Agronomy

12 pages, 1320 KiB

Open AccessArticle

The Mechanism Involved in High-Lycopene Tomato Mutants for Broomrape Resistance

by Lianfeng Shi, Xin Li, Jinrui Bai, Xiaoxiao Lu, Chunyang Pan, Junling Hu, Chen Zhang, Can Zhu, Yanmei Guo, Xiaoxuan Wang, Zejun Huang, Yongchen Du, Lei Liu and Junming Li

Agronomy 2025, 15(5), 1250; https://doi.org/10.3390/agronomy15051250 (registering DOI) - 21 May 2025

Abstract

The root parasitic weed Phelipanche aegyptiaca (Pers.) Pomel poses a serious threat to solanaceous crops, leading to yield losses of up to 80% in tomato (Solanum lycopersicum L.). Strigolactones (SLs), derived from the carotenoid metabolic pathway, serve as key host-recognition signals for [...] Read more.

The root parasitic weed Phelipanche aegyptiaca (Pers.) Pomel poses a serious threat to solanaceous crops, leading to yield losses of up to 80% in tomato (Solanum lycopersicum L.). Strigolactones (SLs), derived from the carotenoid metabolic pathway, serve as key host-recognition signals for root-parasitic plants. This study investigated the molecular mechanisms of host resistance, focusing on the suppression of SL biosynthesis through altered carotenoid metabolism in the high-pigment tomato mutants hp-1 and og^c. Both pot experiment and in vitro seed germination assays demonstrated that the mutants exhibited reduced susceptibility to P. aegyptiaca and triggered lower germination rates in broomrape seeds compared to the wild-type cultivar AC. Quantitative RT-PCR analysis revealed a significant downregulation of SL biosynthesis genes (SlD27, SlCCD7, SlCCD8, SlMAX1, SlP450, SlDI4) in hp-1 at various parasitic stages post-inoculation, with a more pronounced suppression observed in hp-1 than in og^c. Notably, the extent of downregulation correlated with the enhanced resistance phenotype in hp-1. These findings highlight a synergistic resistance mechanism involving the coordinated regulation of carotenoid metabolism and SL biosynthesis, providing new insights into the molecular defense network underlying tomato-broomrape interactions. Full article

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

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16 pages, 2308 KiB

Open AccessArticle

Mechanical Chiseling Versus Root Bio-Tillage on Soil Physical Quality and Soybean Yield in a Long-Term No-Till System

by Gustavo Ferreira da Silva, Bruno Cesar Ottoboni Luperini, Jéssica Pigatto de Queiroz Barcelos, Fernando Ferrari Putti, Sacha J. Mooney and Juliano Carlos Calonego

Agronomy 2025, 15(5), 1249; https://doi.org/10.3390/agronomy15051249 (registering DOI) - 21 May 2025

Abstract

Occasional mechanical intervention can help alleviate compaction symptoms in no-till systems, but its effects compared to well-established crop rotation systems are uncertain. Thus, the aim of this study was to evaluate the effects of mechanical and biological chiseling of the soil (via millet [...] Read more.

Occasional mechanical intervention can help alleviate compaction symptoms in no-till systems, but its effects compared to well-established crop rotation systems are uncertain. Thus, the aim of this study was to evaluate the effects of mechanical and biological chiseling of the soil (via millet and sunn hemp cover crops) on soil physical properties, root development, and soybean yield in a long-term experiment. The treatments consisted of crops rotations used in the spring harvest: (I) triticale (autumn–winter), millet (spring), and soybean (summer); (II) triticale (autumn–winter), sunn hemp (spring), and soybean (summer); and (III) triticale (autumn–winter), fallow/soil chiseling (spring), and soybean (summer). Mechanical chiseling reduced bulk density and penetration resistance in the upper 0.10 m layer by 6% and 37%, respectively. However, its effects did not extend below this depth. Conversely, millet and sunn hemp maintained higher penetration resistance in surface layers but reduced resistance in deeper layers (0.20–0.40 m) by up to 27% compared to chiseling. These cover crops also improved root growth (up to 71% higher root dry mass), soil microporosity, and total porosity. Notably, sunn hemp enhanced water infiltration (151 mm accumulated) and basic infiltration rate (180 cm h⁻¹), outperforming chiseling by 30% and 85%, respectively. Soybean yield was highest under sunn hemp, with an 18% increase over chiseling. Thus, growing millet and sunn hemp in a long-term production system can improve the soil’s physical properties, ensuring better infiltration, storage, and availability of water in the soil for plants. Full article

