Agronomy

16 pages, 3315 KiB

Open AccessArticle

Using Machine Learning to Assess the Effects of Biochar-Based Fertilizers on Crop Production and N₂O Emissions in China

by Yuan Zeng, Sujuan Chen, Yunpeng Li, Li Xiong, Cheng Liu, Muhammad Azeem, Xiaoting Jie, Mei Chen, Longjiang Zhang and Jianfei Sun

Agronomy 2025, 15(5), 1238; https://doi.org/10.3390/agronomy15051238 - 19 May 2025

Abstract

The growing global population and increasing agricultural demands have made nitrogen fertilizers essential for modern agriculture. However, nearly 50% of applied nitrogen fertilizers are lost to the environment, causing pollution and greenhouse gas (GHG) emissions. Biochar-based fertilizers (BBFs), combining biochar with chemical fertilizers, [...] Read more.

The growing global population and increasing agricultural demands have made nitrogen fertilizers essential for modern agriculture. However, nearly 50% of applied nitrogen fertilizers are lost to the environment, causing pollution and greenhouse gas (GHG) emissions. Biochar-based fertilizers (BBFs), combining biochar with chemical fertilizers, enhance nutrient efficiency, boost crop yields, and reduce N₂O emissions. However, comprehensive field studies on BBF impacts remain limited. This study uses a global dataset of BBF field experiments to build predictive models with three machine learning algorithms for crop yields and N₂O emissions, and to assess BBFs’ potential to increase yields and mitigate emissions in China’s major crops. The artificial neural network (ANN) model outperformed random forest (RF) and support vector machine (SVM) in predicting N₂O emissions (R²: 0.99; EF: 0.99), while all models showed high accuracy for crop yields (R², EF: 0.98–0.99). Variable importance analysis revealed that BBF C/N and BBF N/Mineral N explained 4.25% and 3.95% of yield variation, and 3.19% and 0.55% of N₂O emission variation, respectively. BBFs could increase China’s major crop yields by 4.3–5.0% and reduce N₂O emissions by 3.7–6.3%, based on simulations. Challenges like high costs and limited adaptability persist, necessitating optimized production, standardized protocols, and expanded trials. Full article

(This article belongs to the Special Issue New Pathways Towards Carbon Neutrality in Agricultural Systems)

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22 pages, 3422 KiB

Open AccessArticle

Estimation of Reference Crop Evapotranspiration in the Yellow River Basin Based on Machine Learning and Its Regional and Drought Adaptability Analysis

by Jun Zhao, Huayu Zhong and Congfeng Wang

Agronomy 2025, 15(5), 1237; https://doi.org/10.3390/agronomy15051237 - 19 May 2025

Abstract

In recent years, the Yellow River Basin has experienced frequent extreme climate events, with an increasing intensity and frequency of droughts, exacerbating regional water scarcity and severely constraining agricultural irrigation efficiency and sustainable water resource utilization. The accurate estimation of reference crop evapotranspiration [...] Read more.

In recent years, the Yellow River Basin has experienced frequent extreme climate events, with an increasing intensity and frequency of droughts, exacerbating regional water scarcity and severely constraining agricultural irrigation efficiency and sustainable water resource utilization. The accurate estimation of reference crop evapotranspiration (ET₀) is crucial for developing scientifically sound irrigation strategies and enhancing water resource management capabilities. This study utilized daily scale meteorological data from 31 stations across the Yellow River Basin spanning the period 1960–2023 to develop various machine learning models. The study constructed four machine learning models—random forest (RF), a Support Vector Machine (SVM), Gradient Boosting (GB), and Ridge Regression (Ridge)—using the meteorological variables required by the Priestley–Taylor (PT) and Hargreaves (HG) equations as inputs. These models represent a range of algorithmic structures, from nonlinear ensemble methods (RF, GB) to kernel-based regression (SVR) and linear regularized regression (Ridge). The objective was to comprehensively evaluate their performance and robustness in estimating ET₀ under different climatic zones and drought conditions and to compare them with traditional empirical formulas. The main findings are as follows: machine learning models, particularly nonlinear approaches, significantly outperformed the PT and HG methods across all climatic regions. Among them, the RF model demonstrated the highest simulation accuracy, achieving an R² of 0.77, and reduced the mean daily ET₀ estimation error by 0.057 mm/day and 0.076 mm/day compared to the PT and HG models, respectively. Under drought-year scenarios, although all models showed slight performance degradation, nonlinear machine learning models still surpassed traditional formulas, with the R² of the RF model decreasing marginally from 0.77 to 0.73, indicating strong robustness. In contrast, linear models such as Ridge Regression exhibited greater sensitivity to changes in feature distributions during drought years, with estimation accuracy dropping significantly below that of the PT and HG methods. The results indicate that in data-sparse regions, machine learning approaches with simplified inputs can serve as effective alternatives to empirical formulas, offering superior adaptability and estimation accuracy. This study provides theoretical foundations and methodological support for regional water resource management, agricultural drought mitigation, and climate-resilient irrigation planning in the Yellow River Basin. Full article

(This article belongs to the Special Issue Application of Deep and Machine Learning in Crop Monitoring and Management)

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48 pages, 7578 KiB

Open AccessArticle

Research on the Precise Regulation of Korla Fragrant Pear Quality Based on Sensitivity Analysis and Artificial Neural Network Model

by Mingyang Yu, Yang Li, Lanfei Wang, Weifan Fan, Zengheng Wang, Hao Wang, Kailu Guo, Liang Fu and Jianping Bao

Agronomy 2025, 15(5), 1236; https://doi.org/10.3390/agronomy15051236 - 19 May 2025

Abstract

This study investigated the soil–leaf–fruit relationship in Korla fragrant pears (Pyrus sinkiangensis Yu) to establish a scientific cultivation framework by analyzing soil nutrients (alkali-hydrolyzable nitrogen, available phosphorus, available potassium, and pH at 0–60 cm depth) across key phenological stages (fruit setting, expansion, [...] Read more.

This study investigated the soil–leaf–fruit relationship in Korla fragrant pears (Pyrus sinkiangensis Yu) to establish a scientific cultivation framework by analyzing soil nutrients (alkali-hydrolyzable nitrogen, available phosphorus, available potassium, and pH at 0–60 cm depth) across key phenological stages (fruit setting, expansion, and maturation), combined with leaf and fruit quality indicators. Artificial neural network modeling demonstrated strong predictive capability (R² > 0.85), while sensitivity analysis quantified the relative contributions of different factors, revealing that titratable acidity was optimized when available potassium (30–47 mg/kg) in 40–60 cm soil during fruit setting coincided with pH 7.4–7.8 in 20–40 cm, or when pH 7.3–7.7 in 40–60 cm at fruit setting interacted with alkali-hydrolyzable nitrogen (33.0–53.2 mg/kg) in 40–60 cm during maturation. Fruit shape index improvement required available potassium (40–60 mg/kg) in 40–60 cm at maturation combined with leaf total nitrogen (2.0–6.5 mg/kg) at fruit setting, or specific maturation-stage alkali-hydrolyzable nitrogen levels paired with fruit setting SPAD (Soil and Plant Analysis Development) values (30–41). Furthermore, synergistic effects between expansion stage available phosphorus in 40–60 cm soil and leaf SPAD (Soil and Plant Analysis Development) values simultaneously enhanced the soluble solids content while reducing peel thickness. These findings provide precise nutrient management thresholds for quality optimization, offering practical guidance for orchard management to enhance Korla fragrant pears quality through targeted agricultural practices. Full article

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

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18 pages, 5042 KiB

Open AccessArticle

The Overexpression of an EnvZ-like Protein Improves the Symbiotic Performance of Mesorhizobia

by José Rodrigo da-Silva, Esther Menéndez, Solange Oliveira and Ana Alexandre

Agronomy 2025, 15(5), 1235; https://doi.org/10.3390/agronomy15051235 - 19 May 2025

Abstract

The two-component signal transduction system EnvZ/OmpR is described to mediate response to osmotic stress, although it regulates genes involved in other processes such as virulence, fatty acid uptake, exopolysaccharide production, peptide transportation, and flagella production. Considering that some of these processes [...] Read more.

