Agriculture

13 pages, 293 KiB

Open AccessArticle

Amino Acids as Biostimulants: Effects on Growth, Chlorophyll Content, and Antioxidant Activity in Ocimum basilicum L.

by Justina Deveikytė, Aušra Blinstrubienė and Natalija Burbulis

Agriculture 2025, 15(14), 1496; https://doi.org/10.3390/agriculture15141496 - 11 Jul 2025

It is necessary to explore possibilities to increase agricultural production in environmentally friendly ways while maintaining the quality standards of plant raw materials. The effect of amino acids on sweet basil (Ocimum basilicum L.) development may stimulate biomass accumulation and enhance the [...] Read more.

It is necessary to explore possibilities to increase agricultural production in environmentally friendly ways while maintaining the quality standards of plant raw materials. The effect of amino acids on sweet basil (Ocimum basilicum L.) development may stimulate biomass accumulation and enhance the biosynthesis of secondary metabolites. Investigated varieties “Rosie”, “Red Opal”, “Bordeaux”, “Dark Opal”, “Red Rubin”, “Genovese”, “Cinamon”, “Italiano Classico”, “Marseillais”, and “Thai” were cultivated in a controlled-environment growth chamber and the impact of isoleucine, methionine, glutamine, tryptophan, phenylalanine was studied on biomass accumulation, chlorophyll and phenolic content, and antioxidant activity. Five to six true leaves plants were treated once with an aqueous solution containing 100 mg L⁻¹ of the mentioned amino acids or received no treatment. Our results show that methionine or tryptophan improved the most fresh and dry weight of shoot system of sweet basil plants. Methionine increased chlorophyl a content in 6 of 10 sweet basil varieties, while glutamine had the greatest results in chlorophyl b content. Phenylalanine increased total phenolic content in most treated plants, as well as antioxidant activity. Amino acids may be applied as useful biostimulants in modern agriculture, as they play an important role in ensuring sustainable crop productivity, fostering beneficial plant properties. Full article

(This article belongs to the Special Issue Bioactive Compounds in Fruits, Vegetables, Berries and Leaves: The Role of Growing Conditions and Harvest Timing)

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23 pages, 2557 KiB

Open AccessArticle

Impact of Phenological and Lighting Conditions on Early Detection of Grapevine Inflorescences and Bunches Using Deep Learning

by Rubén Íñiguez, Carlos Poblete-Echeverría, Ignacio Barrio, Inés Hernández, Salvador Gutiérrez, Eduardo Martínez-Cámara and Javier Tardáguila

Agriculture 2025, 15(14), 1495; https://doi.org/10.3390/agriculture15141495 - 11 Jul 2025

Abstract

Reliable early-stage yield forecasts are essential in precision viticulture, enabling timely interventions such as harvest planning, canopy management, and crop load regulation. Since grape yield is directly related to the number and size of bunches, the early detection of inflorescences and bunches, carried [...] Read more.

Reliable early-stage yield forecasts are essential in precision viticulture, enabling timely interventions such as harvest planning, canopy management, and crop load regulation. Since grape yield is directly related to the number and size of bunches, the early detection of inflorescences and bunches, carried out even before flowering, provides a valuable foundation for estimating potential yield far in advance of veraison. Traditional yield prediction methods are labor-intensive, subjective, and often restricted to advanced phenological stages. This study presents a deep learning-based approach for detecting grapevine inflorescences and bunches during early development, assessing how phenological stage and illumination conditions influence detection performance using the YOLOv11 architecture under commercial field conditions. A total of 436 RGB images were collected across two phenological stages (pre-bloom and fruit-set), two lighting conditions (daylight and artificial night-time illumination), and six grapevine cultivars. All images were manually annotated following a consistent protocol, and models were trained using data augmentation to improve generalization. Five models were developed: four specific to each condition and one combining all scenarios. The results show that the fruit-set stage under daylight provided the best performance (F1 = 0.77, R² = 0.97), while for inflorescences, night-time imaging yielded the most accurate results (F1 = 0.71, R² = 0.76), confirming the benefits of artificial lighting in early stages. These findings define optimal scenarios for early-stage organ detection and support the integration of automated detection models into vineyard management systems. Future work will address scalability and robustness under diverse conditions. Full article

(This article belongs to the Section Digital Agriculture)

18 pages, 2286 KiB

Open AccessArticle

Life Cycle Assessment of Key Mediterranean Agricultural Products at the Farm Level Using GHG Measurements

by Georgios Bartzas, Maria Doula and Konstantinos Komnitsas

Agriculture 2025, 15(14), 1494; https://doi.org/10.3390/agriculture15141494 - 11 Jul 2025

Abstract

Agricultural greenhouse gas (GHG) emissions contribute significantly to climate change and underline the importance of reliable measurements and mitigation strategies. This life cycle assessment (LCA)-based study evaluates the environmental impacts of four key Mediterranean agricultural products, namely olives, sweet potatoes, corn, and grapes [...] Read more.

