Agriculture

20 pages, 10953 KiB

Open AccessArticle

Design and Performance Test of Variable-Capacity Spoon-Type Oat Precision Hill Seeder

by Wenxue Dong, Anbin Zhang, Fei Liu, Xuan Zhao, Yuxing Ren, Hongbin Bai, Dezheng Xuan, Xiang Kong, Shuhan Yang and Xu Yang

Agriculture 2025, 15(9), 986; https://doi.org/10.3390/agriculture15090986 (registering DOI) - 2 May 2025

Abstract

Conventional oat seeders suffer from poor seeding uniformity, a large coefficient of variation in seed volume, and significant seed wastage. To address these issues, a variable-capacity spoon-type oat precision hill seeder was designed based on the agronomic requirements of oat hole seeding and [...] Read more.

Conventional oat seeders suffer from poor seeding uniformity, a large coefficient of variation in seed volume, and significant seed wastage. To address these issues, a variable-capacity spoon-type oat precision hill seeder was designed based on the agronomic requirements of oat hole seeding and the structural characteristics of hill seeders. Through force analysis and theoretical calculations, the angular velocity range of the variable-capacity spoon-type oat precision hill seeder was determined to be within 0–6.9 rad/s. An experiment was conducted using the angular velocity of the hill seeder, the inclination angle of the seed guide spoon, and the length of the bridging groove as test factors. The ranges of these factors for optimal seed displacement performance were established. Based on the Box–Behnken test principle, a response surface test was designed using Design-Expert software (Design-Expert 13). Experimental results identified the optimal operating parameters as follows: an angular velocity of 4.9 rad/s for the hill seeder, a guide spoon inclination angle of 71.0°, and a bridging groove length of 10.9 mm. Under these conditions, the qualified rate, leakage rate, and multiple rates were 92.2%, 5.3%, and 2.6%, respectively. The results of the field trial showed that the seeding qualified rate was 91.2%, the leakage rate was 4.6%, and the multiple rate was 4.2%. The errors between the field test results and the simulation test results were −1.0%, −0.7%, and 1.6%, respectively, meeting the requirements for oat seeding. Full article

(This article belongs to the Section Agricultural Technology)

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26 pages, 6135 KiB

Open AccessArticle

Phenolic Content and Phenolic Acid Composition of Einkorn and Emmer Ancient Wheat Cultivars—Investigation of the Effects of Various Factors

by Gyöngyi Györéné Kis, Szilvia Bencze, Péter Mikó, Magdaléna Lacko-Bartošová, Nuri Nurlaila Setiawan, Andrea Lugasi and Dóra Drexler

Agriculture 2025, 15(9), 985; https://doi.org/10.3390/agriculture15090985 (registering DOI) - 1 May 2025

Abstract

Interest in ancient wheat species is growing because of their unique agronomic and nutritional qualities, and they could be potential sources of antioxidants. The aim of this research was to determine the total, bound, and free phenolic content (TP, FP, BP), the bound [...] Read more.

Interest in ancient wheat species is growing because of their unique agronomic and nutritional qualities, and they could be potential sources of antioxidants. The aim of this research was to determine the total, bound, and free phenolic content (TP, FP, BP), the bound and free phenolic acid (BPA, FPA) content, and the phenolic acid (PA) composition of einkorn and emmer cultivars sourced from a two-year pesticide-free organic variety trial. TPs, FPs, and BPs were analyzed using spectrophotometry, and PAs were determined using HPLC/MS/MS. The results showed that highest mean TP, FP, and BP contents were found in an emmer cultivar, while generally, einkorn varieties had lower phytonutrient values than emmer and bread wheat control. Emmer had the highest TPA, FPA, and BPA contents, followed by control wheat and einkorn landraces. Our gap-filling research was the analysis of the individual PA values in all free and bound fractions. Ferulic acid was the predominant phenolic acid, followed by p-coumaric acid, syringic acid, sinapic acid, and p-hydroxybenzoic acid, whereas salicylic acid and caffeic acid had the lowest concentrations. In the future, we propose to continue this research to gain deeper insights into the changes in phytonutrient properties related to the growing conditions of these cultivars. Full article

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

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16 pages, 1591 KiB

Open AccessArticle

Cereal and Rapeseed Yield Forecast in Poland at Regional Level Using Machine Learning and Classical Statistical Models

by Edyta Okupska, Dariusz Gozdowski, Rafał Pudełko and Elżbieta Wójcik-Gront

Agriculture 2025, 15(9), 984; https://doi.org/10.3390/agriculture15090984 (registering DOI) - 1 May 2025

Abstract

This study performed in-season yield prediction, about 2–3 months before the harvest, for cereals and rapeseed at the province level in Poland for 2009–2024. Various models were employed, including machine learning algorithms and multiple linear regression. The satellite-derived normalized difference vegetation index (NDVI) [...] Read more.

