Agronomy

15 pages, 2060 KB

Open AccessArticle

Evaluating Frequency Sampling for Botanical Composition Assessment in Heterogeneous Tropical Grasslands

by Diana Marcela Valencia-Echavarría, Yury Tatiana Granja-Salcedo, Julián Andrés Castillo Vargas, Sorany Milena Barrientos Grajales and Andrea Milena Sierra-Alarcón

Agronomy 2026, 16(13), 1293; https://doi.org/10.3390/agronomy16131293 (registering DOI) - 5 Jul 2026

Abstract

Aims: This study aimed to evaluate the agreement of a frequency sampling method (FR) as a tool for species identification while measuring undisturbed sward height. Methods: The botanical composition of both grazing systems was evaluated during the pre-grazing and post-grazing periods [...] Read more.

Aims: This study aimed to evaluate the agreement of a frequency sampling method (FR) as a tool for species identification while measuring undisturbed sward height. Methods: The botanical composition of both grazing systems was evaluated during the pre-grazing and post-grazing periods using two methods: the Dry Weight Rank (DWR) and FR. A non-parametric Friedman test was applied to compare evaluation methods and grazing moments. Differences in detection frequencies between methods were assessed using McNemar’s test for paired binary data. Results: The evaluation method did not influence the relative abundance of the three most abundant plant species identified: U. decumbens, Paspalum genus, and Commelinaceae weeds. A high positive Lin’s concordance correlation coefficient (CCC) was observed between the two methods in U. decumbens, Paspalum genus, U. brizantha cv. Marandú, U. plantaginea, U. arrecta, and U. humidicola (CCC ≥ 0.70). We observed lower agreement for some functional groups, particularly Commelinaceae weeds (CCC = 0.38), narrow-leaf weeds (CCC = 0.46), and Cyperaceae weeds (CCC = 0.17). Canonical correlation analysis (CCA) between the chemical composition of leaves and the botanical composition estimated by the DWR revealed two significant canonical functions (p < 0.01), with canonical correlations of 0.692 and 0.478 for the first and second functions, respectively. When botanical composition estimated by the FR was used as a regressor for leaf chemical composition, three significant canonical functions (p < 0.01) were identified, with canonical correlations of 0.632, 0.529, and 0.425 for the first, second, and third functions, respectively. Conclusions: FR represents a practical and complementary approach for assessing botanical composition and plant diversity in heterogeneous tropical grasslands, particularly for the rapid monitoring of dominant species. However, lower agreement was observed for some low-abundance functional groups, indicating reduced FR sensitivity for certain plant types. Full article

(This article belongs to the Section Grassland and Pasture Science)

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34 pages, 12963 KB

Open AccessArticle

Interpretable Deep Learning for Varroa Mite Detection: Integrating Deblurring, Morphology-Preserving Preprocessing, and Explainability Analysis

by Hong-Gu Lee, Jeong-Yong Shin, Woon-Tak Han, Su-Bae Kim, Min-Jee Kim, Giyoung Kim and Changyeun Mo

Agronomy 2026, 16(13), 1292; https://doi.org/10.3390/agronomy16131292 (registering DOI) - 5 Jul 2026

Abstract

Varroa destructor is the most devastating ectoparasite of Apis mellifera, and early detection is critical for colony survival. This study systematically investigated how image preprocessing, model architecture, and feature map resolution jointly affect classification accuracy and Grad-CAM++ explainability in deep-learning-based Varroa detection. [...] Read more.

Varroa destructor is the most devastating ectoparasite of Apis mellifera, and early detection is critical for colony survival. This study systematically investigated how image preprocessing, model architecture, and feature map resolution jointly affect classification accuracy and Grad-CAM++ explainability in deep-learning-based Varroa detection. From comb-surface images of 20 A. mellifera colonies, 3400 region-of-interest images were processed through 12 preprocessing pipelines combining deblurring, histogram normalization, morphology-preserving resizing, and non-morphological resizing. Nineteen CNN architectures, including VarroaNet — a custom lightweight model with configurable channel attention — were screened across all pipelines, and the top six further evaluated at four feature-map resolutions (7 × 7 to 56 × 56); the two stages together comprised 1,548 classification training runs across 516 configurations. Resizing consistently improved classification accuracy, whereas histogram normalization degraded it. VarroaNet (r = 8) achieved the highest mean accuracy across configurations (97.28%) with the lowest cross-configuration variability (CV = 1.47%). The 28 × 28 resolution was jointly optimal for classification and localization at minimal computational overhead, whereas 56 × 56 degraded performance. Notably, classification accuracy and localization quality did not always coincide—the highest-accuracy configuration (ShuffleNet-V2-x1.0 at 14 × 14, 97.34%) achieved an IoU@30 of only 0.160, underscoring the need for explicit localization evaluation. Morphology-preserving resizing achieved higher localization efficiency with zero morphological distortion. The recommended configuration—VarroaNet (r = 8) at 28 × 28 with deblurred MR preprocessing—achieved the highest localization performance (Pointing Game = 0.927), indicating correct attention to the mite region in 92.7% of infested test images. Full article

(This article belongs to the Section Precision and Digital Agriculture)

