Agriculture

19 pages, 1190 KB

Open AccessArticle

Identification and Characterization of the CRY Gene Family Involved in Safflower Flavonoid Biosynthesis

by Mamar Laeeq Zia, Debin Wang, Zixi Lin, Rubab Arshad, Xiaoyan Wang, Jiao Liu, Jianjiang Wei, Rui Qin and Hong Liu

Agriculture 2026, 16(2), 260; https://doi.org/10.3390/agriculture16020260 (registering DOI) - 20 Jan 2026

Abstract

The cryptochromes (CRYs) perceive blue light to regulate various developmental and metabolic events. However, the role of CRYs in flavonoid biosynthesis and flower pigmentation in safflower (Carthamus tinctorius L.) remains unknown. In this study, we determined flower color diversity among 485 safflower [...] Read more.

The cryptochromes (CRYs) perceive blue light to regulate various developmental and metabolic events. However, the role of CRYs in flavonoid biosynthesis and flower pigmentation in safflower (Carthamus tinctorius L.) remains unknown. In this study, we determined flower color diversity among 485 safflower genotypes using the integrated CIELAB color space parameters and cluster analysis. On this basis, distinct colors were categorized into four groups, namely white (WW), yellow (YY), orange–red (OR), and yellow–red (YR). A genome-wide association study (GWAS) via 933,444 high-quality SNPs showed CtCRY2 as a flower color variation gene. Subsequently, genomic analysis identified three genes of the CRY family, including CtCRY1.1, CtCRY1.2, and CtCRY2. In silico analysis, such as gene structure, phylogeny and cis-acting elements, suggested CtCRY1.1 as a key candidate in pigment biosynthesis and was, therefore, selected for functional validation. Overexpression of CtCRY1.1 in Arabidopsis accumulated a high flavonoid content, particularly upregulating the expression of CHS, FLS, and ANS, proving its role as a positive regulator of flavonoid biosynthesis in safflower. These findings provide insights into the molecular mechanisms underlying flower color regulation in safflower and highlight CtCRY1.1 as a new target to enhance pigment-related traits in plants. Full article

(This article belongs to the Section Crop Genetics, Genomics and Breeding)

► Show Figures

Graphical abstract

17 pages, 3715 KB

Open AccessArticle

A Two-Stage Farmer Assistant for Kidding Detection: Enhancing Farming Productivity and Animal Welfare

by João Ferreira, Pedro Gonçalves, Mário Antunes, Ana T. Belo and Maria R. Marques

Agriculture 2026, 16(2), 259; https://doi.org/10.3390/agriculture16020259 - 20 Jan 2026

Abstract

Kidding in goats is a highly significant event with major economic implications and strong impacts on the welfare of both the offspring and the mothers. Monitoring the process is extremely demanding, as it is impossible to predict precisely when it will occur. For [...] Read more.

Kidding in goats is a highly significant event with major economic implications and strong impacts on the welfare of both the offspring and the mothers. Monitoring the process is extremely demanding, as it is impossible to predict precisely when it will occur. For this reason, the automatic detection of kidding has the potential to generate substantial productivity gains while also improving animal well-being. Artificial intelligence techniques based on accelerometry data have been explored for identifying the event, but these approaches typically rely on data loggers, which cannot trigger real-time alerts or assistance. Embedding detection mechanisms directly into wearable devices enables much faster identification and supports energy-efficient operations. However, this approach also introduces considerable challenges, particularly due to the strict constraints of wearable devices in terms of weight, cost, and battery life. The present work documents the development of a real-time, automatic kidding-detection mechanism in which the detection workload is distributed between the collar and an edge device. System evaluation demonstrated the feasibility of this distributed architecture, confirming that both components can cooperate effectively to achieve reliable detection. The system achieved a Matthews Correlation Coefficient performance of 0.91, highlighting the robustness and practical viability of the proposed solution. Full article

(This article belongs to the Section Farm Animal Production)

► Show Figures

Figure 1

17 pages, 4376 KB

Open AccessArticle

The FPF Gene Family in Tomato: Genome-Wide Identification and the Role of SlFPF1 in Gibberellin-Dependent Growth

by Yali Zhu, Yuanyuan Kong, Xingping Liu, Aiying Cui, Cuifang Chang, Xuemei Hou and Weibiao Liao

Agriculture 2026, 16(2), 258; https://doi.org/10.3390/agriculture16020258 - 20 Jan 2026

Abstract

Flowering promoting factor 1 (FPF1) is a key regulator of plant flowering time. While the functions of the FPF family have been characterized in species such as Arabidopsis and rice, systematic studies on the tomato FPF family remain limited. In this study, we [...] Read more.

