Agronomy

32 pages, 962 KB

Open AccessReview

An Ecosystem Framework for Tomato Precision Agriculture: Integrating Measurement, Understanding, Optimization, Prediction, and Diagnosis

by Sangyoon Lee, Hongseok Mun, Joonmo Kang and Byeongeun Moon

Agronomy 2026, 16(10), 965; https://doi.org/10.3390/agronomy16100965 (registering DOI) - 12 May 2026

Abstract

Tomato (Solanum lycopersicum L.) production faces increasing pressure from resource scarcity and climate change, creating demand for more precise and adaptive management. However, adoption in commercial systems remains limited because many advanced technologies are costly, poorly interoperable, or difficult for growers to [...] Read more.

Tomato (Solanum lycopersicum L.) production faces increasing pressure from resource scarcity and climate change, creating demand for more precise and adaptive management. However, adoption in commercial systems remains limited because many advanced technologies are costly, poorly interoperable, or difficult for growers to interpret. This review addresses that gap by organizing recent advances into a five-stage production ecosystem framework: Measurement, Understanding, Optimization, Prediction, and Diagnosis. Unlike previous precision agriculture reviews that mainly summarize sensing, modeling, artificial intelligence, and robotics as separate topics, this framework emphasizes stage-linked integration and decision support relevance across practical tomato production. Measurement establishes the data foundation through sensor networks and imaging; Understanding converts observations into physiological insight using process-based models; Optimization applies these insights to water, nutrient, and microclimate management. Prediction uses machine learning and explainable artificial intelligence to anticipate yield, quality, and stress responses, while Diagnosis supports timely disease detection and vision-based intervention. Overall, this review shows that progress in tomato precision agriculture depends less on isolated algorithmic advances than on cost-effective, modular, interpretable, and operationally feasible systems for commercial deployment. Full article

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

23 pages, 8187 KB

Open AccessArticle

DCFENet: A Dual-Branch Collaborative Feature Enhancement Network for Farmland Boundary Detection

by Mengyao Lan, Bangjun Huang and Peng Wu

Agronomy 2026, 16(10), 964; https://doi.org/10.3390/agronomy16100964 (registering DOI) - 12 May 2026

Abstract

Farmland resources are fundamental to human survival and play a vital role in ensuring global food security. However, farmland boundary detection remains a significant technical challenge due to the low proportion of boundary pixels, multi-scale variations, and weak boundary continuity. To address these [...] Read more.

Farmland resources are fundamental to human survival and play a vital role in ensuring global food security. However, farmland boundary detection remains a significant technical challenge due to the low proportion of boundary pixels, multi-scale variations, and weak boundary continuity. To address these issues, this study proposes DCFENet, a dual-branch collaborative feature enhancement network. Specifically, a multi-scale feature fusion attention module TA-ASPP (Task-Aware Atrous Spatial Pyramid Pooling) is designed, which effectively enhances the network’s perception of farmland boundary features by integrating multi-scale dilated convolutions with skeleton-aware attention. In addition, a dual-branch decoding structure is proposed to enhance boundary localization and global topology modeling through boundary-aware gating and cross-branch feature fusion, thereby improving the boundary continuity. Furthermore, a collaborative constraint mechanism is proposed for dual-branch decoding, which supervises the two decoders using boundary loss and skeleton loss, thereby enhancing structural consistency and topology preservation. Experimental results demonstrate that DCFENet achieves precision, recall, and boundary IoU of 74.5%, 68.1%, and 77.4%, respectively, representing an improvement of 26.8%, 36.3%, and 13.2% compared with ResNet18_UNet. It also outperforms mainstream methods such as UNet, EdgeNAT, and EDTER. In terms of computational efficiency, DCFENet contains 26.43 M parameters and 37.43 G FLOPs, with a memory usage of 1.03 GB and an inference speed of 97.97 FPS, achieving a good balance between accuracy and efficiency. The results demonstrate the efficiency and accuracy of DCFENet in extracting farmland boundaries from high-resolution remote sensing images, providing technical support for farmland management and the advancement of precision and digital agriculture. Full article

(This article belongs to the Special Issue Remote Sensing and GIS in Sustainable and Precision Agriculture)

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20 pages, 16648 KB

Open AccessArticle

Automated High-Frequency RGB Imaging for Biomass Estimation in Hydroponics

by Andrius Grigas, Tomas Krilavičius, Eimantas Zaranka, Danylo Abramov, Sarwan Shafeeq, Dainius Savickas, Indrė Bručienė, Veronika Bryskina, Deividas Valiuška and Rūta Juozaitienė

Agronomy 2026, 16(10), 963; https://doi.org/10.3390/agronomy16100963 (registering DOI) - 12 May 2026

Abstract

Accurate, non-destructive estimation of crop biomass is essential for automated high-frequency monitoring and optimization in controlled-environment agriculture, yet standardized approaches remain limited for short-cycle hydroponic systems. This study introduces a reproducible and fully automated method for estimating the biomass of hydroponically grown wheat [...] Read more.