Agricultural greenhouse gas (GHG) emissions contribute significantly to climate change and underline the importance of reliable measurements and mitigation strategies. This life cycle assessment (LCA)-based study evaluates the environmental impacts of four key Mediterranean agricultural products, namely olives, sweet potatoes, corn, and grapes using GHG measurements at four pilot fields located in different regions of Greece. With the use of a cradle-to-gate approach six environmental impact categories, more specifically acidification potential (AP), eutrophication potential (EP), global warming potential (GWP), ozone depletion potential (ODP), photochemical ozone creation potential (POCP), and cumulative energy demand (CED) as energy-based indicator are assessed. The functional unit used is 1 ha of cultivated land. Any potential carbon offsets from mitigation practices are assessed through an integrated low-carbon certification framework and the use of innovative, site-specific technologies. In this context, the present study evaluates three life cycle inventory (LCI)-based scenarios: Baseline (BS), which represents a 3-year crop production period; Field-based (FS), which includes on-site CO₂ and CH₄ measurements to assess the effects of mitigation practices; and Inventoried (IS), which relies on comprehensive datasets. The adoption of carbon mitigation practices under the FS scenario resulted in considerable reductions in environmental impacts for all pilot fields assessed, with average improvements of 8% for olive, 5.7% for sweet potato, 4.5% for corn, and 6.5% for grape production compared to the BS scenario. The uncertainty analysis indicates that among the LCI-based scenarios evaluated, the IS scenario exhibits the lowest variability, with coefficient of variation (CV) values ranging from 0.5% to 7.3%. In contrast, the FS scenario shows slightly higher uncertainty, with CVs reaching up to 15.7% for AP and 14.7% for EP impact categories in corn production. The incorporation of on-site GHG measurements improves the precision of environmental performance and supports the development of site-specific LCI data. This benchmark study has a noticeable transferability potential and contributes to the adoption of sustainable practices in other regions with similar characteristics. Full article

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

27 pages, 1957 KiB

Open AccessArticle

Vegetable Productivity, Soil Physicochemical and Biochemical Properties, and Microbiome in Response to Organic Substitution in an Intensive Greenhouse Production System

by Xing Liu, Haohui Xu, Yanan Cheng, Ying Zhang, Yonggang Li, Fei Wang, Changwei Shen and Bihua Chen

Agriculture 2025, 15(14), 1493; https://doi.org/10.3390/agriculture15141493 - 11 Jul 2025

Abstract

Partial substitution of mineral N fertilizer with manure (organic substitution) is considered as an effective way to reduce N input in intensive agroecosystems. Here, based on a 3-year field experiment, we assessed the influence of different organic substitution ratios (15%, 30%, 45%, and [...] Read more.

Partial substitution of mineral N fertilizer with manure (organic substitution) is considered as an effective way to reduce N input in intensive agroecosystems. Here, based on a 3-year field experiment, we assessed the influence of different organic substitution ratios (15%, 30%, 45%, and 60%, composted chicken manure applied) on vegetable productivity and soil physicochemical and biochemical properties as well as microbiome (metagenomic sequencing) in an intensive greenhouse production system (cucumber-tomato rotation). Organic substitution ratio in 30% got a balance between stable vegetable productivity and maximum N reduction. However, higher substitution ratios decreased annual vegetable yield by 23.29–32.81%. Organic substitution (15–45%) improved soil fertility (12.18–19.94% increase in soil total organic carbon content) and such improvement was not obtained by higher substitution ratio. Soil mean enzyme activity was stable to organic substitution despite the activities of some selected enzymes changed (catalase, urease, sucrase, and alkaline phosphatase). Organic substitution changed the species and functional structures rather than diversity of soil microbiome, and enriched the genes related to soil denitrification (including nirK, nirS, and nosZ). Besides, the 30% of organic substitution obviously enhanced soil microbial network complexity and this enhancement was mainly associated with altered soil pH. At the level tested herein, organic substitution ratio in 30% was suitable for greenhouse vegetable production locally. Long-term influence of different organic substitution ratios on vegetable productivity and soil properties in intensive greenhouse system needs to be monitored. Full article

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

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15 pages, 2413 KiB

Open AccessArticle

Soil Inoculated with Streptomyces rochei D74 Invokes the Defense Mechanism of Helianthus annuus Against Orobanche cumana

by Jiao Xi, Tengqi Xu, Zanbo Ding, Chongsen Li, Siqi Han, Ruina Liang, Yongqing Ma, Quanhong Xue and Yanbing Lin

Agriculture 2025, 15(14), 1492; https://doi.org/10.3390/agriculture15141492 - 11 Jul 2025

Abstract

Orobanche cumana Wallr. is a root parasitic plant that causes considerable yield losses of up to 50% in sunflower Helianthus annuus plantations. The holoparasite fulfills its entire demand for water, minerals, and organic nutrients from the host’s vascular system. Agronomic practices alone are [...] Read more.