This study performed in-season yield prediction, about 2–3 months before the harvest, for cereals and rapeseed at the province level in Poland for 2009–2024. Various models were employed, including machine learning algorithms and multiple linear regression. The satellite-derived normalized difference vegetation index (NDVI) and climatic water balance (CWB), calculated using meteorological data, were treated as predictors of crop yield. The accuracy of the models was compared to identify the optimal approach. The strongest correlation coefficients with crop yield were observed for the NDVI at the beginning of March, ranging from 0.454 for rapeseed to 0.503 for rye. Depending on the crop, the highest R² values were observed for different prediction models, ranging from 0.654 for rapeseed based on the random forest model to 0.777 for basic cereals based on linear regression. The random forest model was best for rapeseed yield, while for cereal, the best prediction was observed for multiple linear regression or neural network models. For the studied crops, all models had mean absolute errors and root mean squared errors not exceeding 6 dt/ha, which is relatively small because it is under 20% of the mean yield. For the best models, in most cases, relative errors were not higher than 10% of the mean yield. The results proved that linear regression and machine learning models are characterized by similar predictions, likely due to the relatively small sample size (256 observations). Full article

(This article belongs to the Section Digital Agriculture)

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12 pages, 1884 KiB

Open AccessArticle

The Effect of 3′,4′-Methylenedioxychalcone Derivatives on Mycelial Growth and Conidial Germination of Monilinia fructicola: An In Silico and In Vitro Study

by Catalina Ferreira, Valentina Silva, Evelyn Muñoz, Gissella Valle, Manuel Martínez-Lobos, Francisca Valdés, Katy Díaz, Iván Montenegro, Patricio Godoy, Nelson Caro and Alejandro Madrid

Agriculture 2025, 15(9), 983; https://doi.org/10.3390/agriculture15090983 (registering DOI) - 1 May 2025

Abstract

Monilinia fructicola causes brown rot on a wide variety of stone fruits, causing several losses in the field and during storage of fruits. Due to the diverse biological activity of chalcones and their derivatives, they have emerged as a promising alternative for controlling [...] Read more.

Monilinia fructicola causes brown rot on a wide variety of stone fruits, causing several losses in the field and during storage of fruits. Due to the diverse biological activity of chalcones and their derivatives, they have emerged as a promising alternative for controlling phytopathogenic fungi. The aim of this study was to synthesize 3′,4′-methylenedioxychalcone derivatives and evaluate their in vitro inhibitory effect on mycelial growth and the conidial germination of M. fructicola. Additionally, a molecular docking study and the prediction of lipophilicity were carried out to investigate their chemical behavior. The results showed that compound F exhibited the most potent antifungal activity, with EC₅₀ and MIC values of 20.61 µg/mL and <10 µg/mL for mycelial growth and conidial germination, respectively, presenting an adequate lipophilicity (Log p values = 2.79), which would allow proper diffusion through the fungal cell membrane. The in silico study revealed a great number of interactions between compound F and the different active sites of the succinate dehydrogenase enzyme, suggesting a favorable interaction with a binding energy score value of −6.9 kcal/mol, similar to CBE, the native ligand of this enzyme. These types of compounds could provide preventive protection in various stone and other crops. Full article

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

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17 pages, 1189 KiB

Open AccessArticle

The Effects of Two New Fertilizers on the Growth and Fruit Quality of Actinidia eriantha Benth

by Hui Liu, Lan Li, Dujun Xi, Chen Zhang, Shasha He, Dawei Cheng, Jiabo Pei and Jinyong Chen

Agriculture 2025, 15(9), 982; https://doi.org/10.3390/agriculture15090982 (registering DOI) - 30 Apr 2025

Abstract

This study investigated the physiological responses of Actinidia eriantha Benth. cv. ‘Zaoxu’ to water-soluble fertilizer (OWS) and microbial fertilizer (MF) under field conditions from 2022 to 2023. Utilizing a randomized block design, four sequential applications of OWS (T1, T2, and T3) and MF [...] Read more.

This study investigated the physiological responses of Actinidia eriantha Benth. cv. ‘Zaoxu’ to water-soluble fertilizer (OWS) and microbial fertilizer (MF) under field conditions from 2022 to 2023. Utilizing a randomized block design, four sequential applications of OWS (T1, T2, and T3) and MF (T4 and T5) were applied at distinct dilution ratios during the shoot elongation phase. A multivariate analytical framework was employed to assess treatment effects on growth dynamics and fruit quality. Experimental data revealed that OWS applied at 1000× dilution significantly enhanced the growth of mother-bearing shoots and the bearing branch group. During the fruit development stage, both the longitudinal and transverse diameters exhibited differential expansion patterns, with the maximal dimensional increases observed under the 1000× and 1500× dilution OWS treatments. The 1000× dilution OWS treatment demonstrated a superior single-fruit weight, achieving a mean single-fruit weight of 57.07 g—a 32.23% increase relative to the control. Fruit quality analyses further indicated elevated concentrations of sugar components, ascorbic acid, and total phenols in the 1000× dilution OWS treatment group. Principal component analysis (PCA) generated a composite quality index (Z-value) yielding the following treatment ranking: T2 > T3 > T5 > T1 > T4 > control. These findings collectively indicate that the 1000× dilution OWS application demonstrated superior efficiency in enhancing both plant growth and fruit quality in ‘Zaoxu’, providing empirical support for optimized fertilization protocols in commercial cultivation systems. Full article

