Agriculture

25 pages, 1543 KB

Open AccessArticle

Precision Feeding on Pig Fattening Farms: Can Simplified Implementation Enhance Productivity and Reduce Pollutant Emissions?

by Gema Montalvo, María Rodríguez, Carlos Piñeiro, Salvador Calvet, María J. Sanz and Paloma Garcia-Rebollar

Agriculture 2025, 15(18), 1935; https://doi.org/10.3390/agriculture15181935 (registering DOI) - 12 Sep 2025

Abstract

This study evaluated a simplified precision feeding (PF) strategy on pig fattening farms to assess its effects on economic performance and pollutant emissions. PF in pig production can reduce nitrogen (N) intake, excretion, and slurry-related environmental impacts, yet its implementation is difficult due [...] Read more.

This study evaluated a simplified precision feeding (PF) strategy on pig fattening farms to assess its effects on economic performance and pollutant emissions. PF in pig production can reduce nitrogen (N) intake, excretion, and slurry-related environmental impacts, yet its implementation is difficult due to the need for daily diet adjustments to match pigs’ changing requirements. This work tested a simplified PF approach: two commercial feeds, a nutrient-rich pre-grower and a nutrient-poor finisher, were blended weekly based on the lysine needs of two groups of pigs, defined by initial body weight. During the fattening period, blend feeding (BF) sustained growth and feed intake at levels comparable to those with conventional three-phase feeding, but heavy pigs under BF showed reduced feed efficiency. Nitrogen excretion and slurry ammonia (NH₃) emissions did not differ significantly, but BF increased methane and carbon dioxide emissions in the slurry from heavy pigs. The results show that simplified PF can provide economic benefits without compromising performance, but BF formulation should also address potential NH₃ and greenhouse gas emissions during slurry storage. The integration of artificial intelligence-driven tools for real-time diet adjustments at the farm level would be of great interest to enhance sustainability and efficiency, because the economic benefits of PF application were evident. Full article

(This article belongs to the Section Farm Animal Production)

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27 pages, 11645 KB

Open AccessArticle

Structural Design and Parameter Optimization of In-Row Deep Fertilizer Application Device for Maize

by Shengxian Wu, Zihao Dou, Shulong Fei, Feng Shi, Xinbo Zhang, Ze Liu and Dongyan Huang

Agriculture 2025, 15(18), 1934; https://doi.org/10.3390/agriculture15181934 - 12 Sep 2025

Abstract

To enhance the stability and consistency of topdressing depth during maize fertilization, an inter-row deep fertilizer application unit was designed. Through analysis of the coherence between subsurface pressure and topdressing depth stability obtained from stability performance tests, structural optimizations were implemented on the [...] Read more.

To enhance the stability and consistency of topdressing depth during maize fertilization, an inter-row deep fertilizer application unit was designed. Through analysis of the coherence between subsurface pressure and topdressing depth stability obtained from stability performance tests, structural optimizations were implemented on the deep application unit. This resulted in an integrated vibration damping device incorporating a magnetorheological damper (MR damper fertilizer application unit). The MR damper fertilizer application unit was validated through simulation testing. Using an orthogonal experimental design approach, soil bin tests were conducted to identify the preferred parameter ensemble for this unit. Subsequent field trials under these optimized parameters enabled comparative performance evaluation of both fertilizer application units under actual operating conditions. The simulation results indicated that the MR damper fertilizer application unit achieved reductions in the standard deviation of the gauge wheel’s force on the ground by 39.6%, 41.0%, and 44.6% at three distinct operational speeds, respectively. The soil bin tests identified the optimal operational parameters as follows: MR damper current of 0.6 A, vibration damping system spring stiffness of 8 N/mm, and a working speed of 7.2 km/h. Field testing results indicated that, when utilizing the optimal parameters, the MR damper fertilizer application unit achieved a 6.9% improvement in the rate of qualified topdressing depth and a 3.8% reduction in the depth variation coefficient compared to the conventional deep fertilizer application unit. Compared to traditional fertilizer applicators, this study effectively addresses issues of poor fertilization depth uniformity and low qualification rates caused by severe gauge wheel bouncing due to uneven terrain during field operations. Full article

(This article belongs to the Section Agricultural Technology)

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19 pages, 2140 KB

Open AccessArticle

Socio-Economic Assessment of the Agriculture Sector and the Bioeconomy in East Africa—A Gender-Focused Approach

by Rocio Diaz-Chavez

Agriculture 2025, 15(18), 1933; https://doi.org/10.3390/agriculture15181933 - 12 Sep 2025

Abstract

Data on social dimensions of the bioeconomy, particularly gender equity and poverty, two core Sustainable Development Goals, remains limited and difficult to operationalise. This paper presents a desk-based assessment of social risks related to gender inequality and working conditions in agriculture, using the [...] Read more.

Data on social dimensions of the bioeconomy, particularly gender equity and poverty, two core Sustainable Development Goals, remains limited and difficult to operationalise. This paper presents a desk-based assessment of social risks related to gender inequality and working conditions in agriculture, using the Social Hotspot Database (2021) alongside sectoral data on cereals, wheat, and paddy rice. Agriculture was examined as a key component of the bioeconomy in five East African countries: Burundi, Kenya, Rwanda, Tanzania, and Uganda, all signatories of the East Africa Regional Bioeconomy Strategy. Additional data from FAO and ILO were incorporated to strengthen the analysis. Results indicate persistent gender inequality, with women continuing to face systemic disadvantages compared to men in agricultural production, potentially constraining the development of a sustainable bioeconomy in the region. Regarding working conditions, all countries demonstrated high risks of failing to achieve living wages, leaving many workers in poverty. However, a significant limitation lies in the absence of sex disaggregated data and datasets explicitly addressing bioenergy or the bioeconomy. Evidence from international organisations suggests that restricted access to education, limited financial resources, and enduring cultural norms exacerbate the gender gap in agriculture. This paper concludes that advancing education, expanding access to finance, and strengthening gender parity are critical pathways to mitigate social risks and to support inclusive bioeconomy development in East Africa. Full article

(This article belongs to the Special Issue Current Prospects of Social-Ecologically More Sustainable Agriculture and Urban Agriculture)

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27 pages, 2277 KB

Open AccessArticle

Circular Economy Assessment of Biochar-Enhanced Compost in Viticulture Using Ecocanvas

by Alexy Apolo-Romero, Nieves García-Casarejos and Pilar Gargallo

Agriculture 2025, 15(18), 1932; https://doi.org/10.3390/agriculture15181932 - 11 Sep 2025

Abstract

This study evaluates the application of circular economy principles in the wine sector through a demonstrative case developed within the LIFE Climawin project. The initiative focuses on the local valorization of vineyard residues by producing biochar from vine pruning and using it to [...] Read more.