Accurate, non-destructive estimation of crop biomass is essential for automated high-frequency monitoring and optimization in controlled-environment agriculture, yet standardized approaches remain limited for short-cycle hydroponic systems. This study introduces a reproducible and fully automated method for estimating the biomass of hydroponically grown wheat sprouts (HWSs) using high-frequency RGB imaging. The workflow integrates image preprocessing, tray segmentation, and canopy feature extraction with synchronized load-cell measurements to enable continuous, non-invasive growth tracking. To account for irrigation events and associated weight fluctuations, raw mass signals were processed using a second-order low-pass Bessel filter, preserving underlying biomass trends while removing short-term oscillations. Across 3024 paired image–mass observations collected under commercial cultivation conditions, several canopy coverage, color-based indices (AGI, Proxy NDVI), and texture features exhibited strong predictive relationships with biomass. Features reflecting greenness, canopy density, and color uniformity were positively associated with plant mass, whereas brightness- and red-channel features showed consistent negative relationships. Feature selection using an elastic-net approach identified a compact subset of informative predictors, improving model stability and interpretability. Under a nested cross-validation framework based on contiguous interval splits within sprout-growth cohorts, support vector regression (SVR) achieved the best predictive performance, with an sMAPE of 3.64% and an RMSE of 0.16 kg. Additional experiments under altered illumination conditions showed that including light intensity as an explicit covariate improved model robustness across lighting regimes. These results demonstrate that combining elastic-net feature selection with environmental covariates provides a robust and transferable framework for visual biomass estimation in hydroponic HWS. More broadly, the proposed pipeline enables non-destructive crop monitoring and supports the development of intelligent, feedback-driven control strategies for hydroponic production systems. Full article

(This article belongs to the Special Issue Application and Innovation of Digital Technologies in Controlled Environment Agriculture)

17 pages, 5516 KB

Open AccessArticle

BTH-Induced Resistance in Rice Impairs Magnaporthe oryzae Metabolic Fitness and Suppresses Key Virulence Genes

by Ruiming Zhang, Yao Sun, Yanan He, Yaping Li, Yongbin Peng, Chongke Zheng, Lixia Xie, Conghui Jiang, Jinjun Zhou, Guanhua Zhou, Wei Sun, Chang-Jie Jiang and Xianzhi Xie

Agronomy 2026, 16(10), 962; https://doi.org/10.3390/agronomy16100962 (registering DOI) - 12 May 2026

Abstract

Induced resistance primes host immunity for enhanced protection; however, how pathogens respond to this primed state remains poorly understood. Here, we investigated the molecular responses of the rice blast fungus Magnaporthe oryzae during infection of benzothiadiazole (BTH)-primed rice. Seed priming with BTH conferred [...] Read more.

Induced resistance primes host immunity for enhanced protection; however, how pathogens respond to this primed state remains poorly understood. Here, we investigated the molecular responses of the rice blast fungus Magnaporthe oryzae during infection of benzothiadiazole (BTH)-primed rice. Seed priming with BTH conferred long-lasting resistance against M. oryzae at the four-leaf stage. Time-course transcriptomic analyses (12–48 hpi) identified 699 differentially expressed genes (DEGs) in M. oryzae, revealing a distinct temporal transition during infection of BTH-primed rice. The fungal transcriptional response shifted from early growth and environmental sensing to enhanced protein turnover, metabolic repression, energy depletion, and genomic instability, indicating progressive impairment of fungal fitness by host immunity. From these DEGs, eight BTH-suppressed candidate virulence genes (MoBVG1–8) were selected for functional characterization. Gene overexpression analyses showed that two genes, MoBVG2 and MoBVG6, significantly increased pathogenicity on BTH-primed rice, while knockout analyses confirmed that both are required for full pathogenicity on non-primed control plants. MoBVG2 encodes a reactive oxygen species (ROS)-scavenging effector, and MoBVG6 encodes an environmental sensor, highlighting the importance of ROS detoxification and environmental perception for successful host colonization. Functional analyses further revealed that MoBVG2 contribute to vegetative growth, while MoBVG6 is required for proper appressorium development. Together, these findings suggest that BTH-induced resistance restricts blast disease by impairing fungal metabolic fitness and suppressing key virulence genes, providing novel insights into the pathogen-side molecular mechanisms underlying chemically induced resistance in plants. Full article

(This article belongs to the Special Issue New Insights into Fungal Pathogenicity, Pathogen–Host Interactions, and Host Immunity)

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22 pages, 5316 KB

Open AccessArticle

Suitability Analysis of Rice Cropping Patterns in China Using the MaxEnt Model

by Yunyi Cai, Tao Qian, Chuanhai Hua, Haokai Zhu, Bing Liu, Liujun Xiao, Yan Zhu, Weixing Cao and Chongya Jiang

Agronomy 2026, 16(10), 961; https://doi.org/10.3390/agronomy16100961 (registering DOI) - 12 May 2026

Abstract

Climate warming is reshaping hydrothermal resources for rice production, but the climatic responses of different rice-based cropping systems have not been sufficiently compared. This study evaluated the climatic suitability and dominant climatic controls of four major rice cropping systems in China: single-season rice, [...] Read more.

