage system to significantly reduce the operational footprint and enhance maneuverability. Key design parameters were optimized based on broccoli morphological data and experimental measurements of the maximum stem cutting force. Furthermore, dynamic simulations were employed to validate the operational trajectory and ensure interference-free motion. Field tests demonstrated an operational success rate of 93.33% and a cutting success rate of 92.86%. The end effector successfully operated in dense planting environments, effectively avoiding interference with adjacent broccoli heads. This research provides a robust and promising solution that advances the automation of broccoli harvesting, paving the way for the commercial adoption of robotic harvesting technologies.
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(This article belongs to the Topic New Research on Automated and Efficient Agricultural Machineries)
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Data-Efficient Sowing Position Estimation for Agricultural Robots Combining Image Analysis and Expert Knowledge
by
Shuntaro Aotake, Takuya Otani, Masatoshi Funabashi and Atsuo Takanishi
Agriculture 2025, 15(14), 1536; https://doi.org/10.3390/agriculture15141536 - 16 Jul 2025
Abstract
We propose a data-efficient framework for automating sowing operations by agricultural robots in densely mixed polyculture environments. This study addresses the challenge of enabling robots to identify suitable sowing positions with minimal labeled data by integrating image-based field sensing with expert agricultural knowledge.
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We propose a data-efficient framework for automating sowing operations by agricultural robots in densely mixed polyculture environments. This study addresses the challenge of enabling robots to identify suitable sowing positions with minimal labeled data by integrating image-based field sensing with expert agricultural knowledge. We collected 84 RGB-depth images from seven field sites, labeled by synecological farming practitioners of varying proficiency levels, and trained a regression model to estimate optimal sowing positions and seeding quantities. The model’s predictions were comparable to those of intermediate-to-advanced practitioners across diverse field conditions. To implement this estimation in practice, we mounted a Kinect v2 sensor on a robot arm and integrated its 3D spatial data with axis-specific movement control. We then applied a trajectory optimization algorithm based on the traveling salesman problem to generate efficient sowing paths. Simulated trials incorporating both computation and robotic control times showed that our method reduced sowing operation time by 51% compared to random planning. These findings highlight the potential of interpretable, low-data machine learning models for rapid adaptation to complex agroecological systems and demonstrate a practical approach to combining structured human expertise with sensor-based automation in biodiverse farming environments.
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(This article belongs to the Special Issue Research Progress on Agricultural Equipments for Precision Planting and Harvesting)
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