Agronomy

31 pages, 875 KiB

Open AccessReview

Soybean Molecular Breeding Through Genome Editing Tools: Recent Advances and Future Perspectives

by Chan Yong Kim, Sivabalan Karthik and Hyeran Kim

Agronomy 2025, 15(8), 1983; https://doi.org/10.3390/agronomy15081983 - 18 Aug 2025

Soybean (Glycine max L.) is an essential crop for global food, feed, and industrial applications, but its production is increasingly challenged by climate change and environmental stresses. Traditional breeding and transgenic approaches have contributed to improvements in yield and quality; however, limitations [...] Read more.

Soybean (Glycine max L.) is an essential crop for global food, feed, and industrial applications, but its production is increasingly challenged by climate change and environmental stresses. Traditional breeding and transgenic approaches have contributed to improvements in yield and quality; however, limitations in genetic diversity and regulatory hurdles for genetically modified organisms (GMOs) underscore the need for innovative strategies to address these challenges. Genome editing technologies, particularly CRISPR/Cas9, have revolutionized soybean molecular breeding by enabling precise modifications of genes related to key agronomic traits such as yield, seed composition, and stress tolerance. These advances have accelerated the development of soybean varieties with enhanced nutritional value and adaptability. Recent progress includes improvements in editing efficiency, specificity, and the ability to target multiple genes simultaneously. However, the application of genome editing remains concentrated in a few model cultivars, and challenges persist in optimizing transformation protocols, minimizing off-target effects, and validating edited traits under field conditions. Future directions involve expanding the genetic base, integrating genome editing with synthetic biology, and addressing regulatory and public acceptance issues. Overall, genome editing offers significant potential for sustainable soybean improvement, supporting food security and agricultural resilience in the face of global challenges. Full article

(This article belongs to the Special Issue Molecular Advances in Crop Protection and Agrobiotechnology)

18 pages, 1745 KiB

Open AccessArticle

Metagenomic Insight into the Impact of Soil Nutrients and Microbial Community Structure on Greenhouse Gas Emissions: A Case Study in Giant Rice–Fish Co-Cultured Mode

by Andong Wang, Dongsheng Zou, Manyun Zhang, Yinling Luo, Sunyang Li, Jingchen Zou, Xiaopeng Zhang and Bin Chen

Agronomy 2025, 15(8), 1982; https://doi.org/10.3390/agronomy15081982 - 18 Aug 2025

Abstract

This study investigates the impact of environmental changes induced by systematic manipulation of flooding depth and breeding density on greenhouse gas emissions in the field-based giant rice–fish hybrid farming model. Compared with traditional agricultural practices, increasing cultured density in giant rice–fish co-cultivation significantly [...] Read more.

This study investigates the impact of environmental changes induced by systematic manipulation of flooding depth and breeding density on greenhouse gas emissions in the field-based giant rice–fish hybrid farming model. Compared with traditional agricultural practices, increasing cultured density in giant rice–fish co-cultivation significantly alleviated the adverse consequences of flooding on soil nutrient dynamics, microbial activity community structure, and greenhouse gas emissions. Relative to the traditional alternating wet and dry irrigation, the soil concentrations of ammonium, total nitrogen, and phosphate significantly increased. Cultured fish had significantly increased soil microbial biomass carbon, nitrogen, and phosphorus contents and improved soil β-glucosidase and aryl-sulfatase activates relative to flooding alone. Cultured fish increased the relative abundances of Actinobacteria, Nitrospirae, Planctomycetes, Verrucomicrobia, and Aminicenantes. An increasing cultured fish density reduced cumulative methane and nitrous oxide emissions and GWP (global warming potential). Relative to the continuous flooding throughout the growing period, cumulative methane emissions and GWP in the flooding with high-density cultured fish were reduced by 5.32% and 1.48%, respectively. Notably, this co-cultivation strategy has the potential to transform traditional practices for sustainable agriculture. Nevertheless, it is imperative to remain vigilant about the potential consequences of greenhouse gas emissions associated with these innovative practices. Continuous monitoring and refinement are essential to ensure the long-term sustainability and viability of this agricultural approach. Full article

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

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20 pages, 4900 KiB

Open AccessArticle

Development of a Real-Time Irrigation Strategy Based on Cumulative Reference Evapotranspiration (ET₀) for Cabbage Cultivation in Paddy-Converted Fields

by Xin Wang, Yongjae Lee, To Kang and Jongseok Park

Agronomy 2025, 15(8), 1981; https://doi.org/10.3390/agronomy15081981 - 18 Aug 2025

Abstract

This study developed an efficient cultivation strategy for cabbage production in paddy fields. To address poor drainage, discarded coir substrates (CS) were reused and compared with conventional paddy soil (PS). Four irrigation levels (ETc140, ETc100, ETc60, and ETc0) were applied to both CS [...] Read more.