(This article belongs to the Section Crop Production)

18 pages, 1439 KiB

Open AccessArticle

Exploring Soil Hydro-Physical Improvements Under No-Tillage: A Sustainable Approach for Soil Health

by Gabriel-Dumitru Mihu, Tudor George Aostăcioaei, Cosmin Ghelbere, Anca-Elena Calistru, Denis Constantin Țopa and Gerard Jităreanu

Agriculture 2025, 15(9), 981; https://doi.org/10.3390/agriculture15090981 (registering DOI) - 30 Apr 2025

Abstract

No-tillage (NT) is a key practice in conservation agriculture that minimizes soil disturbance, thereby enhancing soil structure, porosity, and overall quality. However, its long-term effects on soil pore networks and hydro-physical functions remain underexplored. This study evaluated the impacts of NT and conventional [...] Read more.

No-tillage (NT) is a key practice in conservation agriculture that minimizes soil disturbance, thereby enhancing soil structure, porosity, and overall quality. However, its long-term effects on soil pore networks and hydro-physical functions remain underexplored. This study evaluated the impacts of NT and conventional tillage (CT) on soil hydro-physical properties using undisturbed soil columns, X-ray computed tomography, and standard physical measurements. A field experiment was conducted under an eight-year continuous cropping system, with a four-year rotation [winter wheat (Triticum aestivum L.)—maize (Zea mays L.)—sunflower (Helianthus annuus L.)—peas (Pisum sativum L.)], comparing NT and CT treatments with three replications. Soil parameters including bulk density (BD), moisture content, total porosity (SP), water-stable aggregates (WSA), and saturated hydraulic conductivity (Ksat) were measured. Results showed that NT increased BD (1.45 g/cm³) compared to CT (1.19 g/cm³), likely due to reduced soil disturbance. Moisture content under NT was up to 78% higher than CT. Saturated hydraulic conductivity was also higher in NT, with 17% and 43% increases observed at harvest in 2022 and 2023, respectively, except in the 0–30 cm layer immediately after sowing. Micro-CT analysis revealed a 34–115% increase in macropores (>1025 μm) under NT at 10–40 cm depth. These findings demonstrate that long-term NT improves key soil hydro-physical properties, supporting its integration into sustainable farming systems to balance productivity and environmental stewardship. Full article

(This article belongs to the Special Issue Soil Amendments Addition Affecting Soil Physical and Chemical Properties)

22 pages, 1290 KiB

Open AccessArticle

Impact of High Temperatures on Tourist Flows in Urban and Rural Areas: Climate Adaptation Strategies in China

by Man Wei and Tai Huang

Agriculture 2025, 15(9), 980; https://doi.org/10.3390/agriculture15090980 (registering DOI) - 30 Apr 2025

Abstract

The impact of high temperatures on tourist flows in urban and rural areas is both complex and multi-dimensional, yet research remains limited regarding their spatial and temporal differences. This study aims to analyze the changes in tourist flows between urban and rural areas [...] Read more.

The impact of high temperatures on tourist flows in urban and rural areas is both complex and multi-dimensional, yet research remains limited regarding their spatial and temporal differences. This study aims to analyze the changes in tourist flows between urban and rural areas under high-temperature conditions and to identify the key factors driving these patterns, contributing to climate-resilient tourism planning. Using Shanghai, China, as a case study, we constructed an attraction-based tourist flow model with Baidu migration data, integrating a self-organizing feature map for urban–rural classification and Pearson correlation analysis to examine influencing factors. The results showed that high temperatures significantly reduced tourist flows in both urban and rural areas, with a more pronounced impact observed in rural areas. This reduction altered spatial patterns, shifting from a multicentric distribution to an urban-centered concentration. Furthermore, high temperatures affected the timing of tourist flows differently across regions. In urban areas, tourist flows tended to start earlier, and key driving factors, such as facility services and economic levels, remained stable and continued to exert a dominant influence. In contrast, rural tourist flows were delayed under high-temperature conditions, with tourists showing a preference for cooler attractions further from urban centers. These findings highlight the need for targeted climate adaptation strategies, including improving cooling infrastructure in urban areas and promoting eco-friendly, sustainable tourism initiatives in rural regions. This study offers empirical evidence to support policy efforts aimed at fostering coordinated urban–rural tourism development and advancing sustainable adaptation to climate change. Full article

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

30 pages, 5180 KiB

Open AccessArticle

The Effect of Fertilization with Antibiotic-Contaminated Manure on Microbial Processes in Soil

by Jadwiga Wyszkowska, Dariusz Mikulski, Agata Borowik, Magdalena Zaborowska, Jan Kucharski, Krzysztof Kozłowski, Magdalena Bilecka, Anna Gajda, Konrad Pietruk, Piotr Jedziniak, Katarzyna Ognik and Jan Jankowski

Agriculture 2025, 15(9), 979; https://doi.org/10.3390/agriculture15090979 (registering DOI) - 30 Apr 2025

Abstract

Antibiotics are a great blessing for humanity, and they have saved millions of human lives. Antimicrobials have enabled humans to produce animal-based foods that are free of pathogens. However, antibiotics also have a number of weaknesses. The use of antimicrobials in livestock production [...] Read more.