Climate warming is reshaping hydrothermal resources for rice production, but the climatic responses of different rice-based cropping systems have not been sufficiently compared. This study evaluated the climatic suitability and dominant climatic controls of four major rice cropping systems in China: single-season rice, double-season rice, rice–wheat rotation, and rice–maize rotation. Occurrence records were extracted from ChinaCP and screened using a 30 m rice distribution mask. MaxEnt models were calibrated using agroclimatic variables for the historical baseline and end-century SSP2-4.5 and SSP5-8.5 scenarios. The models showed good presence–background discrimination, with mean training AUC values of 0.868 ± 0.001, 0.959 ± 0.001, 0.969 ± 0.002, and 0.963 ± 0.003 for the four systems, respectively. Dominant climatic controls differed among systems. Single-season rice, rice–wheat rotation, and rice–maize rotation were mainly associated with heat accumulation, with temperature ≥0 °C (AT₀) showing the highest permutation importance of 55.9%, 59.8%, and 77.0%, respectively. Double-season rice showed a distinct response constrained by precipitation and monthly temperature conditions, with annual precipitation contributing 69.8%. Scenario-based projections for 2081–2100 indicated a system-specific redistribution of climatic suitability, with northward increases most evident for single-season rice and rice–wheat rotation, localized changes for double-season rice, and increased suitability for rice–maize rotation mainly in Southwest and parts of South China. Changes were stronger under SSP5-8.5. These findings show that rice-based cropping systems should not be treated as a single uniform category in climate-change suitability assessments. Full article

(This article belongs to the Special Issue Application of Machine Learning and Modelling in Food Crops)

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36 pages, 19416 KB

Open AccessArticle

Spatial, Temporal, and Vertical Variability of Greenhouse Microclimate and Artificial Neural Network-Based Prediction Under Korean Summer and Winter Conditions

by Md Nasim Reza, Md Razob Ali, Hongbin Jin, Sakib Robin, Md Aminur Rahman, Hyeunseok Choi and Sun-Ok Chung

Agronomy 2026, 16(10), 960; https://doi.org/10.3390/agronomy16100960 (registering DOI) - 12 May 2026

Abstract

Understanding greenhouse microclimatic variability is essential for precise environmental monitoring and control. This study evaluated temperature, relative humidity, CO₂ concentration, and light intensity variability in Korean greenhouses during summer and winter, and developed artificial neural network (ANN) models to predict indoor temperature [...] Read more.

Understanding greenhouse microclimatic variability is essential for precise environmental monitoring and control. This study evaluated temperature, relative humidity, CO₂ concentration, and light intensity variability in Korean greenhouses during summer and winter, and developed artificial neural network (ANN) models to predict indoor temperature and relative humidity at different layers. A glass greenhouse and an arched-frame double-layer plastic greenhouse were monitored during summer and winter, respectively. A wireless sensor network was deployed at multiple spatial positions and vertical layers, and layer-specific artificial neural network (ANN) models were developed to predict indoor temperature and relative humidity at the top, middle, and bottom layers. The measured results revealed clear temperature and humidity stratification, with the top layer generally showing a higher temperature and lower humidity than the middle and bottom layers. In summer, temperatures reached 36.4 °C, while relative humidity ranged from 55% to 92%, while in winter, temperature varied from 3.4 °C to 35.0 °C and relative humidity ranged from 73% to 91%. Spatial contour mapping showed clear microclimatic gradients, and ANOVA with Tukey’s HSD tests confirmed significant differences among sensor locations (p < 0.05). The ANN models predicted indoor temperature with high accuracy, with R² values generally above 0.95, while humidity prediction showed larger errors. Full article

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

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23 pages, 2266 KB

Open AccessArticle

Impact of Organic Digestate on Soil and Crop Nitrogen During Critical Periods of Winter Oilseed Rape Growth

by Witold Szczepaniak, Remigiusz Łukowiak and Hanna Klikocka

Agronomy 2026, 16(10), 959; https://doi.org/10.3390/agronomy16100959 (registering DOI) - 12 May 2026

Abstract

We hypothesized that the application of digestate (D) to winter oilseed rapeseed would have the same effect on seed production as nitrogen fertilizer (N_f). It impacts yield by altering the mass of readily available N in the vegetative and reproductive periods [...] Read more.

We hypothesized that the application of digestate (D) to winter oilseed rapeseed would have the same effect on seed production as nitrogen fertilizer (N_f). It impacts yield by altering the mass of readily available N in the vegetative and reproductive periods of plant growth. This allows for a good yield forecast. This hypothesis was assessed in field experiments with rapeseed carried out in 2015/2016, 2016/2017, and 2017/2018. The experiment included three N fertilization systems (FSs): AN, based on ammonium nitrate (AN); D, with digestate-based N; DAN, using ²/₃ of digestate + ¹/₃ of AN—and five N_f doses: 0, 80, 120, 160, and 240 kg N ha⁻¹. The net seed yield increase due to N application was 1.44 t ha⁻¹ in the AN system, 1.53 t ha⁻¹ in D, and 1.77 t ha⁻¹ in DAN. The optimal N rates were 160, 250, and 224 kg N ha⁻¹. The N economy of winter oilseed rapeseed was assessed in two periods: vegetative—before anthesis (from the rosette stage to the beginning of anthesis, BBCH 30–BBCH 60) and reproductive (from the beginning of anthesis to full maturity, BBCH 60–BBCH 89). The mass of available N at the beginning of anthesis increased by 54.3% (151 kg N ha⁻¹ to 233 N ha⁻¹) and doubled (151 kg N ha⁻¹ to 302 kg N ha⁻¹) compared to its value at the rosette stage, taking into account the mass of N in the rapeseed canopy and its total mass in the soil/rapeseed continuum. No differences in NUE were found for the tested N carriers. The net increase in N available resources resulting from the application of N fertilizer was 55.1, 104.9, 102.8, and 93.0 kg N ha⁻¹ for respective plots fertilized with 60, 120, 180, and 240 kg N ha⁻¹. Three N indices were measured at the beginning of rapeseed anthesis—N in crop biomass (NAF, r = 0.87 ***), N balance (Nb60, r = 0.87 ***), and N released from soil resources (Ngain60, r = 0.79 ***)—and showed potential for seed yield (SEY) prediction. The linear dependence of SEY on these indicators indicates that the potential of the rapeseed canopy to effectively accumulate N during the vegetative growth was too low. This limitation was fully confirmed by analogous N management indicators, but developed for rapeseed during the seed-filling period. The key indicator of SEY at harvest was the N mass in rapeseed biomass (NAH, r = 0.95 ***). N from digestate acted as a slow-release fertilizer, giving it an advantage over ammonium nitrate. In summary, digestate is an optimal N carrier under conditions of average rapeseed yield. Full article