This study evaluates the application of circular economy principles in the wine sector through a demonstrative case developed within the LIFE Climawin project. The initiative focuses on the local valorization of vineyard residues by producing biochar from vine pruning and using it to enrich compost derived from winemaking by-products and sheep manure. The combined application of these soil amendments aims to improve soil structure, enhance carbon sequestration, and reduce reliance on synthetic fertilizers. A systemic evaluation was conducted using the Ecocanvas methodology—a conceptual framework for mapping circular business models across environmental, economic, and social dimensions. The analysis integrated a targeted literature review, examination of technical data, direct field observations of composting and biochar production, and semi-structured interviews with key stakeholders. Results indicate multiple benefits from localized residue valorization, including improved compost quality, reduced greenhouse gas emissions, potential contributions to long-term soil health, and enhanced resource efficiency. The analysis also highlights economic opportunities, such as reduced dependency on external inputs, and social value creation through local stakeholder engagement. Furthermore, the study identifies factors that enable or constrain the replication and scaling of this model. These findings contribute to frameworks for advancing circular, economically viable, and socially inclusive climate-resilient agricultural systems. Full article

(This article belongs to the Special Issue Innovative Approaches to Circular Agriculture: Incorporating Green Chemistry and Waste Valorization)

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15 pages, 1626 KB

Open AccessArticle

Microbial Load, Physical–Chemical Characteristics, Ammonia, and GHG Emissions from Fresh Dairy Manure and Digestates According to Different Environmental Temperatures

by Eleonora Buoio, Elena Ighina and Annamaria Costa

Agriculture 2025, 15(18), 1931; https://doi.org/10.3390/agriculture15181931 - 11 Sep 2025

Abstract

This study evaluated chemical and physical parameters, volatile fatty acids (VFAs), pathogens indicators, ammonia, and greenhouse gas (GHG: CO₂, CH₄, N₂O) emissions from fresh and digested dairy manure under controlled laboratory conditions, simulating storage at 18 °C [...] Read more.

This study evaluated chemical and physical parameters, volatile fatty acids (VFAs), pathogens indicators, ammonia, and greenhouse gas (GHG: CO₂, CH₄, N₂O) emissions from fresh and digested dairy manure under controlled laboratory conditions, simulating storage at 18 °C and 28 °C. Manure and digestate samples were collected during summer 2023 from three dairy farms in Northern Italy, all operating similar mono-substrate, mesophilic anaerobic digesters at 42 °C with short hydraulic retention times (HRT) of ~30 days, instead of the longer HRTs commonly used (up to 90 days). Gas emissions were measured using a static chamber method over 40 min sessions, and cumulative GHG losses were converted to CO₂ equivalents. Anaerobic digestion significantly increased ammonia emissions (p < 0.001), in comparison with fresh manure samples. Anaerobic digestion affected pH variations, while reducing CH₄ and N₂O emissions by up to 67% and 50%, respectively. Storage at 28 °C increased total GHG fluxes by 74% for fresh manure and 66% for digestate. Residual methane emissions suggest incomplete digestion, likely due to short HRT and low digestion temperatures. Among pathogens, only clostridia showed significant reduction post-digestion. Overall, anaerobic digestion effectively lowers the global warming potential (GWP) of dairy manure, but higher environmental temperatures exacerbate ammonia and GHG emissions during storage, highlighting the need for optimized post-digestion handling in warm climates. Full article

(This article belongs to the Section Farm Animal Production)

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27 pages, 5557 KB

Open AccessArticle

Estimating the Tree Canopy Acceleration Required for Optimal Mechanical Harvesting Performance

by Naji Mordi Naji Al-Dosary, Thomas Francis Burks and Saad Abdulrahman Al-Hamed

Agriculture 2025, 15(18), 1930; https://doi.org/10.3390/agriculture15181930 - 11 Sep 2025

Abstract

Mechanical harvesting of ripe fruit should significantly increase fruit picking productivity and reduce harvesting times and operating costs. This study presents the optimal average gravitational acceleration of grapefruit tree branches obtained with a self-propelled citrus canopy shaker that varied the number, vibrational speed, [...] Read more.

Mechanical harvesting of ripe fruit should significantly increase fruit picking productivity and reduce harvesting times and operating costs. This study presents the optimal average gravitational acceleration of grapefruit tree branches obtained with a self-propelled citrus canopy shaker that varied the number, vibrational speed, and canopy penetration depth of the beating arms. Accelerometer sensors measured vibration and acceleration, and the fast Fourier transform (FFT) algorithm analyzed the vibration data. The acceleration values reflected the behavior of the tree branches in response to harvester shaking and varied with different harvester configurations and accelerometer placements in the tree canopy. The magnitude of the gravitational acceleration (g) increased significantly by increasing the number of shaking beaters, the shaker’s penetration into the tree canopy, and increasing the harvester’s shaking speed. The initial 14 beaters only provided acceleration values of 8.00 g maximum, 1.93 g minimum, and 5.044 g averages. Using 26 beaters yielded a maximum of 14.09 g, a minimum of 6.27 g, and an average of 8.65 g. Increasing the shaking speed also increased the forces applied to the tree canopy. An average of 7.387 g, achieved at 45.3 in/s, increased to 8.004 g at 65.9 in/s. Higher (g) values resulted in increased grapefruit fruit dislodgement, with 100% fruit removal on some trees. Full article

(This article belongs to the Section Agricultural Technology)

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16 pages, 2720 KB

Open AccessArticle

Multi-Trait Phenotypic Extraction and Fresh Weight Estimation of Greenhouse Lettuce Based on Inspection Robot

by Xiaodong Zhang, Xiangyu Han, Yixue Zhang, Lian Hu and Tiezhu Li

Agriculture 2025, 15(18), 1929; https://doi.org/10.3390/agriculture15181929 - 11 Sep 2025

Abstract

In situ detection of growth information in greenhouse crops is crucial for germplasm resource optimization and intelligent greenhouse management. To address the limitations of poor flexibility and low automation in traditional phenotyping platforms, this study developed a controlled environment inspection robot. By means [...] Read more.

In situ detection of growth information in greenhouse crops is crucial for germplasm resource optimization and intelligent greenhouse management. To address the limitations of poor flexibility and low automation in traditional phenotyping platforms, this study developed a controlled environment inspection robot. By means of a SCARA robotic arm equipped with an information acquisition device consisting of an RGB camera, a depth camera, and an infrared thermal imager, high-throughput and in situ acquisition of lettuce phenotypic information can be achieved. Through semantic segmentation and point cloud reconstruction, 12 phenotypic parameters, such as lettuce plant height and crown width, were extracted from the acquired images as inputs for three machine learning models to predict fresh weight. By analyzing the training results, a Backpropagation Neural Network (BPNN) with an added feature dimension-increasing module (DE-BP) was proposed, achieving improved prediction accuracy. The R² values for plant height, crown width, and fresh weight predictions were 0.85, 0.93, and 0.84, respectively, with RMSE values of 7 mm, 6 mm, and 8 g, respectively. This study achieved in situ, high-throughput acquisition of lettuce phenotypic information under controlled environmental conditions, providing a lightweight solution for crop phenotypic information analysis algorithms tailored for inspection tasks. Full article

(This article belongs to the Special Issue Intelligent Equipment and Automation Technology in Farmland Production)

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22 pages, 1337 KB

Open AccessArticle

Reasons Behind Differences in the Use of the “Carbon Farming and Nutrient Management” Eco-Scheme Across the Polish Territory

by Monika Małgorzata Wojcieszak-Zbierska, Patrycja Beba and Arkadiusz Sadowski

Agriculture 2025, 15(18), 1928; https://doi.org/10.3390/agriculture15181928 - 11 Sep 2025

Abstract

Today, there is an ongoing discourse on the notion of carbon farming on an international scale. The underlying factors contributing to this phenomenon are numerous. Firstly, the degradation of intensively farmed soils is increasing, and secondly, there is a clear need to restore [...] Read more.