Flowering promoting factor 1 (FPF1) is a key regulator of plant flowering time. While the functions of the FPF family have been characterized in species such as Arabidopsis and rice, systematic studies on the tomato FPF family remain limited. In this study, we comprehensively analyzed the FPF family in tomato (Solanum lycopersicum L.), identifying five SlFPF members in the tomato genome. Phylogenetic analysis classified these genes into five distinct subgroups, and chromosome mapping revealed their distribution across three chromosomes, with the highest density on chromosome 1. Promoter analysis identified a range of putative cis-acting elements related to abiotic stress and hormonal responses. Differential expression analysis of various tissues showed that the five SlFPF genes exhibit varying expression levels, where SlFPF1 had a significantly higher expression compared to the others. Following treatments with abiotic stresses (NaCl, PEG, dark, and low light) and phytohormones (GA, MeJA, ABA, and SA), SlFPF1 expression is notably higher under GA treatment than under other conditions. Based on these findings, SlFPF1 and GA treatments were selected for further functional analysis. The results show that GA treatment significantly promotes multiple morphological traits, including root length, stem diameter, leaf area, plant height, dry weight, and fresh weight. However, silencing SlFPF1 expression led to a reduction in all these traits. Moreover, in SlFPF1-silenced plants, GA treatment failed to enhance root length, leaf area, fresh weight, and dry weight, indicating that GA-dependent growth promotion in tomato plants relies on SlFPF1. This study provides a theoretical foundation for understanding the SlFPF gene family and its role in plant growth and stress responses. Full article

(This article belongs to the Section Crop Genetics, Genomics and Breeding)

► Show Figures

Figure 1

22 pages, 387 KB

Open AccessArticle

The Impact of Digital Literacy on Farmers’ Green Production Behaviours: Evidence from Guizhou, China

by Li Zhu, Weiyong Yu and Jinxiu Yang

Agriculture 2026, 16(2), 257; https://doi.org/10.3390/agriculture16020257 - 20 Jan 2026

Abstract

The increasing momentum of agricultural digital transformation and green development necessitates investigations into how farmers’ digital literacy influences their engagement in green production behaviours, which is critical for achieving the high-quality development of modern agriculture. Utilising primary survey data collected from farmers in [...] Read more.

The increasing momentum of agricultural digital transformation and green development necessitates investigations into how farmers’ digital literacy influences their engagement in green production behaviours, which is critical for achieving the high-quality development of modern agriculture. Utilising primary survey data collected from farmers in rural areas of Guizhou Province, China, this study investigated how digital literacy affects farmers’ green production behaviours. The findings are as follows: (1) Digital literacy exerts a significant positive impact on farmers’ adoption of green production behaviours. Regarding the hierarchical effect, the order of influence is as follows: digital security awareness > basic digital skills > digital application and innovation. (2) The facilitating effect of digital literacy is primarily achieved through two pathways: the peer effect and the guidance effect. (3) Farmers with higher education levels are more impacted by digital literacy than farmers with lower education levels. (4) The impact of digital literacy is more positively significant for young and older farmers than for middle-aged groups. Based on these research findings, it is recommended that future policy formulation and technology extension efforts should prioritise support for specific regions and groups, such as mountainous areas, small-scale operations, low-education backgrounds, and the elderly. Such targeted approaches are crucial for encouraging wider adoption of green production behaviours among farmers. Full article

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

► Show Figures

Figure 1

20 pages, 3611 KB

Open AccessArticle

Screening of High-Virulence and High-Yield Beauveria bassiana Strains for the Biocontrol of Cylas formicarius (Fabricius)

by Yu-Tzu Yang, Chiao-Yen Chen, Jen Tzeng and Yung-Yu Yang

Agriculture 2026, 16(2), 256; https://doi.org/10.3390/agriculture16020256 - 19 Jan 2026

Abstract

Cylas formicarius (Fabricius) is a major pest of sweet potato in Taiwan, where no microbial control agents are currently available. This study aimed to identify Beauveria bassiana strains with both high virulence and high spore productivity using a rice-based solid-state fermentation (SSF) system. [...] Read more.

Cylas formicarius (Fabricius) is a major pest of sweet potato in Taiwan, where no microbial control agents are currently available. This study aimed to identify Beauveria bassiana strains with both high virulence and high spore productivity using a rice-based solid-state fermentation (SSF) system. Eleven field isolates were characterized using morphological traits and multilocus sequence analyses (Bloc, RPB1, RPB2, and TEF). Laboratory assays compared conidial production on 1/2 potato dextrose agar (PDA) and rice substrates and evaluated pathogenicity against adult C. formicarius by estimating median lethal time (LT₅₀) and the onset of mortality (t₀) based on cumulative mortality data. Substantial variation was observed among isolates in growth performance, sporulation, and virulence. Strain TyEf0054 consistently exhibited high pathogenicity, causing more than 80% mortality with conidia produced from both 1/2 PDA and rice, and showed rapid killing activity, with LT₅₀ values of approximately 5.5–5.9 days and t₀ values ranging from 3.55 to 3.99 days. In contrast, some strains exhibited high sporulation and virulence on 1/2 PDA but failed to produce conidia or showed severe clumping during rice-based solid-state fermentation, indicating poor suitability for large-scale production. These results demonstrate that high laboratory virulence alone is insufficient for strain selection and highlight the importance of integrating killing speed, final mortality, and SSF compatibility as joint selection criteria. Overall, TyEf0054 represents a promising locally adapted candidate for the development of commercial mycoinsecticides for sustainable management of sweet potato weevil in Taiwan. Full article

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

► Show Figures

Figure 1

15 pages, 724 KB

Open AccessArticle

Chemical Characterization of Extracts Derived from Apple, Sour Cherry, and Sweet Cherry Seed Oils

by Marek Szmigielski, Marek Domin, Piotr Kiczorowski, Marta Krajewska, Jolanta Piekut, Marzena Smolewska and Małgorzata Szczepanik

Agriculture 2026, 16(2), 255; https://doi.org/10.3390/agriculture16020255 - 19 Jan 2026

Abstract

Numerous sectors of the food processing and oleochemical industries require oils with specific physicochemical properties. Fruit processing generates substantial waste potentially containing valuable raw materials for oil extraction. The significant volumes of apples and cherries processed in Poland prompted an assessment of their [...] Read more.