(This article belongs to the Special Issue Fertilizer Innovation and Practice in Sustainable Intensified Agriculture)

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18 pages, 824 KB

Open AccessArticle

Effect of Foliar Biostimulants on Soybean Growth and Yield Across Different Tillage Systems

by Sushil Thapa, Racquel Gorden, Michelle Santiago and Anna C. Ortiz

Agronomy 2026, 16(10), 958; https://doi.org/10.3390/agronomy16100958 (registering DOI) - 12 May 2026

Abstract

Climate variability and widespread synthetic agrochemical use have increased interest in biostimulants (BS) that enhance plant growth, stress tolerance, and yield by stimulating natural plant processes. A two-site field study, conducted under no-till and tilled systems, evaluated the effects of the foliar biostimulant [...] Read more.

Climate variability and widespread synthetic agrochemical use have increased interest in biostimulants (BS) that enhance plant growth, stress tolerance, and yield by stimulating natural plant processes. A two-site field study, conducted under no-till and tilled systems, evaluated the effects of the foliar biostimulant “Source” on soybean growth and yield at three phosphorus (P) rates (0%, 50%, and 100% of soil test recommendations) because of its potential to replace phosphorus inputs. A complementary greenhouse study was conducted to evaluate the effect of the biostimulant on different soybean hybrids. Measured at various dates after planting (DAP), leaf relative water content (LRWC) and normalized difference vegetation index (NDVI) mostly showed insignificant responses to P treatments, but significant responses to BS. Grain yield increased significantly with individual BS and P applications in both tillage systems. Under no-till conditions, BS increased yield by 13.0% (3.05 vs. 2.70 Mg ha⁻¹), and P100 increased yield by 13.5% (3.0 vs. 2.65 Mg ha⁻¹). Under tilled conditions, BS and P100 increased yield by 19.6% (2.75 vs. 2.30 Mg ha⁻¹) and 19.2% (2.72 vs. 2.28 Mg ha⁻¹), respectively, compared with the control. Yield gains were primarily driven by increased pod density and grain number. Greenhouse experiments supported these trends, with BS-treated plants producing more grains per plant (187.6 vs. 171.3) and higher yield per plant (28.8 vs. 25.7 g). Results indicated that biostimulant application improved physiological performance and increased soybean yields, comparable to full-rate phosphorus, highlighting its potential as a sustainable approach under increasing environmental and input-related challenges. Full article

(This article belongs to the Special Issue Effects of Different Biostimulants and Biochars on Crop Production and Quality)

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18 pages, 2747 KB

Open AccessArticle

Plant Volatiles and Essential Oils Induce Sex-Specific Behavioral Responses and Concentration-Dependent Toxicity in the Invasive Pest Bagrada hilaris

by Camila C. Santander, Marta V. Albornoz, M. Fernanda Flores, Eduardo Oyanedel, Wilson Barros-Parada and Armando Alfaro-Tapia

Agronomy 2026, 16(10), 957; https://doi.org/10.3390/agronomy16100957 (registering DOI) - 12 May 2026

Abstract

Bagrada hilaris (Burmeister) (Hemiptera: Pentatomidae) is an invasive pest that causes significant damage to Brassica crops worldwide. This study evaluated behavioral and toxicological responses of adults B. hilaris to plant volatiles and essential oils (EOs). Y-tube olfactometer assays revealed sex-specific responses to plant-emitted [...] Read more.

Bagrada hilaris (Burmeister) (Hemiptera: Pentatomidae) is an invasive pest that causes significant damage to Brassica crops worldwide. This study evaluated behavioral and toxicological responses of adults B. hilaris to plant volatiles and essential oils (EOs). Y-tube olfactometer assays revealed sex-specific responses to plant-emitted volatiles: females were repelled by Coriandrum sativum and Petroselinum crispum, while males responded to Pelargonium hortorum. Essential oils exhibited non-linear concentration-dependent effects, with C. sativum EO inducing repellency at 40–80 µg/µL and P. hortorum at 160–320 µg/µL. In contrast, repellency index was not influenced by sex, but strongly driven by concentration, with C. sativum and P. hortorum most effective, and P. crispum showing weaker yet consistent responses. Toxicity assays demonstrated greater male susceptibility, with lower LC₅₀ and LC₉₀ values for C. sativum and P. hortorum. Gas chromatography-mass spectrometry (GC-MS) analysis of EOs matrix identified linalool, β-citronellol, trans-geraniol, and myristicin as the predominant constituents. Importantly, repellency occurred at lower concentrations than mortality thresholds, indicating distinct behavioral and physiological mechanisms. These findings support integrating C. sativum and P. hortorum essential oils into sustainable pest management strategies for B. hilaris. Full article

(This article belongs to the Special Issue Sustainable Management of Arthropod Pests in Agriculture)