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24 pages, 2262 KB

Open AccessReview

Reframing Weed Detection: From Feature-Based Vision to Crop-Guided Intelligence in Precision Agriculture

by Yanjun Duan, Wenpeng Zhu, Shugui Ding, Mian Li, Kang Han, Xiaoyue Lai, Yuxin Liao, Fuhao Gong, Zhong Li, Maocheng Zhao, Bin Wu and Xiaojun Jin

Agronomy 2026, 16(13), 1291; https://doi.org/10.3390/agronomy16131291 (registering DOI) - 5 Jul 2026

Abstract

Weeds remain one of the primary constraints on crop productivity, making accurate detection and spatial localization essential for precision weeding systems. Over the past decades, weed detection has evolved from traditional feature-based image processing to deep learning-driven visual recognition, substantially improving detection accuracy [...] Read more.

Weeds remain one of the primary constraints on crop productivity, making accurate detection and spatial localization essential for precision weeding systems. Over the past decades, weed detection has evolved from traditional feature-based image processing to deep learning-driven visual recognition, substantially improving detection accuracy under controlled and semi-controlled conditions. However, most existing approaches still follow a weed-centric paradigm in which models are trained to explicitly recognize diverse weed species or weed classes. Such strategies face persistent limitations caused by extreme weed morphological variability, crop-weed similarity, high annotation cost, and spatial-temporal heterogeneity across fields, seasons, and cropping systems. This review therefore reframes weed detection as a broader transition from feature-based vision and direct weed recognition toward crop-guided, context-aware, and decision-oriented intelligence. Specifically, we synthesize the literature from three perspectives: (i) methodological evolution, including handcrafted features, machine learning, deep learning, segmentation, and multimodal sensing; (ii) paradigm transformation, from weed-centric detection to crop-guided inference based on crop structure, crop rows, and non-crop vegetation; and (iii) deployment-oriented integration, including edge devices, latency-accuracy-energy trade-offs, and robotic actuation. We further summarize representative public datasets, method categories, crop-guided studies, and edge-platform reporting requirements. Finally, we outline a decision-aware hybrid framework in which crop-guided perception provides low-latency weed localization, while species-level recognition is conditionally activated when required by herbicide selection, resistance management, or high-risk weed control. This synthesis clarifies both the value and the limitations of crop-guided weed detection and outlines actionable directions for scalable, robust, and field-deployable intelligent weeding systems. Full article

(This article belongs to the Section Precision and Digital Agriculture)

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16 pages, 3345 KB

Open AccessArticle

Long-Term Fertilizer Postponing Reshapes Spatial and Temporal Patterns of Bacterial Communities and N-Cycling Potential in Paddy Soils

by Yan Zhou, Lei Xu, Junhui Chen and Ganghua Li

Agronomy 2026, 16(13), 1290; https://doi.org/10.3390/agronomy16131290 (registering DOI) - 4 Jul 2026

Abstract

Optimizing nitrogen (N) management is essential for sustaining rice productivity and improving soil N retention in paddy ecosystems, yet whether long-term fertilizer postponing (FP) regulates bacterial community assembly and microbial N-cycling potential in a compartment-dependent manner remains unclear. Using soils from an 11-year [...] Read more.

Optimizing nitrogen (N) management is essential for sustaining rice productivity and improving soil N retention in paddy ecosystems, yet whether long-term fertilizer postponing (FP) regulates bacterial community assembly and microbial N-cycling potential in a compartment-dependent manner remains unclear. Using soils from an 11-year field experiment, we investigated bacterial communities and eight N-cycling genes in bulk and rhizosphere soils across three rice growth stages. Compared with conventional fertilization (CF), FP significantly increased grain yield, plant N accumulation, soil NH₄⁺-N (8.1%), microbial biomass N (MBN, 4.3%), and urease activity (30.3%). N-cycling genes showed pronounced temporal variation, generally peaking at the heading stage. FP increased the abundance of genes involved in N fixation, nitrification, and denitrification in bulk soil but reduced most N-cycling genes in the rhizosphere. Although bacterial α-diversity was unchanged, FP significantly altered bacterial community composition. Network and redundancy analysis further showed that bacterial community assembly and N-cycling potential were closely associated with soil C and N status. These findings indicate that long-term FP improves rice productivity by enhancing soil N availability and reshaping bacterial community assembly and microbial N-cycling potential in a compartment-dependent manner, providing new insights into the microbial mechanisms underlying sustainable N management in paddy soils. Full article

(This article belongs to the Section Soil and Plant Nutrition)

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21 pages, 1812 KB

Open AccessArticle

Antioxidant Potential of Actinidia arguta Fruit Extracts: A Comparative Study of Cultivar-Dependent Differences

by Małgorzata Olszowy-Tomczyk, Irena Maria Choma, Izabella Świątek, Dominika Siwek and Agnieszka Szopa

Agronomy 2026, 16(13), 1289; https://doi.org/10.3390/agronomy16131289 (registering DOI) - 4 Jul 2026

Abstract

Actinidia arguta has recently gained considerable popularity among consumers due to the high nutritional value of its fruits, recognized as a rich source of bioactive compounds, including antioxidants. This study aimed to determine and compare the antioxidant activity (AA) of fruit extracts obtained [...] Read more.