Numerous sectors of the food processing and oleochemical industries require oils with specific physicochemical properties. Fruit processing generates substantial waste potentially containing valuable raw materials for oil extraction. The significant volumes of apples and cherries processed in Poland prompted an assessment of their seeds’ suitability as oil sources. Seed dry matter, protein, and oil content were determined. The extracted oils were analyzed for acid value (AV), peroxide value (PV), oxidative stability, fatty acid composition, and sterol and tocopherol content. The predominant higher fatty acids identified in the sour cherry and sweet cherry kernel oils were linoleic acid (C18:2, n-6), with mean concentrations of 45.82% and 29.23%, respectively, and oleic acid (C18:1, n-9), accounting for 41.54% and 46.59%, respectively. Additional fatty acids detected included palmitic acid C16:0 (6.23% and 5.91%), palmitoleic acid C16:1, n-7 (0.29%), stearic acid C18:0 (1.36% and 3.11%), arachidic acid C20:0 (1.13%), α-eleostearic acid C18:3 (5.07% and 9.48%), and α-linolenic acid C18:3, n-3 (4.09%). Given the substantial proportion of the oil fraction containing numerous potentially biologically active compounds, including nutritionally valuable fatty acids, tocopherols, and phytosterols, apple, sour cherry, and sweet cherry seeds demonstrate considerable potential as raw materials for applications in the food, pharmaceutical, and cosmetics industries. Full article

(This article belongs to the Special Issue Phytochemical Changes in Vegetables and Fruits During Post-Harvest Storage and Processing)

22 pages, 1916 KB

Open AccessArticle

Transcriptomic Insights into the Dynamic Regulatory Mechanisms of Longissimus Dorsi Muscle Development in Jinhua Pigs

by Yihan Fu, Fen Wu, Zhe Zhang, Qishan Wang, Yuchun Pan, Zhen Wang and Huanfa Gong

Agriculture 2026, 16(2), 254; https://doi.org/10.3390/agriculture16020254 - 19 Jan 2026

Abstract

Pigs are a major source of animal protein for humans and serve as valuable biomedical models. Compared to Western commercial pig breeds, Jinhua pigs are characterized by superior meat quality due to dynamic muscle development and fat deposition. However, studies investigating dynamic transcriptional [...] Read more.

Pigs are a major source of animal protein for humans and serve as valuable biomedical models. Compared to Western commercial pig breeds, Jinhua pigs are characterized by superior meat quality due to dynamic muscle development and fat deposition. However, studies investigating dynamic transcriptional regulation of swine meat quality traits across developmental stages remain limited. In this work, we collected longissimus dorsi muscle tissue from three Jinhua and three Landrace × Yorkshire pigs at 1, 90, and 180 days of age, respectively. We have uncovered differentially expressed genes and transcripts, alternative splicing events, and gene fusion events across development stages utilizing RNA sequencing data. CKM exhibited consistent breed-specific alternative splicing and gene fusion events across all three stages, representing a stable regulator of muscle development in Jinhua pigs. On the other hand, our findings highlight day 90 as a critical “window phase” for muscle development and meat quality differences between Jinhua and Landrace × Yorkshire pigs at this stage, exhibiting the greatest number of inter-breed differences in transcriptomic genetic regulation. Additionally, time series analysis revealed that genes with peak expression at day 90 were significantly enriched in pathways associated with muscle development and function. Finally, we identified PFKM, PRKAG3, and CKM as candidate genes with age-specific expression and post-transcriptional regulation that likely influence muscle development. This study advances understanding of transcriptional regulation in pig muscle with implications for meat quality improvement. Full article

(This article belongs to the Special Issue Current Research and Strategies for Improving Farm Animal Meat Quality)

22 pages, 8145 KB

Open AccessArticle

Research on Greenhouse Eggplant Fruit Detection and Tracking-Based Counting Using an Improved YOLOv5s-DeepSORT

by Jianfei Zhu, Long Bai, Caishan Liu, Chengxu Nian, Keke Zhang and Sibo Yang

Agriculture 2026, 16(2), 253; https://doi.org/10.3390/agriculture16020253 - 19 Jan 2026

Abstract

Accurate fruit counting is essential for yield evaluation and automated management in greenhouse eggplant production. This study presents a lightweight detection and counting method based on an improved YOLOv5s–DeepSORT framework. To reduce computational cost while preserving accuracy, we replace the YOLOv5s backbone with [...] Read more.