Today, there is an ongoing discourse on the notion of carbon farming on an international scale. The underlying factors contributing to this phenomenon are numerous. Firstly, the degradation of intensively farmed soils is increasing, and secondly, there is a clear need to restore their biodiversity. A multitude of pollutants stemming from agricultural production have incited the implementation of targeted measures, notably by the European Commission. Consequently, the adoption of the European Green Deal in Poland has prompted the agricultural sector to implement a series of modifications to its practices, with the objective of enhancing soil cultivation and animal husbandry methods. In response to these changes, the introduction of carbon farming practices is being proposed. These practices, which are to be implemented in Polish agriculture with the support of EU subsidies, are intended to mitigate the effects of climate change. This prompts further inquiry into the potential evolution of carbon farming practices and the extent of farmer interest in them. According to the available data, in Poland, 56% of the total agricultural area was covered by payments under the carbon farming eco-scheme. However, support was accessed by barely 31% of farms with an area of more than 1 ha. In turn, from a regional perspective, data analysis reveals significant regional differences in the use of support. Therefore, the purpose of this paper is to explore the structural, environmental, and production reasons behind differences in the use of the “Carbon farming and nutrient management“ eco-scheme across the Polish territory. The headline result is that participation is strongly associated with farm structure, moderately with production performance, and only weakly with environmental status. Full article

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

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23 pages, 4421 KB

Open AccessArticle

Dynamic Modeling of Agricultural Fresh and Dry Biomass Under Variable Nutrient Supply

by Andrew Sharkey, Asher Altman, Yuming Sun and Yongsheng Chen

Agriculture 2025, 15(18), 1927; https://doi.org/10.3390/agriculture15181927 - 11 Sep 2025

Abstract

Data-driven empirical models, including those based on reaction kinetics, are well-regarded for their ability to make accurate predictions and uncover underlying relationships. While such models have been extensively employed for microbial communities, their use in agricultural populations remains comparatively limited. In this study, [...] Read more.

Data-driven empirical models, including those based on reaction kinetics, are well-regarded for their ability to make accurate predictions and uncover underlying relationships. While such models have been extensively employed for microbial communities, their use in agricultural populations remains comparatively limited. In this study, researchers analyzed data from hydroponic lettuce cultivation experiments observing nitrogen-, phosphorus-, and potassium-limited growth. Dynamic μ models, which incorporated nutrient-fueled growth and maturity-based rate decay, were adapted to accommodate a variable nutrient supply, as would be expected for nutrient recovery efforts using domestic wastewater. To test these models, researchers analyzed multiple approaches, differing variations in analyses, and other agricultural models against observed biomass measurements. The resulting Dynamic μ biomass models showed significantly less error than all other tested models, were validated against three variable nutrient treatments, and were evaluated against expected wastewater concentrations. Wastewater-cultivated lettuce was predicted to grow between 20 and 72% of fresh mass compared to lettuce grown under ideal nutrient concentrations, and models identified 41.7 days to maximize dry biomass, with a final harvest time of 44.0 days to maximize fresh biomass. Finally, this research demonstrates the application of agricultural modeling for profit estimation and informing decisions on supplemental nutrient use, providing guidance for nutrient recovery from wastewater. Full article

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

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29 pages, 35542 KB

Open AccessArticle

A Novel Remote Sensing Framework Integrating Geostatistical Methods and Machine Learning for Spatial Prediction of Diversity Indices in the Desert Steppe

by Zhaohui Tang, Chuanzhong Xuan, Tao Zhang, Xinyu Gao, Suhui Liu, Yaobang Song and Fang Guo

Agriculture 2025, 15(18), 1926; https://doi.org/10.3390/agriculture15181926 - 11 Sep 2025

Abstract

Accurate assessments are vital for the effective conservation of desert steppe ecosystems, which are essential for maintaining biodiversity and ecological balance. Although geostatistical methods are commonly used for spatial modeling, they have limitations in terms of feature extraction and capturing non-linear relationships. This [...] Read more.

Accurate assessments are vital for the effective conservation of desert steppe ecosystems, which are essential for maintaining biodiversity and ecological balance. Although geostatistical methods are commonly used for spatial modeling, they have limitations in terms of feature extraction and capturing non-linear relationships. This study therefore proposes a novel remote sensing framework that integrates geostatistical methods and machine learning to predict the Shannon–Wiener index in desert steppe. Five models, Kriging interpolation, Random Forest, Support Vector Machine, 3D Convolutional Neural Network and Graph Attention Network, were employed for parameter inversion. The Helmert variance component estimation method was introduced to integrate the model outputs by iteratively evaluating residuals and assigning relative weights, enabling both optimal prediction and model contribution quantification. The ensemble model yielded a high prediction accuracy with an R² of 0.7609. This integration strategy improves the accuracy of index prediction, and enhances the interpretability of the model regarding weight contributions in space. The proposed framework provides a reliable, scalable solution for biodiversity monitoring and supports scientific decision-making for grassland conservation and ecological restoration. Full article

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

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19 pages, 2899 KB

Open AccessArticle

Digestate-Derived Compost Modulates the Retention/Release Process of Organic Xenobiotics in Amended Soil

by Elisabetta Loffredo, Emanuela Campanale, Claudio Cocozza and Nicola Denora

Agriculture 2025, 15(18), 1925; https://doi.org/10.3390/agriculture15181925 - 11 Sep 2025

Abstract

This study examined the effects of 2, 4 and 8% digestate-derived compost (DCP) on the retention/release of the fungicide penconazole (PEN), the herbicide S-metolachlor (S-MET) and the endocrine disruptor bisphenol A (BPA) in two agricultural soils sampled in Valenzano (SOV) and Trani (SOT), [...] Read more.

This study examined the effects of 2, 4 and 8% digestate-derived compost (DCP) on the retention/release of the fungicide penconazole (PEN), the herbicide S-metolachlor (S-MET) and the endocrine disruptor bisphenol A (BPA) in two agricultural soils sampled in Valenzano (SOV) and Trani (SOT), in Sothern Italy. DCP alone showed a conspicuous adsorption of the three xenobiotics, followed by their slow and scarce release. Sorption isotherm data of the compounds on unamended and DCP-amended soils were well described by the Freundlich model. Compared to unamended soil, the addition of the highest dose (8%) DCP to SOV increased the distribution coefficient, Kd, values of PEN, S-MET and BPA by 281%, 192% and 176%, respectively, while for SOT, the increases were 972%, 786% and 563%, respectively. Desorption of PEN and S-MET from all treatments was slow and partial (hysteresis), and only slightly reduced or unaffected by the addition of DCP, whereas BPA was almost entirely undesorbed in all treatments. Highly significant correlations between the adsorption coefficients of the three compounds in all soil treatments and the corresponding organic C contents confirm the prominent role of native and anthropogenic OM in the adsorption of contaminants and, consequently, in the control of their transfer into natural waters and/or entry in crop plants. Full article

(This article belongs to the Special Issue Innovative Solutions for Sustainable Agriculture: From Waste to Biostimulants, Biofertilisers and Bioenergy)

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14 pages, 739 KB

Open AccessArticle

Do Pastures Diversified with Native Wildflowers Benefit Honeybees (Apis mellifera)?

by Raven Larcom, Parry Kietzman, Megan O’Rourke and Benjamin Tracy

Agriculture 2025, 15(18), 1924; https://doi.org/10.3390/agriculture15181924 - 11 Sep 2025

Abstract

Tall fescue-dominated pasturelands are widespread in the eastern United States and typically lack substantial plant diversity. Establishing native wildflowers into tall fescue pastures has the potential to benefit bee populations and boost pollinator ecosystem services. In this study, tall fescue pastures at five [...] Read more.