Orobanche cumana Wallr. is a root parasitic plant that causes considerable yield losses of up to 50% in sunflower Helianthus annuus plantations. The holoparasite fulfills its entire demand for water, minerals, and organic nutrients from the host’s vascular system. Agronomic practices alone are not effective in controlling this pest. This study investigated the mechanism of a verified plant growth-promoting strain, Streptomyces rochei D74, on the inhibition of the parasitism of O. cumana in a co-culture experiment. We conducted potted and sterile co-culture experiments using sunflower, O. cumana, and S. rochei D74. Our results suggest that the inoculated bacteria invoked the sunflower systemic resistance (SAR and ISR) by increasing the activity of resistance-related enzymes (SOD, POD, PPO, and PAL), the gene expression of systemic resistance marker genes (PR-1 and NPR1), ethylene synthesis genes (HACS. 1 and ACCO1), and JA synthesis genes (pin2 and lox). The expression levels of ISR marker genes (lox, HACS. 1, ACCO1, and pin2) increased by 1.66–7.91-fold in the seedling stage. Simultaneously, S. rochei D74 formed a protective layer on the sunflower root surface, preventing O. cumana from connecting to the vascular system of the sunflower roots. In addition, S. rochei D74 reduced 5DS synthesis of the strigol precursor substance, resulting in a reduction in O. cumana germination. These results demonstrated that the S. rochei D74 strain improved systemic resistance and decreased seed germination to prevent O. cumana parasitism. Full article

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

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25 pages, 24048 KiB

Open AccessArticle

SD-LSTM: A Dynamic Time Series Model for Predicting the Coupling Coordination of Smart Agro-Rural Development in China

by Chunlin Xiong, Yilin Zhang and Weijie Wang

Agriculture 2025, 15(14), 1491; https://doi.org/10.3390/agriculture15141491 - 11 Jul 2025

Abstract

The rapid advancement of digital information technology in rural China has positioned smart agro-rural development as a key driver of agricultural modernization. This study focuses on the theme of digital rural construction (DRC) and high-quality agricultural development (HAD), combining the two into smart [...] Read more.

The rapid advancement of digital information technology in rural China has positioned smart agro-rural development as a key driver of agricultural modernization. This study focuses on the theme of digital rural construction (DRC) and high-quality agricultural development (HAD), combining the two into smart agriculture and rural development. Utilizing panel data from 31 Chinese provinces from 2011 to 2022, a comprehensive evaluation index system is constructed to assess development levels. The entropy weight method and kernel density estimation are employed to evaluate indicator performance and capture dynamic distribution patterns. A coupling coordination model is used to analyze the spatio-temporal evolution of the interaction between the two systems, while a hybrid SD-LSTM (System Dynamics–Long Short-Term Memory) model forecasts coordination trends over the next six years. Results reveal a steady upward trend in both systems, with coordination levels improving from “moderate imbalance” to “moderate coordination.” A distinct spatial pattern emerges, characterized by “high in the east, low in the west” and a mismatch between high coupling and low coordination. Forecasts suggest a continued progression toward “good coordination.” The findings offer policy implications for enhancing digital village initiatives, accelerating rural technological diffusion, and strengthening regional collaboration—providing valuable insights into advancing China’s smart rural transformation and agricultural modernization. Full article

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

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4 pages, 170 KiB

Open AccessCorrection

Correction: Rodrigues et al. Applying Remote Sensing, Sensors, and Computational Techniques to Sustainable Agriculture: From Grain Production to Post-Harvest. Agriculture 2024, 14, 161

by Dágila Melo Rodrigues, Paulo Carteri Coradi, Newiton da Silva Timm, Michele Fornari, Paulo Grellmann, Telmo Jorge Carneiro Amado, Paulo Eduardo Teodoro, Larissa Pereira Ribeiro Teodoro, Fábio Henrique Rojo Baio and José Luís Trevizan Chiomento

Agriculture 2025, 15(14), 1490; https://doi.org/10.3390/agriculture15141490 - 11 Jul 2025

Abstract

The authors have recognized several errors in the original publication [...] Full article

(This article belongs to the Special Issue Agricultural Products Processing and Quality Detection)

18 pages, 4535 KiB

Open AccessArticle

Selenium Alleviates Low-Temperature Stress in Rice by Regulating Metabolic Networks and Functional Genes

by Naixin Liu, Qingtao Yu, Baicui Chen, Chengxin Li, Fanshan Bu, Jingrui Li, Xianlong Peng and Yuncai Lu

Agriculture 2025, 15(14), 1489; https://doi.org/10.3390/agriculture15141489 - 11 Jul 2025

Abstract

Low temperature is a major abiotic stress affecting rice productivity. Selenium (Se) treatment has been shown to enhance plant resilience to cold stress. In this study, low concentrations of selenium (ColdSe1) alleviated the adverse effects of cold stress on rice seedlings, improving fresh [...] Read more.