Antibiotics are a great blessing for humanity, and they have saved millions of human lives. Antimicrobials have enabled humans to produce animal-based foods that are free of pathogens. However, antibiotics also have a number of weaknesses. The use of antimicrobials in livestock production can have adverse consequences for the natural environment. The aim of this study is to evaluate the applicability of manure from turkeys administered monensin (M), enrofloxacin (E), and doxycycline (D) as soil fertilizer and to determine the impact of these antibiotics on the physicochemical, microbiological, and biochemical properties of soil in a pot experiment. The following treatments were established: unfertilized soil (S), soil fertilized with turkey manure free of antibiotics (C), soil fertilized with turkey manure containing only M (M), soil fertilized with turkey manure containing M and E (ME), and soil fertilized with turkey manure containing M, E, and D (MED). The experimental plant was Zea mays. The study demonstrated that the soil application of turkey manure containing all three antibiotics (MED) did not inhibit the growth of Zea mays, did not lead to adverse changes in the physicochemical properties of soil, and did not disrupt the abundance or diversity of culturable microorganisms, despite the fact that these antibiotics were identified in both the soil and Zea mays roots. The application of manure containing M, E, and D in the cultivation of Zea mays contributed to the transfer and presence of E and D in soil and maize roots. Antibiotics were not detected in above-ground plant parts. Monensin was not identified in soil or plant samples. The tested manure induced significant changes in the biochemical index of soil quality and in the microbiome of non-culturable bacteria and fungi at both phylum and genus levels. These results indicate that manure from turkeys administered M, E, and D should be used with caution to avoid permanent changes in the microbiome and biochemical properties of soil. Manure contaminated with antimicrobials can be used in the production of fodder crops that do not accumulate antibiotics in above-ground parts. Full article

(This article belongs to the Section Agricultural Soils)

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24 pages, 4945 KiB

Open AccessArticle

Does Digital Village Construction Affect the Sustainable Intensification of Cultivated Land Use? Evidence from Rural China

by Hui Yang, Jingye Li, Stefan Sieber and Kaisheng Long

Agriculture 2025, 15(9), 978; https://doi.org/10.3390/agriculture15090978 (registering DOI) - 30 Apr 2025

Abstract

Digital village construction (DVC) is a crucial pathway for increasing farmland productivity, reducing agricultural waste, and ultimately achieving sustainable development goals (SDGs). However, its effects on the sustainable intensification of cultivated land use (SICLU) remain unclear. To bridge this gap, this study investigated [...] Read more.

Digital village construction (DVC) is a crucial pathway for increasing farmland productivity, reducing agricultural waste, and ultimately achieving sustainable development goals (SDGs). However, its effects on the sustainable intensification of cultivated land use (SICLU) remain unclear. To bridge this gap, this study investigated the impact effects and mechanisms of DVC on SICLU across 358 counties in China using ordinary least squares and mediating effect models. The results showed the following: (1) DVC and its four sub-indices had significant and positive impacts on SICLU, which were validated through a series of robustness tests. (2) Heterogeneity analysis showed that DVC significantly improved SICLU in the eastern and central regions, as well as in regions with abundant and relatively scarce resource endowments, whereas no such effect was observed in the western region. (3) The relationship between DVC and SICLU was mediated by farmers’ income, technological innovation, and agricultural informatization. These insights highlight the importance of accelerating DVC to enhance SICLU. Full article

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

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18 pages, 1340 KiB

Open AccessArticle

Brewer’s Grains on Growth Performance, Nutrient Digestibility, Blood Metabolites, and Fecal Microbiota in Simmental Crossbred Cattle Finished in Feedlot

by Zitao Fan, Sha He, Qingjie Lin, Shiying Lin, Luwei Zhu, Rui Yang, Bingxia Chen, Dingcheng Ye and Pingting Guo

Agriculture 2025, 15(9), 977; https://doi.org/10.3390/agriculture15090977 (registering DOI) - 30 Apr 2025

Abstract

This study was conducted to evaluate the impacts of brewer’s grains (BG) on growth performance, apparent nutrient digestibility, immunity, antioxidant capacity, and fecal microbiota of Simmental crossbred cattle and the economic benefits. A completely randomized design was adopted in our study. Twenty-four 15-month-old [...] Read more.

This study was conducted to evaluate the impacts of brewer’s grains (BG) on growth performance, apparent nutrient digestibility, immunity, antioxidant capacity, and fecal microbiota of Simmental crossbred cattle and the economic benefits. A completely randomized design was adopted in our study. Twenty-four 15-month-old finishing Simmental crossbred male cattle (body weight, 433.43 ± 32.47 kg) were randomly assigned to three groups: control group (basal diet), 10% BG group (supplemented with 10% BG on a dry matter basis), and 15% BG group (supplemented with 15% BG on a dry matter basis). The trial lasted for 48 days, with serum samples collected on days 24 and 48 and fecal samples collected from days 46 to 48. Diets did not influence the average daily gain, dry matter intake, feed efficiency, and serum antioxidant parameters (p > 0.05). The 15% BG group showed significantly higher acid detergent fiber digestibility (p < 0.01) and elevated serum albumin levels on day 48 (p = 0.047) compared with the control group. As for fecal microbiota, there was a lower Chao index (p = 0.040) and a higher abundance of Romboutsia in the 15% BG group (p = 0.025). Moreover, the feed costs of cattle fell by 9.34% and 14.66% after 10% and 15% BG supplementation, respectively. On the whole, BG supplementation demonstrated no significant effects on growth performance or animal health in finishing cattle. The 15% inclusion level demonstrated the greatest cost reduction potential. We, therefore, recommend adopting 15% BG supplementation as the optimal strategy to enhance economic returns in cattle production systems. Full article

