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Agriculture
Volume 15
Issue 11
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Agriculture, Volume 15, Issue 11 (June-1 2025) – 120 articles

Cover Story (view full-size image): Cover crops are widely used as a tool with which to improve sustainability due to their benefits for the environment, cropping systems, and soil health. Cover crops are part of rotation strategies, and the primary goal is not to maintain or increase the biomass of the cover crops but to provide biomass for cash crops. However, this can be counterproductive if nutrient fixation or uptake leads to subsequent nutrient stress and yield decline. Variety selection and management practices must be integrated to mitigate these effects. In addition, regional studies are needed to determine the best varieties and management practices for accurately understanding the effects of cover crops. View this paper
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23 pages, 1557 KiB  
Article
The Effect of Agricultural Mechanization Services on the Technical Efficiency of Cotton Production
by Yaxue Zhu, Guangyao Wang, Huijuan Du, Jiajia Liu and Qingshan Yang
Agriculture 2025, 15(11), 1233; https://doi.org/10.3390/agriculture15111233 - 5 Jun 2025
Viewed by 310
Abstract
As the process of agricultural modernization accelerates, exploring the impact of agricultural mechanization services on production technology efficiency has become a key issue for enhancing agricultural productivity and promoting sustainable agricultural development. The study focuses on cotton growers in the Tarim River Basin [...] Read more.
As the process of agricultural modernization accelerates, exploring the impact of agricultural mechanization services on production technology efficiency has become a key issue for enhancing agricultural productivity and promoting sustainable agricultural development. The study focuses on cotton growers in the Tarim River Basin and systematically explores the impact and driving mechanisms of agricultural mechanization services (AMSs) on cotton production’s technical efficiency within the framework of the social–ecological system (SES). By employing a combination of stochastic frontier analysis (SFA) and propensity score matching (PSM), the research indicates that the adoption of AMSs significantly enhances the production technical efficiency of cotton farmers. Among the sample that adopted this service, as much as 53.04% of the farmers have their production efficiency within the range of [0.8, 0.9], demonstrating a high production capability. In contrast, the production efficiency values of the farmers who did not adopt such services are more dispersed, with inefficient samples accounting for 11.48%. Furthermore, while the technical efficiency levels across different regions are similar, there are significant efficiency differences within regions. A further analysis indicates that the age of the household head, their education level, the number of agricultural laborers in the family, the proportion of income from planting, and irrigation convenience have a positive impact on farmers’ adoption of AMSs, while the degree of land fragmentation has a negative impact. Therefore, AMSs are not only a core pathway to enhance cotton production’s technical efficiency but also an important support for promoting agricultural modernization in arid areas and strengthening farmers’ risk-resistance capabilities. Future policies should focus on optimizing service delivery, enhancing technical adaptability, and promoting regional collaboration to drive the high-quality development of the cotton industry and support sustainable rural revitalization. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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25 pages, 5134 KiB  
Article
The Establishment of a Discrete Element Model of Wheat Grains with Different Moisture Contents: A Research Study
by He Li, Guangmeng Guo, Lu Xun, Junhao Lu, Huanhuan Chen and Gongpei Cui
Agriculture 2025, 15(11), 1232; https://doi.org/10.3390/agriculture15111232 - 5 Jun 2025
Viewed by 282
Abstract
The high moisture content of wheat grains in extreme weather, such as continuous rain, can easily cause mildew, and we lack accurate discrete element parameters when conducting a simulation analysis of the rapid dehumidification of high-moisture grains. Based on the material characteristics of [...] Read more.
The high moisture content of wheat grains in extreme weather, such as continuous rain, can easily cause mildew, and we lack accurate discrete element parameters when conducting a simulation analysis of the rapid dehumidification of high-moisture grains. Based on the material characteristics of wheat grains with a moisture content ranging from 10.41% to 32.51%, the key parameters of discrete element simulation were calibrated. Firstly, the stacking angle under different moisture contents was determined by a physical experiment, and the regression equation was established as R2 = 0.9981. Subsequently, three significant parameters, namely, the static friction coefficient between the grains and steel plates, the static friction coefficient between grains, and the rolling friction coefficient, were selected from nine parameters using the Plackett–Burman test and the steepest climbing test. Furthermore, the stacking angle–discrete element parameter model was established using the Box–Behnken test as R2 = 0.98, with a relative error of less than or equal to 3.28%. Finally, the moisture content–discrete element parameter model was derived and constructed based on the moisture content–stacking angle model and the stacking angle–discrete element parameter model, with a relative error of less than or equal to 3.92%. The results indicate that the discrete element simulation parameters of wheat grains can be directly predicted by the moisture content and used for the discrete element simulation testing of high-moisture wheat grains. This universal calibration method not only provides convenient and reliable technical support for optimizing the emergency rapid dehumidification process for high-moisture wheat grains but also provides a reference method for the calibration of other grains. Full article
(This article belongs to the Special Issue Agricultural Products Processing and Quality Detection)
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17 pages, 265 KiB  
Article
Effect of Preceding Crops, Soil Packing and Tillage System on Soil Compaction, Organic Carbon Content and Maize Yield
by Krzysztof Orzech, Maria Wanic and Dariusz Załuski
Agriculture 2025, 15(11), 1231; https://doi.org/10.3390/agriculture15111231 - 5 Jun 2025
Viewed by 137
Abstract
Crop rotation and simplified tillage affect soil properties and consequently crop yields. The use of heavy machinery in the tillage can affect soil degradation and reduce soil productivity. The aim of this study was to investigate the effect of soil packing and different [...] Read more.
Crop rotation and simplified tillage affect soil properties and consequently crop yields. The use of heavy machinery in the tillage can affect soil degradation and reduce soil productivity. The aim of this study was to investigate the effect of soil packing and different soil tillage methods applied before the sowing of maize cultivated after grassland and in monoculture on soil compaction, soil organic carbon content, and maize yield. A strip–split–plot experiment was conducted on-farm in northeastern Poland from 2017 to 2021. The soil compaction was measured in the soil layers: 0–10, 10–20 and 20–30 cm in the leaf development stage (BBCH 19), the flowering stage (BBCH 67) and the maize kernel development stage (BBCH 79). The experimental factors were as follows: 1. preceding crop—grassland, maize; 2. degree of soil packing—without soil packing, soil packing after harvesting the preceding crop; 3. different soil tillage—conventional plough tillage method, reduced tillage method. Maize cultivation following a multi-species grassland resulted in a modest 1.47% increase in soil organic carbon content compared to continuous maize monoculture. In monoculture maize, all investigated reduced tillage methods led to increased soil compaction by 0.61–0.67 MPa. However, this adverse effect was mitigated by prior grassland cultivation. Maize grown after a multi-species grassland exhibited 14% higher silage mass yields. Considering the reduction in soil compaction and the enhanced yield potential, this preceding crop is recommended for maize cultivation. Although soil packing did not significantly impact maize yields, reduced tillage methods, such as subsoiling at 40 cm, medium ploughing at 20 cm, and passive tillage, led to a significant reduction in silage mass compared to other treatments. Full article
(This article belongs to the Section Agricultural Soils)
15 pages, 3275 KiB  
Article
Fermented Mixed Feed Increased Egg Quality and Intestinal Health of Laying Ducks
by Changfeng Xiao, Yunying Xu, Changsuo Yang, Daqian He and Lihui Zhu
Agriculture 2025, 15(11), 1230; https://doi.org/10.3390/agriculture15111230 - 5 Jun 2025
Viewed by 234
Abstract
This study investigated the effects of adding fermented mixed feed (FMF, composed of several unconventional protein feeds, such as brown rice, rice bran, rice bran meal, sunflower meal, cottonseed meal, and corn starch residue) into the diet of Longyan Shan-ma ducks on their [...] Read more.