Tall fescue-dominated pasturelands are widespread in the eastern United States and typically lack substantial plant diversity. Establishing native wildflowers into tall fescue pastures has the potential to benefit bee populations and boost pollinator ecosystem services. In this study, tall fescue pastures at five on-farm sites in Virginia, USA, were planted with wildflowers native to North America and paired with sites with conventional tall fescue pastures. Honeybee apiaries were established at the ten locations, and variables related to hive strength were measured over two years. The main study objectives were to: (1) compare metrics of hive strength between diversified and conventional pastures, (2) determine whether honeybees used native-sown wildflowers as a source of pollen, and (3) explore whether native-sown wildflowers were visited more by honeybees and other pollinators compared with nonnative, unsown forbs. Diversified pastures had many more plant species and blooms compared with conventional pastures, but this had little effect on hive parameters. Pollen DNA metabarcoding revealed that honeybee diets were similar regardless of whether hives were associated with diversified or conventional pastures. Honeybees foraged mostly on plants in the surrounding landscape—especially white clover (Trifolium repens) and less so on native wildflowers. Native-sown wildflowers received more visits from native pollinators, however. We hypothesize that the native-sown wildflowers had little impact on hive strength metrics because honeybees had access to abundant, white clover blooms and other flowering species in these landscapes. Native wildflowers that bloom in late summer/early autumn after white clover blooms diminish may be of greater value to honeybees in pasture settings. Full article

(This article belongs to the Special Issue Honey Bees and Wild Pollinators in Agricultural Ecosystems)

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21 pages, 5797 KB

Open AccessArticle

Optimizing Controlled-Release Urea and Conventional Urea Ratios Enhances Nitrogen Use Efficiency and Yield in Peanut

by Mingxuan Gu, Lu Luo, Ruiyuan Fang, Fengzhen Liu, Zhen Tan, Zheng Wu, Mengjian Zheng, Kun Zhang and Yongshan Wan

Agriculture 2025, 15(18), 1923; https://doi.org/10.3390/agriculture15181923 - 11 Sep 2025

Abstract

Combined application of controlled-release urea (CRU) and urea (U) improves yield and nitrogen use efficiency (NUE) in various crops, but the optimal blending ratio and related mechanisms in peanut production, particularly regarding antioxidant enzyme responses, remain insufficiently studied. To address this, a two-year [...] Read more.

Combined application of controlled-release urea (CRU) and urea (U) improves yield and nitrogen use efficiency (NUE) in various crops, but the optimal blending ratio and related mechanisms in peanut production, particularly regarding antioxidant enzyme responses, remain insufficiently studied. To address this, a two-year field experiment was conducted with six fertilization treatments at a nitrogen rate of 120 kg·ha⁻¹: CK (no nitrogen), T1 (100% U), T2 (100% CRU), T3 (50% CRU + 50% U), T4 (70% CRU + 30% U), and T5 (30% CRU + 70% U). The results showed that compared with T1, the blended treatments significantly increased yield by 5.41–10.88% and improved NUE by 35.90–64.37%, with T4 performing the best. The T4 treatment significantly enhanced photosynthetic characteristics, promoted dry matter accumulation, coordinated nitrogen supply across growth stages, strengthened nitrogen metabolism enzyme activity, and delayed leaf senescence. At harvesting stage, the activities of SOD, POD, and CAT in T4 were 12.82%, 22.37%, and 23.32% higher, respectively, than those in T1, while MDA content decreased by 11.29%. This study demonstrates that in the ridge-furrow plastic film mulching cultivation system of Shandong’s brown soil, coapplying 70% CRU with 30% U is an effective nitrogen management strategy for peanuts. This approach achieves high and stable yields by improving nitrogen metabolism and antioxidant capacity. The findings provide a theoretical basis and technical reference for sustainable intensification of peanut production in similar ecological regions and cultivation systems. Full article

(This article belongs to the Section Crop Production)

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18 pages, 6196 KB

Open AccessArticle

Seasonal Variation in Nutritional, Physicochemical, and Mineral Composition of Honeybee Pollen in Southern Kazakhstan

by Gaukhar Moldakhmetova, Aibyn Torekhanov, Aigul Tajiyeva, Ulzhan Nuraliyeva, Oleg Krupskiy, Gulim Khalykova, Nurgul Myrzabayeva and Maxat Toishimanov

Agriculture 2025, 15(18), 1922; https://doi.org/10.3390/agriculture15181922 - 10 Sep 2025

Abstract

Honeybee pollen is widely recognized as a functional apicultural product due to its rich nutritional profile, but its composition is strongly influenced by seasonality and floral availability. Understanding these seasonal dynamics is critical for optimizing the nutritional and bioactive quality of bee-collected pollen. [...] Read more.

Honeybee pollen is widely recognized as a functional apicultural product due to its rich nutritional profile, but its composition is strongly influenced by seasonality and floral availability. Understanding these seasonal dynamics is critical for optimizing the nutritional and bioactive quality of bee-collected pollen. This study investigated the seasonal variation in the physicochemical and mineral composition of honeybee pollen collected monthly from April to September 2024 from an apiary in the Tulkibas district, Turkistan region, Kazakhstan. Pollen samples were analyzed for key quality parameters, including moisture, protein, fat, fiber, carbohydrates, starch, ash, and minerals (Ca, P, K, Mg, Na, Cu, Fe, Zn). Moisture, protein, fat, fiber, starch, and ash were determined using standard AOAC methods, while minerals were quantified by flame atomic absorption spectrophotometry (Ca, Cu, Fe, Mg, Zn; Analytik Jena novAA 350), flame emission spectrophotometry (Na, K), and the molybdenum blue colorimetric method (P). The moisture content decreased significantly from 10.34 ± 1.74% in April to 5.23 ± 0.86% in June (p = 0.0030), while protein increased from 20.28 ± 2.13% to a peak of 23.66 ± 1.70% in June (p = 0.0268). The fat content reached its maximum in July at 8.67 ± 0.11% (p = 0.0446), and carbohydrates peaked at 14.41 ± 0.11% in the same month. Among minerals, Fe and Zn showed substantial increases, with iron rising from 47.51 ± 5.69 mg/kg in April to 143.39 ± 6.58 mg/kg in July (p = 0.0388), and Zn from 38.56 ± 2.36 mg/kg to 57.14 ± 8.54 mg/kg (p = 0.0302). Principal Component Analysis (PCA) and Pearson correlation confirmed strong seasonal clustering and nutrient interrelationships. These findings highlight the superior nutritional value of mid- to late-season pollen and underscore the importance of the harvest timing in optimizing the bioactive profile of bee-collected pollen for apicultural and functional food applications. Full article

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

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23 pages, 2092 KB

Open AccessArticle

The Interplay of Pro-Innovative Behavior, Trust, and Farm Viability for Sustainability in the United Winemaking Agricultural Cooperative of Samos

by Sofia Karampela, Thanasis Kizos and Alex Koutsouris

Agriculture 2025, 15(18), 1921; https://doi.org/10.3390/agriculture15181921 - 10 Sep 2025

Abstract

This study explores the complex interplay between innovation, pro-innovative behavior, social capital, and farm viability in contributing to sustainability within agricultural cooperatives. Focusing on the United Winemaking Agricultural Cooperative of Samos (UWC SAMOS), a historic cooperative on the Greek island of Samos, this [...] Read more.