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15 pages, 7209 KB

Open AccessArticle

Silicon Dioxide Nanoparticles Mitigate PEG-Induced Drought Stress in Carya cathayensis by Improving Physiological Characteristics and Ultrastructure

by Yecheng Wang, Zhenyang Pu, Minjie Lai, Qunhao Wan, Junle Chen, Longjun Cheng and Zhengjia Wang

Agronomy 2026, 16(10), 956; https://doi.org/10.3390/agronomy16100956 (registering DOI) - 12 May 2026

Abstract

Drought frequently threatens the yield and quality of Carya cathayensis Sarg. cultivated in mountainous regions. To search for effective drought-resistant regulators is of great significance for alleviating short-term seasonal drought in C. cathayensis during dry seasons, thereby stabilizing its yield and quality. Silicon [...] Read more.

Drought frequently threatens the yield and quality of Carya cathayensis Sarg. cultivated in mountainous regions. To search for effective drought-resistant regulators is of great significance for alleviating short-term seasonal drought in C. cathayensis during dry seasons, thereby stabilizing its yield and quality. Silicon dioxide nanoparticles (SiO₂ NPs) mitigate abiotic stress in plants. To give insight into the regulatory role of SiO₂ NPs in mitigating drought stress, polyethylene glycol 6000 (PEG-6000) was used to simulate varying degrees of drought conditions, and the growth phenotype, photosynthetic physiological characteristics, antioxidant defense system, and cellular ultrastructure of C. cathayensis leaves were analyzed to evaluate the impacts of foliar-applied exogenous SiO₂ NPs. The results indicated that, compared with severe drought, 200 mg/L SiO₂ NP application to plants under severe drought treatment significantly increased superoxide dismutase and peroxidase activities and chlorophyll and nitrogen contents, while malondialdehyde levels decreased. Furthermore, SiO₂ NP application notably enhanced the net photosynthetic rate, stomatal conductance, and electron transport efficiency. This effectively alleviated both stomatal and non-stomatal limitations, thereby mitigating drought-induced photosynthetic inhibition. Additionally, Transmission electron microscopy revealed that SiO₂ NPs effectively preserved the structural integrity of chloroplasts, mitochondria, and nuclei, reducing drought-induced ultrastructural damage. In conclusion, exogenous SiO₂ NPs enhance drought tolerance in C. cathayensis by synergistically modulating photosynthesis, antioxidant defense, and cellular structural stability. Full article

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

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18 pages, 61486 KB

Open AccessArticle

Characterization and Genetic Analysis of Traits in Autotetraploid Progeny of a Gossypium herbaceum L.

by Lili Feng, Lexiang Wang, Jiamin Li, Xianglong Li, Erhua Rong and Yuxiang Wu

Agronomy 2026, 16(10), 955; https://doi.org/10.3390/agronomy16100955 (registering DOI) - 11 May 2026

Abstract

Polyploidization is a key pathway for species formation and genetic innovation; approximately 70% of angiosperms have undergone at least one whole-genome duplication event during their evolutionary history. To determine the genetic and phenotypic stability of artificially induced autotetraploids across generations, this study utilized [...] Read more.

Polyploidization is a key pathway for species formation and genetic innovation; approximately 70% of angiosperms have undergone at least one whole-genome duplication event during their evolutionary history. To determine the genetic and phenotypic stability of artificially induced autotetraploids across generations, this study utilized a colchicine-induced autotetraploid of Gossypium herbaceum as experimental material and conducted systematic comparative analyses of morphological, cytological, and molecular marker characteristics in the S3 and S4 generations. The results showed that, compared with the 2×, seed weight in the S3 generation increased by 59.4% (to 89.22 mg), and in the S4 generation increased by 65.0% (to 92.40 mg), while there was no significant difference in fiber length. The leaf area of tetraploids decreased significantly during the flower-bell stage. Observation of pollen mother cell meiosis revealed that the proportions of normal tetrads in the S3 and S4 generations were 73.80% and 81.80%, respectively, and the proportions of normal pollen grains were 79.60% and 80.60%, respectively. Cytological stability was markedly improved in the S4 generation. A total of 34 alleles were amplified by SSR molecular marker analysis, of which 23 (67.60%) were polymorphic. The primers NBRI_G1015 and NAU1164 exhibited the highest polymorphism rates, at 87.50% and 83.30%, respectively. The average genetic diversity index (He) was 0.1411, indicating a highly inbred genetic background. The banding patterns of S3 and S4 are highly consistent, with strong signal intensity; not only do they amplify bands consistent with those of diploids, but they also exhibit specific new bands and band deletions. In summary, this autotetraploid material exhibits stable morphological advantages and genetic uniformity. As generations progress, its meiotic behavior and genetic structure tend to stabilize. The S4 generation exhibits greater cytological stability and genetic uniformity than the S3 generation, making it a highly promising new germplasm resource for cotton polyploid breeding. Full article

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

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23 pages, 6648 KB

Open AccessArticle

Phosphate Mining Residues as Novel Substrate for Advanced Vertical Flow Constructed Wetlands: A Circular Economy Approach

by Meryem Hdidou, Mohamed Chaker Necibi, Jérôme Labille, Amal An-nori, Bouchaib Gourich and Nicolas Roche

Agronomy 2026, 16(10), 954; https://doi.org/10.3390/agronomy16100954 (registering DOI) - 11 May 2026

Abstract

Constructed wetlands offer a sustainable, decentralized solution for wastewater treatment and reuse in Morocco. This study evaluated mesocosm-scale advanced vertical flow constructed wetlands (AVFCWs) incorporating locally sourced reactive media to assess phosphate mining residues as a novel substrate. Accordingly, four configurations were compared: [...] Read more.