This study investigated the effects of adding fermented mixed feed (FMF, composed of several unconventional protein feeds, such as brown rice, rice bran, rice bran meal, sunflower meal, cottonseed meal, and corn starch residue) into the diet of Longyan Shan-ma ducks on their egg quality and intestinal health. The ducks were randomly divided into two groups: one group served as the control and received a standard diet, while the other group received a diet in which 4% of the feed was substituted with FMF. Compared to unfermented feed, FMF had elevated lactic acid levels and reduced phytic acid and crude fiber, along with higher amounts of crude protein and a range of amino acids, including serine, histidine, arginine, alanine, valine, methionine, cysteine, isoleucine, and lysine. FMF significantly enhanced egg production and improved the overall egg quality, such as eggshell strength and thickness. It also enhanced total antioxidant capacity and glutathione peroxidase concentrations in serum while reducing serum urea nitrogen and interleukin-1β levels. Histological analysis showed that FMF supplementation improved the ileal villus height-to-crypt depth ratio. Microbiota analysis demonstrated that FMF had a significant impact on β-diversity by increasing Firmicutes, Actinobacteriota, and Desulfobacterota and decreasing Proteobacteria and Myxococcota at the phylum level. The abundance of Corynebacterium, Lactobacillus, and Gallicola was found to be elevated due to FMF at the genus level, whereas Kocuria, Rothia, Helicobacter, and Escherichia-Shigella were decreased. Additionally, diets supplemented with FMF resulted in higher intestinal valeric acid levels among ducks. Our findings indicate that incorporating FMF into laying duck diets can enhance production performance, egg quality, and gut health. Full article
(This article belongs to the Section Farm Animal Production)
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15 pages, 1878 KiB  
Article
The Influence of Weather Conditions and Available Soil Water on Vitis vinifera L. Albillo Mayor in Ribera del Duero DO (Spain) and Potential Changes Under Climate Change: A Preliminary Analysis
by María Concepción Ramos
Agriculture 2025, 15(11), 1229; https://doi.org/10.3390/agriculture15111229 - 4 Jun 2025
Viewed by 269
Abstract
Climate variability and trends are of increasing concern in grape-growing areas, although each cultivar can respond differently. In order to establish appropriate adaptation measures, it is necessary to know the relationship between climate variables and grape composition for each cultivar. This research attempts [...] Read more.
Climate variability and trends are of increasing concern in grape-growing areas, although each cultivar can respond differently. In order to establish appropriate adaptation measures, it is necessary to know the relationship between climate variables and grape composition for each cultivar. This research attempts to provide information in this regard for the Albillo Mayor variety grown in the Ribera del Duero DO (Spain) and its potential changes under the shared socioeconomic pathways (SSPs) that lead to different radiative forcing targets. The response of this variety was evaluated in two plots during five seasons (2020–2024). For each year, the phenological dates and grape composition (berry weight, pH, titratable acidity, malic acid, alcoholic content, and the total polyphenol index) were evaluated and related to climate variables including maximum and minimum temperature and precipitation and the resulting water availability averaged over different periods within the growing season. Maximum and minimum temperatures in the pre-veraison period led to lower titratable acidity and malic acid, which, in addition, were favored by lower water availability in the same period. These conditions, on the contrary, led to an increase in the probable alcoholic degree, which is associated with a decrease in berry size. In addition, more available water during the ripening period increases the berry weight, which was also negatively affected by the difference between the maximum and minimum temperature in the same period. By 2050, with the predicted decrease in precipitation and increase in temperature, Albillo Mayor may undergo a decrease in acidity >14% and an increase in the probable alcoholic degree of about 5% in the SSP2-4.5 scenario (energy-balanced development, leading to a radiative forcing of 4.5 Wm−2), while changes could be up to 1.5 and 1.1 times greater, respectively, in the SSP5-8.5 scenario (heavily reliant in fossil-fueled development, leading to a radiative forcing of 8.5 Wm−2). Full article
(This article belongs to the Special Issue Sustainable Viticulture for Climate Change Adaptation)
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20 pages, 1314 KiB  
Article
Seeds Mineral Profile and Ash Content of Thirteen Different Genotypes of Cultivated and Wild Cardoon over Three Growing Seasons
by Marina Giménez-Berenguer, Salvatore Alfio Salicola, Claudia Formenti, María José Giménez, Giovanni Mauromicale, Pedro Javier Zapata, Sara Lombardo and Gaetano Pandino
Agriculture 2025, 15(11), 1228; https://doi.org/10.3390/agriculture15111228 - 4 Jun 2025
Viewed by 194
Abstract
Cultivated and wild cardoons are versatile plants with significant economic and bioactive potential. They have gained attention in recent years for their nutritional value and potential health benefits due to their high mineral content and unique composition. The aim of this study was [...] Read more.
Cultivated and wild cardoons are versatile plants with significant economic and bioactive potential. They have gained attention in recent years for their nutritional value and potential health benefits due to their high mineral content and unique composition. The aim of this study was to investigate the variations in mineral composition and ash content of thirteen distinct genotypes (four commercial, four wild, and five self-developed by Catania University) of cultivated and wild cardoon seeds over three consecutive growing seasons. The results showed that ash content and macro and micro-elements are significantly influenced by environmental conditions, genetic factors, and the interaction between both. For example, ash content showed notable fluctuations over the three seasons, with the lowest value recorded in season 2, probably linked to the higher rainfall level with respect to seasons 1 and 3. The genotypes self-developed showed the highest mean content of all micro-mineral elements under study, with Zn and Cu peaking in Linea 7. In general, it was reported that cardoon seeds are a valuable source of macro and micro-elements, highlighting, in particular, the potential of the genotypes developed by Catania University. This research provides, for the first time, valuable insights into the long-term consistency and variability of mineral content and ash composition in cardoon seeds, contributing to a more comprehensive understanding of their nutritional value and potential applications. Full article
(This article belongs to the Section Seed Science and Technology)
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18 pages, 2671 KiB  
Article
Evaluation of Temporal Changes in Evapotranspiration and Crop Water Requirements in the Context of Changing Climate: Case Study of the Northern Bucharest–Ilfov Development Region, Romania
by Florentina Iuliana Mincu, Daniel Constantin Diaconu, Dana Maria Oprea Constantin and Daniel Peptenatu
Agriculture 2025, 15(11), 1227; https://doi.org/10.3390/agriculture15111227 - 4 Jun 2025
Viewed by 295
Abstract
Climate change has a complex impact on the agricultural crop system, with knowledge of the processes being necessary to assist decisions that guide the adaptation of society to profound structural changes. This study aims to highlight the main changes generated by the modification [...] Read more.
Climate change has a complex impact on the agricultural crop system, with knowledge of the processes being necessary to assist decisions that guide the adaptation of society to profound structural changes. This study aims to highlight the main changes generated by the modification of climatic parameters (increasing air temperature, humidity and precipitation and decreasing wind speed) on agricultural crops in a region with important changes in its economic profile due to urban extension and land use modification. The analysis methodology is based on the Cropwat software to highlight the temporal variability of crop evapotranspiration, effective rain and water requirements for different crops—strawberry, sunflower and pea—and the possibility of using other types of crops with higher yield and lower water needs. The methodology used highlights this fact, showing that major changes are needed in the choice of crop schemes and future technological processes in the current context of climate change. The current results of the study, conducted over a period of 30 years (1991–2020), showed that the climatic, land use and economic changes in the study area have led to a decrease in evapotranspiration and crop water requirements due to the amounts of precipitation that can provide for the water needs of strawberry, sunflower and pea crops. The irrigation requirements during the analysis period 1991–2020 varied from <10 mm/year to 120 mm/year for strawberry crops, and can exceed 300 mm/year for sunflower and pea crops, having higher values in years with a precipitation deficit (effective rain less than 100 mm). Analyzing the irrigation requirements during the vegetation growing seasons shows that for pea and strawberry the trend is decreasing, but without a significance level. Only for the sunflower crop is an increasing trend recorded in the initial and late stages. The results obtained provide a methodological framework as well as concrete information for decision-makers in the field of agriculture who must build adaptation mechanisms for climate challenges. Full article
(This article belongs to the Special Issue Agricultural Citizen Science and Sustainable Regional Development—Second Edition)
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15 pages, 3045 KiB  
Article
The Peptide-Encoding CLE25 Gene Modulates Drought Response in Cotton
by Dayong Zhang, Qingfeng Zhu, Pu Qin, Lu Yu, Weixi Li and Hao Sun
Agriculture 2025, 15(11), 1226; https://doi.org/10.3390/agriculture15111226 - 4 Jun 2025
Viewed by 211
Abstract
CLAVATA3 (CLV3)/endosperm surrounding region (CLE) peptides have been reportedly involved in plant growth and development, as well as responses to abiotic stresses. However, the stress resilience of most CLE genes in cotton remains largely unknown. Here, induced expression pattern analysis showed that GhCLE25 [...] Read more.