This study developed an efficient cultivation strategy for cabbage production in paddy fields. To address poor drainage, discarded coir substrates (CS) were reused and compared with conventional paddy soil (PS). Four irrigation levels (ETc140, ETc100, ETc60, and ETc0) were applied to both CS and PS to evaluate their interactive effects. An automated irrigation system was deployed, integrating a weather sensor and solenoid valves via a LoRa-based IoT network. Hourly ET₀ was calculated based on Penman–Monteith in real time, and an irrigation event was triggered when cumulative ET₀ reached 1 mm (CS) or 3 mm (PS). The automated irrigation system showed stable performance. Hourly ET₀ estimates were 97% consistent with Korea Meteorological Administration data. The actual total irrigation depth (ID_actual) remained within 2% of the calculated depth (ID). Under moderate irrigation depths (ETc60 and ETc100), the reuse of CS significantly improved cabbage photosynthetic efficiency. Both CS-ETc60 and CS-ETc100 treatments maintained superior yield performance compared with other treatments. This integrated strategy not only offers a practical solution for improving water use efficiency but also enhances the multifunctional utilization of paddy fields, supporting the transition toward more sustainable agricultural practices. Full article

(This article belongs to the Section Innovative Cropping Systems)

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17 pages, 16198 KiB

Open AccessArticle

Identifying Agronomic Strategy for a Low-Carbon Economy Under the Effects of Climate Change by Using a Simulation-Optimization Hybrid Model

by Haomiao Cheng, Siyu Sun, Wei Jiang, Qilin Yu, Wei Ma, Shaoyuan Feng, Fusheng Wang and Zuping Xu

Agronomy 2025, 15(8), 1980; https://doi.org/10.3390/agronomy15081980 - 18 Aug 2025

Abstract

Agronomic practices and future climate change lead to divergent responses in crop growth and greenhouse gas (GHG) emissions, which challenge a sustainable low-carbon agricultural economy. Therefore, this study developed a simulation-optimization hybrid model to identify long-term best management practices (BMPs) for economic and [...] Read more.

Agronomic practices and future climate change lead to divergent responses in crop growth and greenhouse gas (GHG) emissions, which challenge a sustainable low-carbon agricultural economy. Therefore, this study developed a simulation-optimization hybrid model to identify long-term best management practices (BMPs) for economic and social benefits under the effects of future climate change. This model, i.e., RZWQM2 coupled with an orthogonal optimization algorithm (RZWQM2-OOA), integrates four core components, including an orthogonal sampling module, climate prediction module, RZWQM2 simulation module, and optimization analysis module. The model enabled a high-fidelity simulation of crop growth and carbon emissions across complex management practice-climate combinations, while efficiently identifying BMPs and circumventing dimensionality challenges through orthogonality and balanced dispersion mechanisms. To validate the applicability of the developed model, it was applied to a real-world, irrigated, continuous corn (Zea mays L.) production system in the USA. Results indicated that the maximum increases in direct and indirect economic benefits (F₁ and F₂) and potential social benefits (F₃) were 35.7%, 42.6%, and 155.5%, respectively, compared to the actual practice. Fertilization amount was the key regulating factor for direct economic and potential social benefits, which exhibited the largest contribution rates (44.3% for direct economic benefit and 53.9% for potential social benefit). Irrigation exerted the most significant influence on indirect economic benefits (Contribution rate = 53.9%). This study provides a replicable and scalable methodology for policy-makers to balance the trade-offs between the economy and carbon emissions in agricultural sustainability. Full article

(This article belongs to the Special Issue Modeling Soil-Water-Salt Interactions for Agricultural Sustainability)

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20 pages, 5917 KiB

Open AccessArticle

Montmorillonite and Composite Amino Acid Overcome the Challenges of Straw Return in Cold-Region Soil: Synergistic Mechanisms of Rapid Straw Humification and Carbon Sequestration

by Xingyan Chen, Tchoumtchoua Foka Joseline Galliane, Chongyang Zhao, Yanhui Feng and Mingtang Li

Agronomy 2025, 15(8), 1979; https://doi.org/10.3390/agronomy15081979 - 17 Aug 2025

Abstract

This study aimed to develop an effective method to overcome the challenge of straw return in cold-region soil. We systematically investigated the synergistic mechanism of montmorillonite (MMT) and composite amino acid (CAA) on straw humification and carbon sequestration through a low-temperature litterbag field [...] Read more.

This study aimed to develop an effective method to overcome the challenge of straw return in cold-region soil. We systematically investigated the synergistic mechanism of montmorillonite (MMT) and composite amino acid (CAA) on straw humification and carbon sequestration through a low-temperature litterbag field experiment. The results indicate that the combined treatment (MMT-CAA) significantly increased the decomposition rate of straw by 42.1% compared to the control (CK), with MMT showing particular efficacy in lignin degradation (28.3% reduction), while the CAA preferentially decomposed cellulose (19.7% reduction). An FTIR analysis of the decomposition products confirmed these findings. Water-soluble organic carbon (WEOC) and its three-dimensional fluorescence spectra exhibited a 25.0% increase in MMT-CAA and enhanced aromaticity of humic acid-like substances. Humic substances and their ¹³C-NMR revealed that MMT-CAA enhanced humic acid formation and molecular stability by 31.4% (with a 47.8% increase in aromaticity). A further redundancy analysis and symbiotic network of microorganisms demonstrated that MMT-CAA increased the abundance of lignocellulose-degrading phyla (Actinomycetes and Stramenomycetes) and the formation of a complex co-degradation network. Field corn planting trials indicated that MMT-CAA increased plant height by 55.1%, stem thickness by 58.7%, leaf area by 70.2%, and the SPAD value by 41.1%. Additionally, MMT significantly reduced CO₂ and N₂O emission fluxes by 35.6% and 15.8%, respectively, while MMT-CAA increased CH₄ uptake fluxes by 13.4%. This study presents an innovative strategy, providing mechanistic insights and practical solutions to synergistically address the challenges of slow straw decomposition and carbon loss in cold regions. Full article