Actinidia arguta has recently gained considerable popularity among consumers due to the high nutritional value of its fruits, recognized as a rich source of bioactive compounds, including antioxidants. This study aimed to determine and compare the antioxidant activity (AA) of fruit extracts obtained from 10 different cultivars. The results of spectrophotometric assays were compared to those of effect-directed dot-blot and thin-layer chromatography (TLC). Total phenolic content (TPC) was determined spectrophotometrically (Folin–Ciocalteu) and verified through high-performance liquid chromatography and TLC. All studied extracts exhibited AA, although significant intracultivar differences were observed. Both spectrophotometric and chromatographic analyses consistently indicated the Vitikiwi and Geneva cultivars as having the highest antioxidant potential among the tested samples. However, chromatographic analyses revealed that the strong AA of the Vitikiwi was not associated with a high polyphenol content, but rather with its exceptionally high level of ascorbic acid, which led to an overestimation of phenolic content in the Folin–Ciocalteu assay. These findings demonstrate that AA in A. arguta fruits may result from different classes of bioactive compounds depending on the cultivar. The study emphasizes the importance of using both spectrophotometric and chromatographic methods to accurately evaluate antioxidant potential in plant-derived food products. Full article

(This article belongs to the Section Plant-Crop Biology and Biochemistry)

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17 pages, 3507 KB

Open AccessArticle

Agro-Morphological and Physicochemical Evaluation of Blueberry Varieties (Vaccinium corymbosum L.) in the Ecuadorian Andes

by Wilson Vásquez-Castillo, Anahí Serrano-Ibarra, Sebastián Escobar, Pablo Moncayo and María Raquel Meléndez-Jácome

Agronomy 2026, 16(13), 1288; https://doi.org/10.3390/agronomy16131288 (registering DOI) - 4 Jul 2026

Abstract

Blueberries are in high demand across the world due to their nutritional characteristics. The objective of this study was to conduct an agro-morphological and physicochemical characterisation of blueberry varieties (Vaccinium corymbosum L.) in the high Andean valleys of Ecuador, aiming to evaluate [...] Read more.

Blueberries are in high demand across the world due to their nutritional characteristics. The objective of this study was to conduct an agro-morphological and physicochemical characterisation of blueberry varieties (Vaccinium corymbosum L.) in the high Andean valleys of Ecuador, aiming to evaluate their productive performance and agro-industrial potential. The introduced varieties Eureka Maxx, Eureka Gold and Biloxi were studied across different agricultural systems and substrates under a randomised block design. The agro-morphological characterisation included variables such as latent buds, vegetative shoots, flowering onset and yield. Eureka Maxx grown in pots had the highest productivity (37.1%). In the physicochemical analysis, which considered diameter, firmness, pH, acidity, soluble solids, moisture and colour, it was observed that Eureka Maxx and Eureka Gold outperformed Biloxi in size (23.7 mm), firmness (15.2 N), and concentration of soluble solids (11.9 Brix). In general, plant growth and development are influenced by the environmental conditions of the site and crop management practices. Our results show that the varieties Eureka Maxx and Eureka Gold are viable alternatives for blueberry production in the high Andean valleys of Ecuador, to replace the Biloxi and Emerald varieties. Full article

(This article belongs to the Section Horticultural and Floricultural Crops)

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28 pages, 7283 KB

Open AccessArticle

Fusion of Canopy Multispectral and Environmental Time-Series Data for Predicting Substrate Moisture Content and Electrical Conductivity in Greenhouse Strawberry

by Chi-Won Choi, Su-Min Chin, Kyeong-Ha Lee and Dae-Hyun Jung

Agronomy 2026, 16(13), 1287; https://doi.org/10.3390/agronomy16131287 - 3 Jul 2026

Abstract

Accurate monitoring of substrate moisture content and electrical conductivity (EC) is essential for irrigation and nutrient management in soilless strawberry cultivation. However, conventional sensor-based approaches are limited in spatial coverage. This study developed a multimodal prediction framework integrating canopy multispectral imaging (713–920 nm) [...] Read more.

Accurate monitoring of substrate moisture content and electrical conductivity (EC) is essential for irrigation and nutrient management in soilless strawberry cultivation. However, conventional sensor-based approaches are limited in spatial coverage. This study developed a multimodal prediction framework integrating canopy multispectral imaging (713–920 nm) with greenhouse environmental and irrigation time-series data to estimate substrate state non-destructively. Four spectral preprocessing schemes were evaluated, and the first derivative of digital number values was adopted as the primary preprocessing condition. Five regression models, including a proposed spectrum-query cross-attention long short-term memory network (SpecAtten-LSTM), were compared across six spectral input configurations and five non-spectral baselines. Substrate EC was predicted accurately from either modality alone. Extreme gradient boosting reached R² = 0.9710, and the environment-only baselines achieved comparable performance, suggesting that either modality contained sufficient information for EC prediction. For substrate moisture content, the highest performance was obtained when spectral and environmental information were combined. SpecAtten-LSTM achieved the highest accuracy (R² = 0.7463) under the full multimodal configuration, and an ablation analysis confirmed that its cross-attention and fusion modules drove this gain. Permutation importance identified relative humidity as the dominant environmental variable for both targets. The results indicate that canopy-level observations can be used to estimate root-zone substrate conditions and that spectral information provides additional value primarily for substrate moisture content prediction. Full article