The two-component signal transduction system EnvZ/OmpR is described to mediate response to osmotic stress, although it regulates genes involved in other processes such as virulence, fatty acid uptake, exopolysaccharide production, peptide transportation, and flagella production. Considering that some of these processes are known to be important for a successful symbiosis, the present study addresses the effects of extra envZ-like gene copies in the Mesorhizobium–chickpea symbiosis. Five Mesorhizobium-transformed strains, expressing the envZ-like gene from M. mediterraneum UPM-Ca36T, were evaluated in terms of symbiotic performance. Chickpea plants inoculated with envZ-transformed strains (PMI6envZ⁺ and EE7envZ⁺) showed a significantly higher symbiotic effectiveness as compared to the corresponding control. In plants inoculated with PMI6envZ+, a higher number of infection threads was observed, and nodules were visible 4 days earlier. Overall, our results showed that the overexpression of Env-like protein may influence the symbiotic process at different stages, leading to strain-dependent effects. This study contributes to elucidating the role of an EnvZ-like protein in the rhizobia–legume symbioses. Full article

(This article belongs to the Section Agricultural Biosystem and Biological Engineering)

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18 pages, 5323 KiB

Open AccessArticle

Surface Defect and Malformation Characteristics Detection for Fresh Sweet Cherries Based on YOLOv8-DCPF Method

by Yilin Liu, Xiang Han, Longlong Ren, Wei Ma, Baoyou Liu, Changrong Sheng, Yuepeng Song and Qingda Li

Agronomy 2025, 15(5), 1234; https://doi.org/10.3390/agronomy15051234 - 19 May 2025

Abstract

The damaged and deformed fruits of fresh berries severely restrict the economic value of produce, and accurate identification and grading methods have become a global research hotspot. To address the challenges of rapid and accurate defect detection in intelligent cherry sorting systems, this [...] Read more.

The damaged and deformed fruits of fresh berries severely restrict the economic value of produce, and accurate identification and grading methods have become a global research hotspot. To address the challenges of rapid and accurate defect detection in intelligent cherry sorting systems, this study proposes an enhanced YOLOv8n-based framework for sweet cherry defect identification. First, the dilation-wise residual (DWR) module replaces the conventional C2f structure, allowing for the adaptive capture of both local and global features through multi-scale convolution. This enhances the recognition accuracy of subtle surface defects and large-scale damages on cherries. Second, a channel attention feature fusion mechanism (CAFM) is incorporated at the front end of the detection head, which enhances the model’s ability to identify fine defects on the cherry surface. Additionally, to improve bounding box regression accuracy, powerful-IoU (PIoU) replaces the traditional CIoU loss function. Finally, self-distillation technology is introduced to further improve the mode’s generalization capability and detection accuracy through knowledge transfer. Experimental results show that the YOLOv8-DCPF model achieves precision, mAP, recall, and F1 score rates of 92.6%, 91.2%, 89.4%, and 89.0%, respectively, representing improvements of 6.9%, 5.6%, 6.1%, and 5.0% over the original YOLOv8n baseline network. The proposed model demonstrates high accuracy in cherry defect detection, providing an efficient and precise solution for intelligent cherry sorting in agricultural engineering applications. Full article

(This article belongs to the Special Issue Facility Agriculture Robots and Autonomous Unmanned Management for Crops)

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13 pages, 658 KiB

Open AccessArticle

Melatonin Elicitation Differentially Enhances Flavanone and Its Endogenous Content in Lemon Tissues Through Preharvest and Postharvest Applications

by Vicente Agulló, María Emma García-Pastor and Daniel Valero

Agronomy 2025, 15(5), 1233; https://doi.org/10.3390/agronomy15051233 - 19 May 2025

Abstract

The growing prevalence of metabolic diseases underscores the necessity for enhancing the nutritional value of widely consumed foods. The present study investigated the impact of melatonin elicitation on the accumulation of flavanones and endogenous melatonin in lemons. Preharvest treatments of 0.1 and 1 [...] Read more.

The growing prevalence of metabolic diseases underscores the necessity for enhancing the nutritional value of widely consumed foods. The present study investigated the impact of melatonin elicitation on the accumulation of flavanones and endogenous melatonin in lemons. Preharvest treatments of 0.1 and 1 mM were applied, followed by postharvest treatment of 1 mM, either individually or in combination, and then cold storage. The quantification of bioactive compounds was conducted in various plant components, namely juice, albedo, flavedo, and leaves, employing HPLC-DAD and HPLC-MS/MS methodologies. Preharvest application of 1 mM melatonin resulted in a 26% increase in flavanone concentration in juice at harvest, while postharvest treatment induced a 19% increase during storage. The combination of both treatments resulted in elevated levels of flavanone (a 27% increase). With regard to melatonin levels, the combined treatments resulted in a significant increase in all tissues; however, the postharvest application alone achieved the highest concentration (6.99 µg L⁻¹), particularly in the juice. The results of this study demonstrate the efficacy of melatonin elicitation, particularly in postharvest treatments, as a practical strategy to enhance the functional quality of lemons. This approach has the potential to facilitate the development of health-promoting foods and the valorisation of citrus byproducts. Further research is required to elucidate the role of melatonin in modulating the bioavailability and health effects of lemon phytochemicals in humans. Full article

(This article belongs to the Special Issue New Trends in Molecular Biochemistry and Physiology of Pre- and Post-Harvest Fruits and Vegetables)

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16 pages, 1047 KiB

Open AccessArticle

Effects and Mechanism of Nitrogen Regulation on Seed Yield and Quality of Rapeseed (Brassica napus L.)

by Chunli Wang, Xiaojun Wang, Jianli Yang, Zhi Zhang and Miaomiao Chen

Agronomy 2025, 15(5), 1232; https://doi.org/10.3390/agronomy15051232 - 19 May 2025

Abstract

Appropriate nitrogen is required and important in grain yield formation of crops. To elucidate nitrogen regulation of seed yield and quality of rapeseed (Brassica napus L.), field trials were consecutively conducted in two years with three nitrogen levels of 0, 180, and [...] Read more.

Appropriate nitrogen is required and important in grain yield formation of crops. To elucidate nitrogen regulation of seed yield and quality of rapeseed (Brassica napus L.), field trials were consecutively conducted in two years with three nitrogen levels of 0, 180, and 240 kg ha⁻¹ (the N0, N180, and N240 treatments). The nitrogen application (N-app) induced increasing trend in the nitrogen accumulation in flowering plants (N-acc), number of siliques per plant (silique-num), number of branches per plant (branch-num), number of seeds per silique (seed-num), and seed yield of rapeseed; there were significant correlational relationships between these indexes (excepting seed-num). The N-app, N-acc, and silique-number showed higher effects on the seed yield. The effect of N-app was mainly achieved through influence on the silique-num, branch-num, and seed-num. When the N-app was increased from 180 to 240 kg ha⁻¹, the nitrogen utilization efficiency (NUE) and the partial productivity of nitrogen fertilizer (PPN) of the rapeseed varieties tested showed a decreasing trend; the NR (nitrate reductase) gene expression level and the NR and GS (glutamine synthetase) activity in leaves was significantly increased under the N180 and N240 treatments compared to the N0 treatment, which achieved peak values at 180 kg ha⁻¹ of N-app. The N-app hardly influenced the seed quality, as well as the gene expression and activity of the enzymes ACCase (acetyl-CoA carboxylase), FAD2 (oleic acid desaturase), and FAD3 (omega-3 fatty acid desaturase) in young seed. In conclusion, N-app induced significant increase in seed yield of rapeseed, the NR gene expression level and the NR and GS activity in leaves was improved; the NUE of rapeseed variety showed decreasing trend with increase in N-app level; while N-app hardly influenced the seed quality. Full article

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

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24 pages, 5362 KiB

Open AccessArticle

Genomic Architecture of AP2/ERF Superfamily Genes in Marigold (Tagetes erecta) and Insights into the Differential Expression Patterns of AP2 Family Genes During Floral Organ Specification

by Hang Li, Guoqing Chen, Shirui Hu, Cuicui Liu, Manzhu Bao and Yanhong He

Agronomy 2025, 15(5), 1231; https://doi.org/10.3390/agronomy15051231 - 18 May 2025

Abstract

The APETALA2/Ethylene-Responsive Factor (AP2/ERF) superfamily is one of the largest transcription factor families in plants, playing diverse roles in development, stress response, and metabolic regulation. Despite their ecological and economic importance, AP2/ERF genes remain uncharacterized in marigold (Tagetes erecta), [...] Read more.