(This article belongs to the Section Agroecology Innovation: Achieving System Resilience)

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14 pages, 8373 KiB

Open AccessArticle

Machine-Learning-Based Multi-Site Corn Yield Prediction Integrating Agronomic and Meteorological Data

by Chenyu Ma, Zhilan Ye, Qingyan Zi and Chaorui Liu

Agronomy 2025, 15(8), 1978; https://doi.org/10.3390/agronomy15081978 - 16 Aug 2025

Abstract

Accurate maize yield forecasting under climate uncertainty remains a critical challenge for global food security, yet existing studies predominantly rely on single-model frameworks, limiting generalizability and actionable insights. This study selected three regions, specifically Dali, Lijiang, and Zhaotong, and collected data on 12 [...] Read more.

Accurate maize yield forecasting under climate uncertainty remains a critical challenge for global food security, yet existing studies predominantly rely on single-model frameworks, limiting generalizability and actionable insights. This study selected three regions, specifically Dali, Lijiang, and Zhaotong, and collected data on 12 agronomic traits of 114 varieties, along with eight sets of meteorological data, covering the period from 2019 to 2023. We employed three machine learning models: Random Forest (RF), Support Vector Machine (SVM), and XGBoost. The results revealed a strong correlation between yield and multiple agronomic traits, particularly grain weight per spike (GWPS) and hundred-kernel weight (HKW). Notably, the XGBoost model emerged as the top performer across all three regions. The model achieved the lowest RMSE (0.22–191.13) and a good R² (0.98–0.99), demonstrating exceptional predictive accuracy for yield-related traits. The comparative analysis revealed that XGBoost exhibited superior accuracy and stability compared to RF and SVM. Through feature importance analysis, four critical determinants of yield were identified: GWPS, shelling percentage (SP), growth period (GP), and plant height (PH). Furthermore, partial dependence plots (PDPs) provided deeper insights into the nonlinear interactive effects between GWPS, SP, GP, PH, and yield, offering a more comprehensive understanding of their complex relationships. This study presents an innovative, data-driven methodology designed to accurately forecast corn yield across diverse locations. This approach offers valuable scientific insights that can significantly enhance precision agricultural practices by enabling the precise tailoring of fertilizer usage and irrigation strategies. The results highlight the importance of integrating agronomic and meteorological data in yield forecasting, paving the way for development of agricultural decision-support systems in the context of future climate change scenarios. This study presents an innovative, data-driven methodology designed to accurately forecast corn yield across diverse locations. This approach offers valuable scientific insights that can significantly enhance precision agricultural practices by enabling the precise tailoring of fertilizer usage and irrigation strategies. Full article

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

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12 pages, 1110 KiB

Open AccessArticle

Endophyte Viability in Grass Seeds: Storage Conditions Affecting Survival and Control Methods

by Barbara Wiewióra and Grzegorz Żurek

Agronomy 2025, 15(8), 1977; https://doi.org/10.3390/agronomy15081977 - 15 Aug 2025

Abstract

Research has evaluated the efficacy of various methods for eliminating endophytes from grass seeds, as well as changes in endophyte viability during seed storage under different conditions, indicating significant variation in different procedures and cultivars. Chemical seed treatment (tebuconazole and thiram) completely eliminated [...] Read more.

Research has evaluated the efficacy of various methods for eliminating endophytes from grass seeds, as well as changes in endophyte viability during seed storage under different conditions, indicating significant variation in different procedures and cultivars. Chemical seed treatment (tebuconazole and thiram) completely eliminated viable fungal mycelia, leaving no trace in any tested cultivar. Non-chemical methods, such as drying and microwave treatment, only partially reduced mycelial viability by 30.3% and 33.1%, respectively, with no statistically significant difference between them. A significant positive correlation was observed between the initial mycelial viability and its reduction. Lolium perenne cv. Vigor showed no impact from non-chemical methods, while Festuca rubra cv. Anielka exhibited the greatest reduction (79% after microwave treatment). Seed storage also impacted endophyte survival. Storage at +7 °C, +23 °C, and −20 °C reduced viability by 27.4%, 31.7%, and 37.3%, respectively. Positive correlations existed between initial viability and post-storage reductions. Similarly to elimination methods, cv. Vigor showed resistance to storage conditions. However, −20 °C storage proved least favorable for endophyte survival, particularly for Festuca pratensis cv. Artema, cv. Anielka, and Festuca ovina cv. Jolka. To maintain the viability of beneficial endophytes during seed storage, we must carefully control storage conditions, especially ambient temperature. Full article