CLAVATA3 (CLV3)/endosperm surrounding region (CLE) peptides have been reportedly involved in plant growth and development, as well as responses to abiotic stresses. However, the stress resilience of most CLE genes in cotton remains largely unknown. Here, induced expression pattern analysis showed that GhCLE25 was obviously responsive to osmotic and salt treatments, indicating that GhCLE25 was involved in abiotic stress tolerance. Furthermore, silencing GhCLE25 or the exogenous application of CLE25p effectively led to reduced and enhanced drought tolerance, respectively, as indicated by the activities of the plants’ POD, SOD, CAT, and MDA contents, as well as their height and fresh weight. We found that the knockdown of GhCLE25 promoted seedling growth and development, with a higher plant height and fresh weight in GhCLE25-silenced plants in comparison to control plants. In addition, a comparative transcriptome analysis of TRV:00 versus TRV:GhCLE25 and Mock versus CLE25p revealed that the CLE25-mediated signaling pathway is mainly involved in defense response and phytohormone signaling. Collectively, these findings indicate diverse roles of CLE25 in regulating plant growth and response to environmental stimuli and highlight the potential utilization of CLE25 to improve drought stress in modern agriculture via CLE25p spraying. Full article
(This article belongs to the Topic Advances in Industrial Crops Physioecology and Sustainable Cultivation)
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20 pages, 875 KiB  
Article
Preparation and Characterization of Liquid Fertilizers Produced by Anaerobic Fermentation
by Juan Francisco López-Rubio, Cristina Cebrián-Tarancón, Gonzalo L. Alonso, Maria Rosario Salinas and Rosario Sánchez-Gómez
Agriculture 2025, 15(11), 1225; https://doi.org/10.3390/agriculture15111225 - 4 Jun 2025
Viewed by 316
Abstract
Biol is a liquid product, obtained by anaerobic fermentation of local inputs, which improves the health of agroecosystems, which is an emerging area in agronomy. The aim of this study consists of the preparation of two biols from inoculums of cow dung (BCD) [...] Read more.
Biol is a liquid product, obtained by anaerobic fermentation of local inputs, which improves the health of agroecosystems, which is an emerging area in agronomy. The aim of this study consists of the preparation of two biols from inoculums of cow dung (BCD) and native forest duff (BNF) by using specific biodigesters and commercial inputs. The biol characterization was made in terms of mineral (ionic and complex forms), amino acids, hormones and volatile compounds, along with Pfeiffer circular chromatography during fermentation monitoring. The results showed a pH acidic in both biols (4.5–5.5), which is higher for BCD. Also, this biol had higher content in several macro- and micronutrients in ionic (nitrates, phosphates, calcium, iron and sodium) and complex forms (calcium, iron and potassium). Both have interesting content in amino acids and hormones. The absence of microorganisms in the final products could be due to the presence of volatile compounds such as pyrazines and sulfoxides. Along with this, other volatile compounds such as esters were identified, which can be responsible for their pleasant odor. The novelty of this work is to provide a protocol for obtaining biols and to demonstrate their potential to be used as biofertilizers. Full article
(This article belongs to the Section Agricultural Technology)
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17 pages, 6187 KiB  
Article
Dynamics of Aromia bungii (Faldermann, 1835) (Coleoptera, Cerambycidae) Distribution in China Amidst Climate Change: Dual Insights from MaxEnt and Meta-Analysis
by Zhipeng He, Xinju Wei, Yaping Li, Xinqi Deng and Zhihang Zhuo
Agriculture 2025, 15(11), 1224; https://doi.org/10.3390/agriculture15111224 - 4 Jun 2025
Viewed by 213
Abstract
Aromia bungii Faldermann (Coleoptera, Cerambycidae) is one of the most serious stem-boring pests that infests Rosaceae fruit trees and ornamental trees. This study, based on occurrence data for this species, employed the MaxEnt model and meta-analysis method to predict the [...] Read more.
Aromia bungii Faldermann (Coleoptera, Cerambycidae) is one of the most serious stem-boring pests that infests Rosaceae fruit trees and ornamental trees. This study, based on occurrence data for this species, employed the MaxEnt model and meta-analysis method to predict the distribution range and centroid movement of A. bungii under the current and future climates in China. The study also analyzed the impact of environmental variables on its distribution. The meta-analysis results revealed that A. bungii has the highest distribution density within the altitude range of 0 to 300 m. The MaxEnt model identified six key environmental variables influencing the distribution of A. bungii, namely the minimum temperature of the coldest month (bio6), mean temperature of the wettest quarter (bio8), precipitation of the wettest month (bio13), precipitation of the driest month (bio14), precipitation seasonality (coefficient of variation) (bio15), and altitude. Under the current climate conditions, the most suitable distribution range of A. bungii is located between 92.6–120.38° E and 16.17–44.46° N, with highly suitable areas predominantly found in the North China Plain, the Shandong Hills, the area around the Bohai Sea, and the middle–lower reaches of the Yangtze River, covering a total area of 41.43 × 104 km2. Scenarios related to the future climate indicate a shift in the suitable habitats of A. bungii towards higher latitudes, with the centroid of the potentially suitable area shifting towards the northeast. This study provides supporting information for the control and management of this pest. Full article
(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)
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25 pages, 1595 KiB  
Review
Research Status and Development Trends of Deep Reinforcement Learning in the Intelligent Transformation of Agricultural Machinery
by Jiamuyang Zhao, Shuxiang Fan, Baohua Zhang, Aichen Wang, Liyuan Zhang and Qingzhen Zhu
Agriculture 2025, 15(11), 1223; https://doi.org/10.3390/agriculture15111223 - 4 Jun 2025
Viewed by 388
Abstract
With the acceleration of agricultural intelligent transformation, deep reinforcement learning (DRL), leveraging its adaptive perception and decision-making capabilities in complex environments, has emerged as a pivotal technology in advancing the intelligent upgrade of agricultural machinery and equipment. For example, in UAV path optimization, [...] Read more.
With the acceleration of agricultural intelligent transformation, deep reinforcement learning (DRL), leveraging its adaptive perception and decision-making capabilities in complex environments, has emerged as a pivotal technology in advancing the intelligent upgrade of agricultural machinery and equipment. For example, in UAV path optimization, DRL can help UAVs plan more efficient flight paths to cover more areas in less time. To enhance the systematicity and credibility of this review, this paper systematically examines the application status, key issues, and development trends of DRL in agricultural scenarios, based on the research literature from mainstream Chinese and English databases spanning from 2018 to 2024. From the perspective of algorithm–hardware synergy, the article provides an in-depth analysis of DRL’s specific applications in agricultural ground platform navigation, path planning for intelligent agricultural end-effectors, and autonomous operations of low-altitude unmanned aerial vehicles. It highlights the technical advantages of DRL by integrating typical experimental outcomes, such as improved path-tracking accuracy and optimized spraying coverage. Meanwhile, this paper identifies three major challenges facing DRL in agricultural contexts: the difficulty of dynamic path planning in unstructured environments, constraints imposed by edge computing resources on algorithmic real-time performance, and risks to policy reliability and safety under human–machine collaboration conditions. Looking forward, the DRL-driven smart transformation of agricultural machinery will focus on three key aspects: (1) The first aspect is developing a hybrid decision-making architecture based on model predictive control (MPC). This aims to enhance the strategic stability and decision-making interpretability of agricultural machinery (like unmanned tractors, harvesters, and drones) in complex and dynamic field environments. This is essential for ensuring the safe and reliable autonomous operation of machinery. (2) The second aspect is designing lightweight models that support edge-cloud collaborative deployment. This can meet the requirements of low-latency responses and low-power operation in edge computing scenarios during field operations, providing computational power for the real-time intelligent decision-making of machinery. (3) The third aspect is integrating meta-learning with self-supervised mechanisms. This helps improve the algorithm’s fast generalization ability across different crop types, climates, and geographical regions, ensuring the smart agricultural machinery system has broad adaptability and robustness and accelerating its application in various agricultural settings. This paper proposes research directions from three key dimensions-“algorithm capability enhancement, deployment architecture optimization, and generalization ability improvement”-offering theoretical references and practical pathways for the continuous evolution of intelligent agricultural equipment. Full article
(This article belongs to the Special Issue How Optical Sensors and Deep Learning Enhance the Production Management in Smart Agriculture)
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10 pages, 659 KiB  
Communication
The Effects of CO2-Enriched Water Irrigation on Agricultural Crop Growth
by Laura Feodorov, Anca Maria Patrascu, Alina-Roxana Banciu, Dragos Radulescu, Catalina Stoica, Indraneel Sen, Yasmina Dimitrova, Matteo Fasano and Mihai Nita-Lazar
Agriculture 2025, 15(11), 1222; https://doi.org/10.3390/agriculture15111222 - 3 Jun 2025
Viewed by 196
Abstract
CO2, a major industrial (waste)water treatment process byproduct, significantly contributes to climate change, desertification and overall water depletion. Therefore, there is a significant interest in decreasing CO2 amounts, generated by various technological processes, through a wide range of methods from [...] Read more.