Low temperature is a major abiotic stress affecting rice productivity. Selenium (Se) treatment has been shown to enhance plant resilience to cold stress. In this study, low concentrations of selenium (ColdSe1) alleviated the adverse effects of cold stress on rice seedlings, improving fresh weight, plant height, and chlorophyll content by 36.9%, 24.3%, and 8.4%, respectively, while reducing malondialdehyde (MDA) content by 29.1%. Se treatment also increased the activities of antioxidant enzymes, including catalase (CAT), superoxide dismutase (SOD), and peroxidase (POD), by 25.2%, 42.7%, and 33.3%, respectively, and upregulated flavonoids, soluble sugars, cysteine (Cys), glutathione (GSH), and oxidized glutathione (GSSG). Transcriptome analysis revealed that ColdSe1 treatment upregulated genes associated with amino and nucleotide sugar metabolism, glutathione metabolism, and fructose and mannose metabolism. It also alleviated cold stress by modulating the MAPK signaling pathway, phytohormone signaling, and photosynthesis-related pathways, enriching genes and transcription factors linked to antioxidant metabolism and photosynthesis. Metabolomic analyses showed that ColdSe1 positively influenced amino acid glucose metabolism, glycerolipid metabolism, hormonal pathways, and alanine/glutamate pathways under cold stress, while also upregulating metabolites associated with plant secondary metabolites (e.g., flavonoids, phenolic compounds) and antioxidant metabolism (e.g., α-linolenic acid metabolism). In contrast, high selenium concentrations (ColdSe2) disrupted phenylpropanoid biosynthesis, α-linolenic acid metabolism, and ABC transporter function, exacerbating cold-stress injury. This study highlights the critical role of Se in mitigating cold stress in rice, offering a theoretical basis for its application as an agricultural stress reliever. Full article

(This article belongs to the Special Issue Genetic Research and Breeding to Improve Stress Resistance in Rice)

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20 pages, 337 KiB

Open AccessArticle

How Does Farmers’ Digital Literacy Affect Green Grain Production?

by Wenqi Wang and Meng Zhang

Agriculture 2025, 15(14), 1488; https://doi.org/10.3390/agriculture15141488 - 11 Jul 2025

Abstract

Grain production is crucial for national security and stability. Studying the impact of digital literacy on green production by grain farmers is of great significance for ensuring food security and achieving green agricultural development. This article utilizes data from the 2020 China Rural [...] Read more.

Grain production is crucial for national security and stability. Studying the impact of digital literacy on green production by grain farmers is of great significance for ensuring food security and achieving green agricultural development. This article utilizes data from the 2020 China Rural Revitalization Survey (CRRS), selecting a sample of 1811 farming households engaged in grain cultivation. Employing methods such as the ordered Probit model and mediating effect model, it analyzes the impact of digital literacy on green grain production from the perspectives of transformation drivers and pathways. The results show, first, that digital literacy significantly promotes farmers’ green production behaviors, and the findings remain valid after multiple robustness tests. Second, a mechanism analysis reveals that digital literacy drives farmers’ green production by reconstructing their benefit cognition and green cognition and promoting the application of green mechanization technologies. Third, a heterogeneity analysis indicates that the larger the farmers’ operation scale and the stronger their economic capacity, the more significant the promoting effect of digital literacy on their green production. Accordingly, it is necessary to accelerate the improvement of farmers’ digital literacy, reduce green production costs, popularize green mechanization technologies, and promote the green transformation of grain production. Full article

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

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14 pages, 2881 KiB

Open AccessArticle

Nano-Titanium Dioxide Regulates the Phenylpropanoid Biosynthesis of Radish (Raphanus sativus L.) and Alleviates the Growth Inhibition Induced by Polylactic Acid Microplastics

by Lisi Jiang, Wenyuan Li, Yuqi Zhang, Zirui Liu, Yangwendi Yang, Lixin Guo, Chang Guo, Zirui Yu and Wei Fu

Agriculture 2025, 15(14), 1478; https://doi.org/10.3390/agriculture15141478 - 11 Jul 2025

Abstract

Nano-titanium dioxide (nano-TiO₂) can alleviate oxidative damage in plants subjected to abiotic stress, interfere with related gene expression, and change metabolite content. Polylactic acid (PLA) microplastics can inhibit plant growth, induce oxidative stress in plant cells, and alter the biophysical properties [...] Read more.

Nano-titanium dioxide (nano-TiO₂) can alleviate oxidative damage in plants subjected to abiotic stress, interfere with related gene expression, and change metabolite content. Polylactic acid (PLA) microplastics can inhibit plant growth, induce oxidative stress in plant cells, and alter the biophysical properties of rhizosphere soil. In this study, untargeted metabolomics (LC-MS) and RNA-seq sequencing were performed on radish root cells exposed to nano-TiO₂ and PLA. The results showed that nano-TiO₂ alleviated the growth inhibition of radish roots induced by PLA. Nano-TiO₂ alleviated PLA-induced oxidative stress, and the activities of SOD and POD were decreased by 28.6% and 36.0%, respectively. A total of 1673 differentially expressed genes (DEGs, 844 upregulated genes, and 829 downregulated genes) were detected by transcriptome analysis. Metabolomics analysis showed that 5041 differential metabolites were involved; they mainly include terpenoids, fatty acids, alkaloids, shikimic acid, and phenylpropionic acid. Among them, phenylpropanoid biosynthesis as well as flavone and flavonol biosynthesis were the key metabolic pathways. This study demonstrates that nano-TiO₂ mitigates PLA phytotoxicity in radish via transcriptional and metabolic reprogramming of phenylpropanoid biosynthesis. These findings provide important references for enhancing crop resilience against pollutants and underscore the need for ecological risk assessment of co-existing novel pollutants in agriculture. Full article