The APETALA2/Ethylene-Responsive Factor (AP2/ERF) superfamily is one of the largest transcription factor families in plants, playing diverse roles in development, stress response, and metabolic regulation. Despite their ecological and economic importance, AP2/ERF genes remain uncharacterized in marigold (Tagetes erecta), a valuable ornamental and medicinal plant in the Asteraceae family known for its unique capitulum-type inflorescence with distinct ray and disc florets. Here, we conducted a comprehensive genome-wide analysis of the AP2/ERF superfamily in marigold and identified 177 AP2/ERF genes distributed across 11 of the 12 chromosomes. Phylogenetic analysis revealed their classification into the AP2 (28 genes), ERF (143 genes), RAV (4 genes), and Soloist (2 genes) families based on domain architecture. Gene structure and motif composition analyses demonstrated group-specific patterns that correlated with their evolutionary relationships. Chromosome mapping and synteny analyses revealed that segmental duplications significantly contributed to AP2/ERF superfamily gene expansion in marigold, with extensive collinearity observed between marigold and other species. Expression profiling across different tissues and developmental stages indicated distinct spatio-temporal expression patterns, with several genes exhibiting tissue-specific expression in Asteraceae-specific structures. In floral organs, TeAP2/ERF145 exhibited significantly higher expression in ray floret corollas compared to disc florets, while TeAP2/ERF103 showed stamen-specific expression in disc florets. Protein interaction network analysis revealed AP2 as a central hub with extensive predicted interactions with MADS-box and TCP family proteins. These findings suggest that AP2 family genes may collaborate with MADS-box and CYC2 genes in regulating the characteristic floral architecture of marigold, establishing a foundation for future functional studies and molecular breeding efforts to enhance ornamental and agricultural traits in this economically important plant. Full article

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

23 pages, 13662 KiB

Open AccessArticle

Adaptive SOM-GA Hybrid Algorithm for Grasping Sequence Optimization in Apple Harvesting Robots: Enhancing Efficiency in Open-Field Orchards

by Li Zhang, Zhihui He, Haobin Zhu, Zhanhong Wei, Juan Lu and Xiongkui He

Agronomy 2025, 15(5), 1230; https://doi.org/10.3390/agronomy15051230 - 18 May 2025

Abstract

To address the challenge of low operational efficiency in apple harvesting robots, this study proposes an adaptive grasping sequence planning methodology that combines Self-Organizing Maps (SOMs) and genetic algorithms (GAs). The proposed adaptive SOM—GA hybrid algorithm aims to minimize cycle time by optimizing [...] Read more.

To address the challenge of low operational efficiency in apple harvesting robots, this study proposes an adaptive grasping sequence planning methodology that combines Self-Organizing Maps (SOMs) and genetic algorithms (GAs). The proposed adaptive SOM—GA hybrid algorithm aims to minimize cycle time by optimizing the path planning between the fruit detection and grasping phases. First of all, we propose a density-aware adaptive mechanism that dynamically adjusts planning strategies based on fruit count thresholds. In addition, the proposed grasping sequence planning framework for high-density dwarf cultivation (HDDC) orchards is validated through threshold sensitivity analysis and empirical analysis of over 500 real-world fruit distribution samples. Finally, comparative experiments demonstrate that our proposed method reduces path length in high-density scenarios. Statistical analysis reveals a bimodal fruit distribution, which aligns the algorithm’s adaptive thresholds with real-world operational demands. These advancements improve theoretical research and enhance the commercial viability in agricultural robotics. Full article

(This article belongs to the Special Issue Agricultural Environment and Intelligent Plant Protection Equipment—2nd Edition)

25 pages, 1383 KiB

Open AccessArticle

Optimizing Water and Nitrogen Application to Furrow-Irrigated Summer Corn Using the AquaCrop Model

by Yifei Zhao, Shunsheng Wang and Aili Wang

Agronomy 2025, 15(5), 1229; https://doi.org/10.3390/agronomy15051229 - 18 May 2025

Abstract

Summer maize is an important grain crop in the North China Plain, but the problem of irrational application of water and fertilizer is becoming increasingly serious. Optimizing water and nitrogen management not only improves yield but also reduces water and fertilizer waste and [...] Read more.

Summer maize is an important grain crop in the North China Plain, but the problem of irrational application of water and fertilizer is becoming increasingly serious. Optimizing water and nitrogen management not only improves yield but also reduces water and fertilizer waste and environmental pollution. The Aquacrop model was calibrated and validated using a two-year summer maize field trial, and 16 different water and nitrogen scenarios were simulated and analyzed. In particular, the field trials were divided into 10 water–nitrogen treatments. The results showed that (1) the model has good applicability to the growth process of summer maize in the North China Plain. (2) Excessive water and nitrogen application would reduce yield by 5.6–33.7%, nitrogen bias productivity by 8.1–32.5%, and water use efficiency by 6.4–84.6%. (3) The optimal irrigation and nitrogen application program for furrow-irrigated summer maize is an irrigation quota of 360 mm in conjunction with nitrogen application of 240 kg/ha. This study provides a theoretical basis for a water-saving, fertilizer-saving, high-yield water and fertilizer management system for summer maize in the North China Plain. Full article

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

16 pages, 3314 KiB

Open AccessReview

Plant Aux/IAA Gene Family: Significance in Growth, Development and Stress Responses

by Zelong Zhuang, Jianwen Bian, Zhenping Ren, Wanling Ta and Yunling Peng

Agronomy 2025, 15(5), 1228; https://doi.org/10.3390/agronomy15051228 - 18 May 2025

Abstract

Auxin plays a crucial role throughout the entire life cycle of plants. The auxin/indole-3-acetic acid (Aux/IAA) gene family serves as a negative regulator of auxin response and is one of the earliest auxin-responsive gene families. It regulates the expression of auxin-responsive [...] Read more.

Auxin plays a crucial role throughout the entire life cycle of plants. The auxin/indole-3-acetic acid (Aux/IAA) gene family serves as a negative regulator of auxin response and is one of the earliest auxin-responsive gene families. It regulates the expression of auxin-responsive genes by specifically binding to auxin response factors. This review summarizes the protein structural characteristics of the Aux/IAA gene family and its typical and atypical transduction mechanisms in auxin signaling. Additionally, it examines the role of Aux/IAA in regulating plant growth and development, as well as its function in modulating plant resistance to abiotic stress through hormonal signaling pathways. Our findings indicate that the Aux/IAA gene family plays a significant role in plant growth and development, as well as in abiotic stress resistance. However, research on the functional roles of the Aux/IAA gene family in crops such as rice, wheat, and maize remains relatively scarce. Furthermore, we identified key questions and proposed new research directions regarding the Aux/IAA gene family, aiming to provide insights for future research on plant hormone signaling and molecular breeding in crop design. Full article

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

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25 pages, 4506 KiB

Open AccessArticle

Optimizing Cropping Systems Using Biochar for Wheat Production Across Contrasting Seasons in Ethiopian Highland Agroecology

by Desalew Fentie, Fekremariam Asargew Mihretie, Yudai Kohira, Solomon Addisu Legesse, Mekuanint Lewoyehu, Tassapak Wutisirirattanachai and Shinjiro Sato

Agronomy 2025, 15(5), 1227; https://doi.org/10.3390/agronomy15051227 - 18 May 2025

Abstract

Biochar has recently emerged as a promising resource for enhancing crop productivity by improving the soil quality. However, there is limited understanding of how varying application rates of biochar combined with inorganic fertilizers impact crop productivity across diverse biophysical contexts. This study investigated [...] Read more.