(This article belongs to the Special Issue Plant–Microbiota Interactions Under Abiotic Stress)

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22 pages, 2262 KiB

Open AccessArticle

Stage-Specific Light Intensity Optimization for Yield and Energy Efficiency in Plant Factory Potato Pre-Basic Seed Production

by Song Chen, Jiating Lin and Zhigang Xu

Agronomy 2025, 15(8), 1976; https://doi.org/10.3390/agronomy15081976 - 15 Aug 2025

Abstract

This study investigated the effects of light intensity regulation on yield and energy efficiency during potato pre-basic seed propagation in plant factories. Using virus-free ‘Favorita’ potato seedlings as experimental material, gradient light intensities (200, 300, and 400 μmol·m²·s⁻¹) were [...] Read more.

This study investigated the effects of light intensity regulation on yield and energy efficiency during potato pre-basic seed propagation in plant factories. Using virus-free ‘Favorita’ potato seedlings as experimental material, gradient light intensities (200, 300, and 400 μmol·m²·s⁻¹) were applied at four developmental stages: the seedling stage (SS), tuber formation stage (TFS), tuber growth stage (TGS), and harvest stage (HS), to explore the physiological mechanisms of stage-specific light intensity regulation and energy utilization efficiency. The results revealed that: (1) The per-plant tuber yield of the high yield group reached 72.91 g (T59 treatment), representing a 25% increase compared to the medium yield group and a 168% increase compared to the low yield group. Additionally, the high yield group exhibited superior leaf area, photosynthetic rate, and accumulation of sucrose and starch. (2) The impact of light intensity on tuber development exhibited stage specificity: low light intensity (200 μmol·m⁻²·s⁻¹) during TFS promoted early tuber initiation, while a high light intensity (400 μmol·m⁻²·s⁻¹) enhanced tuber formation efficiency. Increasing the light intensity during TGS facilitated the accumulation of sucrose and starch in tubers. (3) Energy use efficiency (EUE) increased significantly with yield, with the high yield group reaching 3.2 g MJ⁻¹, representing 52% and 88% improvements over the medium yield (2.1 g MJ⁻¹) and low yield (1.7 g MJ⁻¹) groups, respectively. A “stage-specific precision light supplementation” strategy was proposed, involving moderate light reduction (200 μmol·m⁻²·s⁻¹) during TFS and light enhancement (300 μmol·m⁻²·s⁻¹) during TGS to coordinate source-sink relationships and optimize carbohydrate metabolism. This study provides a theoretical basis for efficient potato production in plant factories. Full article

(This article belongs to the Special Issue Potato in a Changing Climate: Adaptation Strategies and Crop Optimization)

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17 pages, 2455 KiB

Open AccessArticle

Variations in Solar Radiation and Their Effects on Rice Growth in Agro-Photovoltaics System

by Yamin Jia, Xiaoli Gao, Junkang He, Jiufu Luo, Xin Sui and Peilan Su

Agronomy 2025, 15(8), 1975; https://doi.org/10.3390/agronomy15081975 - 15 Aug 2025

Abstract

Agro-photovoltaics (APV) or agrivoltaic systems integrate crop cultivation with solar energy production, offering a promising solution through the dual-use of land. This two-year study (2023 and 2024) examined the effects of an APV system on rice production. The results indicated that APV arrays [...] Read more.

Agro-photovoltaics (APV) or agrivoltaic systems integrate crop cultivation with solar energy production, offering a promising solution through the dual-use of land. This two-year study (2023 and 2024) examined the effects of an APV system on rice production. The results indicated that APV arrays created spatially variable light environments, with shadow lengths following predictable solar azimuth patterns and cloudy conditions mitigating shading effects through enhanced diffuse light. Compared with CK (non-shadow area), inter-panel plots (BP) maintained 77% photosynthetic efficiency and 85.4% plant height, whereas the areas beneath the panel showed a significant decrease in the relative chlorophyll content (SPAD values), photosynthesis rates, and yield. BP plots preserved a 78% fruiting rate through adaptive stomatal regulation, whereas LP zones (directly under the low eave) exhibited 35% higher intercellular CO₂ because of the limited assimilation in shading. Rice yield losses were correlated with shading intensity, driven by reduced panicles and grain filling. Moreover, the APV system achieved a high land equivalent ratio of 148–149% by combining 65–66% rice yield with 82.5% photovoltaics output. Based on the microenvironment created by the APV system, optimal crop types and fertilisation are essential for enhancing agricultural yields and improving land use efficiency. Full article

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

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17 pages, 1808 KiB

Open AccessArticle

White Lupin and Hairy Vetch as Green Manures: Impacts on Yield and Nutrient Cycling in an Organic Almond Orchard

by Soraia Raimundo, Margarida Arrobas, António Castro Ribeiro and Manuel Ângelo Rodrigues

Agronomy 2025, 15(8), 1974; https://doi.org/10.3390/agronomy15081974 - 15 Aug 2025

Abstract

Organic farming systems, which prohibit synthetic fertilizers, often rely on legumes for their ability to fix atmospheric nitrogen (N). In orchards, legumes can be established as cover crops between tree rows to enhance nutrient cycling. This study evaluated the effects of two legume [...] Read more.