(This article belongs to the Special Issue Crop Responses and Adaptations to Environmental Stresses: New Insights and Approaches)

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26 pages, 3747 KiB

Open AccessArticle

Design Optimization and Performance Evaluation of an Automated Pelleted Feed Trough for Sheep Feeding Management

by Xinyu Gao, Chuanzhong Xuan, Jianxin Zhao, Yanhua Ma, Tao Zhang and Suhui Liu

Agriculture 2025, 15(14), 1487; https://doi.org/10.3390/agriculture15141487 - 10 Jul 2025

Abstract

The automatic feeding device is crucial in grassland livestock farming, enhancing feeding efficiency, ensuring regular and accurate feed delivery, minimizing waste, and reducing costs. The shape and size of pellet feed render it particularly suitable for the delivery mechanism of automated feeding troughs. [...] Read more.

The automatic feeding device is crucial in grassland livestock farming, enhancing feeding efficiency, ensuring regular and accurate feed delivery, minimizing waste, and reducing costs. The shape and size of pellet feed render it particularly suitable for the delivery mechanism of automated feeding troughs. The uniformity of pellet flow is a critical factor in the study of automatic feeding troughs, and optimizing the movement characteristics of the pellets contributes to enhanced operational efficiency of the equipment. However, existing research often lacks a systematic analysis of the pellet size characteristics (such as diameter and length) and flow behavior differences in pellet feed, which limits the practical application of feed troughs. This study optimized the angle of repose and structural parameters of the feeding trough using Matlab simulations and discrete element modeling. It explored how the stock bin slope and baffle opening height influence pellet feed flow characteristics. A programmable logic controller (PLC) and human–machine interface (HMI) were used for precise timing and quantitative feeding, validating the design’s practicality. The results indicated that the Matlab method could calibrate the Johnson–Kendall–Roberts (JKR) model’s surface energy. The optimal slope was found to be 63°, with optimal baffle heights of 28 mm for fine and medium pellets and 30 mm for coarse pellets. The experimental metrics showed relative errors of 3.5%, 2.8%, and 4.2% (for average feed rate) and 8.2%, 7.3%, and 1.2% (for flow time). The automatic feeding trough showed a feeding error of 0.3% with PLC-HMI. This study’s optimization of the automatic feeding trough offers a strong foundation and guidance for efficient, accurate pellet feed distribution. Full article

(This article belongs to the Section Agricultural Technology)

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25 pages, 990 KiB

Open AccessReview

Toward Sustainable Broiler Production: Evaluating Microbial Protein as Supplementation for Conventional Feed Proteins

by Daniela-Mihaela Grigore, Maria-Luiza Mircea and Elena Narcisa Pogurschi

Agriculture 2025, 15(14), 1486; https://doi.org/10.3390/agriculture15141486 - 10 Jul 2025

Abstract

The increasing demand for sustainable poultry production has urged the exploration of alternative feed strategies supporting animal performance and environmental goals. The first section outlines the protein requirements in broiler nutrition (19–25% crude protein) and the physiological importance of balanced amino acid profiles. [...] Read more.

The increasing demand for sustainable poultry production has urged the exploration of alternative feed strategies supporting animal performance and environmental goals. The first section outlines the protein requirements in broiler nutrition (19–25% crude protein) and the physiological importance of balanced amino acid profiles. Vegetal conventional protein sources are discussed in terms of their nutritional value (12.7–20.1 MJ/kg), limitations (antinutritional factors), and availability. Emerging trends in broiler nutrition highlight the integration of supplements and the need for innovative feed solutions as support for the improvement in broiler body weight and feed efficiency increase. Microbial protein sources: yeast biomass (41–60% of 100 g dry weight), microbial mixed cultures (32–76% of 100 g dry weight), and beer by-products, such as brewer’s spent yeast (43–52% of 100 g dry weight), offer promising nutritional profiles, rich in bioactive compounds (vitamin B complex, minerals, enzymes, and antioxidants), and may contribute to improved gut health, immunity, and feed efficiency when used as dietary supplements. The review also addresses the regulatory and safety considerations associated with the use of microbial protein in animal feed, emphasizing EU legislation and standards. Finally, recent findings on the impact of microbial protein supplementation on broiler growth performance, carcass traits, and overall health status are discussed. This review supports the inclusion of microbial protein sources as valuable co-nutrients that complement conventional feed proteins, contributing to more resilient and sustainable broiler production and broiler meat products. Full article

(This article belongs to the Special Issue Alternative and Novel Feeds for Poultry: Nutritive Value, Product Quality and Environmental Aspects)

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14 pages, 618 KiB

Open AccessArticle

Reliability of Acid-Insoluble Ashes and Undigestible Neutral Detergent Fibre as Internal Markers for Estimation of Digestibility in Beef Cattle Fed High-Concentrate Diets

by Amira Arbaoui and Antonio de Vega

Agriculture 2025, 15(14), 1485; https://doi.org/10.3390/agriculture15141485 - 10 Jul 2025

Abstract

Digestibility, together with intake, is the main factor affecting animal productivity. It can be assessed in vivo by measuring total feed intake and faecal output (time-consuming and labour-intensive) or with the aid of substances known as markers. Internal markers such as acid insoluble [...] Read more.