Biochar has recently emerged as a promising resource for enhancing crop productivity by improving the soil quality. However, there is limited understanding of how varying application rates of biochar combined with inorganic fertilizers impact crop productivity across diverse biophysical contexts. This study investigated the effects of different rates of water hyacinth-derived biochar and fertilizer application on wheat production during the rainy and dry seasons. Four biochar rates (0, 5, 10, and 20 t ha⁻¹), three NPS fertilizer rates (0, 100, and 200 kg ha⁻¹), and two irrigation levels (50% and 100%; for the dry season only) were evaluated for wheat yield and profitability with a randomized complete block design. Soil amendment with both biochar and fertilizer improved wheat grain yield by 6.4% in the dry season and by 173% in the rainy season. Optimal grain yields were achieved with 10 t ha⁻¹ of biochar and 200 kg ha⁻¹ of fertilizer in the rainy season, whereas in the dry season, the highest yield was observed with 20 t ha⁻¹ of biochar and 200 kg ha⁻¹ of fertilizer under the full water requirement. Specifically, for the dry season, plant height, leaf area, soil plant analysis development (SPAD) of leaf value, dry biomass, spike length, spikelet number, and grain number significantly improved due to biochar and fertilizer application. Furthermore, reducing irrigation to 50% did not significantly affect growth and yield components when the soil was amended with biochar. The highest net return (5351 and 3084 USD ha⁻¹) was achieved with 10 t ha⁻¹ of biochar and 200 kg ha⁻¹ of fertilizer during the rainy and dry seasons, respectively. This study suggests that maximum yield improvement and economic benefits can be obtained through the combination of biochar application, appropriate fertilizer rates, and water management strategies in rainfed and irrigated cropping systems. Full article

(This article belongs to the Special Issue Energy Crops in Sustainable Agriculture)

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23 pages, 1594 KiB

Open AccessArticle

Effects of Biochar on Soil Quality in a Maize Soybean Rotation on Mollisols

by Likun Hou, Yuchao Wang, Zhipeng Wang, Ruichun Gao, Xin Zhou, Siyu Yang, Xu Luo, Zhenfeng Jiang and Zhihua Liu

Agronomy 2025, 15(5), 1226; https://doi.org/10.3390/agronomy15051226 - 18 May 2025

Abstract

Rotation and organic material addition (e.g., biochar) are major measures to improve soil quality, but the improvement effects and mechanisms of their combination on soil quality remain unclear; the relationship between the physical, chemical, and biological parameters was has not been adequately detected [...] Read more.

Rotation and organic material addition (e.g., biochar) are major measures to improve soil quality, but the improvement effects and mechanisms of their combination on soil quality remain unclear; the relationship between the physical, chemical, and biological parameters was has not been adequately detected in terms of the change in quality after biochar addition. This study selected corn straw biochar as the material and established two biochar application methods: biochar mixed in 0–20 cm soil depth (B1) and biochar mixed in 0–40 cm soil depth (B2). After 3 years of maize–bean rotation, soil samples from 0–20 cm and 20–40 cm were collected to determine the soil’s physical, chemical, and biological properties, as well as crop yields. Principal component analysis was used to establish a minimum data set for the systematic analysis of soil quality and its factors. The results showed that compared with the control (CK), biochar reduced soil bulk density by 3.1% and electrical conductivity by 19.5–28.25% while increasing soil organic matter content by 7.2%, ammonium nitrogen content by 6.7–12.0%, available nitrogen content by 6.7–18.5%, available phosphorus content by 15.6–23.8%, available potassium content by 11.6–17.3%, soil urease activity by 12.25–21.6%, soil sucrase activity by 6.8–30.8%, soil neutral phosphatase activity by 5.6–9.7%, and soil catalase activity by 13.6%. Four indicators, namely bulk density, water content, pH, and nitrate nitrogen, were selected from 16 soil-quality-related indicators to form the minimum data set (MDS), and the soil quality index was calculated. Biochar application significantly increased the soil quality index (SQI) of rotation soil by 14.6–63.3% and crop yields by 5.6–7.2%. A random forest analysis of soil indicators and crop yields, combined with partial least squares structural equation modeling, revealed that biological indicators—particularly catalase activity—showed significant positive correlations with crop yields. Based on these multi-dimensional analyses, the interaction between rotation systems and biochar application improves the quality of mollisol soil plow layers by reducing bulk density and increasing catalase activity. Full article

(This article belongs to the Section Innovative Cropping Systems)

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20 pages, 3310 KiB

Open AccessArticle

CdGLK1 Transcription Factor Confers Low-Light Tolerance in Bermudagrass via Coordinated Upregulation of Photosynthetic Genes and Enhanced Antioxidant Enzyme Activity

by Peng Han, Jun Liu, Jingjin Yu, Zhongpeng Liu, Fahui He and Zhimin Yang

Agronomy 2025, 15(5), 1225; https://doi.org/10.3390/agronomy15051225 - 17 May 2025

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Abstract

As a widely cultivated warm-season turfgrass, bermudagrass (Cynodon spp.) faces significant challenges in shaded environments due to its inherent low-light sensitivity. While improving photosynthetic adaptation represents a promising strategy to address this limitation, the associated regulatory mechanisms remain insufficiently characterized. In this [...] Read more.

As a widely cultivated warm-season turfgrass, bermudagrass (Cynodon spp.) faces significant challenges in shaded environments due to its inherent low-light sensitivity. While improving photosynthetic adaptation represents a promising strategy to address this limitation, the associated regulatory mechanisms remain insufficiently characterized. In this study, we found that the overexpression of CdGLK1 significantly improved low-light tolerance in bermudagrass by increasing shoot weight, root weight, chlorophyll a, chlorophyll b, net photosynthetic rate (Pn), and maximum quantum yield of photosystem II (Fv/Fm). Furthermore, coordinated upregulation of both C₃ and C₄ pathway enzymes was observed under low-light stress, accompanied by enhanced antioxidant capacity and reduced photoxidative damage. Transcriptomic profiling further revealed CdGLK1-mediated activation of photosynthetic machinery components spanning light harvesting, electron transport, and carbon fixation modules. These findings establish CdGLK1 as a master integrator of photoprotection and metabolic adaptation under light-limiting conditions, providing both mechanistic insights and practical strategies for developing shade-resilient turfgrass cultivars. Full article

(This article belongs to the Special Issue New Strategies for Forage Breeding and Cultivation Under Challenging Conditions)

19 pages, 6793 KiB

Open AccessArticle

Identification and Analysis of Endoplasmic-Reticulum-Stress- and Salt-Stress-Related Genes in Solanum tuberosum Genome: StbZIP60 Undergoes Splicing in Response to Salt Stress and ER Stress

by Peiyan Guan, Dongbo Zhao, Chenxi Zhang, Zhennan Qiu, Qingshuai Chen, Inna P. Solyanikova, Peinan Sun, Peipei Cui, Ru Yu, Xia Zhang, Yanmei Li and Linshuang Hu

Agronomy 2025, 15(5), 1224; https://doi.org/10.3390/agronomy15051224 - 17 May 2025

Viewed by 62

Abstract

Salt stress can trigger endoplasmic reticulum (ER) stress and affect potato yield. The endomembrane system is tightly regulated in response to salt stress for maintaining cellular homeostasis. However, little is known about the genes involved in the ER-mediated cytoprotective pathways in potato plants. [...] Read more.