Accurate fruit counting is essential for yield evaluation and automated management in greenhouse eggplant production. This study presents a lightweight detection and counting method based on an improved YOLOv5s–DeepSORT framework. To reduce computational cost while preserving accuracy, we replace the YOLOv5s backbone with MobileNetV3, insert an Efficient Channel Attention (ECA) module to enhance discriminative fruit features, and substitute the neck C3 block with C2f to strengthen multi-scale feature fusion. Compared with the original YOLOv5s, our improved YOLOv5s increases precision by 2.3% while reducing the number of parameters and FLOPs by 37.0% and 50.9%, respectively. For counting, we integrate DeepSORT with a counting-zone strategy that increments the count once per target when the bounding-box center first enters the counting zone, thereby mitigating identity switches (ID switches) and suppressing duplicate counts. Experimental results demonstrate that the proposed method enables accurate and real-time eggplant fruit counting in complex greenhouse scenes, providing practical support for automated yield assessment on inspection robots. Full article

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

► Show Figures

Figure 1

31 pages, 5575 KB

Open AccessArticle

Explainable Deep Learning and Edge Inference for Chilli Thrips Severity Classification in Strawberry Canopies

by Uchechukwu Ilodibe, Daeun Choi, Sriyanka Lahiri, Changying Li, Daniel Hofstetter and Yiannis Ampatzidis

Agriculture 2026, 16(2), 252; https://doi.org/10.3390/agriculture16020252 - 19 Jan 2026

Abstract

Traditional plant scouting is often a costly and labor-intensive task that requires experienced specialists to diagnose and manage plant stresses. Artificial intelligence (AI), particularly deep learning and computer vision, offers the potential to transform scouting by enabling rapid, non-intrusive detection and classification of [...] Read more.

Traditional plant scouting is often a costly and labor-intensive task that requires experienced specialists to diagnose and manage plant stresses. Artificial intelligence (AI), particularly deep learning and computer vision, offers the potential to transform scouting by enabling rapid, non-intrusive detection and classification of early stress symptoms from plant images. However, deep learning models are often opaque, relying on millions of parameters to extract complex nonlinear features that are not interpretable by growers. Recently, eXplainable AI (XAI) techniques have been used to identify key spatial regions that contribute to model predictions. This project explored the potential of convolutional neural networks (CNNs) for classifying the severity of chilli thrips damage in strawberry plants in Florida and employed XAI techniques to interpret model decisions and identify symptom-relevant canopy features. Four CNN architectures, YOLOv11, EfficientNetV2, Xception, and MobileNetV3, were trained and evaluated using 2353 square RGB canopy images of different sizes (256, 480, 640 and 1024 pixels) to classify symptoms as healthy, moderate, or severe. Trade-offs between image size, model parameter count, inference speed, and accuracy were examined in determining the best-performing model. The models achieved accuracies ranging from 77% to 85% with inference times of 5.7 to 262.3 ms, demonstrating strong potential for real-time pest severity estimation. Gradient-Weighted Class Activation Mapping (Grad-CAM) visualization revealed that model attention focused on biologically relevant regions such as fruits, stems, leaf edges, leaf surfaces, and dying leaves, areas commonly affected by chilli thrips. Subsequent analysis showed that model attention spread from localized regions in healthy plants to wide diffuse regions in severe plants. This alignment between model attention and expert scouting logic suggests that CNNs internalize symptom-specific visual cues and can reliably classify pest-induced plant stress. Full article

(This article belongs to the Special Issue Automation Strategy Using Machine Learning in Horticultural Crop Cultivation)

► Show Figures

Graphical abstract

19 pages, 3569 KB

Open AccessArticle

Alternating Partial Root-Zone Irrigation Improves Alfalfa Water Use Efficiency by Regulating Root Water Uptake, Photosynthetic Traits, and Endogenous Hormones

by Xingyu Ge, Chen Liang, Shuzhen Zhang, Lijun Li, Xianwei Peng, Binghan Wen, Youping An, Dongxu Huang and Ruixuan Xu

Agriculture 2026, 16(2), 251; https://doi.org/10.3390/agriculture16020251 (registering DOI) - 19 Jan 2026

Abstract

Alfalfa (Medicago sativa L.) is an important forage crop with significant economic value. Alternating partial root-zone irrigation (APRI) is a promising water-saving technique that has been shown to improve water use efficiency in various crops. In this study, the effects of APRI [...] Read more.