(This article belongs to the Special Issue Conservation Agricultural Practices for Improving Crop Production and Quality—2nd Edition)

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17 pages, 2229 KiB

Open AccessArticle

Effect of Ethephon on Sensitivity Difference of Lodging Resistance in Different Maize Inbred Lines

by Siyao Liu, Feng Guo, Mengzhu Chai, Shiwei Gu, Dacheng Wang, Zihao Wang, Yidan Chen, Tenglong Xie, Deguang Yang and Qian Zhang

Agronomy 2025, 15(5), 1248; https://doi.org/10.3390/agronomy15051248 - 21 May 2025

Abstract

Lodging imposes substantial constraints on maize yield potential and agronomic efficiency, critically undermining productivity and resource optimization in cultivation systems. This study aimed to elucidate the mechanism whereby ethephon enhances lodging resistance and analyze the sensitivity differences to ethephon among distinct maize inbred [...] Read more.

Lodging imposes substantial constraints on maize yield potential and agronomic efficiency, critically undermining productivity and resource optimization in cultivation systems. This study aimed to elucidate the mechanism whereby ethephon enhances lodging resistance and analyze the sensitivity differences to ethephon among distinct maize inbred lines. Through exogenous application of ethephon (200 and 400 mg/L, S1 and S2 treatments) to four classic maize inbred lines (Zheng58, Chang7-2, PH6WC, and PH4CV), we systematically evaluated its effects on plant morphology, stalk biomechanical properties, and lignin biosynthesis. Results demonstrated that ethephon optimized plant morphology through reductions in plant height, ear height, leaf area, leaf angle, and internode length. Significant augmentations in stalk bending resistance (a maximum increase of 52.61% in PH4CV) and puncture strength (most pronounced in Zheng58) were mechanistically associated with increased lignin content and enhanced activity of key biosynthetic enzymes [cinnamyl alcohol dehydrogenase (CAD), phenylalanine ammonia-lyase (PAL), and 4-coumarate-CoA ligase (4CL)], with PH6WC exhibiting the most robust enzymatic response. These findings underscored genotype-specific regulatory effects of ethephon, bridging the knowledge gap regarding its molecular–physiological interplay with maize genotypes. The study provides critical insights for precision breeding and optimization strategies employing plant growth regulators to improve maize lodging resistance. Full article

(This article belongs to the Section Farming Sustainability)

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17 pages, 2141 KiB

Open AccessSystematic Review

Biodegradation Potential of Glyphosate by Bacteria: A Systematic Review on Metabolic Mechanisms and Application Strategies

by Karolayne Silva Souza, Milena Roberta Freire da Silva, Manoella Almeida Candido, Hévellin Talita Sousa Lins, Gabriela de Lima Torres, Kátia Cilene da Silva Felix, Kaline Catiely Campos Silva, Ricardo Marques Nogueira Filho, Rahul Bhadouria, Sachchidanand Tripathi, Rishikesh Singh, Milena Danda Vasconcelos Santos, Isac Palmeira Santos Silva, Amanda Vieira de Barros, Lívia Caroline Alexandre de Araújo, Fabricio Motteran and Maria Betânia Melo de Oliveira

Agronomy 2025, 15(5), 1247; https://doi.org/10.3390/agronomy15051247 - 21 May 2025

Abstract

The biodegradation of glyphosate by bacteria is an emerging bioremediation strategy necessitated by the intensive use of this herbicide in global agriculture. This study systematically reviews the literature to identify bacteria with the potential to degrade glyphosate. The PRISMA protocol was utilized, considering [...] Read more.