This study explores the complex interplay between innovation, pro-innovative behavior, social capital, and farm viability in contributing to sustainability within agricultural cooperatives. Focusing on the United Winemaking Agricultural Cooperative of Samos (UWC SAMOS), a historic cooperative on the Greek island of Samos, this research aimed to measure and operationalize these concepts using literature-derived indicators and complex indexes. A mixed-method approach was employed, collecting data via semi-structured questionnaires and in-depth interviews. The findings revealed a highly intricate relationship among these factors which quantitative analysis alone could not fully capture. The findings revealed a complex interplay, with female respondents showing better results in all the created composite indexes of our study. Surprisingly, the respondents of our sample who were more than 60 years old had the highest values in the composite indexes of pro-innovative behavior and economic viability and a relatively high value in the social capital index, and considering the educational level of the interviewees, the proportion with a Master’s or an equivalent level had the highest results in the pro-innovative behavior index and trust but not in economic viability. Crucially, qualitative data highlighted the underlying mechanism of “institutionalization of cooperative members” as a significant mediating factor, explaining the weak innovation and low social trust observed. This study concludes that a holistic understanding of sustainability in agricultural cooperatives necessitates a deep integration of both quantitative measures and qualitative exploration of socio-cultural dynamics, offering a refined conceptual framework for future research and policy. Full article

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

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29 pages, 9899 KB

Open AccessArticle

Research on the Design of an Omnidirectional Leveling System and Adaptive Sliding Mode Control for Tracked Agricultural Chassis in Hilly and Mountainous Terrain

by Renkai Ding, Xiangyuan Qi, Xuwen Chen, Yixin Mei, Anze Li, Ruochen Wang and Zhongyang Guo

Agriculture 2025, 15(18), 1920; https://doi.org/10.3390/agriculture15181920 - 10 Sep 2025

Abstract

To address the suboptimal leveling performance and insufficient slope stability of existing agricultural machinery chassis in hilly and mountainous regions, this study proposes an innovative omnidirectional leveling system based on a “double-layer frame” crawler-type agricultural chassis. The system employs servo-electric cylinders as its [...] Read more.

To address the suboptimal leveling performance and insufficient slope stability of existing agricultural machinery chassis in hilly and mountainous regions, this study proposes an innovative omnidirectional leveling system based on a “double-layer frame” crawler-type agricultural chassis. The system employs servo-electric cylinders as its actuation components. A control model for the servo-electric cylinders has been established, accompanied by the design of an adaptive sliding mode controller (ASMC). A co-simulation platform was developed utilizing Matlab/Simulink and Adams to evaluate system performance. Comparative simulations were conducted between the ASMC and a conventional PID controller, followed by comprehensive machine testing. Experimental results demonstrate that the proposed double-layer frame crawler chassis achieves longitudinal leveling adjustments of up to 25° and lateral adjustments of 20°. Through structural optimization and the application of ASMC (in contrast to PID), both longitudinal and lateral leveling response times were reduced by 1.12 s and 0.95 s, respectively. Furthermore, leveling velocities increased by a factor of 1.5 in the longitudinal direction and by a factor of 1.3 in the lateral direction, while longitudinal and lateral angular accelerations decreased by 15.8% and 17.1%, respectively. Field tests confirm the system’s capability for adaptive leveling on inclined terrain, thereby validating the enhanced performance of the proposed omnidirectional leveling system. Full article

(This article belongs to the Section Agricultural Technology)

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15 pages, 4037 KB

Open AccessArticle

Methods of Work Area Division Under a Human–Machine Cooperative Mode of Intelligent Agricultural Machinery Equipment

by Jing He, Jiarui Zou, Zhun Cheng, Jiatao Huang, Runmao Zhao, Guoqing Wang and Jie He

Agriculture 2025, 15(18), 1919; https://doi.org/10.3390/agriculture15181919 - 10 Sep 2025

Abstract

To address the problems of incomplete coverage of complex plots and low efficiency in unmanned agricultural machinery operations, the study proposes the Human–Machine Collaboration (HMC). Targeting different types of plots, the study designed the method of area division based on the Breseham algorithm [...] Read more.

To address the problems of incomplete coverage of complex plots and low efficiency in unmanned agricultural machinery operations, the study proposes the Human–Machine Collaboration (HMC). Targeting different types of plots, the study designed the method of area division based on the Breseham algorithm and the polygonal clipping algorithm. In addition, the study proposed a secondary division method of the area based on alternating point judgment and risk area evaluation function to ensure the security of the HMC. The experimental results show that the coverage rate of HMC is 100% and the field operation work efficiency is higher than 86% under different shapes of plots (rectangle, right trapezoid and ordinary quadrilateral). In the three shapes of plots, the operation scores of the HMC in the open edge area are 96.08, 163.39, and 137.4, respectively; the operation scores in other areas are 104.73, 89.88, 97.77, respectively; and the comprehensive scores are 162.36, 204.33, and 189.85, respectively, which are higher than those of unmanned operation and manned operation, showing comparatively better performance. The area division under the HMC meets the operational requirements, and the research provides technical support for unmanned farm development. Full article

(This article belongs to the Topic Intelligent Agriculture: Perception Technologies and Agricultural Equipment for Crop Production Processes)

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13 pages, 1662 KB

Open AccessArticle

Camera-Based Sow (Sus scrofa domesticus Erxleben) Posture Analysis and Prediction of Artificial Insemination Timing

by Sookeun Song, Minseo Jo, Bong-kuk Lee, Sangkeum Lee and Hyunbean Yi

Agriculture 2025, 15(18), 1918; https://doi.org/10.3390/agriculture15181918 - 10 Sep 2025

Abstract

Determining sow (Sus scrofa domesticus Erxleben) estrus status requires considerable labor investment, and continuous real-time monitoring is impractical. Workers typically identify estrus at scheduled intervals and determine artificial insemination timing based on experience. However, experience-based methods are subjective, vary with operator expertise, [...] Read more.

Determining sow (Sus scrofa domesticus Erxleben) estrus status requires considerable labor investment, and continuous real-time monitoring is impractical. Workers typically identify estrus at scheduled intervals and determine artificial insemination timing based on experience. However, experience-based methods are subjective, vary with operator expertise, and impede standardized management in large-scale farms. This study employs cameras and deep learning to detect sows and analyze postural changes, enabling estrus detection and optimal insemination timing prediction. Experimental results indicate that the proposed method achieved an accuracy of 70% (42/60), where the recommended insemination timing differed by less than 24 h from human decisions. This approach facilitates data-driven estrus detection and insemination scheduling, potentially reducing labor intensity and improving reproductive outcomes, particularly beneficial for labor-intensive and large-scale swine production systems. Full article

(This article belongs to the Section Farm Animal Production)

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41 pages, 47436 KB

Open AccessReview

Research Progress on Path Planning and Tracking Control Methods for Orchard Mobile Robots in Complex Scenarios

by Yayun Shen, Yue Shen, Yafei Zhang, Chenwei Huo, Zhuofan Shen, Wei Su and Hui Liu

Agriculture 2025, 15(18), 1917; https://doi.org/10.3390/agriculture15181917 - 10 Sep 2025

Abstract

Orchard mobile robots (OMR) represent a critical research focus in the field of modern intelligent agricultural equipment, offering the potential to significantly enhance operational efficiency through the integration of path planning and tracking control navigation methods. However, the inherent complexity of orchard environments [...] Read more.

Orchard mobile robots (OMR) represent a critical research focus in the field of modern intelligent agricultural equipment, offering the potential to significantly enhance operational efficiency through the integration of path planning and tracking control navigation methods. However, the inherent complexity of orchard environments presents substantial challenges for robotic systems. Researchers have extensively investigated the robustness of various path planning and tracking control techniques for OMR in complex scenes, aiming to improve the robots’ security, stability, efficiency, and adaptability. This paper provides a comprehensive review of the state-of-the-art path planning and tracking control strategies for OMR in such environments. First, it discusses the advances in both global and local path planning methods designed for OMR navigating through complex orchard scenes. Second, it examines tracking control approaches in the context of different motion models, with an emphasis on the application characteristics and current trends in various scene types. Finally, the paper highlights the technical challenges faced by OMR in autonomous tasks within these complex environments and emphasizes the need for further research into navigation technologies that integrate artificial intelligence with end-to-end control systems. This fusion is identified as a promising direction for achieving efficient autonomous operations in orchard environments. Full article

(This article belongs to the Section Agricultural Technology)

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22 pages, 1124 KB

Open AccessArticle

Assessment of Emerging Contaminants in Treated Wastewater Through Plant-Based Biotests

by Irene Tercero and Pilar Mañas

Agriculture 2025, 15(18), 1916; https://doi.org/10.3390/agriculture15181916 - 9 Sep 2025

Viewed by 227

Abstract

Given the increasing concern about the presence of emerging contaminants in wastewater and their persistence in the environment, this study aimed to assess the effects of two anxiolytic pharmaceuticals commonly used in human therapy—Tranxilium (dipotassium clorazepate) and Zolpidem (zolpidem tartrate)—on plant development. Lettuce [...] Read more.