(This article belongs to the Special Issue Advances in Soil and Water Sensor Technologies for Precision Agriculture and Environmental Monitoring)

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20 pages, 2339 KB

Open AccessFeature PaperArticle

Projected Range Expansion of the Red Palm Weevil (Rhynchophorus ferrugineus) Across the Arabian Peninsula Under Future Climate Scenarios

by Hathal M. Al Dhafer, Amr Mohamed, Ioannis Eleftherianos and Mahmoud S. Abdel-Dayem

Agronomy 2026, 16(13), 1286; https://doi.org/10.3390/agronomy16131286 - 3 Jul 2026

Abstract

The red palm weevil, Rhynchophorus ferrugineus (Olivier, 1791), is among the most destructive pests of date palm (Phoenix dactylifera L.) globally, posing a severe and escalating threat to agricultural productivity across the Arabian Peninsula. Despite its well-documented economic impact, the potential influence [...] Read more.

The red palm weevil, Rhynchophorus ferrugineus (Olivier, 1791), is among the most destructive pests of date palm (Phoenix dactylifera L.) globally, posing a severe and escalating threat to agricultural productivity across the Arabian Peninsula. Despite its well-documented economic impact, the potential influence of climate change on its future distributional dynamics within this region remains poorly quantified. This study employed Maximum Entropy (MaxEnt) species distribution modelling to assess current and projected habitat suitability for R. ferrugineus across the Arabian Peninsula (~3.2 million km²) under two contrasting Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5) for the mid-century (2050) and late-century (2070). The model was calibrated using 52 spatially thinned occurrence records and six non-collinear environmental predictors selected following Variance Inflation Factor (VIF) analysis, with sampling bias corrected through a kernel density-based background weighting approach. Model performance was robust, with mean training and test AUC values of 0.921 ± 0.023 and 0.840 ± 0.052, respectively, and a mean TSS of 0.583 ± 0.046. Precipitation of the coldest quarter (Bio 19) and precipitation seasonality (Bio 15) emerged as the most influential predictors of habitat suitability, followed by elevation. Currently, approximately 727,589.8 km² (26.11%) of the peninsula is classified as suitable habitat, concentrated along the eastern Arabian Gulf coastline and the western Red Sea plain. Under SSP1-2.6, suitable habitat is projected to expand by 16.34% and 31.60% by 2050 and 2070, respectively. Under the high-emission SSP5-8.5 scenario, expansions are considerably more pronounced, reaching 34.11% by 2050 and 60.15% by 2070, with total suitable area approaching 1,158,474.8 km² (41.58%) by late-century. Habitat contraction was negligible across all scenarios, indicating a unidirectional range expansion dynamic. These findings highlight the substantial threat posed by climate-driven habitat expansion of R. ferrugineus and provide spatially explicit projections to inform proactive biosecurity planning and pest management strategies for date palm cultivation across the Arabian Peninsula. Full article

(This article belongs to the Topic Climate Change Impacts and Adaptation: Interdisciplinary Perspectives, 2nd Edition)

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19 pages, 10885 KB

Open AccessArticle

Research on Forage Hyperspectral Imagery Identification Based on Dual-Attention Auto-Encoding Dense Convolution Network

by Yilei Liu, Chen Chen, Jiangping Liu, Xin Pan and Rigeng Wu

Agronomy 2026, 16(13), 1285; https://doi.org/10.3390/agronomy16131285 - 3 Jul 2026

Abstract

The grassland ecosystem plays a crucial role in providing ample forage resources for grassland animal husbandry, ensuring its development. Identifying grassland forage is essential for understanding forage resources and cultivating high-quality forage. To address the low accuracy of forage image identification and the [...] Read more.

The grassland ecosystem plays a crucial role in providing ample forage resources for grassland animal husbandry, ensuring its development. Identifying grassland forage is essential for understanding forage resources and cultivating high-quality forage. To address the low accuracy of forage image identification and the issue of some features being ignored during image preprocessing, we have proposed a novel forage identification model, Dual-attention Auto-Encoding Dense Convolution Network (DAEDN), which has not been applied to forage identification before. DAEDN simultaneously calculates the weighted features of both channels and spaces, and utilizes Auto-Encodings to better capture the data, thereby enhancing the feature extraction capability and identification classification performance for grassland forage data. Additionally, it enhances the analysis of edge, texture, and other detailed features by leveraging the feature reuse and direct connection of each layer in the dense convolution structure. We evaluated the model performance through six evaluation parameters including overall accuracy (OA) and average accuracy (AA) and verified the effectiveness of the model by comparing it with popular convolutional neural network models. Experimental results show that the identification accuracy of DAEDN is 98.31%. Experiments proved that DAEDN enhanced ability to extract forage features, improved identification accuracy, and offered a new approach for the identification research of forage hyperspectral images. Full article