(This article belongs to the Special Issue Effects of New Feeds or Additives on Farm Animal Performance and Carcasses Composition)

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20 pages, 29897 KiB

Open AccessArticle

Accurate Parcel Extraction Combined with Multi-Resolution Remote Sensing Images Based on SAM

by Yong Dong, Hongyan Wang, Yuan Zhang, Xin Du, Qiangzi Li, Yueting Wang, Yunqi Shen, Sichen Zhang, Jing Xiao, Jingyuan Xu, Sifeng Yan, Shuguang Gong and Haoxuan Hu

Agriculture 2025, 15(9), 976; https://doi.org/10.3390/agriculture15090976 (registering DOI) - 30 Apr 2025

Abstract

Accurately extracting parcels from satellite images is crucial in precision agriculture. Traditional edge detection fails in complex scenes with difficult post-processing, and deep learning models are time-consuming in terms of sample preparation and less transferable. Based on this, we designed a method combining [...] Read more.

Accurately extracting parcels from satellite images is crucial in precision agriculture. Traditional edge detection fails in complex scenes with difficult post-processing, and deep learning models are time-consuming in terms of sample preparation and less transferable. Based on this, we designed a method combining multi-resolution remote sensing images based on the Segment Anything Model (SAM). Using cropland masking, overlap prediction and post-processing, we achieved 10 m-resolution parcel extraction with SAM, with performance in plain areas comparable to existing deep learning models (P: 0.89, R: 0.91, F1: 0.91, IoU: 0.87). Notably, in hilly regions with fragmented cultivated land, our approach even outperformed these models (P: 0.88, R: 0.76, F1: 0.81, IoU: 0.69). Subsequently, the 10 m parcels results were utilized to crop the high-resolution image. Based on the histogram features and internal edge features of the parcels, used to determine whether to segment downward or not, and at the same time, by setting the adaptive parameters of SAM, sub-meter parcel extraction was finally realized. Farmland boundaries extracted from high-resolution images can more accurately characterize the actual parcels, which is meaningful for farmland production and management. This study extended the application of deep learning large models in remote sensing, and provided a simple and fast method for accurate extraction of parcels boundaries. Full article

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

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13 pages, 2855 KiB

Open AccessArticle

Research on Video Behavior Detection and Analysis Model for Sow Estrus Cycle Based on Deep Learning

by Kaidong Lei, Bugao Li, Shan Zhong, Hua Yang, Hao Wang, Xiangfang Tang and Benhai Xiong

Agriculture 2025, 15(9), 975; https://doi.org/10.3390/agriculture15090975 (registering DOI) - 30 Apr 2025

Abstract

Against the backdrop of precision livestock farming, sow behavior analysis holds significant theoretical and practical value. Traditional production methods face challenges such as low production efficiency, high labor intensity, and increased disease prevention risks. With the rapid advancement of optoelectronic technology and deep [...] Read more.

Against the backdrop of precision livestock farming, sow behavior analysis holds significant theoretical and practical value. Traditional production methods face challenges such as low production efficiency, high labor intensity, and increased disease prevention risks. With the rapid advancement of optoelectronic technology and deep learning, more technologies are being integrated into smart agriculture. Intelligent large-scale pig farming has become an effective means to improve sow quality and productivity, with behavior recognition technology playing a crucial role in intelligent pig farming. Specifically, monitoring sow behavior enables an effective assessment of health conditions and welfare levels, ensuring efficient and healthy sow production. This study constructs a 3D-CNN model based on video data from the sow estrus cycle, achieving analysis of SOB, SOC, SOS, and SOW behaviors. In typical behavior classification, the model attains accuracy, recall, and F1-score values of (1.00, 0.90, 0.95; 0.96, 0.98, 0.97; 1.00, 0.96, 0.98; 0.86, 1.00, 0.93), respectively. Additionally, under conditions of multi-pig interference and non-specifically labeled data, the accuracy, recall, and F1-scores for the semantic recognition of SOB, SOC, SOS, and SOW behaviors based on the 3D-CNN model are (1.00, 0.90, 0.95; 0.89, 0.89, 0.89; 0.91, 1.00, 0.95; 1.00, 1.00, 1.00), respectively. These findings provide key technical support for establishing the classification and semantic recognition of typical sow behaviors during the estrus cycle, while also offering a practical solution for rapid video-based behavior detection and welfare monitoring in precision livestock farming. Full article

(This article belongs to the Special Issue Computer Vision Analysis Applied to Farm Animals)

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21 pages, 4404 KiB

Open AccessArticle

Potential of Baled Silage to Preserve White Grape Pomace for Ruminant Feeding

by Marina Galvez-Lopez, Alfonso Navarro, Raquel Muelas, Amparo Roca, Cristofol Peris, Gema Romero and José Ramón Díaz

Agriculture 2025, 15(9), 974; https://doi.org/10.3390/agriculture15090974 (registering DOI) - 30 Apr 2025

Abstract

The use of agro-industrial by-products in animal feed represents a useful alternative to enhance the sustainability of the agri-food chain. Grape pomace represents an environmental problem mainly for wine-producing countries. Because of the high water content and the seasonality of this feedstuff, ensiling [...] Read more.