Salt stress can trigger endoplasmic reticulum (ER) stress and affect potato yield. The endomembrane system is tightly regulated in response to salt stress for maintaining cellular homeostasis. However, little is known about the genes involved in the ER-mediated cytoprotective pathways in potato plants. Previously characterized genes involved in the ER stress signaling pathway in Arabidopsis were used as prototypes. We identified 29 genes involved in ER stress response in the potato genome. Transcriptome data analysis showed that the expression levels of related genes were significantly different in different tissues. Most genes can response to β-aminobutyric acid, benzothiadiazole, salt, and mannitol. qRT-PCR assay revealed that they could respond to NaCl and tunicamycin, which was consistent with the fact that the promoter region of related genes contained ER-stress- and abiotic-stress-related cis-elements. Furthermore, we found that StbZIP60 has a splicing form, StbZIP60s, under salt and ER stress, which can be spliced at the CxGxxG site in the C terminus to create a frame shift through the excision of 23 base pairs. StbZIP60 is localized in the cytoplasm and nucleus, whereas most of the StbZIP60s translocated to the nucleus. This study provides a basis for further analyses of the functions of salt-stress- and ER-stress-related genes in potato plants. Full article

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

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21 pages, 7916 KiB

Open AccessReview

Effects of Microbial Agents on Soil Improvement—A Review and Bibliometric Analysis

by Mengdi Tan, Tianjiao Feng, Cong Wang, Xiaozhen Hao and Hang Yu

Agronomy 2025, 15(5), 1223; https://doi.org/10.3390/agronomy15051223 - 17 May 2025

Viewed by 75

Abstract

Microbial agents play a crucial role in improving soil quality, increasing soil fertility, enhancing crop yields, and reducing the incidence of diseases. The ecological benefits of these products contribute to environmental protection and to the promotion of sustainable agricultural development. Since the beginning [...] Read more.

Microbial agents play a crucial role in improving soil quality, increasing soil fertility, enhancing crop yields, and reducing the incidence of diseases. The ecological benefits of these products contribute to environmental protection and to the promotion of sustainable agricultural development. Since the beginning of the 21st century, research in the academic community on the use of microbial agents for soil improvement has increased, yet a systematic summary of the progress in this field is lacking. In this paper, we review trends in microbial agent applications, focusing on their classification, mechanisms of action, and practical implementations. To achieve this, we conduct a bibliometric analysis based on the SCI-EXPANDED database of the Web of Science, using tools such as VOSviewer for visualization. We focus on microbial agents for soil improvement and analyze publication trends, research hotspots, and annual variations in relevant studies published between 2003 and 2024. The results show that (1) the number of publications on microbial soil improvement has steadily increased over the years, indicating that the academic community has maintained a high level of interest in this field. Keywords such as “soil”, “diversity”, “carbon”, and “nitrogen” have been central research hotspots in the past 20 years. The research has been highly concentrated in a few countries, including China and the United States, as well as in key institutions such as the Chinese Academy of Sciences and the United States Department of Agriculture. (2) We further analyze the principles governing microbial agent efficacy, address limitations in their application, and propose strategies to advance research in this field. Finally, several suggestions are proposed to promote the further development of research on microbial agents for soil improvement. Full article

(This article belongs to the Special Issue Innovations and Obstacles: Microbial Communities in the Journey of Soil Remediation)

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17 pages, 1159 KiB

Open AccessArticle

Optimization of Subsurface Drainage Parameters in Saline–Alkali Soils to Improve Salt Leaching Efficiency in Farmland in Southern Xinjiang

by Han Guo, Guangning Wang, Zhenliang Song, Pengfei Xu, Xia Li and Liang Ma

Agronomy 2025, 15(5), 1222; https://doi.org/10.3390/agronomy15051222 - 17 May 2025

Viewed by 96

Abstract

In arid regions, soil salinization and inefficient water use are major challenges to sustainable agricultural development. Optimizing subsurface drainage system layouts is critical for improving saline soil reclamation efficiency. This study conducted field experiments from 2023 to 2024 to evaluate the effects of [...] Read more.

In arid regions, soil salinization and inefficient water use are major challenges to sustainable agricultural development. Optimizing subsurface drainage system layouts is critical for improving saline soil reclamation efficiency. This study conducted field experiments from 2023 to 2024 to evaluate the effects of varying subsurface drainage configurations—specifically, burial depths (1.0–1.5 m) and pipe spacings (20–40 m)—on drainage and salt removal efficiency in silty loam soils of southern Xinjiang, aiming to develop an optimized scheme balancing water conservation and desalination. Five treatments (A1–A5) were established to measure evaporation, drainage, and salt discharge during both spring and winter irrigation. These variables were analyzed using a water balance model and multifactorial ANOVA to quantify the interactive effects of drainage depth and spacing. The results indicated that treatment A5 (1.5 m depth, 20 m spacing) outperformed all the others in terms of both the drainage-to-irrigation ratio (R_d/i) and the drainage salt efficiency coefficient (DSEC), with a two-year average R_d/i of 32.35% across two spring and two winter irrigation events, and a mean DSEC of 3.28 kg·m⁻³. The 1.5 m burial depth significantly improved salt leaching efficiency by increasing the salt control volume and reducing capillary rise. The main effect of burial depth on both R_d/i and DSEC was highly significant (p < 0.01), whereas the effect of spacing was not statistically significant (p > 0.05). Although the limited experimental duration and the use of a single soil type may affect the generalizability of the findings, the recommended configuration (1.5 m burial depth, 20 m spacing) shows strong potential for broader application in silty loam regions of southern Xinjiang and provides technical support for subsurface drainage projects aimed at reclaiming saline soils in arid regions. Full article

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

19 pages, 3797 KiB

Open AccessArticle

The Influence of Unmanned Aerial Vehicle Wind Field on the Pesticide Droplet Deposition and Control Effect in Cotton Fields

by Haoran Li, Ying Li, Muhammad Zeeshan, Longfei Yang, Zhishuo Gao, Yuting Yang, Guoqiang Zhang, Chunjuan Wang and Xiaoqiang Han

Agronomy 2025, 15(5), 1221; https://doi.org/10.3390/agronomy15051221 - 17 May 2025

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Abstract

Unmanned aerial vehicles (UAVs) offer significant advantages in agricultural pest control. The present study investigated the influence of rotor-induced wind fields from multirotor UAVs (six-rotor T30, eight-rotor T40, eight-rotor T50, and four-rotor T60) on pesticide droplet deposition and control efficacy in cotton fields. [...] Read more.

Unmanned aerial vehicles (UAVs) offer significant advantages in agricultural pest control. The present study investigated the influence of rotor-induced wind fields from multirotor UAVs (six-rotor T30, eight-rotor T40, eight-rotor T50, and four-rotor T60) on pesticide droplet deposition and control efficacy in cotton fields. The results revealed that UAVs with stronger wind fields (e.g., T60) significantly improved droplet deposition in the middle and lower canopy layers, with penetration rates of 54.09–56.04% which were notably higher than the penetration rate observed for the T30 (45.83–44.76%). UAVs exhibited a pesticide utilization efficiency of 75.47–77.86% indicating a 32.2% improvement over the boom sprayers, which achieved a utilization efficiency of 58.88%. While the boom sprayers initially showed a better pest control efficacy, the efficacy gap narrowed after 7 days, with T40 achieving 91.55%, comparable to the efficacy of boom sprayers (93.36%). Following a second spraying, UAVs achieved defoliation rates exceeding 93% and boll opening rates exceeding 90%, similar to that of boom sprayers. This study underscores the critical role of wind field intensity in influencing the spraying performance, with UAVs featuring stronger wind fields exhibiting superior droplet penetration and distribution uniformity. These findings provide valuable scientific insights for optimizing UAV spraying in cotton fields. Full article

(This article belongs to the Special Issue Novel Studies in High-Performance and Precision Plant Protection Products Application—2nd Edition)

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20 pages, 3521 KiB

Open AccessArticle

Using Constrained K-Means Clustering for Soil Texture Mapping with Limited Soil Samples

by Fubin Zhu, Changda Zhu, Zihan Fang, Wenhao Lu and Jianjun Pan

Agronomy 2025, 15(5), 1220; https://doi.org/10.3390/agronomy15051220 - 17 May 2025

Viewed by 68

Abstract

Soil texture is one of the most important physical properties of soil and plays a crucial role in determining its suitability for crop cultivation. Currently, supervised classification machine learning methods are most commonly used in digital soil mapping. However, these methods may not [...] Read more.