(This article belongs to the Section Precision and Digital Agriculture)

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20 pages, 3103 KB

Open AccessArticle

Path to Achieving Higher Productivity in Popcorn Under Drought Conditions

by Jhean Torres Leite, Antônio Teixeira do Amaral Junior, Valter Jário de Lima, Divino Rosa dos Santos Junior, Uéliton Alves de Oliveira, Flávia Nicácio Viana, Monique de Souza Santos, Letícia Peixoto Gomes, Danielle Leal Lamêgo, Carolina Macedo Carvalho, Wallace de Paula Bernado, Eliemar Campostrini, Henrique Duarte Vieira and Samuel Henrique Kamphorst

Agronomy 2026, 16(13), 1284; https://doi.org/10.3390/agronomy16131284 - 3 Jul 2026

Abstract

This study investigated alternative pathways involving adaptive traits associated with drought tolerance in popcorn cultivated under soil water restriction through path analysis. Direct and indirect effects were estimated for morpho-agronomic variables, including ear height (EH), grain number per ear (GNE), grain number per [...] Read more.

This study investigated alternative pathways involving adaptive traits associated with drought tolerance in popcorn cultivated under soil water restriction through path analysis. Direct and indirect effects were estimated for morpho-agronomic variables, including ear height (EH), grain number per ear (GNE), grain number per row (GNR), 100-grain weight, grain yield (GY), and popping expansion (PE); physiological variables, such as relative chlorophyll content, anthocyanin and flavonoid indices, and maximum quantum efficiency of photosystem II; and root-related traits, including brace- and crown-root number and root angle. Fifty popcorn inbred lines were assessed under contrasting water conditions (WC), namely well-watered and water-stressed environments, during two crop seasons (CS). Water restriction was imposed before male anthesis, and the permanent wilting point was reached at 63 days after sowing in CS-2020 and at 100 days in CS-2021. Significant genotype × WC × CS interactions were identified for all evaluated traits. The effects of drought varied according to the developmental stage at which stress occurred. Under both water regimes, indirect selection through EH and GNR favored gains in GY, whereas EH and GNE showed the strongest indirect contributions to PE. Among the evaluated variables, EH emerged as a promising trait for indirect selection in popcorn breeding under water-limited conditions. Full article

(This article belongs to the Special Issue Agroecological Strategies for Improving Nutritional Quality of Cereal Crops)

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28 pages, 11766 KB

Open AccessArticle

First-Year Effects of Biochar, Biosolids, and Greenwaste on Tall Fescue Under Deficit Irrigation: Part I

by Jaime Barros Silva Filho, Jonathan Montgomery, Marco Schiavon and Milton E. McGiffen, Jr.

Agronomy 2026, 16(13), 1283; https://doi.org/10.3390/agronomy16131283 - 3 Jul 2026

Abstract

As urban water scarcity intensifies, selecting soil amendments is critical for drought-stressed landscapes. This study evaluated biochar, biosolids, and greenwaste effects on canopy quality, soil moisture, nitrogen dynamics, and root architecture in tall fescue (Schedonorus arundinaceus) in the post-establishment year. Eight [...] Read more.

As urban water scarcity intensifies, selecting soil amendments is critical for drought-stressed landscapes. This study evaluated biochar, biosolids, and greenwaste effects on canopy quality, soil moisture, nitrogen dynamics, and root architecture in tall fescue (Schedonorus arundinaceus) in the post-establishment year. Eight amendment treatments were assessed under two irrigation regimes (50% and 85% of reference evapotranspiration [ET₀]) in a split-split-plot arrangement with four replications. No treatment maintained acceptable visual quality (VQ ≥ 6.0) under the 50% ET₀ deficit. A 10 cm greenwaste layer raised soil moisture up to 27% above the control but produced the lowest mineral nitrogen concentrations at both depths, and this advantage did not translate into improved canopy performance. High-rate biochar (24.71 t ha⁻¹) maintained superior NDVI during peak stress, whereas biosolids generated the highest soil nitrogen (Total N up to 9.62 mg kg⁻¹) but restricted root length, surface area, and branching. Because no amendment optimizes moisture, nitrogen, and rooting together, selection should target the limiting factor: pair greenwaste with nitrogen, use biochar where nitrogen is limited, and reserve biosolids for nitrogen-poor, non-root-limited sites. Under a 41% irrigation reduction, no amendment sustained acceptable quality, so managers should temper expectations or choose more drought-tolerant species rather than soil management alone. Full article

(This article belongs to the Special Issue Bio-Based Fertilizers and Soil Health: Innovations for Nutrient Recycling and Environmental Protection)

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21 pages, 2212 KB

Open AccessReview

cis-Regulatory Elements in Crops: From Natural Variation to Precision Engineering

by Alessandra Boccaccini, Benedetta Pizziconi, Michela Molinari, Sara Cimini and Laura De Gara

Agronomy 2026, 16(13), 1282; https://doi.org/10.3390/agronomy16131282 - 3 Jul 2026

Abstract

A deeper understanding of the molecular mechanisms shaping plant development is relevant for enhancing agricultural productivity. Among these mechanisms, the regulation of gene expression plays a central role in determining phenotypes and plasticity. This specificity is largely mediated by transcription factors (TFs) that [...] Read more.