The use of agro-industrial by-products in animal feed represents a useful alternative to enhance the sustainability of the agri-food chain. Grape pomace represents an environmental problem mainly for wine-producing countries. Because of the high water content and the seasonality of this feedstuff, ensiling might be a technology to preserve its nutritional quality for a long time, and this must be considered and studied on a commercial scale. This study aimed to characterise the ensiling process of white grape pomace, evaluate its suitability for inclusion in the ruminant diet and compare its shelf life to untreated storage conditions. White grape pomace silos were made with baled silage (300 kg approx.). Samples were analysed at days 0, 7, 14, 35, 60 and 180 of conservation to determine microbial populations, fermentation metabolites, nutritional components and bioactive properties. The collected data were analysed using a general linear model, considering the effect of the treatment, sampling days and their interaction (Proc. GLM, SAS v9.4). White grape pomace showed good suitability for ensiling, and stabilisation was achieved on day 35. The microbial populations and fermentative components observed in silage treatments adhered to the expected standards for high-quality ensiling processes. There were no significant losses of dry matter, and no significant differences were observed in the nutritional composition for ruminant feeding. A small reduction in antioxidant potential was observed and considered irrelevant in terms of the bioactive properties of the silages. Additionally, the cost analysis demonstrated that white grape pomace silage could serve as a more economical feedstuff compared to conventional forages, considering its nutritional value. So, the ensiling of white grape pomace in baled silage is a suitable and cost-effective technique that allows its preservation over a long period. Full article

(This article belongs to the Section Farm Animal Production)

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12 pages, 1269 KiB

Open AccessArticle

Effects of Phosphorus Addition on Inorganic Phosphorus Fractions and Phosphorus Accumulation in Alfalfa in Alkaline Soils

by Haifeng He and Xing Xu

Agriculture 2025, 15(9), 973; https://doi.org/10.3390/agriculture15090973 (registering DOI) - 29 Apr 2025

Abstract

Distribution and availability of soil inorganic phosphorus fractions significantly influence plant phosphorus uptake and crop yield, particularly in alkaline soils, where phosphorus availability is often constrained by soil chemical properties. This study investigated the contribution of different phosphorus fractions to phosphorus uptake and [...] Read more.

Distribution and availability of soil inorganic phosphorus fractions significantly influence plant phosphorus uptake and crop yield, particularly in alkaline soils, where phosphorus availability is often constrained by soil chemical properties. This study investigated the contribution of different phosphorus fractions to phosphorus uptake and yield of alfalfa by applying four phosphorus addition levels: 0 kg/hm², 50 kg/hm², 100 kg/hm² and 150 kg/hm², designated as P0, P50, P100, and P150, respectively, over two consecutive years. Correlation analysis and multiple linear regression analysis were employed to analyze the data. The results revealed that in alkaline soils, inorganic phosphorus fractions were dominated by aluminum-bound phosphate (Al-Pi) and decacalcium phosphate (Ca₁₀-Pi), with storage contribution rates of 33.92% and 37.11%, respectively. In contrast, the cumulative storage contribution rates of dicalcium phosphate (Ca₂-Pi), octocalcium phosphate (Ca₈-Pi), iron-bound phosphorus (Fe-Pi) and occluded phosphorus (O-P) accounted for 28.97%. Although the storage contribution rate of Ca₁₀-Pi was relatively low, its output contribution rate was high, rendering it easily absorbed and depleted by plants, thereby serving as an important source of soil phosphorus availability. Among these fractions, O-Pi was identified as the primary source of phosphorus for alfalfa, playing a critical role in P nutrition. Furthermore, Ca₈-Pi exhibited a significant positive correlation with phosphorus uptake in alfalfa (R² = 0.98, p < 0.05) and was identified as a key factor influencing alfalfa yield, making it a reliable predictor for yield estimation. Full article

(This article belongs to the Section Agricultural Soils)

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16 pages, 746 KiB

Open AccessArticle

Effects of Seed Fraction on Sowing Quality and Yield of Three-Line Hybrid Maize

by Katarzyna Panasiewicz, Rafał Sobieszczański, Karolina Ratajczak, Agnieszka Faligowska, Grażyna Szymańska, Jan Bocianowski, Anna Kolanoś and Rafał Pretkowski

Agriculture 2025, 15(9), 972; https://doi.org/10.3390/agriculture15090972 (registering DOI) - 29 Apr 2025

Abstract

Maize is one of the most productive cereal crops, and is increasingly being grown over large areas. Using the right cultivar of high-quality selected seeds for sowing can be crucial for its productivity. The aim of this study was to investigate the effect [...] Read more.