CO2, a major industrial (waste)water treatment process byproduct, significantly contributes to climate change, desertification and overall water depletion. Therefore, there is a significant interest in decreasing CO2 amounts, generated by various technological processes, through a wide range of methods from geological sequestration to biological sequestration. The CO2 (waste)water treatment byproduct sequestration into agricultural CO2-enhanced irrigation water offers several benefits by enhancing crop yield and repurposing emissions. This sustainable approach supports climate neutrality via biological sequestration, promotes circular economy principles, and strengthens the link between agriculture and climate change. In this study, the effect of CO2-enriched water irrigation was analyzed in a complex network of plants germination, soil bacterial populations’ dynamics and soil composition. Results showed that germination rates of plants irrigated with CO2-enriched water were species specific. Sage plants increased their germination and growth when irrigated with CO2-enriched water compared with plants irrigated with plain water. Moreover, CO2 addition favored the development of soil anaerobic bacteria in detriment of aerobic bacteria and subsequently changing organic and nitrogenous compounds soil composition compared to plain water irrigation. For the first time, the germination process influenced by CO2 was correlated with on overall possible CO2 effects on bacterial population growth dynamics and soil quality metabolites availability. Full article
(This article belongs to the Section Agricultural Water Management)
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21 pages, 3278 KiB  
Article
Enhancing Bee Mite Detection with YOLO: The Role of Data Augmentation and Stratified Sampling
by Hong-Gu Lee, Jeong-Yong Shin, Su-Bae Kim, Min-Jee Kim, Moon S. Kim, Hoyoung Lee and Changyeun Mo
Agriculture 2025, 15(11), 1221; https://doi.org/10.3390/agriculture15111221 - 3 Jun 2025
Viewed by 261
Abstract
Beekeeping is facing a serious crisis due to climate change and diseases such as bee mites (Varroa destructor), which have led to declining populations, collapsing colonies, and reduced beekeeping productivity. Bee mites are small, reddish-brown in color, and difficult to distinguish [...] Read more.
Beekeeping is facing a serious crisis due to climate change and diseases such as bee mites (Varroa destructor), which have led to declining populations, collapsing colonies, and reduced beekeeping productivity. Bee mites are small, reddish-brown in color, and difficult to distinguish from bees. Rapid bee mite detection techniques are essential for overcoming this crisis. This study developed a technology for recognizing bee mites and beekeeping objects in beecombs using the You Only Look Once (YOLO) object detection algorithm. The dataset was constructed by acquiring RGB images of beecombs containing mites. Regions of interest with a size of 640 × 640 pixels centered on the bee mites were extracted and labeled as seven classes: bee mites, bees, mite-infected bees, larvae, abnormal larvae, and cells. Image processing, data augmentation, and stratified data distribution methods were applied to enhance the object recognition performance. Four datasets were constructed using different augmentation and distribution strategies, including random and stratified sampling. The datasets were partitioned into training, testing, and validation sets in a 7:2:1 ratio, respectively. A YOLO-based model for the detection of bee mites and seven beekeeping-related objects was developed for each dataset. The F1 scores for the detection of bee mites and seven beekeeping-related objectives using the YOLO model based on original datasets were 94.1% and 91.9%, respectively. The model applied data augmentation, and stratified sampling achieved the highest performance, with F1 scores of 97.4% and 96.4% for the detection of bee mites and seven beekeeping-related objects, respectively. The results underscore the efficacy of using the YOLO architecture on RGB images of beecombs for simultaneously detecting bee mites and various beekeeping-related objects. This advanced mite detection method is expected to contribute significantly to the early identification of pests and disease outbreaks, offering a valuable tool for enhancing beekeeping practices. Full article
(This article belongs to the Section Digital Agriculture)
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22 pages, 3526 KiB  
Article
Indirect Regulation of SOC by Different Land Uses in Karst Areas Through the Modulation of Soil Microbiomes and Aggregate Stability
by Haiyuan Shu, Xiaoling Liang, Lei Hou, Meiting Li, Long Zhang, Wei Zhang and Yali Song
Agriculture 2025, 15(11), 1220; https://doi.org/10.3390/agriculture15111220 - 3 Jun 2025
Viewed by 182
Abstract
Natural restoration of vegetation and plantation are effective land use measures to promote soil organic carbon (SOC) sequestration. How soil physicochemical properties, microorganisms, Glomalin-related soil proteins (GRSPs), and aggregates interact to regulate SOC accumulation and sequestration remains unclear. This study examined five land [...] Read more.
Natural restoration of vegetation and plantation are effective land use measures to promote soil organic carbon (SOC) sequestration. How soil physicochemical properties, microorganisms, Glomalin-related soil proteins (GRSPs), and aggregates interact to regulate SOC accumulation and sequestration remains unclear. This study examined five land uses in the karst region of Southwest China: corn field (CF), corn intercropped with cabbage fields (CICF), orchard (OR), plantation (PL), and natural restoration of vegetation (NRV). The results revealed that SOC, total nitrogen (TN), total phosphorus (TP), total GRSP (T-GRSP), and easily extractable GRSP (EE-GRSP) contents were significantly higher under NRV and PL than in the CF, CICF, and OR, with increases ranging from 10.69% to 266.72%. Land use significantly influenced bacterial α-diversity, though fungal α-diversity remained unaffected. The stability of soil aggregates among the five land uses followed the order: PL > NRV > CF > OR > CICF. Partial least-squares path modeling (PLS-PM) identified land use as the most critical factor influencing SOC. SOC accumulation and stability were enhanced through improved soil properties, increased microbial diversity, and greater community abundance, promoting GRSP secretion and strengthening soil aggregate stability. In particular, soil microorganisms adhere to the aggregates of soil particles through the entanglement of fine roots and microbial hyphae and their secretions (GRSPs, etc.) to maintain the stability of the aggregates, thus protecting SOC from decomposition. Natural restoration of vegetation and plantation proved more effective for soil carbon sequestration in the karst region of Southwest China compared to sloping cropland and orchards. Full article
(This article belongs to the Section Agricultural Soils)
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17 pages, 2188 KiB  
Article
Employment of Biodegradable, Short-Life Mulching Film on High-Density Cropping Lettuce in a Mediterranean Environment: Potentials and Prospects
by Marco Pittarello, Maria Teresa Rodinò, Rossana Sidari, Maria Rosaria Panuccio, Francesca Cozzi, Valentino Branca, Beatrix Petrovičová and Antonio Gelsomino
Agriculture 2025, 15(11), 1219; https://doi.org/10.3390/agriculture15111219 - 3 Jun 2025
Viewed by 231
Abstract
Biodegradable mulch films were developed over the last decades to replace polyethylene, but their short durability and higher costs still limit their diffusion. This work aimed to test an innovative composite mulching film constituted by a mixture of carboxylmethyl cellulose, chitosan and sodium [...] Read more.