Alfalfa (Medicago sativa L.) is an important forage crop with significant economic value. Alternating partial root-zone irrigation (APRI) is a promising water-saving technique that has been shown to improve water use efficiency in various crops. In this study, the effects of APRI on root water uptake, photosynthetic indices, and physiological responses in alfalfa were investigated. Polyethylene glycol (PEG 6000) was used to simulate water stress, and four irrigation treatments were established: conventional irrigation (CI), deficit irrigation (DI), fixed partial root-zone irrigation (FPRI), and APRI. Principal component analysis (PCA) revealed that APRI reduced stomatal conductance (Gs) by 19.82% and transpiration rate (E) by 19.16%, which was associated with increased abscisic acid (ABA) content, thereby enhancing instantaneous water use efficiency (iWUE) by 47.93%. Meanwhile, APRI promoted root growth, leading to a 14.09% increase in root–shoot ratio, which in turn enhanced the photosynthetic rate by 22.06%. APRI enhanced methyl jasmonate (MeJA) content in alfalfa leaves by 45.23%, which was associated with a 24.13% improvement in water absorption capacity. In conclusion, APRI induced positive physiological responses in alfalfa, with the effectiveness ranked as follows: APRI > CI > FPRI > DI. These findings provide a theoretical basis for the rational application of APRI in alfalfa forage production. Full article

(This article belongs to the Topic Tolerance to Drought and Salt Stress in Plants, 2nd volume)

► Show Figures

Figure 1

20 pages, 322 KB

Open AccessArticle

Competitive Asymmetries and the Threat to Supply Chain Resilience: A Comparative Analysis of the EU–Mercosur Trade Agreement’s Impact on the European Union’s and Polish Agri-Food Sectors

by Sebastian Jarzebowski, Marcin Adamski, Łukasz Zaremba, Agata Żak, Brigitte Petersen and Alejandro Guzmán Rivera

Agriculture 2026, 16(2), 250; https://doi.org/10.3390/agriculture16020250 - 19 Jan 2026

Abstract

This study analyzes the competitive asymmetries and trade effects of the proposed EU–Mercosur Trade Agreement on the European Union’s (EU) and Polish agri-food sectors. The comparative analysis reveals that Mercosur holds a significant structural advantage driven by substantially lower labor costs, cheaper agricultural [...] Read more.

This study analyzes the competitive asymmetries and trade effects of the proposed EU–Mercosur Trade Agreement on the European Union’s (EU) and Polish agri-food sectors. The comparative analysis reveals that Mercosur holds a significant structural advantage driven by substantially lower labor costs, cheaper agricultural land, and a climate permitting multiple harvests. This cost advantage is further compounded by weaker regulatory standards (e.g., on pesticides and antibiotics). This structural edge is most pronounced in high-volume commodities, leading to Mercosur trade surpluses in products such as soybeans, sugar cane, and wheat, which pose the primary competitive threats to the EU market. Conversely, the EU maintains an intensive advantage through superior yields in intensive farming (e.g., maize) and specialization in high-value, processed products. This creates quantifiable export opportunities for EU/Polish producers in sectors where Mercosur is a consistent net importer, notably other frozen vegetables, preserved tomatoes, and apples. The findings confirm an asymmetric effect of liberalization, which necessitates a dual strategy of internal structural reform (e.g., the EU Protein Strategy) and the implementation of external protective mechanisms, including strategic Common Agricultural Policy (CAP) adaptations and safeguard clauses, to maintain the long-term competitiveness and Supply Chain Resilience of European agriculture. Full article

(This article belongs to the Special Issue Price and Trade Dynamics in Agricultural Commodity Markets)

13 pages, 852 KB

Open AccessCommunication

Maize Diseases in Northeast China: Current Status and Emerging Threats

by Bingbing Liang, Dongyu Li, Lingxi He, Huaiyu Dong, Lijuan Wang, Le Chen, Kejie Liu and Ping Wang

Agriculture 2026, 16(2), 249; https://doi.org/10.3390/agriculture16020249 - 19 Jan 2026

Abstract

A comprehensive two-year investigation (2024–2025) was conducted across Northeast China’s crucial grain production base to assess the status of maize diseases. Field surveys spanning three provinces and Inner Mongolia revealed a significant shift in the regional disease profile, with diagnosis performed by experienced [...] Read more.

A comprehensive two-year investigation (2024–2025) was conducted across Northeast China’s crucial grain production base to assess the status of maize diseases. Field surveys spanning three provinces and Inner Mongolia revealed a significant shift in the regional disease profile, with diagnosis performed by experienced personnel based on characteristic field symptoms. The results demonstrated that maize white spot (MWS) has emerged as a severe new threat, recording remarkably high disease severity indices exceeding 80 at multiple locations (e.g., LDD25-1: 86.83). Concurrently, gray leaf spot (GLS) was confirmed as the most prevalent foliar disease, forming stable areas of high severity in the eastern mountainous regions where its disease indices consistently surpassed 60 (e.g., LFS25-1: 65.26), thereby exceeding the impact of northern corn leaf blight. In contrast, stalk rot (SR) maintained a low field incidence rate below 10%, while other diseases such as Curvularia leaf spot and maize eyespot were only observed locally or were absent during the 2025 survey period. These findings underscore the emergence of MWS as a critical threat and affirm the dominant status of GLS, offering a scientific foundation for prioritizing disease management strategies in the region. Full article

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

► Show Figures

Figure 1

20 pages, 640 KB

Open AccessArticle

Effects of Bacterial Inoculants and Ground Corn Grain on Fermentation Profile and In Situ Rumen Degradability of Tropical Grass Silage

by Luciano Saraiva dos Santos, Alex Lopes da Silva, Bernardo Magalhães Martins, Kellen Ribeiro Oliveira, Jessica Marcela Vieira Pereira, Odilon Gomes Pereira, Wellington Paulo Fernandes Amorim, João Vitor Coelho Rodrigues, Poliana Teixeira Rocha Salgado, Luis Henrique Rodrigues Silva and Polyana Pizzi Rotta

Agriculture 2026, 16(2), 248; https://doi.org/10.3390/agriculture16020248 - 18 Jan 2026

Abstract

The aim of this study was to evaluate different doses of bacterial inoculants and the inclusion of 8% ground corn grain (GCG) on fermentative characteristics, chemical composition, and in situ ruminal degradability of low-DM elephant grass (cv. BRS Capiaçu) silage. The experiment followed [...] Read more.