Digestibility, together with intake, is the main factor affecting animal productivity. It can be assessed in vivo by measuring total feed intake and faecal output (time-consuming and labour-intensive) or with the aid of substances known as markers. Internal markers such as acid insoluble ash (AIA) or undigestible neutral detergent fibre (uNDF) have been alleged to be preferable for digestibility estimations. The use of AIA and uNDF for digestibility estimation in beef cattle fed high-concentrate and barley straw diets has been rarely documented; hence, the objectives of the present paper were to compare digestibility values obtained by total faecal collection vs. AIA or uNDF (Experiment 1), to compare digestibility values obtained using Cr₂O₃ as an external marker vs. AIA or uNDF (Experiment 2), and to compare digestibility values obtained using AIA vs. uNDF in beef cattle fed high-concentrate and barley straw diets (Experiment 3). Faecal recoveries of AIA and uNDF (Experiment 1) were very variable and likely influenced by contamination of faeces and/or feedstuffs with soil and/or dust. Then, the regressions between digestibility values obtained in metabolism cages or using Cr₂O₃ as an external marker and AIA or uNDF were not significant. The use of these two latter markers for estimation of digestibility in beef cattle fed high-concentrate and barley straw diets is not recommended. Full article

(This article belongs to the Special Issue Assessment of Nutritional Value of Animal Feed Resources)

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34 pages, 1232 KiB

Open AccessArticle

A Knowledge-Driven Smart System Based on Reinforcement Learning for Pork Supply-Demand Regulation

by Haohao Song and Jiquan Wang

Agriculture 2025, 15(14), 1484; https://doi.org/10.3390/agriculture15141484 - 10 Jul 2025

Abstract

With the advancement of Agriculture 4.0, intelligent systems and data-driven technologies offer new opportunities for pork supply-demand balance regulation, while also confronting challenges such as production cycle fluctuations and epidemic outbreaks. This paper introduces a knowledge-driven smart system for pork supply-demand regulation, which [...] Read more.

With the advancement of Agriculture 4.0, intelligent systems and data-driven technologies offer new opportunities for pork supply-demand balance regulation, while also confronting challenges such as production cycle fluctuations and epidemic outbreaks. This paper introduces a knowledge-driven smart system for pork supply-demand regulation, which integrates essential components including a knowledge base, a mathematical-model-based expert system, an enhanced optimization framework, and a real-time feedback mechanism. Around the core of the system, a nonlinear constrained optimization model is established, which uses adjustments to newly retained gilts as decision variables and minimizes supply-demand squared errors as its objective function, incorporating multi-dimensional factors such as pig growth dynamics, epidemic impacts, consumption trends, and international trade into its analytical framework. By harnessing dynamic decision-making capabilities of reinforcement learning (RL), we design an optimization architecture centered on the Q-learning mechanism and dual-strategy pools, which is integrated into the honey badger algorithm to form the RL-enhanced honey badger algorithm (RLEHBA). This innovation achieves an efficient balance between exploration and exploitation in model solving and improves system adaptability. Numerical experiments demonstrate RLEHBA’s superior performance over State-of-the-Art algorithms on the CEC 2017 benchmark. A case study of China’s 2026 pork regulation confirms the system’s practical value in stabilizing the supply-demand balance and optimizing resource allocation. Finally, some targeted managerial insights are proposed. This study constructs a replicable framework for intelligent livestock regulation, and it also holds transformative significance for sustainable and adaptive supply chain management in global agri-food systems. Full article

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

33 pages, 1513 KiB

Open AccessArticle

From Online Markets to Green Fields: Unpacking the Impact of Farmers’ E-Commerce Participation on Green Production Technology Adoption

by Zhaoyu Li, Kewei Gao and Guanghua Qiao

Agriculture 2025, 15(14), 1483; https://doi.org/10.3390/agriculture15141483 - 10 Jul 2025

Abstract

Amid the global push for agricultural green transformation, sustainable agriculture requires not only technological innovation but also market mechanisms that effectively incentivize green practices. Agricultural e-commerce is increasingly viewed as a potential driver of green technology diffusion among farmers. However, the extent and [...] Read more.