A deeper understanding of the molecular mechanisms shaping plant development is relevant for enhancing agricultural productivity. Among these mechanisms, the regulation of gene expression plays a central role in determining phenotypes and plasticity. This specificity is largely mediated by transcription factors (TFs) that bind cis-regulatory elements (CREs), short DNA sequences dispersed within intergenic regions. Despite their key roles, CRE function remains incompletely understood. However, growing evidence indicates that variation in CREs has contributed to crop domestication, adaptation, and trait diversification. This review highlights the role of CRE variation in shaping agronomic traits, discusses current approaches for CRE identification with a focus on multi-omics strategies, and examines recent genome-editing technologies for CRE manipulation and their potential applications in crop improvement. Full article

(This article belongs to the Special Issue Genetics and Breeding of Field Crops in the 21st Century—2nd Edition)

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25 pages, 7269 KB

Open AccessArticle

Agricultural and Hydrogeochemical Controls on Nitrate and Sulfate in a Karst Surface Water–Groundwater System

by Haowen Liu, Longxinyue Qin, Ailin Zhan, Shuang Liu, Qiang Li, Lin Zhang, Cuishan Liu and Junliang Jin

Agronomy 2026, 16(13), 1281; https://doi.org/10.3390/agronomy16131281 - 2 Jul 2026

Abstract

Agricultural karst watersheds are highly vulnerable to nutrient loss because strong surface water–groundwater (SW–GW) connectivity can rapidly transfer nitrogen and sulfur species from soils, agricultural activities, and human settlements into aquatic systems. However, the coupled behavior and contrasting controls of nitrate (NO₃ [...] Read more.

Agricultural karst watersheds are highly vulnerable to nutrient loss because strong surface water–groundwater (SW–GW) connectivity can rapidly transfer nitrogen and sulfur species from soils, agricultural activities, and human settlements into aquatic systems. However, the coupled behavior and contrasting controls of nitrate (NO₃⁻) and sulfate (SO₄²⁻) in such agroecosystems remain insufficiently understood, limiting effective nutrient and groundwater-quality management. In this study, a typical karst agricultural watershed in Southwest China was selected to investigate the sources, transformation processes, and transport pathways of NO₃⁻ and SO₄²⁻ under strong SW–GW interactions. During the rainy season, 44 groundwater and 40 surface water samples were collected for major hydrochemical and nitrate–sulfate stable isotope analyses. An integrated framework combining hydrochemical analysis, self-organizing maps (SOM), positive matrix factorization (PMF), and MixSIAR were used to identify dominant sources, quantify source contributions, and clarify controlling processes. The results showed that groundwater was mainly characterized by carbonate-controlled Ca-HCO₃ facies, whereas surface water exhibited higher mineralization and a shift toward Ca-SO₄ facies, indicating stronger external inputs and rapid hydrological responses. Nitrate was primarily controlled by external nitrogen inputs, with manure and sewage and soil nitrogen contributing 39–62% and 16–33%, respectively. Nitrate was also regulated by nitrification under oxic conditions, while denitrification was negligible. In contrast, sulfate was predominantly governed by geogenic processes, with sulfide oxidation contributing 63–83%, while other sources were minor. These contrasting controls resulted in distinct spatial and process behaviors: nitrate showed source-driven variability associated with agricultural and domestic inputs, whereas sulfate displayed process-driven accumulation mainly controlled by water–rock interactions. Strong SW–GW connectivity enhanced the transfer of anthropogenic nutrient signals, while subsurface mixing and buffering regulated their expression in groundwater and surface water. These findings demonstrate a clear decoupling between nitrate and sulfate controls in agricultural karst systems and provide a scientific basis for nutrient pollution control, groundwater protection, and sustainable agricultural water management in vulnerable karst regions. Full article

(This article belongs to the Special Issue Soil Nutrient Dynamics in Agriculture: Cycling, Chemistry and Climate Resilience)

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18 pages, 6825 KB

Open AccessArticle

Bacillus velezensis BV3 Suppresses Leaf Spot Pathogens via Two Antifungal Metabolites and Promotes Maize Growth

by Mengjing Wu, Yuanlin Qi, Linlin Song, Peng Huang, Jie Zhang, Deyi Yu, Zhaohua Zeng and Jin-Ai Yao

Agronomy 2026, 16(13), 1280; https://doi.org/10.3390/agronomy16131280 - 2 Jul 2026

Abstract

Southern corn leaf blight (SCLB), caused by Bipolaris maydis, poses a serious threat to maize production worldwide. In our previous study, Bacillus velezensis BV3 was isolated and demonstrated strong antagonistic activity against maize leaf spot pathogens and effective disease control in greenhouse [...] Read more.