Constructed wetlands offer a sustainable, decentralized solution for wastewater treatment and reuse in Morocco. This study evaluated mesocosm-scale advanced vertical flow constructed wetlands (AVFCWs) incorporating locally sourced reactive media to assess phosphate mining residues as a novel substrate. Accordingly, four configurations were compared: a sand-based control (CW-A) and three amended systems combining pozzolan with phosphate mining residues (CW-B), clay (CW-C), and biochar (CW-D), operated in batch mode under hydraulic retention times (HRTs) of 24, 48, and 72 h. The incorporation of reactive media significantly improved treatment efficiency, with CW-D achieving high removal efficiencies across most parameters. COD and TSS removal reached 80% and 88%, respectively, while nitrogen removal exceeded 82% in optimal configurations. Phosphorus removal reached 76% in CW-B and 88% in CW-C. The removal of Cd and Cu exceeded 85% in all systems, with phosphate mining residues demonstrating strong potential for metal immobilization. However, despite these high removal efficiencies, the treated effluent did not meet Moroccan reuse standards for cadmium and fecal coliforms, indicating that single-stage AVFCWs are insufficient for safe agricultural reuse and require additional polishing steps. Extended HRT improved AVFCWs’ performance, but increased water loss, reaching up to 28% due to evapotranspiration. Hence, phosphate mining residues emerge as a promising substrate, pending further optimization, while supporting circular economy objectives. Full article

(This article belongs to the Special Issue Old Challenges and Modern Solutions in Farmland Soils: Addressing Heavy Metals, Microplastics, and GHG Emissions)

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29 pages, 3465 KB

Open AccessArticle

Integrated Yield Formation and Multiple Grain Quality Responses of Bread Wheat to Post-Heading Drought Using Multivariate Analyses

by Ali Yiğit

Agronomy 2026, 16(10), 953; https://doi.org/10.3390/agronomy16100953 (registering DOI) - 11 May 2026

Abstract

Spring drought is a major constraint in Mediterranean wheat production, where elevated temperatures and evapotranspiration after heading limit soil water availability during critical generative stages. This study investigated how post-heading drought reshapes the relationships between yield and multiple quality traits (a total of [...] Read more.

Spring drought is a major constraint in Mediterranean wheat production, where elevated temperatures and evapotranspiration after heading limit soil water availability during critical generative stages. This study investigated how post-heading drought reshapes the relationships between yield and multiple quality traits (a total of 22 variables) across ten bread wheat genotypes using multivariate analyses. Field experiments were conducted under rainfed and post-heading drought conditions over two growing seasons. The following traits were evaluated: yield components; flag leaf SPAD; physical, technological, and nutritional quality traits; flour color (L*, a*, b*); phenolic content; and antioxidant activity. Drought caused significant yield reductions, with SPAD, ear yield, grain and test weight emerging as key traits associated with yield formation. Water-limited conditions constrained yield formation in post-heading development stages while promoting certain quality improvements in wheat grain. PCA clearly separated drought and rainfed conditions: drought clustered with bioactive, pigment-related, and mineral traits, whereas rainfed conditions were associated with higher yield, protein content, gluten quality, and technological traits. These findings demonstrate that post-heading drought shifts wheat grain composition toward bioactive and nutritional constituents at the expense of yield-oriented and technological traits, emphasizing the need to select genotypes that sustain both yield stability and nutritional quality under increasing spring water limitations driven by climate change. Full article

(This article belongs to the Special Issue Adaptation Strategies Under Climate Change in Mediterranean Agriculture)

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24 pages, 12896 KB

Open AccessArticle

Spatial Variability of Soil Nutrients in Walnut Orchards in the Middle and Lower Reaches of the Yarlung Zangbo River Valley and Its Association with Fruit Quality

by Kai Yang, Wensheng Yang, Yuao Zou, Qianshun Zhou, Jianqiang Zhu, Qixia Wu and Xiaohong Xu

Agronomy 2026, 16(10), 952; https://doi.org/10.3390/agronomy16100952 (registering DOI) - 11 May 2026

Abstract

This study evaluated the multi-scale spatial heterogeneity of soil fertility in walnut orchards in the middle and lower reaches of the Yarlung Zangbo River valley. The investigation focused on Jiacha, Lang, and Milin counties, covering four river terrace levels and three soil depths [...] Read more.