Amid the global push for agricultural green transformation, sustainable agriculture requires not only technological innovation but also market mechanisms that effectively incentivize green practices. Agricultural e-commerce is increasingly viewed as a potential driver of green technology diffusion among farmers. However, the extent and mechanism of e-commerce’s influence on farmers’ green production remain underexplored. Using survey data from 346 rural households in Inner Mongolia, China, this study develops a conceptual framework of “e-commerce participation–green cognition–green adoption” and employs propensity score matching (PSM) combined with mediation analysis to evaluate the impact of e-commerce participation on green technology adoption. The empirical results yield four main findings: (1) E-commerce participation significantly promotes the adoption of green production technologies, with an estimated 29.52% increase in adoption. (2) Participation has a strong positive effect on water-saving irrigation and pest control technologies at the 5% significance level, a moderate effect on straw incorporation at the 10% level, and no statistically significant impact on plastic film recycling or organic fertilizer use. (3) Compared to third-party sales, the direct e-commerce model more effectively promotes green technology adoption, with an increase of 21.64% at the 5% significance level. (4) Green cognition serves as a mediator in the relationship between e-commerce and green adoption behavior. This study makes contributions by introducing e-commerce participation as a novel explanatory pathway for green technology adoption, going beyond traditional policy-driven and resource-based perspectives. It further highlights the role of cognitive mechanisms in shaping adoption behaviors. The study recommends that policymakers subsidize farmers’ participation in e-commerce, invest in green awareness programs, and support differentiated e-commerce models to enhance their positive impact on sustainable agricultural practices. Full article

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

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19 pages, 2155 KiB

Open AccessArticle

Effects of Degossypolized Cottonseed Protein on the Laying Performance, Egg Quality, Blood Indexes, Gossypol Residue, Liver and Uterine Histopathological Changes, and Intestinal Health of Laying Hens

by Ru Li, Xingyuan Luo, Shiping Bai, Xuemei Ding, Jianping Wang, Qiufeng Zeng, Yue Xuan, Shanshan Li, Sharina Qi, Xiaojuan Bi, Chao He, Xuanming Chen and Keying Zhang

Agriculture 2025, 15(14), 1482; https://doi.org/10.3390/agriculture15141482 - 10 Jul 2025

Abstract

This experiment aimed to investigate the appropriate level of degossypolized cottonseed protein (DGCP) in the diet of laying hens. A total of 600 49-week-old Lohmann pink laying hens were allocated to five treatments, with six replicates per treatment and 20 birds per replicate. [...] Read more.

This experiment aimed to investigate the appropriate level of degossypolized cottonseed protein (DGCP) in the diet of laying hens. A total of 600 49-week-old Lohmann pink laying hens were allocated to five treatments, with six replicates per treatment and 20 birds per replicate. The control group was fed a corn-soybean meal basal diet. Four experimental diets were formulated by replacing 25%, 50%, 75%, and 100% of the soybean meal protein-equivalent capacity with DGCP, where 100% replacement corresponded to the maximum safe inclusion of DGCP. The study period lasted for 8 weeks. The results showed that the feed intake, average egg weight, egg mass, laying rate, and the albumen percentage were significantly reduced in the 100% DGCP group (p < 0.05). Plasma uric acid (UA), total cholesterol (TC), triglyceride (TG), and potassium (K) levels were significantly lower (p < 0.05), and depth of crypt (CD) was significantly higher (p < 0.05) in the 100% DGCP group. The DGCP diet linearly increased the relative abundance of Bacteroidota and Bacteroide and significantly increased the relative abundance of Desulfobacterotas in the cecum contents compared to the control group (p < 0.05). The ACE and Chao1 indices in both the control group and the 100% DGCP group were significantly decreased (p < 0.05). In conclusion, the dietary addition of DGCP can reach up to 114.6 g/kg. Full article

(This article belongs to the Special Issue Alternative and Novel Feeds for Poultry: Nutritive Value, Product Quality and Environmental Aspects)

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20 pages, 7149 KiB

Open AccessArticle

On-Demand Design of Terahertz Metasurface Sensors for Detecting Plant Endogenous and Exogenous Molecules

by Hongyan Gao, Yuanye Liu, Gen Li, Haodong Liu, Yuxi Shang and Zheng Ma

Agriculture 2025, 15(14), 1481; https://doi.org/10.3390/agriculture15141481 - 10 Jul 2025

Abstract

This study presents a neural-network-based method for on-demand design of terahertz metasurface sensors, aimed at detecting plant endogenous and exogenous molecules. The approach uses target performance indicators (constructed via fingerprint peaks) as inputs and structural parameters as outputs, employing a neural network to [...] Read more.