Maize is one of the most productive cereal crops, and is increasingly being grown over large areas. Using the right cultivar of high-quality selected seeds for sowing can be crucial for its productivity. The aim of this study was to investigate the effect of kernel fraction on the seed quality, seed vigor, morphological traits, and seed yield of the trilinear hybrid maize cv. ‘Lokata’. The research factor was the kernel fraction, categorized based on the thousand-kernel weight (TKW) into four groups: I—small; II—medium; III–large; and IV–very large. A three-year experiment showed that increases in the TKW resulted in increases in germination and vigor up to fraction III (large seeds) in maize. Sowing maize seeds with a higher TKW resulted in plants with higher fresh and dry weights in the early stages of maize development; however, this response decreased as growth progressed. The seed yield was significantly correlated with plant height and the number of kernels per cob for all fractions sown, but the fraction did not significantly modify the seed yield of ‘Lokata’ maize. Full article

(This article belongs to the Section Seed Science and Technology)

20 pages, 6169 KiB

Open AccessArticle

Developing an Uncrewed Aerial Vehicle (UAV)-Based Prediction Model for the Rice Harvest Index Using Machine Learning

by Zhaoyang Pan, Zhanhua Lu, Liting Zhang, Wei Liu, Xiaofei Wang, Shiguang Wang, Hao Chen, Haoxiang Wu, Weicheng Xu, Youqiang Fu and Xiuying He

Agriculture 2025, 15(9), 971; https://doi.org/10.3390/agriculture15090971 (registering DOI) - 29 Apr 2025

Abstract

(1) Background: The harvest index is important for measuring the correlation between grain yield and aboveground biomass. However, the harvest index can only be measured after a mature harvest. If it can be obtained in advance during the growth period, it will promote [...] Read more.

(1) Background: The harvest index is important for measuring the correlation between grain yield and aboveground biomass. However, the harvest index can only be measured after a mature harvest. If it can be obtained in advance during the growth period, it will promote research on high harvest indices and variety breeding; (2) Methods: In this study, we proposed a method to predict the harvest index during the rice growth period based on uncrewed aerial vehicle (UAV) remote sensing technology. UAV obtained visible light and multispectral images of different varieties, and the data such as digital surface elevation, visible light reflectance, and multispectral reflectance were extracted after processing for correlation analysis. Additionally, characteristic variables significantly correlated with the harvest index were screened out; (3) Results: The results showed that TCARI (correlation coefficient −0.82), GRVI (correlation coefficient −0.74), MTCI (correlation coefficient 0.83), and TO (correlation coefficient −0.72) had a strong correlation with the harvest index. Based on the above characteristics, this study used a variety of machine learning algorithms to construct a harvest index prediction model. The results showed that the Stacking model performed best in predicting the harvest index (R² reached 0.88) and had a high prediction accuracy. (4) Conclusions: Therefore, the harvest index can be accurately predicted during rice growth through UAV remote sensing images and machine learning technology. This study provides a new technical means for screening high harvest index in rice breeding, provides an important reference for crop management and variety improvement in precision agriculture, and has high application potential. Full article

(This article belongs to the Section Digital Agriculture)

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29 pages, 19793 KiB

Open AccessArticle

Design of a Conveyer Trough Bolt Signal Acquisition System and Bayesian Ensemble Identification Method for Working State

by Yi Lian, Bangzhui Wang, Meiyan Sun, Kexin Que, Sijia Xu, Zhong Tang and Zhilong Huang

Agriculture 2025, 15(9), 970; https://doi.org/10.3390/agriculture15090970 (registering DOI) - 29 Apr 2025

Abstract

Rice combine harvester conveyor troughs and their bolted connections are susceptible to vibration-induced failure due to operational and environmental excitations. Addressing the challenge of predicting the state of the combine harvester’s conveyor trough bolted structure prior to vibration-induced failure, this study addresses this [...] Read more.

Rice combine harvester conveyor troughs and their bolted connections are susceptible to vibration-induced failure due to operational and environmental excitations. Addressing the challenge of predicting the state of the combine harvester’s conveyor trough bolted structure prior to vibration-induced failure, this study addresses this by investigating signal analysis, system design, and condition identification for these critical components. Firstly, multi-point vibration signals from the conveyor trough were acquired and analyzed in the time-frequency domain. The analysis pinpointed the X-direction at the trough-frame connection (Point 5) as the most responsive location, with RMS peaking at 6.650 during header start-up (vs. 0.849 idle). Significant responses were also noted at Point 3 (Y-dir, 4.628) and Point 6 (X-dir, 3.896) under certain conditions (where Z-direction responses were minimal), identifying critical points that form the basis for condition assessment. Secondly, a vibration acquisition system was developed using a high-performance AD7606 ADC and A39C wireless technology. It features 16-bit resolution (0.00076 mm/s theoretical sensitivity), 8-channel synchronous sampling up to 200 kSPS, and rapid (0.8 s) wireless data transmission. This system meets the demands for high-frequency, high-precision monitoring of the bolted structure. Finally, after comparing machine learning algorithms, Support Vector Machine was chosen for its superior performance. Using a one-vs.-one strategy and data from critical points, an operational condition identification model was developed. Validation with field data confirmed high accuracy (96.9–99.7%) for principal states and low misclassification rates (<5%). This allows for precise identification of the bolted structure’s working status. The research presented in this study offers effective methodologies and technical underpinning for the condition monitoring of critical structural components in rice combine harvesters. Full article