This study evaluated the multi-scale spatial heterogeneity of soil fertility in walnut orchards in the middle and lower reaches of the Yarlung Zangbo River valley. The investigation focused on Jiacha, Lang, and Milin counties, covering four river terrace levels and three soil depths within the 0–60 cm layer, and further examined the effects of such heterogeneity on walnut fruit quality. Using integrated multivariate statistical approaches and fuzzy comprehensive evaluation, 321 paired soil and fruit samples collected in September and October of 2023 were analyzed. Overall soil fertility was moderate (0.4 ≤ IFI < 0.6) with a mean integrated fertility index (IFI) of 0.527, but showed pronounced spatial variation. PCA-based composite scores indicated the highest fertility in Milin County, followed by Lang County, with Jiacha County ranking lowest. Soil fertility across 11 towns was classified into five grades. Cluster analysis based on ten standardized soil fertility indicators revealed clear regional aggregation patterns, where close towns exhibited similar fertility conditions. Third-level river terraces exhibited significantly higher fertility than other terrace levels. Available phosphorus was widely deficient, while exchangeable magnesium and available zinc were also low, representing key limiting nutrients with strong regional variability. Spatial differences in soil enzyme activities reflected variation in microbially mediated nutrient cycling, with phosphatase activity negatively correlated with available phosphorus, suggesting potential microbial responses to phosphorus-stressed environments. Soil fertility significantly influenced walnut fruit quality, with alkaline hydrolyzable nitrogen, phosphorus, potassium, and exchangeable calcium and magnesium identified as key drivers. These findings provide a theoretical basis for suggesting a zoned precision fertilization strategy, where prioritizing P, Zn, and Mg inputs in deficient areas could be considered alongside organic fertilisation. Such site-specific management strategies are suggested to support the sustainable development of the walnut industry along the Yarlung Zangbo River valley. Full article

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

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23 pages, 3987 KB

Open AccessArticle

UAV-Based Multi-Source Feature Fusion and Ensemble Learning for Maize Growth Monitoring and Fertilizer Optimization in Saline–Alkali Regions

by Xun Yang, Haixiao Ge, Fenfang Lin, Fei Ma and Changwen Du

Agronomy 2026, 16(10), 951; https://doi.org/10.3390/agronomy16100951 (registering DOI) - 11 May 2026

Abstract

In saline–alkali environments, soil salinity imposes severe abiotic stress on maize growth by inhibiting root activity and nutrient uptake. Traditional destructive sampling methods struggle to enable cross-growth stage, large-scale dynamic fertilizer effect assessment. This study, conducted in saline–alkali farmlands of Inner Mongolia, utilized [...] Read more.

In saline–alkali environments, soil salinity imposes severe abiotic stress on maize growth by inhibiting root activity and nutrient uptake. Traditional destructive sampling methods struggle to enable cross-growth stage, large-scale dynamic fertilizer effect assessment. This study, conducted in saline–alkali farmlands of Inner Mongolia, utilized UAV multispectral remote sensing to extract 20 vegetation indices and 40 texture parameters, constructing a multi-source feature set. An ensemble learning framework integrating Random Forest (RF), Decision Tree (DTR), AdaBoost and Gradient Boosting Regression (GBR) was developed to achieve precise monitoring of maize plant height, leaf area index (LAI), and yield. In addition, the study aimed to evaluate the dynamic effects of seven fertilizer treatments (six controlled-release composite fertilizers, T1–T6, and conventional CK) and to identify the optimal fertilization scheme, with particular emphasis on comparing the two best-performing treatments, T1 and T2. Results showed that: (1) The ensemble model improved prediction robustness, with R² values of 0.88, 0.76, and 0.76 for plant height, LAI, and yield across the entire growth cycle, respectively. The integration of texture features effectively mitigated spectral saturation during peak growth stages (e.g., tasseling and filling). (2) For fertilizer evaluation, T1 performed best in growth and yield at jointing, tasseling, and filling stages, with a yield increase rate of up to 40.18% at the jointing stage. Although T2 slightly outperformed T1 in yield increase at maturity (15.42%), T1 was identified as the optimal fertilizer scheme for the region based on whole-growth-stage growth performance, measured yield, LAI, and yield increase rate. These results demonstrate that UAV-based multi-source feature fusion combined with ensemble learning provides an effective and non-destructive approach for fertilizer evaluation and precision nutrient management in saline–alkali regions. Full article

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

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20 pages, 3124 KB

Open AccessArticle

Vertical Variations of Leaf Photosynthetic and Biochemical Parameters Within Winter Wheat and Paddy Rice Canopies at Different Growth Stages

by Jing Li, Yanlian Zhou, Xuehe Lu, Tingting Zhu, Kai Cao, Shucun Sun, Bo Tang and Weimin Ju

Agronomy 2026, 16(10), 950; https://doi.org/10.3390/agronomy16100950 (registering DOI) - 9 May 2026

Abstract

During crop growth, leaf photosynthetic capacity changes continuously, and the vertical distribution of leaf nitrogen (N_a, in g m⁻²) and chlorophyll (Chl_a, in μg cm⁻²) affects photosynthesis in different canopy layers. Understanding stratified photosynthesis is [...] Read more.

During crop growth, leaf photosynthetic capacity changes continuously, and the vertical distribution of leaf nitrogen (N_a, in g m⁻²) and chlorophyll (Chl_a, in μg cm⁻²) affects photosynthesis in different canopy layers. Understanding stratified photosynthesis is vital for the accurate prediction of crop photosynthetic capacity. We conducted a two-year field study on winter wheat and paddy rice in Eastern China, measuring the leaf maximum carboxylation rate (V_cmax25), maximum electron transport rate (J_max25), N_a, and Chl_a every 7–10 days from greening to maturity. We analyzed vertical variations in these parameters in the upper (T-1), middle (T-2), and lower (T-3) canopy layers and explored relationships between N_a/Chl_a and V_cmax25. The results showed significant vertical variations: V_cmax25 and J_max25 in T-1 were higher than in T-2, and T-2 was higher than T-3. The vertical distribution of N_a and V_cmax25 was more pronounced than that of Chl_a. The correlation between N_a and V_cmax25 increased from T-1 to the lower layers, while the V_cmax25-Chl_a correlation decreased. A single V_cmax25 estimation model based on N_a performed well across layers (R² = 0.619, RMSE = 15.751 µmol m⁻² s⁻¹). Differentiating T-1 from T-2/T-3 improved the Chl_a-based models. N_a was better than Chl_a for characterizing the V_cmax25 vertical variation, with the Chl_a-based models requiring separation of T-1 from T-2/T-3. This study provides key insights for remote sensing of photosynthetic parameters and improves the understanding of crop canopy photosynthesis. Full article