Southern corn leaf blight (SCLB), caused by Bipolaris maydis, poses a serious threat to maize production worldwide. In our previous study, Bacillus velezensis BV3 was isolated and demonstrated strong antagonistic activity against maize leaf spot pathogens and effective disease control in greenhouse experiments. In this study, we evaluated the plant growth-promoting effects of BV3 on two maize cultivars through root application in pot experiments, and investigated the underlying molecular mechanisms using transcriptomic and metabolomic analyses. Inoculation with BV3 significantly promoted maize growth. Moreover, BV3 treatment induced extensive transcriptional and metabolic reprogramming in maize. Transcriptomic analysis identified numerous differentially expressed genes (DEGs) mainly enriched in plant–pathogen interaction, plant hormone signal transduction, MAPK signaling pathway, and phenylpropanoid biosynthesis pathways. Metabolomic analysis revealed substantial changes in metabolite accumulation, particularly in lipids, amino acids, sugars, organic acids, and polyphenols, with enriched pathways including secondary metabolite biosynthesis, phenylpropanoid biosynthesis, and flavonoid biosynthesis. LC/MS and GC/MS analyses further revealed that BV3 produced diverse bioactive compounds. 2,4-DTBP and surfactin exhibited strong antifungal activities, particularly against B. maydis and Exserohilum turcicum. Overall, Bacillus sp. BV3 exhibits strong biocontrol efficacy against maize leaf spot pathogens and significant plant growth-promoting activity, highlighting its potential as an eco-friendly biocontrol agent for the management of southern corn leaf blight. Full article

(This article belongs to the Special Issue Interaction Mechanisms Between Crops and Pathogens)

23 pages, 2656 KB

Open AccessArticle

Trehalose-6-Phosphate Promotes Growth, Yield, and Recovery After Pre-Flowering Drought Stress in Adzuki Bean (Vigna angularis)

by Renfeng Xue, Zongji Jin, Jian Chen, Zhao Li, Dong Deng, Yang Zhao, Ming Feng, Tao Li, Yuning Huang, Chao Zhong and Weide Ge

Agronomy 2026, 16(13), 1279; https://doi.org/10.3390/agronomy16131279 - 2 Jul 2026

Abstract

Trehalose-6-phosphate (T6P) is involved in the regulation of plant growth and stress-related responses; however, its potential role in post-drought recovery remains unclear in legume crops. This study evaluated whether foliar application of native T6P could improve growth, yield formation, and physiological recovery in [...] Read more.

Trehalose-6-phosphate (T6P) is involved in the regulation of plant growth and stress-related responses; however, its potential role in post-drought recovery remains unclear in legume crops. This study evaluated whether foliar application of native T6P could improve growth, yield formation, and physiological recovery in adzuki bean after pre-flowering drought stress. We aimed to propose a model for the production and drought tolerance regulation in adzuki bean. A three-year field experiment was conducted by applying T6P at both the budding stage and the initial pod stage. The plant architecture, yield, photosynthetic characteristics, defensive enzyme activity, sugar metabolism and hormone changes were evaluated. The results indicated that T6P significantly increased root, stem and leaf parameters in adzuki bean it also increased all yield traits except for the main stem branches and sections during post-drought recovery. Indeed, the yield per block of 5μM and 10μM of T6P application during post-drought recovery increased by 20.66% and 31.60% on average compared to the control under well-watered conditions and by 47.68% and 60.20% under drought stress during the three experimental years. Foliar T6P treatment was associated with higher antioxidant enzyme activities and related gene expression, lower ROS accumulation and proline content, and changes in sugar- and hormone-related traits during post-drought recovery. Therefore, foliar application of T6P at the budding and initial pod stages improved growth, yield formation, and post-drought recovery in adzuki bean. The result provide a novel strategy for T6P application for coordinating the high production potential and crop resilience. Full article

(This article belongs to the Special Issue Crop Agronomic Traits and Performances Under Stress)

22 pages, 4318 KB

Open AccessArticle

YOLOv11-Pose-BEH: An Enhanced Multi-Scale Attention Network for Tea Bud Detection and Two-Dimensional Picking Point Localization

by Weihao Liu, Junjie He, Chun Wang, Miao Zhou, Chunyan Zhao, Tianyu Wu, Zhiyong Cao, Xinya Chen, Man Zou, Kai Peng, Shasha Feng and Baijuan Wang

Agronomy 2026, 16(13), 1278; https://doi.org/10.3390/agronomy16131278 - 2 Jul 2026

Abstract

The terrain of tea gardens in Yunnan is complex, and the branches and leaves of tea trees are densely interlaced. Traditional manual picking methods are labor-intensive and inefficient, and can no longer meet the needs of modern tea industry development. To achieve automatic [...] Read more.