Biodegradable mulch films were developed over the last decades to replace polyethylene, but their short durability and higher costs still limit their diffusion. This work aimed to test an innovative composite mulching film constituted by a mixture of carboxylmethyl cellulose, chitosan and sodium alginate, enriched or not with an inorganic N- and P-source to help the microbial breakdown in soil. The trial was carried out using outdoor mesocosms cultivated with lettuce plants with high-density planting. Commercial Mater-Bi® and a polyethylene film were taken as control treatments. Air temperature and humidity monitored daily during the 51 d cropping cycle remained within the ideal range for lettuce growth with no mildew or fungi infection. Visible mechanical degradation of the experimental biopolymers occurred after 3 weeks; however, Mater-Bi® and polyethylene remained unaltered until harvest. Chemical soil variables (TOC, TN, CEC, EC) remained unchanged in all theses, whereas the pH varied. The yield, pigments, total phenols, flavonoids and ROS scavenging activity of lettuce were similar among treatments. Despite their shorter life service (~3 weeks), polysaccharide-based mulching films showed their potential to protect lettuce plants at an early stage and provide yield and nutraceutical values similar to conventionally mulched plants, while allowing a reduced environmental impact and disposal operations. Full article
(This article belongs to the Section Crop Production)
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23 pages, 6314 KiB  
Article
For Precision Animal Husbandry: Precise Detection of Specific Body Parts of Sika Deer Based on Improved YOLO11
by Jinfan Wei, Haotian Gong, Lan Luo, Lingyun Ni, Zhipeng Li, Juanjuan Fan, Tianli Hu, Ye Mu, Yu Sun and He Gong
Agriculture 2025, 15(11), 1218; https://doi.org/10.3390/agriculture15111218 - 3 Jun 2025
Viewed by 322
Abstract
The breeding of sika deer has significant economic value in China. However, the traditional management methods have problems such as low efficiency, easy triggering of strong stress responses, and damage to animal welfare. Therefore, the development of non-contact, automated, and precise monitoring and [...] Read more.
The breeding of sika deer has significant economic value in China. However, the traditional management methods have problems such as low efficiency, easy triggering of strong stress responses, and damage to animal welfare. Therefore, the development of non-contact, automated, and precise monitoring and management technologies has become an urgent need for the sustainable development of this industry. In response to this demand, this study designed a model MFW-YOLO based on YOLO11, aiming to achieve precise detection of specific body parts of sika deer in a real breeding environment. Improvements include: designing a lightweight and efficient hybrid backbone network, MobileNetV4HybridSmall; The multi-scale fast pyramid pooling module (SPPFMscale) is proposed. The WIoU v3 loss function is used to replace the default loss function. To verify the effectiveness of the method, we constructed a sika deer dataset containing 1025 images, covering five categories. The experimental results show that the improved model performs well. Its mAP50 and MAP50-95 reached 91.9% and 64.5%, respectively. This model also demonstrates outstanding efficiency. The number of parameters is only 62% (5.9 million) of the original model, the computational load is 60% (12.8 GFLOPs) of the original model, and the average inference time is as low as 3.8 ms. This work provides strong algorithmic support for achieving non-contact intelligent monitoring of sika deer, assisting in automated management (deer antler collection and preparation), and improving animal welfare, demonstrating the application potential of deep learning technology in modern precision animal husbandry. Full article
(This article belongs to the Section Digital Agriculture)
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21 pages, 5936 KiB  
Article
Research on Intelligent Control Technology for a Rail-Based High-Throughput Crop Phenotypic Platform Based on Digital Twins
by Haishen Liu, Weiliang Wen, Wenbo Gou, Xianju Lu, Hanyu Ma, Lin Zhu, Minggang Zhang, Sheng Wu and Xinyu Guo
Agriculture 2025, 15(11), 1217; https://doi.org/10.3390/agriculture15111217 - 2 Jun 2025
Viewed by 263
Abstract
Rail-based crop phenotypic platforms operating in open-field environments face challenges such as environmental variability and unstable data quality, highlighting the urgent need for intelligent, online data acquisition strategies. This study proposes a digital twin-based data acquisition strategy tailored to such platforms. A closed-loop [...] Read more.
Rail-based crop phenotypic platforms operating in open-field environments face challenges such as environmental variability and unstable data quality, highlighting the urgent need for intelligent, online data acquisition strategies. This study proposes a digital twin-based data acquisition strategy tailored to such platforms. A closed-loop architecture “comprising connection, computation, prediction, decision-making, and execution“ was developed to build DT-FieldPheno, a digital twin system that enables real-time synchronization between physical equipment and its virtual counterpart, along with dynamic device monitoring. Weather condition standards were defined based on multi-source sensor requirements, and a dual-layer weather risk assessment model was constructed using the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation by integrating weather forecasts and real-time meteorological data to guide adaptive data acquisition scheduling. Field deployment over 27 consecutive days in a maize field demonstrated that DT-FieldPheno reduced the manual inspection workload by 50%. The system successfully identified and canceled two high-risk tasks under wind-speed threshold exceedance and optimized two others affected by gusts and rainfall, thereby avoiding ineffective operations. It also achieved sub-second responses to trajectory deviation and communication anomalies. The synchronized digital twin interface supported remote, real-time visual supervision. DT-FieldPheno provides a technological paradigm for advancing crop phenotypic platforms toward intelligent regulation, remote management, and multi-system integration. Future work will focus on expanding multi-domain sensing capabilities, enhancing model adaptability, and evaluating system energy consumption and computational overhead to support scalable field deployment. Full article
(This article belongs to the Section Digital Agriculture)
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11 pages, 396 KiB  
Article
Estimation of Genetic Parameters and Prediction for Body Weight of Angus Cattle
by Xiaofang Feng, Yu Wang, Jie Zhao, Qiufei Jiang, Yafei Chen, Yaling Gu, Penghui Guo and Juanshan Zheng
Agriculture 2025, 15(11), 1216; https://doi.org/10.3390/agriculture15111216 - 2 Jun 2025
Viewed by 259
Abstract
With the growing global population, the demand for beef is increasing, making the genetic improvement of beef cattle crucial for sustainable production. This study aimed to estimate genetic parameters using different models and predict body weight in Angus cattle to enhance the accuracy [...] Read more.
With the growing global population, the demand for beef is increasing, making the genetic improvement of beef cattle crucial for sustainable production. This study aimed to estimate genetic parameters using different models and predict body weight in Angus cattle to enhance the accuracy of genetic evaluation and support optimal breeding and selection programs. We used the inclusion or exclusion of maternal genetic effects, maternal permanent environmental effects, and the presence or absence of covariance between maternal and direct genetic effects to distinguish between the six animal models. The variance components and genetic parameters of 13,607 weight records from Angus cattle were estimated using the Average Information Restricted Maximum Likelihood (AI-REML) method. The best estimated model was selected based on the Akaike Information Criterion (AIC) and Likelihood Ratio Test (LRT). The results of this study revealed that, in addition to individual genetic effects, maternal genetic effects had a significant impact on unbiased and accurate genetic parameter estimates of body weight in Angus cattle. The total heritability estimated with the best model for body weight at birth (BW0), 3 months (BW3), 6 months (BW6), 12 months (BW12), and 18 months (BW18) was 0.215 ± 0.007, 0.340 ± 0.021, 0.239 ± 0.035, 0.362 ± 0.044, and 0.225 ± 0.048, respectively. The maternal heritability ranges from 0.017~0.438 and significantly affects Angus cattle throughout their growth and development stages, with the effect decreasing with increasing age. Positive correlations were observed between body weights at different months of age, ranging from 0.061 to 0.828. BW6 has a high positive genetic correlation with later age weight, and BW6 is a good predictor of later age weight. Thus, it is possible to optimize breeding programs and accelerate genetic progress by selecting for higher 6-month-old live weights for early Angus selection. In addition, our results emphasize the importance of considering maternal effects in genetic evaluation to improve the efficiency and accuracy of selection programs and thereby contribute to sustainable genetic improvement in beef cattle. Full article
(This article belongs to the Section Farm Animal Production)
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23 pages, 4799 KiB  
Article
Path Tracking Control of Agricultural Automatic Navigation Vehicles Based on an Improved Sparrow Search-Pure Pursuit Algorithm
by Junhao Wen, Liwen Yao, Jiawei Zhou, Zidong Yang, Lijun Xu and Lijian Yao
Agriculture 2025, 15(11), 1215; https://doi.org/10.3390/agriculture15111215 - 1 Jun 2025
Viewed by 322
Abstract
A pure pursuit method based on an improved sparrow search algorithm is proposed to address low path-tracking accuracy of intelligent agricultural machinery in complex farmland environments. Firstly, we construct a function relating speed to look-ahead distance and develop a fitness function based on [...] Read more.