Organic farming systems, which prohibit synthetic fertilizers, often rely on legumes for their ability to fix atmospheric nitrogen (N). In orchards, legumes can be established as cover crops between tree rows to enhance nutrient cycling. This study evaluated the effects of two legume cover crops, white lupin (Lupinus albus L.) and hairy vetch (Vicia villosa Roth), compared to a Control treatment with conventional tillage, which is the most commonly used method of soil management in the region, in an organically managed almond [Prunus dulcis (Mill.) D.A.Webb] orchard compliant with European Union standards, in an experiment arranged as a completely randomized design. In the first year, kernel yield was highest in the Control treatment (404 kg ha⁻¹), while significantly lower yields were recorded for white lupin (246 kg ha⁻¹) and hairy vetch (283 kg ha⁻¹), likely due to competition for resources between cover crops and trees. In the second year, however, the trend reversed, with cover crop treatments yielding significantly more (Lupin: 313 kg ha⁻¹; Vetch: 296 kg ha⁻¹) than the Control (199 kg ha⁻¹). The cover crops accumulated over 150 kg ha⁻¹ of N in their tissues, enhancing soil N availability and increasing N concentrations in almond leaves. In addition to N, cover crops influenced the cycling of other nutrients, increasing potassium (K) and boron (B) concentrations while reducing calcium (Ca) and manganese (Mn) in plant tissues. Despite being derived from a two-year study, these results highlight the complexity of interpreting cover crop effects, underscoring the need for further long-term research to provide more comprehensive guidance to growers. Full article

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

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23 pages, 3014 KiB

Open AccessArticle

Spatiotemporal Simulation of Soil Moisture in Typical Ecosystems of Northern China: A Methodological Exploration Using HYDRUS-1D

by Quanru Liu, Zongzhi Wang, Liang Cheng, Ying Bai, Kun Wang and Yongbing Zhang

Agronomy 2025, 15(8), 1973; https://doi.org/10.3390/agronomy15081973 - 15 Aug 2025

Abstract

Global climate change has intensified the frequency and severity of drought events, posing significant threats to agricultural sustainability, particularly for water-sensitive crops such as tea. In northern China, where precipitation is unevenly distributed and evapotranspiration rates are high, tea plantations frequently experience water [...] Read more.

Global climate change has intensified the frequency and severity of drought events, posing significant threats to agricultural sustainability, particularly for water-sensitive crops such as tea. In northern China, where precipitation is unevenly distributed and evapotranspiration rates are high, tea plantations frequently experience water stress, leading to reduced yields and declining quality. Therefore, accurately simulating soil water content (SWC) is essential for drought forecasting, soil moisture management, and the development of precision irrigation strategies. However, due to the high complexity of soil–vegetation–atmosphere interactions in field conditions, the practical application of the HYDRUS-1D model in northern China remains relatively limited. To address this issue, a three-year continuous monitoring campaign (2021–2023) was conducted in a coastal area of northern China, covering both young tea plantations and adjacent grasslands. Based on the measured meteorological and soil data, the HYDRUS-1D model was used to simulate SWC dynamics across 10 soil layers (0–100 cm). The model was calibrated and validated against observed SWC data to evaluate its accuracy and applicability. The simulation results showed that the model performed reasonably well, achieving an R² of 0.739 for the tea plantation and 0.878 for the grassland, indicating good agreement with the measured values. These findings demonstrate the potential of physics-based modeling for understanding vertical soil water processes under different land cover types and provide a scientific basis for improving irrigation strategies and water use efficiency in tea-growing regions. Full article

(This article belongs to the Section Water Use and Irrigation)

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22 pages, 1792 KiB

Open AccessArticle

Automatic Scribble Annotations Based Semantic Segmentation Model for Seedling-Stage Maize Images

by Zhaoyang Li, Xin Liu, Hanbing Deng, Yuncheng Zhou and Teng Miao

Agronomy 2025, 15(8), 1972; https://doi.org/10.3390/agronomy15081972 - 15 Aug 2025

Abstract

Canopy coverage is a key indicator for judging maize growth and production prediction during the seedling stage. Researchers usually use deep learning methods to estimate canopy coverage from maize images, but fully supervised models usually need pixel-level annotations, which requires lots of manual [...] Read more.

Canopy coverage is a key indicator for judging maize growth and production prediction during the seedling stage. Researchers usually use deep learning methods to estimate canopy coverage from maize images, but fully supervised models usually need pixel-level annotations, which requires lots of manual labor. To overcome this problem, we propose ASLNet (Automatic Scribble Labeling-based Semantic Segmentation Network), a weakly supervised model for image semantic segmentation. We designed a module which could self-generate scribble labels for maize plants in an image. Accordingly, ASLNet was constructed using a collaborative mechanism composed of scribble label generation, pseudo-label guided training, and double-loss joint optimization. The cross-scale contrastive regularization can realize semantic segmentation without manual labels. We evaluated the model for label quality and segmentation accuracy. The results showed that ASLNet generated high-quality scribble labels with stable segmentation performance across different scribble densities. Compared to Scribble4All, ASLNet improved mIoU by 3.15% and outperformed fully and weakly supervised models by 6.6% and 15.28% in segmentation accuracy, respectively. Our works proved that ASLNet could be trained by pseudo-labels and offered a cost-effective approach for canopy coverage estimation at maize’s seedling stage. This research enables the early acquisition of corn growth conditions and the prediction of corn yield. Full article