The aim of this study was to evaluate different doses of bacterial inoculants and the inclusion of 8% ground corn grain (GCG) on fermentative characteristics, chemical composition, and in situ ruminal degradability of low-DM elephant grass (cv. BRS Capiaçu) silage. The experiment followed a completely randomized design in a 6 × 3 factorial arrangement (six treatments × three fermentation periods). Treatments were a control without additive (CTR); 0.5 or 1 g/ton of Lentilactobacillus buchneri (LBU0.5 and LBU1); 1 or 2 g/ton of a Lactiplantibacillus plantarum + Pediococcus acidilactici inoculant (LPP1 and LPP2); and 8% GCG. After 60 d of fermentation, in situ ruminal degradability was evaluated using rumen-fistulated lactating cows with incubation times from 0 to 240 h. The GCG treatment increased DM, CP, and ether extract concentrations and reduced NDF, ADF, and lignin contents. Additionally, GCG silage exhibited lower pH, butyric acid, and ammonia nitrogen concentrations, along with higher lactic acid levels. No treatment effects were observed for water-soluble carbohydrates or total DM losses. The effective NDF degradability, degradation rate of the slowly degradable fraction, and undigested NDF after 240 h were not affected by treatments. In conclusion, the inclusion of GCG improved the fermentative profile of low-DM elephant grass silage, whereas bacterial inoculants did not significantly enhance the silage quality under the conditions evaluated. Full article

(This article belongs to the Special Issue Silage Preparation, Processing and Efficient Utilization—2nd Edition)

► Show Figures

Figure 1

15 pages, 9018 KB

Open AccessArticle

Algorithm for Recognizing Green Apples Using Image Segmentation and Object Detection

by Debin Yu, Yangting Liu, Ying Kong, Jiaxing Yin, Chuanxun Xu, Jinxing Wang and Guangming Wang

Agriculture 2026, 16(2), 247; https://doi.org/10.3390/agriculture16020247 - 18 Jan 2026

Abstract

Green apples exhibit a coloration that closely matches their surrounding environment, leading to low recognition accuracy for existing artificial intelligence models. This paper presents a green apple recognition algorithm that integrates an improved U-shaped network (U-Net) and you only look once network (YOLO) [...] Read more.

Green apples exhibit a coloration that closely matches their surrounding environment, leading to low recognition accuracy for existing artificial intelligence models. This paper presents a green apple recognition algorithm that integrates an improved U-shaped network (U-Net) and you only look once network (YOLO) v8 to address this challenge. First, the U-Net is enhanced via Dilated Convolution, Attention Gates, and Residual Connections to blur the background, thereby emphasizing the green apple target. Second, convolutional transformations and an attention mechanism are incorporated into YOLO v8, enabling it to focus more effectively on green apple targets within similarly colored backgrounds. Finally, the improved YOLO v8 is employed to recognize green apple targets segmented by the U-Net, with its performance compared against existing models. Research results show that the proposed algorithm achieves a precision of 92.5% and a Recall of 96.8% in green apple recognition, representing a significant improvement over classical models. To mitigate omission issues and further enhance overall performance, an improved YOLO v8 module is connected in parallel with the primary model. Based on its underlying principles, this approach is also applicable to other green fruits with colors and textures highly similar to their backgrounds, demonstrating strong robustness and generalization capabilities. Full article

(This article belongs to the Special Issue Key Technology Research and Applications of Agricultural Inspection Robots Based on Machine Vision and Artificial Intelligence)

► Show Figures

Figure 1

14 pages, 510 KB

Open AccessArticle

Physiological and Productive Response of Solanum tuberosum L. var. Superchola to Water Deficit in the Andean Highlands of Ecuador

by Mishel Katherine Lascano Muñoz, Charles Jim Cachipuendo Ulcuango and Juan Eduardo Léon Teran

Agriculture 2026, 16(2), 246; https://doi.org/10.3390/agriculture16020246 - 18 Jan 2026

Abstract

In light of the evident water scarcity and the challenges posed by climate change, this study aimed to evaluate the physiological, phenological, and productive responses of the potato crop (var. Superchola) under water deficit conditions, with the goal of optimizing water use in [...] Read more.