A pure pursuit method based on an improved sparrow search algorithm is proposed to address low path-tracking accuracy of intelligent agricultural machinery in complex farmland environments. Firstly, we construct a function relating speed to look-ahead distance and develop a fitness function based on the prototype’s speed and pose deviation. Subsequently, an improved sparrow search algorithm (ISSA) is employed to adjust the pure pursuit model’s speed and look-ahead distance dynamically. Finally, improvements are made to the initialization of the original algorithm and the position update method between different populations. Simulation results indicate that the improved sparrow search algorithm exhibits faster convergence speed and better capability to escape local extrema. The real vehicle test results show that the proposed algorithm achieves an average lateral deviation of approximately 3 cm, an average heading deviation below 5°, an average stabilization distance under 5 m, and an average navigation time of around 46 s during path tracking. These results represent reductions of 51.25%, 30.62%, 49.41%, and 10.67%, respectively, compared to the traditional pure pursuit model. Compared to the pure pursuit model that only dynamically adjusts the look-ahead distance, the proposed algorithm shows reductions of 34.11%, 24.96%, 32.13%, and 11.23%, respectively. These metrics demonstrate significant improvements in path-tracking accuracy, pose correction speed, and path-tracking efficiency, indicating that the proposed algorithm can serve as a valuable reference for path-tracking research in complex agricultural environments. Full article
(This article belongs to the Section Agricultural Technology)
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16 pages, 3020 KiB  
Article
Critical Flow Velocity Analysis of Multi-Span Viscoelastic Micro-Bending Irrigation Pipelines
by Sihao Wu, Bo Fan, Jianhua Cao, Suwei Xiao and Yuhe Cao
Agriculture 2025, 15(11), 1214; https://doi.org/10.3390/agriculture15111214 - 1 Jun 2025
Viewed by 217
Abstract
Irrigation pipelines are critical agricultural hydraulic facilities that often develop minor bending defects due to ground settlement or improper installation. This study employs Lagrange equations for non-material volumes and the Absolute Nodal Coordinate Formulation (ANCF) to model the multi-span viscoelastic micro-bending irrigation pipelines, [...] Read more.
Irrigation pipelines are critical agricultural hydraulic facilities that often develop minor bending defects due to ground settlement or improper installation. This study employs Lagrange equations for non-material volumes and the Absolute Nodal Coordinate Formulation (ANCF) to model the multi-span viscoelastic micro-bending irrigation pipelines, investigating the influence of micro-bending defects on critical flow velocity. The material parameters of the pipeline wall are determined via uniaxial tensile tests, and the effectiveness of the proposed model is validated through comparison with degraded models and field tests. Further numerical analysis demonstrates that modifying the micro-bend defect of the pipeline from a parabolic to a sinusoidal shape yields a 13.9% enhancement in critical flow velocity. This improvement is particularly significant for irrigation projects with limited pipe material options, tight flow design margins, and low economic returns. Full article
(This article belongs to the Section Agricultural Technology)
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28 pages, 962 KiB  
Review
Precision Weeding in Agriculture: A Comprehensive Review of Intelligent Laser Robots Leveraging Deep Learning Techniques
by Chengming Wang, Caixia Song, Tong Xu and Runze Jiang
Agriculture 2025, 15(11), 1213; https://doi.org/10.3390/agriculture15111213 - 1 Jun 2025
Viewed by 461
Abstract
With the advancement of modern agriculture, intelligent laser robots driven by deep learning have emerged as an effective solution to address the limitations of traditional weeding methods. These robots offer precise and efficient weed control, crucial for boosting agricultural productivity. This paper provides [...] Read more.
With the advancement of modern agriculture, intelligent laser robots driven by deep learning have emerged as an effective solution to address the limitations of traditional weeding methods. These robots offer precise and efficient weed control, crucial for boosting agricultural productivity. This paper provides a comprehensive review of recent research on laser weeding applications using intelligent robots. Firstly, we introduce the content analysis method employed to organize the reviewed literature. Subsequently, we present the workflow of weeding systems, emphasizing key technologies such as the perception, decision-making, and execution layers. A detailed discussion follows on the application of deep learning algorithms, including Convolutional Neural Networks (CNNs), YOLO, and Faster R-CNN, in weed control. Here, we show that these algorithms can achieve high accuracy in weed detection, with YOLO demonstrating particularly fast and accurate performance. Furthermore, we analyze the challenges and open problems associated with deep learning detection systems and explore future trends in this research field. By summarizing the role of intelligent laser robots powered by deep learning, we aim to provide insights for researchers and practitioners in agriculture, fostering further innovation and development in this promising area. Full article
(This article belongs to the Section Digital Agriculture)
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15 pages, 5392 KiB  
Article
Validating Data Interpolation Empirical Orthogonal Functions Interpolated Soil Moisture Data in the Contiguous United States
by Haipeng Zhao, Haoteng Zhao and Chen Zhang
Agriculture 2025, 15(11), 1212; https://doi.org/10.3390/agriculture15111212 - 1 Jun 2025
Viewed by 269
Abstract
Accurate and spatially detailed soil moisture (SM) data are essential for hydrological research, precision agriculture, and ecosystem monitoring. The NASA’s Soil Moisture Active Passive (SMAP) product offers unprecedented information on global soil moisture. To provide more detailed information about the cropland SM data [...] Read more.
Accurate and spatially detailed soil moisture (SM) data are essential for hydrological research, precision agriculture, and ecosystem monitoring. The NASA’s Soil Moisture Active Passive (SMAP) product offers unprecedented information on global soil moisture. To provide more detailed information about the cropland SM data for the Contiguous United States (CONUS), a 1-km SMAP product has been produced using the THySM model in support of USDA NASS operations. However, the current 1-km product contains substantial data gaps, which poses challenges for applications that require continuous daily data. Data Interpolation Empirical Orthogonal Functions (DINEOF+) is an interpolation technique that uses singular value decomposition (SVD) to address missing data problems. Previous studies have applied DINEOF+ to reconstruct the 1-km daily SM dataset but without further analysis of the reconstruction errors. In this study, we perform a comprehensive validation of DINEOF+ reconstructed SM by using both the original SMAP data and in situ measurements across the CONUS. Our results show that the reconstructed SM closely aligns with the original SM with R2 > 0.65 and bias ranging from 0.01 to 0.02 m3/m3. When compared to in situ SM, the mean absolute error (MAE) ranges between 0.01 and 0.04 m3/m3 and the time series correlation coefficient ranges from 0.6 to 0.8. Our findings suggest that DINEOF+ effectively recovers missing data and improves the temporal resolution of SM time series. However, we also note that the accuracy of the reconstructed SM is dependent on the quality of the original SMAP data, emphasizing the need for continued improvements in SM retrievals by satellite. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Agricultural Soil and Crop Mapping)
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15 pages, 1014 KiB  
Article
Response of Four Shrubs to Drought Stress and Comprehensive Evaluation of Their Drought Resistance
by Bing Ma, Haibo Hu, Xingyu Liu, Qi Wang, Hongwei Zhou, Sheng Chen, Jiacai Liu and Yuyan Li
Agriculture 2025, 15(11), 1211; https://doi.org/10.3390/agriculture15111211 - 1 Jun 2025
Viewed by 240
Abstract
Drought stress is a crucial factor limiting plant survival and growth, especially during the seedling establishment stage. A deep understanding of different plants’ responses to drought stress and their drought resistance is of great significance for vegetation restoration under drought conditions. This study [...] Read more.