Soil texture is one of the most important physical properties of soil and plays a crucial role in determining its suitability for crop cultivation. Currently, supervised classification machine learning methods are most commonly used in digital soil mapping. However, these methods may not yield optimal predictive performance due to the limited number of soil samples. Therefore, we propose using Constrained K-Means Clustering to combine a small number of labeled samples with a large amount of unlabeled data, thereby achieving improved prediction in soil texture mapping. In this study, we focused on a typical hilly region in northern Jurong City, Jiangsu Province, China, and used Constrained K-Means Clustering as our mapping model. GF-2 remote sensing imagery and the ALOS digital elevation model (DEM), along with their derived variables, were employed as environmental variables. In Constrained K-Means Clustering, the choice of distance method is a key parameter. Here, we used four different distance methods (euclidean, maximum, manhattan, and canberra) and compared the results with those of the random forest (RF) and multilayer perceptron (MLP) models. Notably, the euclidean distance method within Constrained K-Means Clustering achieved the highest overall accuracy (

O A

),

K a p p a

coefficient, and

M a c r o F 1 S c o r e

, with values of 0.77, 0.68, and 0.75, respectively. These methods were higher than those obtained by the RF and MLP models by 0.12, 0.18, and 0.12, and 0.18, 0.26, and 0.18, respectively. This indicates that Constrained K-Means Clustering demonstrates strong predictive performance in soil texture mapping. Moreover, land use (LU), multi-resolution of ridge top flatness index (MRRTF), topographic position index (TPI), and plan curvature (PlC) emerged as the key environmental variables for predicting soil texture. Overall, Constrained K-Means Clustering proves to be an effective digital soil mapping approach, offering a novel perspective for soil texture mapping with limited samples. Full article

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

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18 pages, 6598 KiB

Open AccessArticle

Optimizing the LED Light Spectrum for Enhanced Seed Germination of Lettuce cv. ‘Lollo Bionda’ in Controlled-Environment Agriculture

by Hamid Reza Soufi, Hamid Reza Roosta, Nazim S. Gruda and Mahdiyeh Shojaee Khabisi

Agronomy 2025, 15(5), 1219; https://doi.org/10.3390/agronomy15051219 - 17 May 2025

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Abstract

Light is crucial in controlled-environment agriculture (CEA), affecting germination, growth, and overall plant quality. Here, we explored the optimization of various LED light spectra on the germination traits such as germination percentage, mean germination time, germination index, vigor index, and early seedling growth [...] Read more.

Light is crucial in controlled-environment agriculture (CEA), affecting germination, growth, and overall plant quality. Here, we explored the optimization of various LED light spectra on the germination traits such as germination percentage, mean germination time, germination index, vigor index, and early seedling growth of ‘Lollo Bionda’ lettuce seedlings in a plant factory. A completely randomized design was implemented, involving three replications. LED lamps with different spectral compositions—red (R, peak at 656 nm), red/blue (3:1 ratio, R:B, peak at 656 nm), blue (B, peak at 450 nm), and white (400–700 nm)—were utilized in this study. The combination of red and blue LED lights, along with monochromatic red and blue treatments, significantly enhanced germination traits and early seedling growth compared to white and ambient lighting. The combined spectrum resulted in the highest seedling emergence, the longest shoot and root lengths, and the highest fresh weight. These findings underscore the potential of the LED technology to improve germination efficiency and enhance seedling quality in CEA. Future studies should refine multispectral LED strategies by examining factors such as light intensity and photoperiod, while also elucidating the molecular pathways involved in light-driven germination and early development in lettuce. Full article

(This article belongs to the Special Issue Advancement in Controlled Environment Agriculture (CEA) Automation and Crop Management)

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16 pages, 2493 KiB

Open AccessArticle

Comparative Transcriptome Analysis of Susceptible and Resistant Rutaceae Plants to Huanglongbing

by Huihong Liao, Fuping Liu, Xi Wang, Hongming Huang, Qichun Huang, Nina Wang and Chizhang Wei

Agronomy 2025, 15(5), 1218; https://doi.org/10.3390/agronomy15051218 - 17 May 2025

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Abstract

Huanglongbing (HLB), also known as citrus greening, is a devastating disease affecting the citrus industry worldwide. This study aimed to investigate the transcriptional responses of two Rutaceae species, Ponkan Mandarin (susceptible) and Punctate Wampee (resistant), to HLB infection. Comparative transcriptome analysis was conducted [...] Read more.

Huanglongbing (HLB), also known as citrus greening, is a devastating disease affecting the citrus industry worldwide. This study aimed to investigate the transcriptional responses of two Rutaceae species, Ponkan Mandarin (susceptible) and Punctate Wampee (resistant), to HLB infection. Comparative transcriptome analysis was conducted to identify differentially expressed genes (DEGs) and pathways involved in defense mechanisms. The transcriptome data showed that in the susceptible Ponkan Mandarin, there were 1519 upregulated genes and 700 downregulated genes, while in the resistant Punctate Wampee variety, there were 1611 upregulated genes and 1727 downregulated genes. Upon infection, 297 genes were upregulated in both varieties, while 211 genes were downregulated in both. These genes included transcription factors from different families such as WRKY, ERF, and MYB. Ponkan Mandarin primarily relies on pathways like lignin synthesis and cell wall modification to defend against HLB, whereas Punctate Wampee mainly resists HLB by regulating cellular homeostasis and metabolism. Weighted Gene Co-expression Network Analysis (WGCNA) identified ten potential key resistance genes in the resistant Punctate Wampee variety, including genes involved in lignin biosynthesis and genes related to cellular signaling pathways. These findings not only enhance our understanding of the distinct defense mechanisms employed by citrus species against HLB infection but also offer novel perspectives for developing effective prevention and management strategies against this disease. Full article

(This article belongs to the Special Issue Resistance-Related Gene Mining and Genetic Improvement in Crops)

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19 pages, 7193 KiB

Open AccessArticle

Multimodal Deep Learning Models in Precision Agriculture: Cotton Yield Prediction Based on Unmanned Aerial Vehicle Imagery and Meteorological Data

by Chunbo Jiang, Xiaoshuai Guo, Yongfu Li, Ning Lai, Lei Peng and Qinglong Geng

Agronomy 2025, 15(5), 1217; https://doi.org/10.3390/agronomy15051217 - 17 May 2025

Viewed by 69

Abstract

This study investigates a multimodal deep learning framework that integrates unmanned aerial vehicle (UAV) multispectral imagery with meteorological data to predict cotton yield. The study analyzes the impact of different neural network architectures, including the CNN feature extraction layer, the depth of the [...] Read more.

This study investigates a multimodal deep learning framework that integrates unmanned aerial vehicle (UAV) multispectral imagery with meteorological data to predict cotton yield. The study analyzes the impact of different neural network architectures, including the CNN feature extraction layer, the depth of the fully connected layer, and the method of integrating meteorological data, on model performance. Experimental results show that the model combining UAV multispectral imagery with weekly meteorological data achieved optimal yield prediction accuracy (RMSE = 0.27 t/ha; R² = 0.61). Specifically, models based on AlexNet (Model 9) and CNN2conv (Model 18) exhibited superior accuracy. ANOVA results revealed that deeper fully connected layers significantly reduced RMSE, while variations in CNN architectural complexity had no statistically significant effect. Furthermore, although the models exhibited comparable prediction accuracy (RMSE: 0.27–0.33 t/ha; R²: 0.61–0.69 across test datasets), their yield prediction spatial distributions varied significantly (e.g., Model 9 predicted a mean yield of 3.88 t/ha with a range of 2.51–4.89 t/ha, versus Model 18 at 3.74 t/ha and 2.33–4.76 t/ha), suggesting the need for further evaluation of spatial stability. This study underscores the potential of deep learning models integrating UAV and meteorological data for precision agriculture, offering valuable insights for optimizing spatiotemporal data integration strategies in future research. Full article

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

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22 pages, 1821 KiB

Open AccessArticle

Comparative Nutrient Study of Raphanus sativus L. Sprouts Microgreens, and Roots

by Dominika Kajszczak, Dorota Sosnowska, Radosław Bonikowski, Kamil Szymczak, Barbara Frąszczak, Katarzyna Pielech-Przybylska and Anna Podsędek

Agronomy 2025, 15(5), 1216; https://doi.org/10.3390/agronomy15051216 - 17 May 2025

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Abstract

Radish (Raphanus sativus L.) is an important vegetable crop worldwide. Four red radish cultivars (Carmen, Jutrzenka, Saxa 2, and Warta) were evaluated for their macronutrients (protein, fat, available carbohydrates), as well as ash, and dietary fiber at the sprout, microgreen, and mature [...] Read more.