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

27 pages, 7464 KiB

Open AccessArticle

Keel Petal Fusion in Soybean: Anatomical Insights and Transcriptomic Identification of Candidate Regulators

by Shun-Geng Jia, Li-Na Guo, Xiao-Fei Wang, De-Li Wang, Dan Chen, Wei-Cai Yang and Hong-Ju Li

Agronomy 2025, 15(8), 1971; https://doi.org/10.3390/agronomy15081971 - 15 Aug 2025

Abstract

The fusion of keel petals is a defining trait of Papilionoideae flowers, contributing to floral architecture and promoting self-pollination but hindering hybridization in crops like soybean. Here, we investigated the cellular and molecular basis of keel petal fusion in Glycine max (L.) Merr. [...] Read more.

The fusion of keel petals is a defining trait of Papilionoideae flowers, contributing to floral architecture and promoting self-pollination but hindering hybridization in crops like soybean. Here, we investigated the cellular and molecular basis of keel petal fusion in Glycine max (L.) Merr. cv. Jack using anatomical and transcriptomic approaches. Microscopy revealed that keel petal fusion involves marginal cell reshaping and postgenital adhesion with defective cuticle continuity, consistent with fusion modes in other Papilionoideae species. Comparative transcriptome analysis between fused and unfused petal stages identified 23,328 differentially expressed genes, with lipid and cuticle metabolism genes showing coordinated downregulation during fusion. A set of 384 keel-enriched genes was identified, among which a previously uncharacterized gene, KPEG1 (Keel Preferential Expression Gene 1), was preferentially expressed in fused keel petals. Protein interaction network analysis revealed that KPEG1 co-expresses with epigenetics-related genes, suggesting a regulatory role in fusion through chromatin-mediated mechanisms. These findings uncover the cellular dynamics and transcriptional reprogramming underlying keel petal fusion in soybean and provide a candidate regulator for further functional studies. Full article

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

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19 pages, 2398 KiB

Open AccessArticle

Population Fluctuation of Phytophagous Mites and Their Impact on the Quality Properties of Wild and Cultivated Blackberry Fruits (Rubus spp. L.) in Jalisco, Mexico

by Haidel Vargas-Madriz, Ausencio Azuara-Domínguez, Ángel Félix Vargas-Madriz, Citlally Topete-Corona, Martha Olivia Lázaro-Dzul, Jesús Alberto Acuña-Soto, Crystian Sadiel Venegas-Barrera, Jorge Luis Chávez-Servín and Aarón Kuri-García

Agronomy 2025, 15(8), 1970; https://doi.org/10.3390/agronomy15081970 - 15 Aug 2025

Abstract

Phytophagous mites are considered pests in fruit crops, such as blackberries (Rubus spp. L.). These pests affect fruit quality and commercial value. This study aimed to evaluate the fluctuation of phytophagous mite populations and their impact on the quality of cultivated and [...] Read more.

Phytophagous mites are considered pests in fruit crops, such as blackberries (Rubus spp. L.). These pests affect fruit quality and commercial value. This study aimed to evaluate the fluctuation of phytophagous mite populations and their impact on the quality of cultivated and wild blackberries in Jalisco, Mexico. Monthly sampling was carried out from November 2023 to May 2024. Mite families such as Diptilomiopidae, Eriophyidae, Tydeidae, Tarsonemidae, Tenuipalpidae, and Tetranychidae were identified, with a total of 6438 mites in the samples. An increase in mite populations was observed in March on cultivated blackberries and in April on wild ones, coinciding with the onset of plant development. The Eriophyidae family showed the highest relative abundance, with 34.2% in cultivated blackberries and 31.7% in wild ones in 2024. Quality parameters were evaluated in healthy and damaged blackberries. Damaged cultivated fruits showed lower weight (4.49 ± 1.44 g), smaller diameter (18.11 ± 2.00 mm), lower vitamin C content (4.76 ± 1.53 mg/100 g), and higher acidity (80.07 ± 19.10%). This study enabled the identification and monitoring of different mite families in blackberries, as well as an understanding of their population dynamics and impact on fruit quality. Full article

(This article belongs to the Special Issue Research Progress on Pathogenicity of Fungi in Crops—2nd Edition)

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28 pages, 2107 KiB

Open AccessArticle

A Scale-Adaptive and Frequency-Aware Attention Network for Precise Detection of Strawberry Diseases

by Kaijie Zhang, Yuchen Ye, Kaihao Chen, Zao Li and Hongxing Peng

Agronomy 2025, 15(8), 1969; https://doi.org/10.3390/agronomy15081969 - 15 Aug 2025

Abstract

Accurate and automated detection of diseases is crucial for sustainable strawberry production. However, the challenges posed by small size, mutual occlusion, and high intra-class variance of symptoms in complex agricultural environments make this difficult. Mainstream deep learning detectors often do not perform well [...] Read more.