Given the increasing concern about the presence of emerging contaminants in wastewater and their persistence in the environment, this study aimed to assess the effects of two anxiolytic pharmaceuticals commonly used in human therapy—Tranxilium (dipotassium clorazepate) and Zolpidem (zolpidem tartrate)—on plant development. Lettuce (Lactuca sativa L.) and wheat (Triticum aestivum) were selected as the biotest species. Phytotoxicity assays were also performed on Raphanus sativus. Greenhouse experiments were conducted using different concentrations of both pharmaceuticals, and several physiological and growth parameters were evaluated, including the germination rate, biomass accumulation, SPAD index, and spectrophotometrically measured contents of chlorophyll A, chlorophyll B, and carotenoids. The results indicated that both pharmaceuticals can affect plant growth, with stimulatory effects at intermediate concentrations and phytotoxic effects at higher levels. These findings highlight the importance of considering the impact of emerging contaminants on agricultural ecosystems and their potential risks to environmental and human health. Full article

(This article belongs to the Special Issue Sustainable Water Reuse in Agriculture: Advances in Contaminant Treatment and Management)

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16 pages, 2412 KB

Open AccessArticle

A Strategic Breeding Approach for Improvement of a Native Greek Chamomile (Matricaria chamomilla L.) Population for High-Yield and Optimized Chemical Profile Under Mediterranean Low-Input Conditions

by Nektaria Tsivelika, Ioannis Mylonas, Elissavet Ninou, Athanasios Mavromatis, Eirini Sarrou, Maria Irakli and Paschalina Chatzopoulou

Agriculture 2025, 15(18), 1915; https://doi.org/10.3390/agriculture15181915 - 9 Sep 2025

Viewed by 99

Abstract

Chamomile (Matricaria chamomilla L.) is a popular herb of great economic and medicinal value. Despite its significant potential, there are currently no commercially available varieties specifically adapted to Mediterranean low-input farming systems. The present study aimed to develop a genetically improved breeding [...] Read more.

Chamomile (Matricaria chamomilla L.) is a popular herb of great economic and medicinal value. Despite its significant potential, there are currently no commercially available varieties specifically adapted to Mediterranean low-input farming systems. The present study aimed to develop a genetically improved breeding population derived from indigenous Greek chamomile germplasm, following a multi-year strategy, based on pedigree selection under low-input conditions. This selection process constituted the first phase of the breeding program, during which selection focused on improving inflorescence dry weight and essential oil quality, particularly with respect to α-bisabolol and chamazulene content. After three cycles of selection, considerable genetic gains were achieved. The realized heritability values exceeded 0.5 for all assessed traits, confirming strong genetic control. In the fourth year, representing the second phase of the breeding program, the breeding population—developed through selection during the first phase—was evaluated alongside the initial population and commercial diploid and tetraploid varieties. The breeding population exhibited significant observed gains compared to the initial population: inflorescence dry weight increased by 12.17%, α-bisabolol content by 71.45%, and chamazulene content by 6.57%. Additionally, the breeding population not only surpassed all evaluated diploid genotypes in essential oil composition, but also displayed a chemotypic shift, indicating successful alignment with tetraploid varieties characterized by high-value chemical profiles. Furthermore, this selection process targeting specific commercial chamomile traits indirectly contributed to improvement in plant height and inflorescence morphology. Overall, these results demonstrate that conventional breeding, when applied effectively to native resources, can enhance both agronomic performance and essential oil profile. The newly developed breeding population provides a strong foundation for future cultivar development tailored to Mediterranean low-input sustainable farming systems. Full article

(This article belongs to the Special Issue Genetic Diversity Assessment and Phenotypic Characterization of Crops)

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20 pages, 1156 KB

Open AccessArticle

Effects of Nitrogen Nutrition on the Nutraceutical and Antinutrient Content of Red Beet (Beta vulgaris L.) Baby Leaves Grown in a Hydroponic System

by Martina Puccinelli, Simone Cuccagna, Rita Maggini, Giulia Carmassi, Alberto Pardossi and Alice Trivellini

Agriculture 2025, 15(18), 1914; https://doi.org/10.3390/agriculture15181914 - 9 Sep 2025

Viewed by 237

Abstract

Efficient nitrogen fertilization is critical for maximizing crop productivity while minimizing environmental and health risks. Red beet baby leaves are valued for their vibrant color, flavor, and antioxidant content, particularly betalains, but they are also prone to accumulating antinutritional compounds such as nitrate [...] Read more.

Efficient nitrogen fertilization is critical for maximizing crop productivity while minimizing environmental and health risks. Red beet baby leaves are valued for their vibrant color, flavor, and antioxidant content, particularly betalains, but they are also prone to accumulating antinutritional compounds such as nitrate and oxalate. Excessive nitrogen supply can exacerbate this accumulation, highlighting the need to optimize nitrate input to balance yield, nutritional quality, and safety. This study examined how different nitrate concentrations (1 mM and 10 mM NO₃⁻) in hydroponic systems influence red beet baby leaf yield, quality, and levels of beneficial and harmful compounds. The plants were sampled at 10 and 17 days after planting (DAP), and the effects of the treatments in relation to plant age were assessed. Both sampling time and nitrate concentration significantly influenced red beet baby leaf growth and quality. Extending cultivation to 17 days improved yield and antioxidant levels (phenols, flavonoids, betalains) but also increased soluble oxalates. Low nitrate (1 mM) reduced both yield and antioxidant content, regardless of harvest time. However, after 17 days, low nitrate also lowered total oxalate levels, likely due to increased oxalate oxidase activity. Although 1 mM nitrate reduces fertilizer input, it compromises yield and quality. Therefore, intermediate nitrate levels should be explored to optimize both fertilizer use and product quality. Full article

(This article belongs to the Section Crop Production)

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16 pages, 634 KB

Open AccessArticle

Effects of Adding Lactobacillus Inoculants on the Nutritional Value of Sesbania cannabina and Whole Corn Mixed Silage

by Tianzhu Yin, Shuai Song, Xianwei Song, Duofeng Pan, Qinghua Zhao, Liwen He, Ding Tang, Yajun Jia, Xiaofeng Cao, Xian Deng and Wei Zhang

Agriculture 2025, 15(18), 1913; https://doi.org/10.3390/agriculture15181913 - 9 Sep 2025

Viewed by 134

Abstract

This study evaluated the potential of utilizing Sesbania cannabina, produced during saline–alkali soil improvement, as a high-quality feed resource for ruminants. Mixed silages were prepared by combining S. cannabina and whole corn at ratios of 1:1 and 1:3, with or without a [...] Read more.