(This article belongs to the Section Agricultural Technology)

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16 pages, 7273 KiB

Open AccessArticle

An AI-Based Open-Source Software for Varroa Mite Fall Analysis in Honeybee Colonies

by Jesús Yániz, Matías Casalongue, Francisco Javier Martinez-de-Pison, Miguel Angel Silvestre, Beeguards Consortium, Pilar Santolaria and Jose Divasón

Agriculture 2025, 15(9), 969; https://doi.org/10.3390/agriculture15090969 (registering DOI) - 29 Apr 2025

Abstract

Infestation by Varroa destructor is responsible for high mortality rates in Apis mellifera colonies worldwide. This study was designed to develop and test under field conditions a new free software (VarroDetector) based on a deep learning approach for the automated detection and counting [...] Read more.

Infestation by Varroa destructor is responsible for high mortality rates in Apis mellifera colonies worldwide. This study was designed to develop and test under field conditions a new free software (VarroDetector) based on a deep learning approach for the automated detection and counting of Varroa mites using smartphone images of sticky boards collected in honeybee colonies. A total of 204 sheets were collected, divided into four frames using green strings, and photographed under controlled lighting conditions with different smartphone models at a minimum resolution of 48 megapixels. The Varroa detection algorithm comprises two main steps: First, the region of interest where Varroa mites must be counted is established. From there, a one-stage detector is used, namely YOLO v11 Nano. A final verification was conducted counting the number of Varroa mites present on new sticky sheets both manually through visual inspection and using the VarroDetector software and comparing these measurements with the actual number of mites present on the sheet (control). The results obtained with the VarroDetector software were highly correlated with the control (R² = 0.98 to 0.99, depending on the smartphone camera used), even when using a smartphone for which the software was not previously trained. When Varroa mite numbers were higher than 50 per sheet, the results of VarroDetector were more reliable than those obtained with visual inspection performed by trained operators, while the processing time was significantly reduced. It is concluded that the VarroDetector software Version 1.0 (v. 1.0) is a reliable and efficient tool for the automated detection and counting of Varroa mites present on sticky boards collected in honeybee colonies. Full article

(This article belongs to the Special Issue Recent Advances in Bee Rearing and Production)

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27 pages, 1868 KiB

Open AccessArticle

MACA-Net: Mamba-Driven Adaptive Cross-Layer Attention Network for Multi-Behavior Recognition in Group-Housed Pigs

by Zhixiong Zeng, Zaoming Wu, Runtao Xie, Kai Lin, Shenwen Tan, Xinyuan He and Yizhi Luo

Agriculture 2025, 15(9), 968; https://doi.org/10.3390/agriculture15090968 (registering DOI) - 29 Apr 2025

Abstract

The accurate recognition of pig behaviors in intensive farming is crucial for health monitoring and growth assessment. To address multi-scale recognition challenges caused by perspective distortion (non-frontal camera angles), this study proposes MACA-Net, a YOLOv8n-based model capable of detecting four key behaviors: eating, [...] Read more.

The accurate recognition of pig behaviors in intensive farming is crucial for health monitoring and growth assessment. To address multi-scale recognition challenges caused by perspective distortion (non-frontal camera angles), this study proposes MACA-Net, a YOLOv8n-based model capable of detecting four key behaviors: eating, lying on the belly, lying on the side, and standing. The model incorporates a Mamba Global–Local Extractor (MGLE) Module, which leverages Mamba to capture global dependencies while preserving local details through convolutional operations and channel shuffle, overcoming Mamba’s limitation in retaining fine-grained visual information. Additionally, an Adaptive Multi-Path Attention (AMPA) mechanism integrates spatial-channel attention to enhance feature focus, ensuring robust performance in complex environments and low-light conditions. To further improve detection, a Cross-Layer Feature Pyramid Transformer (CFPT) neck employs non-upsampled feature fusion, mitigating semantic gap issues where small target features are overshadowed by large target features during feature transmission. Experimental results demonstrate that MACA-Net achieves a precision of 83.1% and mAP of 85.1%, surpassing YOLOv8n by 8.9% and 4.4%, respectively. Furthermore, MACA-Net significantly reduces parameters by 48.4% and FLOPs by 39.5%. When evaluated in comparison to leading detectors such as RT-DETR, Faster R-CNN, and YOLOv11n, MACA-Net demonstrates a consistent level of both computational efficiency and accuracy. These findings provide a robust validation of the efficacy of MACA-Net for intelligent livestock management and welfare-driven breeding, offering a practical and efficient solution for modern pig farming. Full article

(This article belongs to the Special Issue Modeling of Livestock Breeding Environment and Animal Behavior)

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