Accurate and automated detection of diseases is crucial for sustainable strawberry production. However, the challenges posed by small size, mutual occlusion, and high intra-class variance of symptoms in complex agricultural environments make this difficult. Mainstream deep learning detectors often do not perform well under these demanding conditions. We propose a novel detection framework designed for superior accuracy and robustness to address this critical gap. Our framework introduces four key innovations: First, we propose a novel attention-driven detection head featuring our Parallel Pyramid Attention (PPA) module. Inspired by pyramid attention principles, our module’s unique parallel multi-branch architecture is designed to overcome the limitations of serial processing. It simultaneously integrates global, local, and serial features to generate a fine-grained attention map, significantly improving the model’s focus on targets of varying scales. Second, we enhance the core feature fusion blocks by integrating Monte Carlo Attention (MCAttn), effectively empowering the model to recognize targets across diverse scales. Third, to improve the feature representation capacity of the backbone without increasing the parametric overhead, we replace standard convolutions with Frequency-Dynamic Convolutions (FDConv). This approach constructs highly diverse kernels in the frequency domain. Finally, we employ the Scale-Decoupled Loss function to optimize training dynamics. By adaptively re-weighting the localization and scale losses based on target size, we stabilize the training process and improve the Precision of bounding box regression for small objects. Extensive experiments on a challenging dataset related to strawberry diseases demonstrate that our proposed model achieves a mean Average Precision (MAP) of 81.1%. This represents an improvement of 2.1% over the strong YOLOv12-n baseline, highlighting its practical value as an effective tool for intelligent disease protection. Full article

(This article belongs to the Special Issue Modern Control of Biotic Stress in Crops: Intelligent Detection and Precision Pesticide Application)

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17 pages, 1029 KiB

Open AccessArticle

Fertilisation Potential of Combined Use of Wood Biomass Ash and Digestate in Maize Cultivation

by Elżbieta Rolka, Mirosław Wyszkowski, Anna Skorwider-Namiotko and Radosław Szostek

Agronomy 2025, 15(8), 1968; https://doi.org/10.3390/agronomy15081968 - 15 Aug 2025

Abstract

In recent years, there has been growing interest in using wood biomass for energy production, which has led to an increase in post-processing waste in the form of wood biomass ash (WBA). Due to the rich composition of WBA, its fertilising potential should [...] Read more.

In recent years, there has been growing interest in using wood biomass for energy production, which has led to an increase in post-processing waste in the form of wood biomass ash (WBA). Due to the rich composition of WBA, its fertilising potential should be considered. In the conducted studies, WBA was used both alone and in combination with digestate (DG). The WBA was obtained from the Municipal Heat Energy Company and the DG from the Agricultural Biogas Plant in the form of unseparated liquid digestate (ULD), separated solid digestate (SSD) and separated liquid digestate (SLD). The studies included four series: (1) WBA, (2) WBA + ULD, (3) WBA + SSD and (4) WBA + SLD. In each series, WBA was introduced in three increasing doses (0.5, 1.0 and 1.5, expressed in hydrolytic acidity units (HACs) and determined based on the general alkalinity of the material). The digestates (DGs) were applied in fixed doses, which were balanced with respect to the nitrogen introduced into the soil. The test plant was the maize (Zea mays L.) variety Garantio, which was grown in a vegetation hall. The obtained results indicate that the combined use of WBA and DGs (especially ULD and SLD) had a positive effect on the plant height, leaf greenness index (SPAD), and thus, maize yield and dry matter content. In the series with DG addition, the maize yield ranged from 615.5 g (WBA + SSD) to 729.6 g pot⁻¹ (WBA + SLD), which was 28–52% higher than in the series with WBA alone. In turn, the application of increasing doses of WBA alone did not significantly affect the biomass yield but significantly increased the content of N (34%), K (60%), Mg (56%), Ca (60%) and Na (4%). In the series with WBA and DGs, the increase in the content of the above-mentioned macronutrients depended on the type of DG and the dose of WBA. The exception among the macronutrients was P, whose content generally decreased (by 4–23%) with an increasing WBA dose, regardless of the test series. The most favourable results in terms of the chemical composition, excluding the P content, were observed following the combined application of WBA and liquid forms of DG (ULD and SLD). Full article

(This article belongs to the Special Issue New Advances in Sustainable Fertilization: Efficiency and Environmental Challenges)

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22 pages, 1037 KiB

Open AccessArticle

Nanoparticle-Driven Modulation of Soil Fertility and Plant Growth: Evaluating Fe₂O₃ and CuO Nanofertilizers in Sandy Loam Soils

by Beata Smolińska

Agronomy 2025, 15(8), 1967; https://doi.org/10.3390/agronomy15081967 - 15 Aug 2025

Abstract

The excessive use of conventional fertilizers has led to low nutrient-use efficiency and significant environmental challenges. To address these limitations, this study aimed to evaluate the effects of Fe₂O₃ and CuO nanoparticles (NPs) as potential nanofertilizers, on the soil chemical [...] Read more.