This study evaluated the potential of utilizing Sesbania cannabina, produced during saline–alkali soil improvement, as a high-quality feed resource for ruminants. Mixed silages were prepared by combining S. cannabina and whole corn at ratios of 1:1 and 1:3, with or without a compound Lactobacillus (LAB) inoculant, and were assessed for fermentation quality, nutrient composition, ruminal degradation, intestinal digestibility, and energy value. Results: The addition of Lactobacillus (LAB) inoculants increased lactic acid content, crude protein effective degradability (CPED), gross energy (GE), and dry matter apparent digestibility (DMAD), while decreasing ammonia nitrogen (NH₃-N), acetic acid (AA), propionic acid (PA), neutral detergent fiber (NDF), acid detergent fiber (ADF), rumen undegradable protein (RUP), intestinal crude protein degradability (ICPD), and intestinal digestible crude protein (IDCP). Increasing the proportion of whole corn increased dry matter (DM) and gross energy (GE), while reducing crude protein (CP), NDF, ADF, Ash, rumen degradable protein (RDP), RUP, IDCP, and the effective ruminal degradability of NDF (NDFED) and ADF (ADFED). Overall, a 1:1 mixing ratio maximized S. cannabina utilization without compromising feeding value, and LAB inoculation ensured successful ensiling while enhancing nutrient utilization. Full article

(This article belongs to the Section Farm Animal Production)

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20 pages, 5345 KB

Open AccessArticle

Design and Development of an Intelligent Robotic Feeding Control System for Sheep

by Haina Jiang, Haijun Li and Guoxing Cai

Agriculture 2025, 15(18), 1912; https://doi.org/10.3390/agriculture15181912 - 9 Sep 2025

Viewed by 144

Abstract

With the widespread adoption of intelligent technologies in animal husbandry, traditional manual feeding methods can no longer meet the demands for precision and efficiency in modern sheep farming. To address this gap, we present an intelligent robotic feeding system designed to enhance feeding [...] Read more.

With the widespread adoption of intelligent technologies in animal husbandry, traditional manual feeding methods can no longer meet the demands for precision and efficiency in modern sheep farming. To address this gap, we present an intelligent robotic feeding system designed to enhance feeding efficiency, reduce labor intensity, and enable precise delivery of feed. This system, developed on the ROS platform, integrates LiDAR-based SLAM with point cloud rendering and an Octomap 3D grid map. It combines an improved bidirectional RRT* algorithm with Dynamic Window Approach (DWA) for efficient path planning and uses 3D LiDAR data along with the RANSAC algorithm for slope detection and navigation information extraction. The YOLOv8s model is utilized for precise sheep pen marker identification, while integration with weighing sensors and a farm management system ensures accurate feed distribution control. The main research contribution lies in the development of a comprehensive, multi-sensor fusion system capable of achieving autonomous feeding in dynamic and complex environments. Experimental results show that the system achieves centimeter-level accuracy in localization and attitude control, with FAST-LIO2 maintaining precision within 1° of attitude angle errors. Compared to baseline performance, the system reduces node count by 17.67%, shortens path length by 0.58 cm, and cuts computation time by 42.97%. At a speed of 0.8 m/s, the robot achieves a maximum longitudinal deviation of 7.5 cm and a maximum heading error of 5.6°, while straight-line deviation remains within ±2.2 cm. In a 30 kg feeding task, the system demonstrates zero feed wastage, highlighting its potential for intelligent feeding in modern sheep farming. Full article

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

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15 pages, 4155 KB

Open AccessArticle

Dynamics and Determinants of Maize Sap Flow Under Soil Compaction in the Black Soil Region of Northeast China

by Xiangming Zhu, Enhua Ran, Wei Peng, Xiangyu Zhao, Tianhao Wang and Qingyang Xie

Agriculture 2025, 15(18), 1911; https://doi.org/10.3390/agriculture15181911 - 9 Sep 2025

Viewed by 160

Abstract

Soil compaction is considered as one of the main factors limiting plant growth. Understanding the variation in sap flow affected by soil compaction is of vital importance for precision agriculture. In this study, a two-year field experiment with three levels of soil compaction [...] Read more.

Soil compaction is considered as one of the main factors limiting plant growth. Understanding the variation in sap flow affected by soil compaction is of vital importance for precision agriculture. In this study, a two-year field experiment with three levels of soil compaction (i.e., NC, no compaction; MC, moderate compaction; and SC, severe compaction) was conducted in the black soil region of Northeast China. Results revealed that soil compaction had a significant impact on soil properties, soil water content, and plant growth parameters, which ultimately affected the sap flow rate of maize. The average daily sap flow rates of MC and SC decreased by 15.89% and 29.12% in comparison to those of NC in 2023, and decreased by 51.53% and 57.11% in comparison to those of NC in 2024, respectively. Net radiation and vapor pressure deficit were the two most important meteorological variables affecting sap flow rate. In addition, the relationship between sap flow rate and meteorological variables was independent of the level of soil compaction stress. Daily sap flow rate exhibited a strong linear relationship with leaf area index and stem diameter, but showed no significant correlation with plant height. Additionally, daily sap flow rate was well correlated with root length density in the 0–60 cm soil layer. Furthermore, daily sap flow rate was significantly affected by soil water content of the 0–60 cm soil layer, but there was no significant correlation between daily sap flow rate and penetration resistance. Moreover, cumulative sap flow rate was negatively correlated with soil bulk density in both the top layer (0–20 cm) and sub-layer (20–40 cm). Our results provide a scientific basis for understanding the relationship between maize sap flow and soil compaction. More precise and systematic characterization of soil compaction, especially in relation to root growth, is needed to explore the underlying mechanisms of soil compaction on plant sap flow in the future. Full article

(This article belongs to the Special Issue Innovative Conservation Cropping Systems and Practices—2nd Edition)

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23 pages, 430 KB

Open AccessArticle

Unmanned Agricultural Robotics Techniques for Enhancing Entrepreneurial Competitiveness in Emerging Markets: A Central Romanian Case Study

by Ioana Madalina Petre, Mircea Boșcoianu, Pompilica Iagăru and Romulus Iagăru

Agriculture 2025, 15(18), 1910; https://doi.org/10.3390/agriculture15181910 - 9 Sep 2025

Viewed by 240

Abstract

Recently, the market for miniaturized Unmanned Agricultural Robots has experienced rapid development worldwide, driven by advances in robotics, artificial intelligence and precision agriculture. These technologies are no longer confined to highly industrialized countries but are increasingly penetrating emerging economies, including Romania, where they [...] Read more.

Recently, the market for miniaturized Unmanned Agricultural Robots has experienced rapid development worldwide, driven by advances in robotics, artificial intelligence and precision agriculture. These technologies are no longer confined to highly industrialized countries but are increasingly penetrating emerging economies, including Romania, where they hold significant potential for transforming farming practices and entrepreneurial competitiveness. The purpose of the present paper is to present strategies for enhancing the competitive advantage of agricultural entrepreneurs in Romania’s Central Region. This is achieved by leveraging competitive advantage through value creation, specifically by deepening strategies for the rapid integration of new miniaturized robotic products. The research employed a mixed-methods approach, combining qualitative and quantitative techniques to investigate the ability of key stakeholders—agricultural entrepreneurs, precision agriculture product/service providers, institutional representatives, and investors—to dynamically adapt to evolving market conditions. The study’s findings reveal a strong interest and readiness among precision agriculture stakeholders to adopt advanced technologies, supported by robust operational knowledge management practices including external knowledge acquisition, strategic partnerships and data protection. Although agricultural entrepreneurs exhibit considerable adaptive and absorptive capacities—evidenced by their openness to innovation and collaboration—persistent barriers such as high equipment costs and limited financing access continue to impede the broad adoption of miniaturized robotic solutions. The study concludes by emphasizing the need for supportive policies and collaborative financing models and it suggests future research on adoption dynamics, cross-country comparisons and the role of education in accelerating agricultural robotics. Full article

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

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12 pages, 1356 KB

Open AccessArticle

Establishing the Effects of Climate and Soil on the Nutritional Composition of an Array of Faba Bean Varieties Grown in Two Different Zones of Andalusia, Spain

by Jazmín Osorio, Gianuario Fortunato, Eleonora Barilli, Diego Rubiales, Elisabete Pinto and Marta W. Vasconcelos

Agriculture 2025, 15(18), 1909; https://doi.org/10.3390/agriculture15181909 - 9 Sep 2025

Viewed by 172

Abstract

Legumes contribute to sustainable agriculture by reducing fertilizer use, enhancing nitrogen fixation, and with high species diversity (~20,000 species). Spain is a leading EU producer, yielding up to 30,000 tons of different legume varieties annually. The Mediterranean climate, particularly in regions like Andalusia, [...] Read more.