(This article belongs to the Special Issue Crop Photosynthesis: Today’s Challenge for Our Future)

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4 pages, 143 KB

Open AccessEditorial

Editorial for the Special Issue on Organic Amendments to Low-Fertility Soils: Current Status and Future Prospects

by Xiquan Wang and Jie Zhou

Agronomy 2026, 16(10), 949; https://doi.org/10.3390/agronomy16100949 (registering DOI) - 9 May 2026

Abstract

Low soil fertility is a widespread problem in many regions worldwide [...] Full article

(This article belongs to the Special Issue Organic Amendments to Low-Fertility Soils: Current Status and Future Prospects)

25 pages, 11007 KB

Open AccessReview

Population-Based Threshold Models for Predicting Weed Emergence: A Synthesis as a Conceptual Framework for the Development of Tools for Site-Specific Management

by Cristian Malavert, Diego Batlla and Roberto L. Benech-Arnold

Agronomy 2026, 16(10), 948; https://doi.org/10.3390/agronomy16100948 - 8 May 2026

Abstract

Effective weed management is crucial for optimizing agricultural productivity and minimizing environmental impacts. Weeds are most effectively managed during their seedling or early growth stages, which can be achieved with the aid of tools for predicting seedling emergence. However, many persistent weed species [...] Read more.

Effective weed management is crucial for optimizing agricultural productivity and minimizing environmental impacts. Weeds are most effectively managed during their seedling or early growth stages, which can be achieved with the aid of tools for predicting seedling emergence. However, many persistent weed species exhibit dormant seedbanks, thus complicating prediction attempts. The number of seedlings emerging in these species is closely tied to seedbank dormancy levels, which are influenced by seasonal variations. Thus, predictive population-based threshold models incorporate seedbank dormancy regulation to accurately forecast seedling “window” emergence. These models use the functional relationship between environmental cues (i.e., temperature, light, alternating temperatures, and soil water content) and seed dormancy behavior. Considering that these environmental signals vary among microsites in the field, these tools can be adapted to predict weed emergence in both temporal and spatial dimensions, thus making them suitable for site-specific weed management. The aim of this review is to synthesize existing modeling approaches and present a conceptual framework for dynamic, site-specific weed emergence predictions, supported by case-study-based applications. The illustrative application shows that incorporating soil water content into dormancy dynamics modifies emergence timing and magnitude, restricting emergence to specific topographic zones and potentially reducing herbicide use by up to 60–70%. This approach can improve the efficiency of herbicide applications and other control measures, reducing costs and environmental impact while enhancing crop yields. This work underscores the potential of integrating environmental cues into sophisticated modeling approaches to address the complexities of weed emergence in diverse agricultural landscapes. Full article

(This article belongs to the Special Issue State-of-the-Art Research on Weed Populations and Community Dynamics)

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25 pages, 1054 KB

Open AccessArticle

DNA Barcoding and Allele-Specific PCR Discrimination of Glasswort Ecotypes from Apulia Region (Southern Italy)

by Angelica Giancaspro, Giulia Conversa, Luigi Giuseppe Duri, Gaetana Ricatti, Antonio Elia, Stefano Pavan and Concetta Lotti

Agronomy 2026, 16(10), 947; https://doi.org/10.3390/agronomy16100947 - 8 May 2026

Abstract

In the scenario of ongoing climate changes, the selection of plant genotypes with high salt tolerance is emerging as the most sustainable strategy to safeguard crop yield and quality and make productive use of salinized soils. Glassworts are annual and perennial halophytes found [...] Read more.

In the scenario of ongoing climate changes, the selection of plant genotypes with high salt tolerance is emerging as the most sustainable strategy to safeguard crop yield and quality and make productive use of salinized soils. Glassworts are annual and perennial halophytes found in inner and coastal wastelands, indistinctly consumed as high-nutritional green vegetables. Traditional taxonomic classification based on morphological traits can be very challenging in glasswort, due to phenotypic plasticity, reduced plant morphology, and inbreeding. In this work, we used DNA-based molecular tools to overcome such constraints and assess inter-generic and inter-specific genetic diversity in a collection of ecotypes from different Apulian areas. A fast and reliable Allele-Specific PCR assay was optimized to enable molecular detection of annual and perennial genera. Species-level classification was obtained through a similarity- and phylogeny-based approach relying on matK and rbcL DNA barcoding. Combined DNA tools identified perennial samples as Sarcocornia fruticosa and Arthrocaulon macrostachyum, along with annual Salicornia europaea, and phylogenetic trees unveiled genetic distances between glassworts, which clustered according to life cycle. The relationship between genotypes and nutritional profiles was finally investigated, suggesting that environmental factors may play a predominant role over taxonomic relatedness in shaping interspecific differences in nutrient composition of the analyzed samples. Full article

(This article belongs to the Special Issue Agro-Environmental Sustainable Exploitation of Halophyte, Medicinal and Aromatic Species from Marginal Areas—2nd Edition)

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