In light of the evident water scarcity and the challenges posed by climate change, this study aimed to evaluate the physiological, phenological, and productive responses of the potato crop (var. Superchola) under water deficit conditions, with the goal of optimizing water use in Tungurahua Province, Ecuador. Crop tolerance to water stress was assessed using drainage lysimeters under a completely randomized block design with three treatments and three replications: 100% ETo, 75% ETo, and 50% ETo. Soil and climatic parameters were characterized, and the crop coefficient (Kc) was calculated and adjusted for each phenological stage. The results showed that, although the full irrigation treatment (100% ETo) yielded the highest production, the application of a moderate water deficit (75% ETo) achieved a 16.2% water saving without significantly affecting crop yield or development. The maximum Kc value recorded was 1.22 during the maximum crop development stage. Full article

(This article belongs to the Topic Irrigation and Fertilization Management for Sustainable Agricultural Production)

► Show Figures

Graphical abstract

20 pages, 7676 KB

Open AccessArticle

Development of a Neural Network-Based Controller for a Greenhouse Irrigation System at Laboratory Scale

by Cesar Gerardo-Parra, Luis Enrique Barreto-Salazar, Lidia Madeleine Flores-López, Julio César Picos-Ponce, David Enrique Castro-Palazuelos and Guillermo Javier Rubio-Astorga

Agriculture 2026, 16(2), 245; https://doi.org/10.3390/agriculture16020245 - 18 Jan 2026

Abstract

Water scarcity and inefficient irrigation practices are major challenges for modern protected agriculture systems. This study designs, implements, and experimentally validates a neural network-based irrigation control strategy in an industrial programmable logic controller (PLC) for a drip irrigation system operating in a laboratory-scale [...] Read more.

Water scarcity and inefficient irrigation practices are major challenges for modern protected agriculture systems. This study designs, implements, and experimentally validates a neural network-based irrigation control strategy in an industrial programmable logic controller (PLC) for a drip irrigation system operating in a laboratory-scale micro-tunnel greenhouse. The objective is to evaluate the real-time performance of an intelligent controller under practical operating conditions and to quantify its impact on water use efficiency and crop growth compared to a conventional on–off strategy. The neural network is trained using 1039 data samples, divided into training (70%), validation (15%), and test (15%) datasets, and is implemented in the PLC to regulate soil moisture through a proportional valve. Experimental validation is carried out over 67 days using a serrano chili pepper (Capsicum annuum L.) crop. Both controllers operate simultaneously under identical environmental and operating conditions. Performance is evaluated using soil moisture stability metrics, including mean squared error (MSE), mean absolute error (MAE), and standard error (SE), water consumption, and crop growth indicators prior to harvest. Results show that the neural network controller achieves higher soil moisture regulation accuracy (MSE = 3.2159%, MAE = 0.7560%, SE = 0.00001687%) and reduces the average daily water consumption per plant by 50.18% compared with the on–off controller. In addition, the absolute growth rate increases by 26.42%, with statistically significant differences. These results demonstrate that neural network-based control can be effectively implemented on industrial hardware and provide tangible benefits for water-efficient and precision irrigation systems. Full article

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

► Show Figures

Figure 1

14 pages, 3584 KB

Open AccessArticle

Transcriptome Data Reveals Hypoxic Adaptability on Embryonic Cardiac Development in Tibetan Chickens

by Xuejiao Chen, Hailu Fan, Hao Zhang, Da Peng and Bo Zhang

Agriculture 2026, 16(2), 244; https://doi.org/10.3390/agriculture16020244 - 18 Jan 2026

Abstract

The Tibetan chicken (TC) is a small indigenous breed native to the Qinghai–Tibet Plateau in China, exhibiting remarkable adaptation to the plateau’s extreme high-altitude environment. Its strong hypoxia tolerance is reflected in the ability to maintain normal embryonic cardiac structure and function during [...] Read more.

The Tibetan chicken (TC) is a small indigenous breed native to the Qinghai–Tibet Plateau in China, exhibiting remarkable adaptation to the plateau’s extreme high-altitude environment. Its strong hypoxia tolerance is reflected in the ability to maintain normal embryonic cardiac structure and function during hypoxic incubation or high-altitude incubation. This study performed transcriptome sequencing of embryonic heart tissues from TC and White Leghorn (WL) incubated for 9, 11, and 16 days in Lhasa (altitude of 3650 m). A total of 1788 differentially expressed genes (DEGs) were identified through inter-breed comparison. Some DEGs were enriched in signaling pathways related to angiogenesis, apelin signaling, and myocardial contraction. Through integrating temporal expression analysis and weighted gene co-expression network analysis (WGCNA), we identified six key candidate DEGs (CREB3L2, MYH7B, CREB1, LOXL2, MICAL2, and AKAP13) that are involved in hypoxic response, myocardial structural remodeling, and regulation of signaling pathways. These genes likely represent core components of the molecular network underlying hypoxic adaptation in TC embryos. Overall, our findings provide a molecular basis for understanding the genetic mechanisms of hypoxic adaptation during embryonic cardiac development in chickens. Full article

(This article belongs to the Special Issue Genetic Resource Evaluation and Germplasm Innovation of Poultry)

► Show Figures

Figure 1

23 pages, 5887 KB

Open AccessArticle

Spatial–Temporal Coupling Characteristics and Interactive Effects of New-Type Urbanization and Cultivated Land Use Efficiency on Food Security

by Yihan Zhao, Yang Peng, Mengduo Li and Shuisheng Fan

Agriculture 2026, 16(2), 243; https://doi.org/10.3390/agriculture16020243 - 18 Jan 2026

Abstract

Against the backdrop of rapid modernization and tightening agricultural resource constraints, coordinating urbanization and grain production is a key challenge for China. Using panel data from 30 Chinese provinces from 2004 to 2023, this study applies the coupling coordination degree (CCD) model and [...] Read more.