Legumes contribute to sustainable agriculture by reducing fertilizer use, enhancing nitrogen fixation, and with high species diversity (~20,000 species). Spain is a leading EU producer, yielding up to 30,000 tons of different legume varieties annually. The Mediterranean climate, particularly in regions like Andalusia, is under increasing pressure from climate change, with extreme temperature variations and drought becoming more frequent. While these changes may jeopardize crop yields, limited information is available on their effects on the nutritional profile of legumes. From 2017 to 2019, six faba bean (Vicia faba) varieties were monitored in two climatically distinct areas of Andalusia to assess the impact of temperature (T) and rainfall (R) on key nutrients and bioactive compounds, including protein, minerals (K, Ca, Mg, Zn, P, Fe, Mn, B), total polyphenol content (TPC), tannins (TA), and saponins (S). Spearman correlations showed that higher T negatively impacted TPC (r = −0.40) and Mg (r = −0.33), while positively influencing Zn (r = 0.27) and Ca (r = 0.22). Rainfall increased TPC and Mg but reduced TA, Zn, and Ca. Canonical correspondence analysis (CCA) and PERMANOVA (p < 0.001) confirmed T, R, and yield as significant factors. These insights support breeding strategies for climate-adapted, nutrient-rich faba beans and the development of more resilient food systems. Full article

(This article belongs to the Section Crop Production)

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15 pages, 568 KB

Open AccessArticle

First Report of Heterodera schachtii (Schmidt, 1879) on Camelina sativa (L.) Crantz in Poland and Assessment of Its Host Suitability for This Nematode

by Renata Dobosz, Roman Krawczyk and Łukasz Flis

Agriculture 2025, 15(18), 1908; https://doi.org/10.3390/agriculture15181908 - 9 Sep 2025

Viewed by 194

Abstract

Heterodera schachtii, a nematode primarily feeding on sugar beet and cruciferous plants, e.g., rapeseed, cabbage, broccoli, mustard, and radish, had a significant impact on Camelina sativa (L.) Crantz. The isolation of H. schachtii cysts from C. sativa roots and a known data [...] Read more.

Heterodera schachtii, a nematode primarily feeding on sugar beet and cruciferous plants, e.g., rapeseed, cabbage, broccoli, mustard, and radish, had a significant impact on Camelina sativa (L.) Crantz. The isolation of H. schachtii cysts from C. sativa roots and a known data gap regarding their development on this plant prompted an investigation into their interaction. A pot experiment conducted under controlled conditions in a growth chamber showed that H. schachtii completes its full development cycle in the roots of spring (UP, Smielowska, Borowska, Omega) and winter (Lemka, Maczuga, Luna, Przybrodzka) camelina cultivars at temperatures of 15, 20, and 25 °C. Female nematodes and cysts were most abundant in the Omega cultivar at 20 °C, averaging 9.25 per plant. Nematode feeding did not affect the height or fresh weight of the plants. Plants of the Przybrodzka cultivar had fewer leaves than the control plants. More siliques were observed on the control plants of the UP cultivar kept at 15 °C and those of the UP and Borowska cultivars at 20 °C. Under natural conditions, the number of eggs and larvae in the soil decreased by approximately 50% during the camelina growth cycle for both spring and winter biotypes. Full article

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

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13 pages, 1829 KB

Open AccessArticle

Biochar Effectively Reduced N₂O Emissions During Heap Composting and NH₃ Emissions During Aerobic Composting

by Zhi Zhang, Haicheng Wu, Yue Zhao, Yupeng Wu, Runting Ming, Donghai Liu, Yan Qiao, Zhuoxi Xiao, Jian Ren, Yunfeng Chen and Cheng Hu

Agriculture 2025, 15(18), 1907; https://doi.org/10.3390/agriculture15181907 - 9 Sep 2025

Viewed by 225

Abstract

The composting process generates considerable amounts of greenhouse gases, presenting challenges for environmental protection. The utilization of biochar not only improves the composting efficiency but also reduces nitrogen (N) loss during composting. This study aimed to examine the impacts of adding biochar on [...] Read more.

The composting process generates considerable amounts of greenhouse gases, presenting challenges for environmental protection. The utilization of biochar not only improves the composting efficiency but also reduces nitrogen (N) loss during composting. This study aimed to examine the impacts of adding biochar on the composting process, gaseous N emissions, and the bacterial community, as well as to clarify the difference between anoxic and aerobic composting. The experiment was conducted with cow dung and corn straw, with four treatments over 45 days: heap composting (C1), heap composting with 10% biochar (BC1), aerobic composting (C2), and aerobic composting with 10% biochar (BC2). The findings showed that adding biochar significantly reduced N₂O emissions during the heap composting, achieving a cumulative emission reduction of 49.51% compared with composting without biochar. Meanwhile, aerobic composting led to a greater decrease in NH₃ emissions, with a cumulative emission reduction of 56.56%. Additionally, there was a marked increase in the abundance of Bacteroidota and Chryseoline. Biochar reduced nitrogen losses, especially N₂O emissions during heap composting and NH₃ emissions during aerobic composting. Full article

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

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28 pages, 5402 KB

Open AccessArticle

Real-Time Strawberry Ripeness Classification and Counting: An Optimized YOLOv8s Framework with Class-Aware Multi-Object Tracking

by Oluwasegun Moses Ogundele, Niraj Tamrakar, Jung-Hoo Kook, Sang-Min Kim, Jeong-In Choi, Sijan Karki, Timothy Denen Akpenpuun and Hyeon Tae Kim

Agriculture 2025, 15(18), 1906; https://doi.org/10.3390/agriculture15181906 - 9 Sep 2025

Viewed by 364

Abstract

Accurate fruit counting is crucial for data-driven decision-making in modern precision agriculture. In strawberry cultivation, a labor-intensive sector, automated, scalable yield estimation is especially critical. However, dense foliage, variable lighting, visual ambiguity of ripeness stages, and fruit clustering pose significant challenges. To overcome [...] Read more.

Accurate fruit counting is crucial for data-driven decision-making in modern precision agriculture. In strawberry cultivation, a labor-intensive sector, automated, scalable yield estimation is especially critical. However, dense foliage, variable lighting, visual ambiguity of ripeness stages, and fruit clustering pose significant challenges. To overcome these, we developed a real-time multi-stage framework for strawberry detection and counting by optimizing a YOLOv8s detector and integrating a class-aware tracking system. The detector was enhanced with a lightweight C3x module, an additional detection head for small objects, and the Wise-IOU (WIoU) loss function, thereby improving performance against occlusion. Our final model achieved a 92.5% mAP@0.5, outperforming the baseline while reducing the number of parameters by 27.9%. This detector was integrated with the ByteTrack multiple object tracking (MOT) algorithm. Our system enabled accurate, automated fruit counting in complex greenhouse environments. When validated on video data, results showed a strong correlation with ground-truth counts (R² = 0.914) and a low mean absolute percentage error (MAPE) of 9.52%. Counting accuracy was highest for ripe strawberries (R² = 0.950), confirming the value for harvest-ready estimation. This work delivers an efficient, accurate, and resource-conscious solution for automated yield monitoring in commercial strawberry production. Full article

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

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