The excessive use of conventional fertilizers has led to low nutrient-use efficiency and significant environmental challenges. To address these limitations, this study aimed to evaluate the effects of Fe₂O₃ and CuO nanoparticles (NPs) as potential nanofertilizers, on the soil chemical composition, nutrient fractionation, enzyme activity, and Lepidium sativum L. growth. The results of the study showed that Fe₂O₃-NPs improved nitrogen bioavailability and enhanced plant biomass, particularly at low to moderate doses. CuO-NPs, in contrast, reduced nitrogen and phosphorus mobility and showed phytotoxic effects at high concentrations. Enzyme activity was suppressed at high NP levels, likely due to oxidative stress. Nutrient fractionation revealed the increased immobilization of phosphorus and the moderate mobilization of potassium and copper, depending on NP type. Based on the results, Fe₂O₃-NPs show potential as a nanofertilizer for enhancing soil fertility and plant growth in sandy loam soils, whereas CuO-NPs require caution due to toxicity risks. Future research should focus on long-term environmental impact, optimal NP concentrations, and their interaction with soil microbial communities. Full article

(This article belongs to the Special Issue New Advances in Sustainable Fertilization: Efficiency and Environmental Challenges)

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31 pages, 1639 KiB

Open AccessReview

Utilizing Different Crop Rotation Systems for Agricultural and Environmental Sustainability: A Review

by Zainulabdeen Kh. Al-Musawi, Viktória Vona and István Mihály Kulmány

Agronomy 2025, 15(8), 1966; https://doi.org/10.3390/agronomy15081966 - 14 Aug 2025

Abstract

Monoculture involves growing the same crop on the same land over at least two crop cycles. Continuous monoculture can increase the population density of pests and pathogens over time, thereby reducing agricultural yields and increasing dependence on chemical inputs. Crop rotation is an [...] Read more.

Monoculture involves growing the same crop on the same land over at least two crop cycles. Continuous monoculture can increase the population density of pests and pathogens over time, thereby reducing agricultural yields and increasing dependence on chemical inputs. Crop rotation is an agricultural practice that involves systematically and sequentially planting different crops in the same field over multiple growing seasons. This review explores the advantages of crop rotation and its contribution to promoting sustainable farming practices, such as legume integration and cover cropping. It is based on a thematic literature review of peer-reviewed studies published between 1984 and 2025. We found that crop rotation can significantly improve soil structure and organic matter content and enhance nutrient cycling. Furthermore, soil organic carbon increased by up to 18% when legumes were included in rotations compared to monoculture systems in Europe, while also mitigating greenhouse gas emissions, enhancing carbon sequestration, and decreasing nutrient leaching and pesticide runoff. Farmers can adopt several strategies to optimise crop rotation benefits, such as diversification of various crops, legume integration, cultivation of cover crops, and rotational grazing. These practices ensure agricultural sustainability and food security and support climate resilience. Full article

(This article belongs to the Section Innovative Cropping Systems)

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23 pages, 3925 KiB

Open AccessArticle

Defense Responses in Prickly Pear (Cucumis metuliferus) to Meloidogyne incognita: Insights from Transcriptomics and Metabolomics Analysis

by Hao Zhang, Qigan Liang, Jihao Chen, Jiming Wang, Yuan Huang, Bin Liu, Xuejun Zhang and Bo Zhou

Agronomy 2025, 15(8), 1965; https://doi.org/10.3390/agronomy15081965 - 14 Aug 2025

Abstract

The root-knot nematode (Meloidogyne incognita) poses a major threat to global agriculture by impairing root function, reducing nutrient uptake, and ultimately limiting seed development and crop productivity. This study investigated the molecular and metabolic defense responses of Cucumis metuliferus (prickly pear) [...] Read more.

The root-knot nematode (Meloidogyne incognita) poses a major threat to global agriculture by impairing root function, reducing nutrient uptake, and ultimately limiting seed development and crop productivity. This study investigated the molecular and metabolic defense responses of Cucumis metuliferus (prickly pear) to M. incognita infection. Gene expression and metabolic pathway reprogramming in M. incognita-infected roots were examined using integrated transcriptomics and metabolomics approaches. The identified genes were involved in stress responses and defense activation. Furthermore, metabolite profiling revealed significant shifts in secondary metabolite production, with an upregulation of defense-related compounds like jasmonic acid, salicylic acid, and prostaglandins. KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis highlighted critical pathways such as biotin metabolism and nucleotide metabolism, underscoring the adaptive metabolic responses of C. metuliferus plants. GO (Gene Ontology) analysis from the integrated transcriptomics and metabolomics data highlighted significant upregulation of enzymatic pathways, transporter activities, and reorganization of cellular structures. Furthermore, KEGG pathway analysis revealed activation of secondary metabolite biosynthesis, immune-related signaling pathways, and metabolic reprogramming including increased carbon metabolism and nucleotide biosynthesis. This study provides a valuable molecular framework for breeding of M. incognita-resistant cultivars, ultimately supporting more stable seed distribution and agricultural productivity in M. incognita-prone regions. Full article

(This article belongs to the Section Pest and Disease Management)

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