The terrain of tea gardens in Yunnan is complex, and the branches and leaves of tea trees are densely interlaced. Traditional manual picking methods are labor-intensive and inefficient, and can no longer meet the needs of modern tea industry development. To achieve automatic recognition of tea buds and leaves and accurate two-dimensional localization of picking points in complex natural environments, this study constructs a lightweight and high-precision tea bud and leaf detection and keypoint localization model, YOLOv11-pose-BEH, based on the YOLOv11-pose model. Based on the original network, this model introduces the BiFPN feature fusion module to achieve efficient bidirectional transmission of multi-scale information. The EMA (Efficient Multi-scale Attention) mechanism is integrated to form the C2PSA_EMA module, improving the effect of multi-scale feature extraction. HetConv is introduced to form the C3K2_HetConv module, enhancing the extraction of local texture and edge features. Compared with the baseline network, the improved YOLOv11-pose-BEH achieved significant improvements in both tea bud object detection and picking point localization. For the tea bud object detection task, the precision, recall, mAP0.5, and F1-score increased by 7.41, 6.39, 5.28, and 6.88 percentage points, respectively. For the picking point localization task, the precision, recall, mAP0.5, and F1-score increased by 5.86, 5.83, 4.83, and 5.85 percentage points, respectively. These results indicate that the proposed model can achieve more accurate and stable tea bud and leaf detection and two-dimensional picking point localization under complex backgrounds, providing efficient and reliable technical support for the visual perception of intelligent tea-picking robots in tea gardens. Full article

(This article belongs to the Collection AI, Sensors and Robotics for Smart Agriculture)

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2 pages, 308 KB

Open AccessCorrection

Correction: Li et al. Effects of GmERF5-Responsive Effector on Soybean Symbiotic Nodulation. Agronomy 2024, 14, 1239

by Candong Li, Yuxin Jin, Weinan Tang, Xuemei Li, Chunyan Liu, Jinhui Wang, Xiaoxia Wu, Qingshan Chen, Feishi Luan and Dawei Xin

Agronomy 2026, 16(13), 1277; https://doi.org/10.3390/agronomy16131277 - 2 Jul 2026

Abstract

In the original publication [...] Full article

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14 pages, 4418 KB

Open AccessArticle

Overexpressing ZmXTH23 Improves Drought and Salt Tolerance in Nicotiana benthamiana

by Qianqian Mao, Fuchao Jiao, Junhua Li, Yuhe Pei, Qiuyue Xing, Jianghao Wang, Huijun Guo, Jun Li, Haoyang Li, Nicola Cannon, Xianmin Chang, Xiyun Song and Xinmei Guo

Agronomy 2026, 16(13), 1276; https://doi.org/10.3390/agronomy16131276 - 2 Jul 2026

Abstract

Maize (Zea mays L.), a critical global food crop, suffers severe yield losses from drought and salt stresses. Xyloglucan endotransglucosylase/hydrolases (XTHs) are cell wall-modifying enzymes regulating plant growth and abiotic stress responses, but the role of -ZmXTH23 in drought and salt [...] Read more.

Maize (Zea mays L.), a critical global food crop, suffers severe yield losses from drought and salt stresses. Xyloglucan endotransglucosylase/hydrolases (XTHs) are cell wall-modifying enzymes regulating plant growth and abiotic stress responses, but the role of -ZmXTH23 in drought and salt tolerance remains unclear. Here, we heterologously expressed ZmXTH23 in Nicotiana benthamiana (N. benthamiana) and confirmed its extracellular localization. ZmXTH23-overexpressing (OE) lines showed significantly increased plant height, root length, and shoot fresh weight under normal conditions. Under 350 mM NaCl or drought stress, the OE lines exhibited enhanced tolerance, with less leaf wilting, higher biomass, and larger leaf area. Physiologically, the OE lines had higher peroxidase (POD) and superoxide dismutase (SOD) activities and relative water content (RWC), but lower malondialdehyde (MDA) content. Additionally, OE seeds maintained ≥50% germination under 120 mM NaCl (WT: 0%) and nearly 100% under 200 mM mannitol. ZmXTH23 improves drought and salt tolerance in N. benthamiana by upregulating antioxidant enzymes and enhancing water retention, making it a promising candidate for maize stress-resistance breeding. Full article

(This article belongs to the Special Issue Advances in Crop Molecular Breeding and Genetics—2nd Edition)

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36 pages, 29389 KB

Open AccessReview

Review of Variable-Stiffness Control for Robotic Arms and Prospects for Application in Agricultural Environments

by Jie Deng, Jizhan Liu, Wenjie Gao, Yong Jiang and Wei Wei

Agronomy 2026, 16(13), 1275; https://doi.org/10.3390/agronomy16131275 - 1 Jul 2026

Abstract

The integration of agricultural science, artificial intelligence, and robotics has accelerated the development of robotic arms, significantly enhancing their potential applications across various fields. In light of the unique characteristics of agricultural environments, this paper provides a comprehensive review of variable-stiffness control methods [...] Read more.

The integration of agricultural science, artificial intelligence, and robotics has accelerated the development of robotic arms, significantly enhancing their potential applications across various fields. In light of the unique characteristics of agricultural environments, this paper provides a comprehensive review of variable-stiffness control methods for robotic arms. A robotic arm with rigid–flexible switching capabilities can effectively address collision safety concerns in complex agricultural environments, offering promising research prospects. This paper outlines recent advancements in variable-stiffness control techniques, including passive control methods based on materials and flexible devices, active control methods based on algorithms, and hybrid methods that combine control algorithms with flexible devices. Additionally, it discusses the technical challenges and future opportunities regarding deploying variable-stiffness robotic arms in agricultural environments. The aim of this paper is to provide theoretical and technical insights to support the efficient and versatile development of agricultural robots. Full article

(This article belongs to the Special Issue Robotics and Automation in Farming—2nd Edition)

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