This study presents a neural-network-based method for on-demand design of terahertz metasurface sensors, aimed at detecting plant endogenous and exogenous molecules. The approach uses target performance indicators (constructed via fingerprint peaks) as inputs and structural parameters as outputs, employing a neural network to map the complex relationship between them. Two single-resonant-peak metasurface sensors were developed to detect abscisic acid and gibberellic acid. The abscisic acid metasurface sensor achieved an average MSE of 5.66 × 10⁻⁶ and R_ER of 0.167%, while the gibberellic acid metasurface sensor had an average MSE of 8 × 10⁻⁷ and R_ER of 0.086%. Their resonant peaks highly matched the substance fingerprint peaks, enabling specific detection. Metasurface sensors’ sensitivities were effectively controlled using correlation analysis and neural networks, achieving remarkable levels of 156.7 and 150.1 GHz/RIU, allowing trace detection. Three dual-resonant-peak metasurface sensors were designed to improve the detection specificity for chlorophyll and folpet and to detect chlorophyll and folpet simultaneously. These metasurface sensors exhibited average MSEs of 1.4 × 10⁻⁵, 1.6 × 10⁻⁶, 1.35 × 10⁻⁵ and R_ERs of 0.27%, 0.088%, 0.20%. The model also worked for four other plant-related molecules, proving its strong generalization ability. Overall, for different application scenarios of exogenous and endogenous molecules in plants, the on-demand design methodology offers a whole new set of ideas for quickly designing and widely applying metasurface sensors with suitable performance indicators. Full article

(This article belongs to the Section Digital Agriculture)

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28 pages, 1311 KiB

Open AccessArticle

Measuring and Analyzing the Spatiotemporal Evolution of Agricultural Green Total Factor Productivity on the Tibetan Plateau (2002–2021)

by Mengmeng Zhang, Jianyu Xiao and Chengqun Yu

Agriculture 2025, 15(14), 1480; https://doi.org/10.3390/agriculture15141480 - 10 Jul 2025

Abstract

This study employs a Super-SBM model to construct a comprehensive evaluation framework—encompassing input factors, desirable outputs, and undesirable outputs—to measure agricultural green total factor productivity (AGTFP) in the Tibet Autonomous Region in the period 2002–2021. We then apply kernel density estimation and Dagum [...] Read more.

This study employs a Super-SBM model to construct a comprehensive evaluation framework—encompassing input factors, desirable outputs, and undesirable outputs—to measure agricultural green total factor productivity (AGTFP) in the Tibet Autonomous Region in the period 2002–2021. We then apply kernel density estimation and Dagum Gini coefficient decomposition to examine its spatiotemporal evolution. The main findings are as follows: (1) AGTFP in Tibet rose overall from 0.949 in 2002 to 1.068 in 2021, with a compound annual growth rate of 0.78%, yet remained below the national average; (2) significant regional heterogeneity emerged, with three typical evolution patterns identified: continual improvement (Nagqu, Qamdo), stable fluctuation (Lhasa, Xigazê), and risk of decline (Lhoka, Nyingchi, Ngari); (3) gains in pure technical efficiency were the primary driver of AGTFP growth, while insufficient scale efficiency was a key constraint; (4) AGTFP exhibited a “convergence–divergence–reconvergence” dynamic, with interregional disparities widening but structural patterns stabilizing; and (5) interregional inequality was the main source of overall disparity—its importance grew over the study period, with the largest gap observed between agrarian and pastoral zones. On this basis, we recommend a “gradient advancement” strategy that prioritizes pure technical efficiency and regional coordination, while promoting zone-specific support tools tailored to local ecological conditions and institutional capacities to ensure inclusive green productivity growth. Full article

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

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21 pages, 5907 KiB

Open AccessArticle

Coverage Path Planning Based on Region Segmentation and Path Orientation Optimization

by Tao Yang, Xintong Du, Bo Zhang, Xu Wang, Zhenpeng Zhang and Chundu Wu

Agriculture 2025, 15(14), 1479; https://doi.org/10.3390/agriculture15141479 - 10 Jul 2025

Abstract

To address the operational demands of irregular farmland with fixed obstacles, this study proposes a full-coverage path planning framework that integrates UAV-based 3D perception and angle-adaptive optimization. First, digital orthophoto maps (DOMs) and digital elevation models (DEMs) were reconstructed from low-altitude aerial imagery. [...] Read more.

To address the operational demands of irregular farmland with fixed obstacles, this study proposes a full-coverage path planning framework that integrates UAV-based 3D perception and angle-adaptive optimization. First, digital orthophoto maps (DOMs) and digital elevation models (DEMs) were reconstructed from low-altitude aerial imagery. The feasible working region was constructed by shrinking field boundaries inward and dilating obstacle boundaries outward. This ensured sufficient safety margins for machinery operation. Next, segmentation angles were scanned from 0° to 180° to minimize the number and irregularity of sub-regions; then a two-level simulation search was performed over 0° to 360° to optimize the working direction for each sub-region. For each sub-region, the optimal working direction was selected based on four criteria: the number of turns, travel distance, coverage redundancy, and planning time. Between sub-regions, a closed-loop interconnection path was generated using eight-directional A* search combined with polyline simplification, arc fitting, Chaikin subdivision, and B-spline smoothing. Simulation results showed that a 78° segmentation yielded four regular sub-regions, achieving 99.97% coverage while reducing the number of turns, travel distance, and planning time by up to 70.42%, 23.17%, and 85.6%. This framework accounts for field heterogeneity and turning radius constraints, effectively mitigating path redundancy in conventional fixed-angle methods. This framework enables general deployment in agricultural field operations and facilitates extensions toward collaborative and energy-optimized task planning. Full article

(This article belongs to the Section Agricultural Technology)

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