Drought stress is a crucial factor limiting plant survival and growth, especially during the seedling establishment stage. A deep understanding of different plants’ responses to drought stress and their drought resistance is of great significance for vegetation restoration under drought conditions. This study selected one-year-old seedlings of Winter Jasmine (Jasminum nudiflorum), Oleander (Nerium oleander), Privet (Ligustrum lucidum), and Redleaf Photinia (Photinia × fraseri) as research objects. Through pot experiments, we investigated the physiological and biochemical responses of these shrubs under different levels of drought stress (control, mild, moderate, and severe drought stress, corresponding to 75%, 60%, 45%, and 30% of field maximum water holding capacity) to comprehensively assess their drought resistance capabilities. The research results indicated that as the level of drought stress increased, significant changes (p < 0.05) occurred in the physiological and biochemical indicators of all four plant species. The chlorophyll content (Chla+b) of Winter Jasmine and Redleaf Photinia gradually decreased with the intensification of stress, while the Chla+b of Oleander showed the most significant decline under moderate stress and Privet was most affected under mild stress. The proline (Pro) and soluble sugar (SS) contents of all four plants exhibited an upward trend, suggesting that the plants coped with drought stress by accumulating these osmoregulatory substances. Drought stress led to damage to plant cell membranes, manifested by an increase in malondialdehyde content (MDA), with Winter Jasmine showing the most pronounced increase. The activities of peroxidase (POD) and superoxide dismutase (SOD) in the four plant species responded differently to drought stress: the POD activity of Oleander and Redleaf Photinia increased with the deepening of stress, while that of Winter Jasmine and Privet decreased. A comprehensive evaluation of the drought tolerance of the four plant species was performed using principal component analysis and affiliation function value methods. The drought tolerance of the four shrubs, from strongest to weakest, was as follows: Redleaf Photinia > Oleander > Privet > Winter Jasmine. This finding provides valuable insights for plant selection in ecological slope protection projects, and Redleaf Photinia and Oleander can be promoted for use in vegetation restoration work under drought conditions. Full article
(This article belongs to the Special Issue Advancements in Best Management Practices for Enhancing Soil Health and Water Quality)
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19 pages, 3604 KiB  
Article
An AI-Enabled Framework for Cacopsylla chinensis Monitoring and Population Dynamics Prediction
by Ruijun Jing, Deyan Peng, Jingtong Xu, Zhengjie Zhao, Xinyi Yang, Yihai Yu, Liu Yang, Ruiyan Ma and Zhiguo Zhao
Agriculture 2025, 15(11), 1210; https://doi.org/10.3390/agriculture15111210 - 1 Jun 2025
Viewed by 223
Abstract
The issue of pesticide and chemical residue in food has drawn increasing public attention, making effective control of plant pests and diseases a critical research focus in agriculture. Monitoring of pest populations is a key factor constraining the precision of pest management strategies. [...] Read more.
The issue of pesticide and chemical residue in food has drawn increasing public attention, making effective control of plant pests and diseases a critical research focus in agriculture. Monitoring of pest populations is a key factor constraining the precision of pest management strategies. Low-cost and high-efficiency monitoring devices are highly desirable. To address these challenges, we focus on Cacopsylla chinensis and design a portable, AI-based detection device, along with an integrated online monitoring and forecasting system. First, to enhance the model’s capability for detecting small targets, we developed a backbone network based on the RepVit block and its variants. Additionally, we introduced a Dynamic Position Encoder module to improve feature position encoding. To further enhance detection performance, we adopt a Context Guide Fusion Module, which enables context-driven information guidance and adaptive feature adjustment. Second, a framework facilitates the development of an online monitoring system centered on Cacopsylla chinensis detection. The system incorporates a hybrid neural network model to establish the relationship between multiple environmental parameters and the Cacopsylla chinensis population, enabling trend prediction. We conduct feasibility validation experiments by comparing detection results with a manual survey. The experimental results show that the detection model achieves an accuracy of 87.4% for both test samples and edge devices. Furthermore, the population dynamics model yields a mean absolute error of 1.94% for the test dataset. These performance indicators fully meet the requirements of practical agricultural applications. Full article
(This article belongs to the Section Digital Agriculture)
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19 pages, 1551 KiB  
Article
Implementation of XGBoost Models for Predicting CO2 Emission and Specific Tractor Fuel Consumption
by Nebojša Balać, Zoran Mileusnić, Aleksandra Dragičević, Mihailo Milanović, Andrija Rajković, Rajko Miodragović and Olivera Ećim-Đurić
Agriculture 2025, 15(11), 1209; https://doi.org/10.3390/agriculture15111209 - 31 May 2025
Viewed by 249
Abstract
Tillage is one of the most energy-intensive operations in crop production, leading to high fuel consumption and the emission of harmful gases such as CO2 and NOx. This study was conducted under real field conditions to explore how soil parameters [...] Read more.
Tillage is one of the most energy-intensive operations in crop production, leading to high fuel consumption and the emission of harmful gases such as CO2 and NOx. This study was conducted under real field conditions to explore how soil parameters influence variations in fuel use and exhaust emissions. A machine learning approach based on the XGBoost algorithm was applied to develop predictive models for CO2 concentrations in exhaust gases and specific fuel consumption. The CO2 prediction model achieved an accuracy exceeding 80%, while the model for fuel consumption reached over 65%. Although not optimized for high precision, these models offer a valuable basis for preliminary assessments and highlight the potential of data-driven approaches for improving energy efficiency and environmental sustainability in agricultural mechanization. Full article
(This article belongs to the Section Agricultural Technology)
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33 pages, 12338 KiB  
Article
Surface Reconstruction and Volume Calculation of Grain Pile Based on Point Cloud Information from Multiple Viewpoints
by Lingmin Yang, Cheng Ran, Ziqing Yu, Feng Han and Wenfu Wu
Agriculture 2025, 15(11), 1208; https://doi.org/10.3390/agriculture15111208 - 31 May 2025
Viewed by 207
Abstract
Accurate estimation of grain volume in storage silos is critical for intelligent monitoring and management. However, traditional image-based methods often struggle under complex lighting conditions, resulting in incomplete surface reconstruction and reduced measurement accuracy. To address these limitations, we propose a B-spline Interpolation [...] Read more.
Accurate estimation of grain volume in storage silos is critical for intelligent monitoring and management. However, traditional image-based methods often struggle under complex lighting conditions, resulting in incomplete surface reconstruction and reduced measurement accuracy. To address these limitations, we propose a B-spline Interpolation and Clustered Means (BICM) method, which fuses multi-view point cloud data captured by RGB-D cameras to enable robust 3D surface reconstruction and precise volume estimation. By incorporating point cloud splicing, down-sampling, clustering, and 3D B-spline interpolation, the proposed method effectively mitigates issues such as surface notches and misalignment, significantly enhancing the accuracy of grain pile volume calculations across different viewpoints and sampling resolutions. The results of this study show that a volumetric measurement error of less than 5% can be achieved using an RGB-D camera located at two orthogonal viewpoints in combination with the BICM method, and the error can be further reduced to 1.25% when using four viewpoints. In addition to providing rapid inventory assessment of grain stocks, this approach also generates accurate local maps for the autonomous navigation of grain silo robots, thereby advancing the level of intelligent management within grain storage facilities. Full article
(This article belongs to the Section Digital Agriculture)
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20 pages, 7197 KiB  
Article
Soil Phosphorus Content, Organic Matter, and Elevation Are Key Determinants of Maize Harvest Index in Arid Regions
by Zhen Huo, Hengbati Wutanbieke, Jian Chen, Dongdong Zhong, Yongyu Chen, Zhanli Song, Xinhua Lv and Hegan Dong
Agriculture 2025, 15(11), 1207; https://doi.org/10.3390/agriculture15111207 - 31 May 2025
Viewed by 206
Abstract
This study systematically investigates the mechanistic effects of multifactor interactions (including soil properties, climatic conditions, and cultivation practices) on the productivity parameters (grain yield, stover yield, dry biomass, harvest index) of maize cultivars of different maturity groups in the arid region of Xinjiang, [...] Read more.