Radish (Raphanus sativus L.) is an important vegetable crop worldwide. Four red radish cultivars (Carmen, Jutrzenka, Saxa 2, and Warta) were evaluated for their macronutrients (protein, fat, available carbohydrates), as well as ash, and dietary fiber at the sprout, microgreen, and mature (root) stages. Fatty acids, organic acids, and sugars were also profiled by using chromatographic methods. Radish roots are characterized by a good chemical composition due to a lower fat content, lower energy value, and higher available carbohydrate content compared to sprouts and microgreens. Microgreens outperformed other forms of radish in terms of organic acids, ash, and soluble dietary fiber, while sprouts contained the most protein. Both immature forms of radish proved to be better sources of fiber than their mature roots. In all radish samples analyzed, glucose, oxalic acid, and oleic acid or alpha-linolenic acid were the dominant sugar, organic acid, and fatty acid, respectively. The results indicate a diverse composition of radish sprouts, microgreens, and roots, and confirm the validity of using red radishes in various forms as valuable components of our diet. Full article

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

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18 pages, 4291 KiB

Open AccessArticle

Identification, Pathogenicity and Fungicide Sensitivity of Colletotrichum Species Causing Anthracnose on Polygonatum cyrtonema Hua

by Huixia Cai, Jinxin Li, Yanling Du, Di Wu, Jinyi Chen, Hong Chen, Kaili Qu, Yuhuan Miao and Dahui Liu

Agronomy 2025, 15(5), 1215; https://doi.org/10.3390/agronomy15051215 - 16 May 2025

Viewed by 36

Abstract

Anthracnose significantly threatens the cultivation of Polygonatum cyrtonema, severely impacting its quality and yield. Between 2022 and 2023, 50 Colletotrichum isolates were obtained from diseased leaves collected in three P. cyrtonema production areas within the Two Lakes region of China (Hubei and [...] Read more.

Anthracnose significantly threatens the cultivation of Polygonatum cyrtonema, severely impacting its quality and yield. Between 2022 and 2023, 50 Colletotrichum isolates were obtained from diseased leaves collected in three P. cyrtonema production areas within the Two Lakes region of China (Hubei and Hunan provinces). Morphological and molecular analyses identified six Colletotrichum species as the causative agents of anthracnose: C. aenigma, C. siamense, C. gloeosporioides, C. spaethianum, C. fructicola, and C. karsti. Among these pathogens, C. fructicola and C. spaethianum were predominant (82%), while C. siamense and C. fructicola exhibited the highest aggressiveness. Physiological investigations revealed that the optimal temperature range for all six pathogens was 25–28 °C. C. spaethianum thrived under acidic conditions, whereas C. aenigma, C. siamense, and C. gloeosporioides preferred alkaline environments. In contrast, C. fructicola and C. karsti showed no significant response to pH variations. Fungicide screening demonstrated that pyraclostrobin, prochloraz, and carbendazim effectively inhibited the growth of Colletotrichum species. These findings elucidate the epidemiological factors, primary pathogens, and effective control agents for P. cyrtonema anthracnose in the Two Lakes region, providing a basis for developing targeted prevention and control strategies. Full article

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

24 pages, 5391 KiB

Open AccessArticle

Design and Implementation of an Intelligent Pest Status Monitoring System for Farmland

by Xinyu Yuan, Zeshen He and Caojun Huang

Agronomy 2025, 15(5), 1214; https://doi.org/10.3390/agronomy15051214 - 16 May 2025

Viewed by 63

Abstract

This study proposes an intelligent agricultural pest monitoring system that integrates mechanical control with deep learning to address issues in traditional systems, such as pest accumulation interference, image contrast degradation under complex lighting, and poor balance between model accuracy and real-time performance. A [...] Read more.

This study proposes an intelligent agricultural pest monitoring system that integrates mechanical control with deep learning to address issues in traditional systems, such as pest accumulation interference, image contrast degradation under complex lighting, and poor balance between model accuracy and real-time performance. A three-axis coordinated separation device is employed, achieving a 92.41% single-attempt separation rate and 98.12% after three retries. Image preprocessing combines the Multi-Scale Retinex with Color Preservation (MSRCP) algorithm and bilateral filtering to enhance illumination correction and reduce noise. For overlapping pest detection, EfficientNetv2-S replaces the YOLOv5s backbone and is combined with an Adaptive Feature Pyramid Network (AFPN), achieving 95.72% detection accuracy, 94.04% mAP, and 127 FPS. For pest species recognition, the model incorporates a Squeeze-and-Excitation (SE) attention module and α-CIoU loss function, reaching 91.30% precision on 3428 field images. Deployed on an NVIDIA Jetson Nano, the system demonstrates a detection time of 0.3 s, 89.64% recall, 86.78% precision, and 1.136 s image transmission delay, offering a reliable solution for real-time pest monitoring in complex field environments. Full article

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

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35 pages, 2860 KiB

Open AccessArticle

Enhancing Soil Health, Growth, and Bioactive Compound Accumulation in Sunflower Sprouts Using Agricultural Byproduct-Based Soil Amendments

by Thidarat Rupngam, Patchimaporn Udomkun, Thirasant Boonupara and Puangrat Kaewlom

Agronomy 2025, 15(5), 1213; https://doi.org/10.3390/agronomy15051213 - 16 May 2025

Viewed by 41

Abstract

This study investigated the effects of organic soil amendments derived from agricultural byproducts—specifically cow manure (CM) at 0% and 1% w/w, and rice husk biochar (RHB) at 0%, 1%, 3%, and 5% w/w—on soil health, plant growth, [...] Read more.

This study investigated the effects of organic soil amendments derived from agricultural byproducts—specifically cow manure (CM) at 0% and 1% w/w, and rice husk biochar (RHB) at 0%, 1%, 3%, and 5% w/w—on soil health, plant growth, and the accumulation of bioactive compounds in sunflower sprouts. The application of 1% CM significantly improved the soil properties—enhancing macroaggregates (MaAs) by 54.5%, mesoaggregates (MeAs) by 16.7%, and soil organic carbon (SOC) by 27.2%. It also increased the shoot and root biomass by 22.3% and 25.8%, respectively, and boosted soil respiration by 67.0%, while reducing the nitrate (NO₃⁻) content by 33.7%. However, the CM also decreased the total phenolic content (TPC) by 21% and chlorophyll by 44.7%. The RHB, particularly at rates of 1–3% w/w, increased the MaAs by 62%, microaggregates (MiAs) by 3%, leaf area by up to 43.9%, root-to-shoot ratio by 26.5%, SOC by 13.1%, and DPPH antioxidant activity by 42.8%, while lowering the MeAs by 9% and NO₃⁻ content by up to 56.1%. In contrast, excessive RHB application (5% w/w) negatively impacted root development. The interaction effects revealed that the combination of 1% w/w CM with 1% w/w RHB maximized the MaAs by 12%, increased the root dry biomass by 101.9%, and also increased the TPC by 40.1% compared to the manure-only treatment. The Principal Component Analysis (PCA) indicated that CM primarily promoted plant growth and respiration, while RHB contributed to organic matter retention and nutrient availability. Applying 1% w/w CM and 1% w/w RHB showed promising effects and is recommended for short-cycle crop production. Full article

(This article belongs to the Section Agricultural Biosystem and Biological Engineering)

15 pages, 477 KiB

Open AccessArticle

Sodium Chloride Enhances Nitrogen Use Efficiency but Reduces Yield Benefits Under Elevated CO₂ in Upland Rice

by Daniel Amorim Vieira, Mayra Alejandra Toro-Herrera, João Paulo Pennacchi, Marília Mickaele Pinheiro Carvalho, Flavia Barbosa Silva Botelho, Paulo Eduardo Ribeiro Marchiori and João Paulo Rodrigues Alves Delfino Barbosa

Agronomy 2025, 15(5), 1212; https://doi.org/10.3390/agronomy15051212 - 16 May 2025

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Abstract

Climate-change-driven elevation of atmospheric CO₂ (e[CO₂]) disrupts rice physiology by impairing nitrogen use efficiency (NUE) and leaf carbon balance. This study investigated how sodium chloride (NaCl) amendment modulates these processes in upland rice (Oryza sativa L. cv. CMG 2085) [...] Read more.