Against the backdrop of rapid modernization and tightening agricultural resource constraints, coordinating urbanization and grain production is a key challenge for China. Using panel data from 30 Chinese provinces from 2004 to 2023, this study applies the coupling coordination degree (CCD) model and a panel vector autoregression model to examine the spatiotemporal coupling characteristics and interaction mechanisms among new-type urbanization (NTU), cultivated land use efficiency (CLUE), and food security (FS). The results show that these three systems have gradually evolved toward coordinated development, with major grain-producing regions consistently leading and entering a moderate coordination stage earlier than other regions. Spatially, CCD exhibits significant positive spatial autocorrelation, characterized by stable “High–High” agglomeration in Northeast China and “Low–Low” agglomeration in southern provinces. Dynamic analysis indicates that system fluctuations are mainly driven by internal inertia, while inter-system interactions are also significant; NTU promotes CLUE, and CLUE and FS exhibit bidirectional causality with complex feedback effects. This study argues for promoting urban–rural factor mobility, advancing green and technology-enabled land use, implementing region-specific development strategies, and establishing a “human–land–grain” early-warning mechanism to safeguard food security during urban expansion. Full article

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

► Show Figures

Figure 1

21 pages, 7451 KB

Open AccessArticle

Distinct Pathways of Cadmium Immobilization as Affected by Wheat Straw- and Soybean Meal-Mediated Reductive Soil Disinfestation

by Tengqi Xu, Jingyi Mei, Cui Li, Lijun Hou, Kun Wang, Risheng Xu, Xiaomeng Wei, Jingwei Zhang, Jianxiao Song, Zuoqiang Yuan, Xiaohong Tian and Yanlong Chen

Agriculture 2026, 16(2), 242; https://doi.org/10.3390/agriculture16020242 - 17 Jan 2026

Abstract

Both organic matter and iron oxide (FeO) dynamics pose key roles in soil cadmium (Cd) bioavailability. However, the microbially driven transformation of soil organic matter and FeO and their linkages to Cd fractions remain unclear under reductive soil disinfestation (RSD) with different organic [...] Read more.

Both organic matter and iron oxide (FeO) dynamics pose key roles in soil cadmium (Cd) bioavailability. However, the microbially driven transformation of soil organic matter and FeO and their linkages to Cd fractions remain unclear under reductive soil disinfestation (RSD) with different organic sources, which limits our mechanistic understanding of Cd immobilization by RSD. To address this gap, we conducted a 45 day microcosm experiment using a paddy soil contaminated with 22.8 mg/kg Cd. Six treatments were established: untreated control (CK), waterlogged (WF), and RSD-amended soils with 0.7% or 2.1% wheat straw (LWD, HWD) or soybean meal (LSD, HSD). We systematically assessed soil Cd fractionation, organic carbon and FeO concentrations, and bacterial community structure, aiming to clarify differences in Cd immobilization efficiency and the underlying mechanisms between wheat straw and soybean meal. For strongly extractable Cd, wheat straw RSD reduced the soil Cd concentrations from 6.02 mg/kg to 4.32 mg/kg (28.2%), whereas soybean meal RSD achieved a maximum reduction to 2.26 mg/kg (62.5%). Additionally, the soil mobility factor of Cd decreased from 44.6% (CK) to 39.2% (HWD) and 32.5% (HSD), while the distribution index increased from 58.5% (CK) to 62.2% (HWD) and 66.8% (HSD). Notably, the HWD treatment increased soil total organic carbon, humus, and humic acid concentrations by 34.8%, 24.6%, and 28.3%, respectively. Regarding amorphous FeO, their concentrations increased by 19.1% and 33.3% relative to CK. RSD treatments significantly altered soil C/N ratios (5.91–12.5). The higher C/N ratios associated with wheat straw stimulated r-strategist bacteria (e.g., Firmicutes, Bacteroidetes), which promoted carbohydrate degradation and fermentation, thereby enhancing the accumulation of humic substances. In contrast, the lower C/N ratios of soybean meal increased dissolved organic carbon and activated iron-reducing bacteria (FeRB; e.g., Anaeromyxobacter, Clostridium), driving iron reduction and amorphous iron oxide formation. PLS-PM analysis confirmed that wheat straw RSD immobilized Cd primarily through humification, whereas soybean meal RSD relied on FeRB-mediated FeO amorphization. These findings suggest that Cd immobilization in soil under RSD may be regulated by microbially mediated organic matter transformation and iron oxide dynamics, which was affected by organic materials of different C/N ratios. Full article

(This article belongs to the Section Agricultural Soils)

► Show Figures

Figure 1

Journal Description

Agriculture

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Further Information

Guidelines

MDPI Initiatives

Follow MDPI