This study systematically investigates the mechanistic effects of multifactor interactions (including soil properties, climatic conditions, and cultivation practices) on the productivity parameters (grain yield, stover yield, dry biomass, harvest index) of maize cultivars of different maturity groups in the arid region of Xinjiang, China. Twelve representative maize-growing counties were selected as study sites, where we collected maize samples to measure HI, grain yield, stover yield, and soil physicochemical properties (e.g., organic matter content, total nitrogen, and available phosphorus). Additionally, climate data (effective accumulated temperature) and agronomic parameters (planting density) were integrated to comprehensively analyze the interactive effects of multiple environmental factors on HI using structural equation modeling (SEM). The results demonstrated significant varietal differences in HI across maturity periods. Specifically, early-maturing cultivars showed the highest average HI (0.58), significantly exceeding those of medium-maturing (0.55) and late-maturing varieties (0.54). Environmental analysis further revealed that soil phosphorus content (both available and total phosphorus), elevation, and organic matter content significantly positively affected HI, whereas soil bulk density and electrical conductivity exhibited negative impacts. Notably, HI exhibited a strong negative correlation with stover yield (R2 = 0.49), but remained relatively stable across different dry matter (DM) and grain yield levels. Despite the strong positive correlation between DM and grain yield (R2 = 0.81), the relative stability of HI suggests that yield improvement requires balanced optimization of both DM and partitioning efficiency. This study provides crucial theoretical foundations for optimizing high-yield maize cultivation systems, regulating fertilizer application rates and their ratios, and improving the configuration of planting density in arid regions. These findings offer practical guidance for sustainable agricultural development in similar environments. Full article
(This article belongs to the Section Agricultural Soils)
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18 pages, 6849 KiB  
Article
Study on the Interactions Process of Coupled Model of Furrow Opener–Soil–Pot Seedling Based on Discrete Approach
by Bin Jiang, Jinping Cai, Xiongfei Chen, Junan Liu, Liping Xiao, Jinlong Lin and Yuqiang Chen
Agriculture 2025, 15(11), 1206; https://doi.org/10.3390/agriculture15111206 - 31 May 2025
Viewed by 237
Abstract
The upright state of pot seedlings in the process of rice mechanized throwing operations has an important influence on the growth rate and yield of rice, and pot seedling uprightness is affected by the influence of soil backfilling during trenching. Due to the [...] Read more.
The upright state of pot seedlings in the process of rice mechanized throwing operations has an important influence on the growth rate and yield of rice, and pot seedling uprightness is affected by the influence of soil backfilling during trenching. Due to the complexity of the furrow opener–soil–pot seedling interaction mechanism in the rice pot seedling planting process, the soil backfilling process is difficult to observe. In order to improve the uprightness of pot seedling planting, this paper constructs a soil model and a soil–pot seedling model step by step, based on the discrete element method (DEM), as well as a coupled model of the pot seedling planting system to study the process of furrow opener–soil–pot seedling planting, the reliability of which is then verified. The results showed that the simulation results of the constructed soil model and soil–pot seedling model deviated from the actual calibration results by <6%, and the model could accurately simulate the pot seedling throwing process. The simulation analysis of the trenching process revealed that the soil backfilling process during trenching showed a three-stage evolution pattern of “backfilling-covering-stabilizing”; in addition, the forward speed of the machine was 0.8 m/s, and the falling speed of the seedling discharge cylinder was 3.5 m/s, which made it possible for the model to simulate the pot seedling throwing process accurately. In addition, when the pot seedling with a forward speed of 0.8 m/s and a drop speed of 3.5 m/s fell into the trench after 0.15 s of trenching, its lateral and longitudinal uprightness were 67.0 ± 1.2° and 65.2 ± 1.5°, respectively. After optimization of the structure of the trenchers, the width, depth, and length of the main body were 40 mm, 37.87 mm, and 32.32 mm, respectively, and the lateral and longitudinal uprightness of the pot seedlings increased to 70.0 ± 1.0° and 69.4 ± 0.8, respectively. The coupled model bench validation test showed that its reliability error was <5%. The coupled model provides technical support for the design and parameter optimization of rice planting equipment. Full article
(This article belongs to the Section Agricultural Technology)
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20 pages, 588 KiB  
Article
Milk Lactose and Inflammatory Marker Changes: Early Indicators of Metabolic and Inflammatory Stress in Early Lactation Dairy Cattle
by Karina Džermeikaitė, Justina Krištolaitytė, Lina Anskienė, Akvilė Girdauskaitė, Samanta Arlauskaitė, Greta Šertvytytė, Gabija Lembovičiūtė, Walter Baumgartner and Ramūnas Antanaitis
Agriculture 2025, 15(11), 1205; https://doi.org/10.3390/agriculture15111205 - 31 May 2025
Viewed by 228
Abstract
Metabolic and inflammatory stress during early lactation poses significant risks to dairy cow health and productivity. This study aimed to assess the physiological, metabolic, and inflammatory differences between dairy cows producing low (LL; <4.5%) and high (HL; ≥4.5%) milk lactose, focusing on C-reactive [...] Read more.
Metabolic and inflammatory stress during early lactation poses significant risks to dairy cow health and productivity. This study aimed to assess the physiological, metabolic, and inflammatory differences between dairy cows producing low (LL; <4.5%) and high (HL; ≥4.5%) milk lactose, focusing on C-reactive protein (CRP), liver function markers, iron metabolism, and reticulorumen health. A total of 71 clinically healthy lactating multiparous cows (20–30 days postpartum) were monitored using real-time physiological sensors, milk composition analysis, blood biomarkers and continuous reticulorumen pH measurement (every 10 min). Cows in the LL group showed significantly higher aspartate transaminase (AST) activity (p = 0.042), lower serum iron (Fe) concentration (p = 0.013), and reduced reticulorumen pH (p = 0.03). Although CRP concentrations did not differ significantly between groups, correlation analysis revealed positive associations with non-esterified fatty acids (NEFA) (r = 0.335, p = 0.043), reticulorumen pH (r = 0.498, p = 0.002), and body temperature (r = 0.372, p = 0.023). Receiver operating characteristic (ROC) analysis identified gamma-glutamyl transferase (GGT) (AUC = 0.66), AST (AUC = 0.63), and NEFA (AUC = 0.58) as moderate predictors of low milk lactose levels. Conversely, Fe (AUC = 0.66) and reticulorumen pH (AUC = 0.64) showed moderate ability to predict higher lactose content. These results support the integration of milk lactose, liver enzymes, and inflammatory biomarkers into precision health monitoring protocols. The combined use of CRP and milk lactose as complementary biomarkers may enhance the early identification of metabolic stress and support more targeted dairy herd health management. Full article
(This article belongs to the Special Issue Innovations in Dairy Cows' Stress, Health, and Nutrition)
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Review
Agricultural Benefits of Shelterbelts and Windbreaks: A Bibliometric Analysis
by Cristian Mihai Enescu, Mircea Mihalache, Leonard Ilie, Lucian Dinca, Cristinel Constandache and Gabriel Murariu
Agriculture 2025, 15(11), 1204; https://doi.org/10.3390/agriculture15111204 - 31 May 2025
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Abstract
Forest shelterbelts and windbreaks play a vital role in protecting ecosystems, mitigating climate change effects, and enhancing agricultural productivity. These vegetative barriers serve as effective tools for soil conservation, reducing wind and water erosion while improving soil fertility. Additionally, they contribute to biodiversity [...] Read more.
Forest shelterbelts and windbreaks play a vital role in protecting ecosystems, mitigating climate change effects, and enhancing agricultural productivity. These vegetative barriers serve as effective tools for soil conservation, reducing wind and water erosion while improving soil fertility. Additionally, they contribute to biodiversity preservation by providing habitat corridors for various plant and animal species. Their role in microclimate regulation, such as temperature moderation and increased humidity retention, further enhances agricultural yields and ecosystem stability. This study examines the historical evolution, design principles, and contemporary applications of forest shelterbelts and windbreaks, drawing insights from scientific research and case studies worldwide. It highlights the economic and environmental benefits, including improved air quality, carbon sequestration, and water management, making them crucial components of sustainable land use strategies. However, challenges such as land use competition, maintenance costs, and policy constraints are also analyzed, underscoring the need for integrated approaches to their management. Through a comprehensive bibliometric analysis of the existing literature and field studies, this paper emphasizes the necessity of strategic planning, community involvement, and adaptive policies to ensure the long-term sustainability of forest shelterbelts and windbreaks. The findings contribute to a broader understanding of their role in combating environmental degradation and promoting ecological resilience in the face of ongoing climate challenges. Full article
(This article belongs to the Special Issue Strategies for Resilient and Sustainable Agri-Food Systems)
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