Climate-change-driven elevation of atmospheric CO₂ (e[CO₂]) disrupts rice physiology by impairing nitrogen use efficiency (NUE) and leaf carbon balance. This study investigated how sodium chloride (NaCl) amendment modulates these processes in upland rice (Oryza sativa L. cv. CMG 2085) under current (400 μmol mol⁻¹) and elevated (700 μmol mol⁻¹) CO₂ concentrations. Using a randomized block design with factorial treatments (CO₂ × NaCl), we analyzed leaf nutrients, gas exchange, chlorophyll fluorescence, and yield parameters. Our findings revealed that 3 mmol L⁻¹ NaCl under ambient CO₂ (1) reduced photorespiration by half, (2) increased grain yield, and (3) enhanced leaf area despite lower leaf N content, indicating improved NUE. Conversely, under e[CO₂], NaCl supplementation decreased rice yield by 15%, demonstrating CO₂-dependent reversal of sodium benefits. Photosynthetic modeling showed higher Vcmax and J values at ambient CO₂, while e[CO₂] increased J/Vcmax, suggesting altered nitrogen allocation to photosynthetic reactions. These results demonstrate that applying low-dose NaCl (3 mmol L⁻¹) can optimize carbon and nitrogen economy under current CO₂ concentrations, although its efficacy diminishes under e[CO₂]. These findings support climate-resilient cultivation strategies for upland rice in tropical and subtropical regions where mild salinity can be used to enhance nitrogen use efficiency and yield under present-day atmospheric conditions. Full article

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

23 pages, 3100 KiB

Open AccessArticle

Integrated Transcriptomic and Metabolomic Analyses Reveal the Positive Effects of 5-Aminolevulinic Acid (ALA) on Shading Stress in Peanut (Arachis hypogaea L.)

by Qi Wu, Liyu Yang, Haiyan Liang, Miao Liu, Dianxu Chen and Pu Shen

Agronomy 2025, 15(5), 1211; https://doi.org/10.3390/agronomy15051211 - 16 May 2025

Viewed by 35

Abstract

Shading stress is a major negative abiotic environmental factor seriously affecting peanut growth, development, and ultimately resulting in a yield decrease in peanut in peanut/maize intercropping systems. However, 5-aminolevulinic acid (ALA) is a potential plant growth regulator that can enhance its tolerance to [...] Read more.

Shading stress is a major negative abiotic environmental factor seriously affecting peanut growth, development, and ultimately resulting in a yield decrease in peanut in peanut/maize intercropping systems. However, 5-aminolevulinic acid (ALA) is a potential plant growth regulator that can enhance its tolerance to various abiotic stresses. However, there is limited information on how ALA affects plant physiology and molecular mechanisms under shading stress. To address this, field experiments were designed involving two shading conditions (CK and AS0, no shading; S40 and AS40, 40% shading) and two ALA foliar sprayed levels (CK and S40, no ALA application; AS0 and AS40, 20 mg L⁻¹ (0.15 mM) ALA application) to investigate the effects of the exogenous application of ALA under shading stress via the evaluation of both transcriptome and metabolome. The research results suggested that the exogenous ALA application under normal light conditions significantly enhanced photosynthesis, while exogenous ALA application could improve the stability of the cell membrane structure and biological function in response to shading stress and thereby enhance shading tolerance of the plant. The results also implied that exogenous ALA regulates the adaptability of peanuts under different light conditions by affecting the concentration of endogenous ALA. This finding improves the understanding of ALA’s regulatory molecular mechanisms and the metabolic pathways of peanuts under shading stress. Our results extend the application of ALA in agricultural production and will provide a reference for crop cultivation, especially for peanut/maize intercropping systems. Full article

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

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15 pages, 5843 KiB

Open AccessArticle

Genome-Wide Characterization and Haplotype Module Stacking Analysis of the KTI Gene Family in Soybean (Glycine max L. Merr.)

by Huilin Tian, Zhanguo Zhang, Shaowei Feng, Jia Song, Xue Han, Xin Chen, Candong Li, Enliang Liu, Linli Xu, Mingliang Yang, Qingshan Chen, Xiaoxia Wu and Zhaoming Qi

Agronomy 2025, 15(5), 1210; https://doi.org/10.3390/agronomy15051210 - 16 May 2025

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Abstract

The Kunitz trypsin inhibitor (KTI) gene family encompasses a category of trypsin inhibitors, and the KTI proteins are important components of the 2S storage protein fraction in soybeans. In this study, fifty members of the GmKTI family were identified in the [...] Read more.

The Kunitz trypsin inhibitor (KTI) gene family encompasses a category of trypsin inhibitors, and the KTI proteins are important components of the 2S storage protein fraction in soybeans. In this study, fifty members of the GmKTI family were identified in the soybean genome, and their physicochemical properties, domain compositions, phylogenetic relationships, gene structures, and expression patterns were comprehensively analyzed to explore their impact on soybean seed protein content. The results revealed significant gene expansion within the GmKTI family in soybean. The gene structures and conserved motifs of GmKTI members exhibited both regularity and diversity, with distinct expression patterns across different soybean tissues. Haplotype analysis identified 7 GmKTI genes significantly associated with seed storage protein content, and the combination of superior haplotypes was found to enhance seed storage protein content. This is crucial for the improvement of soybean varieties and the enhancement of storage protein content. Additionally, the GmKTI family demonstrated evolutionary conservation, with its functions likely linked to light induction, biotic stress, and growth development. This study characterizes the structure, expression, genomic haplotypes, and molecular features of the soybean KTI domain for the first time, providing a foundation for functional analyses of the GmKTI domain in soybean and other plants. Full article

(This article belongs to the Special Issue Genetic Basis of Crop Selection and Evolution)

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22 pages, 5228 KiB

Open AccessArticle

An Analysis of Uncertainties in Evaluating Future Climate Change Impacts on Cotton Production and Water Use in China

by Ruixue Yuan, Keyu Wang, Dandan Ren, Zhaowang Chen, Baosheng Guo, Haina Zhang, Dan Li, Cunpeng Zhao, Shumin Han, Huilong Li, Shuling Zhang, De Li Liu and Yanmin Yang

Agronomy 2025, 15(5), 1209; https://doi.org/10.3390/agronomy15051209 - 16 May 2025

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Abstract

Global Climate Models (GCMs) are a primary source of uncertainty in assessing climate change impacts on agricultural production, especially when relying on limited models. Considering China’s vast territory and diverse climates, this study utilized 22 GCMs and selected three representative cotton-producing regions: Aral [...] Read more.

Global Climate Models (GCMs) are a primary source of uncertainty in assessing climate change impacts on agricultural production, especially when relying on limited models. Considering China’s vast territory and diverse climates, this study utilized 22 GCMs and selected three representative cotton-producing regions: Aral (northwest inland region), Wangdu (Yellow River basin), and Changde (Yangtze River basin). Using the APSIM model, we simulated climate change effects on cotton yield, water consumption, uncertainties, and climatic factor contributions. Results showed significant variability driven by different GCMs, with uncertainty increasing over time and under radiation forcing. Spatial variations in uncertainty were observed: Wangdu exhibited the highest uncertainties in yield and phenology, while Changde had the greatest uncertainties in ET (evapotranspiration) and irrigation amount. Key factors affecting yield varied regionally—daily maximum temperature and precipitation dominated in Aral; precipitation was a major negative factor in Wangdu; and maximum temperature and solar radiation were critical in Changde. This study provides scientific support for developing climate change adaptation measures tailored to cotton production across different regions. Full article

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

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Agronomy, Volume 15, Issue 5 (May 2025) – 238 articles

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