The biodegradation of glyphosate by bacteria is an emerging bioremediation strategy necessitated by the intensive use of this herbicide in global agriculture. This study systematically reviews the literature to identify bacteria with the potential to degrade glyphosate. The PRISMA protocol was utilized, considering relevant articles identified in electronic databases such as PubMed, Scopus, and Science Direct. The research identified 34 eligible studies, highlighting the genera Bacillus, Pseudomonas, and Ochrobactrum as having the greatest potential for glyphosate degradation. These findings were based on analytical techniques such as High-Performance Liquid Chromatography (HPLC) and Nuclear Magnetic Resonance (NMR), which identified and quantified intermediate metabolites, primarily AMPA (aminomethylphosphonic acid), sarcosine, and glyoxylate. This investigation also addressed enzymatic efficiency in biodegradation, emphasizing enzymes like glyphosate oxidoreductase and C-P lyases. The results indicated that South and North America lead in publications on this topic, with Argentina and the United States being the main contributors, reflecting the intense use of glyphosate in these countries. Additionally, studies in Europe and Asia focused on microbial diversity, exploring various bacterial genera. This investigation revealed that despite the promising microbial potential, there are challenges related to environmental condition variations and the cost of large-scale implementation, indicating that continuous research and process optimization are essential for the effective and sustainable application of this biotechnology. Full article

(This article belongs to the Section Weed Science and Weed Management)

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28 pages, 9847 KiB

Open AccessArticle

A Multimodal Parallel Transformer Framework for Apple Disease Detection and Severity Classification with Lightweight Optimization

by Chuhuang Zhou, Xinjin Ge, Yihe Chang, Mingfei Wang, Zhongtian Shi, Mengxue Ji, Tianxing Wu and Chunli Lv

Agronomy 2025, 15(5), 1246; https://doi.org/10.3390/agronomy15051246 - 21 May 2025

Abstract

One of the world’s most important economic crops, apples face numerous disease threats during their production process, posing significant challenges to orchard management and yield quality. To address the impact of complex disease characteristics and diverse environmental factors on detection accuracy, this study [...] Read more.

One of the world’s most important economic crops, apples face numerous disease threats during their production process, posing significant challenges to orchard management and yield quality. To address the impact of complex disease characteristics and diverse environmental factors on detection accuracy, this study proposes a multimodal parallel transformer-based approach for apple disease detection and classification. By integrating multimodal data fusion and lightweight optimization techniques, the proposed method significantly enhances detection accuracy and robustness. Experimental results demonstrate that the method achieves an accuracy of 96%, precision of 97%, and recall of 94% in disease classification tasks. In severity classification, the model achieves a maximum accuracy of 94% for apple scab classification. Furthermore, the continuous frame diffusion generation module enhances the global representation of disease regions through high-dimensional feature modeling, with generated feature distributions closely aligning with real distributions. Additionally, by employing lightweight optimization techniques, the model is successfully deployed on mobile devices, achieving a frame rate of 46 FPS for efficient real-time detection. This research provides an efficient and accurate solution for orchard disease monitoring and lays a foundation for the advancement of intelligent agricultural technologies. Full article

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

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13 pages, 611 KiB

Open AccessArticle

Water Levels More than Earthworms Impact Rice Growth and Productivity: A Greenhouse Study

by Sreypich Sinh, Quang Van Pham, Lan Anh Thi Le, Ruben Puga Freitas, Anne Repellin, Vannak Ann, Nicolas Bottinelli and Pascal Jouquet

Agronomy 2025, 15(5), 1245; https://doi.org/10.3390/agronomy15051245 - 20 May 2025

Abstract

Earthworms are highly active in Southeast Asian paddy fields, yet their activity is challenging to measure in flooded soils. Therefore, this study investigates the influence of the subaquatic earthworm Glyphidrilus papillatus (Michaelsen, 1896) on soil properties and rice (Oryza sativa L.) physiology [...] Read more.

Earthworms are highly active in Southeast Asian paddy fields, yet their activity is challenging to measure in flooded soils. Therefore, this study investigates the influence of the subaquatic earthworm Glyphidrilus papillatus (Michaelsen, 1896) on soil properties and rice (Oryza sativa L.) physiology in Northern Vietnam, specifically focusing on rice cultivation at three distinct water levels: 5 cm above the soil surface (HIGH), at the soil level (ZERO), and 5 cm below the soil surface (LOW). Our findings indicate that water levels significantly affect earthworm activity, with the lowest activity observed at the shallowest water depth, as evidenced by reduced pore production in the soil and fewer casts on the surface. While earthworms are typically associated with enhanced soil fertility, this study did not confirm this relationship. Consequently, despite the substantial reorganization of soil structure, no significant interactions were found between earthworm presence and rice biomass, physiological parameters (such as leaf stomatal conductance to water vapor, chlorophyll content, and maximum quantum yield of PSII), or overall yield. In conclusion, this research highlights the critical role of the water level in influencing both earthworm activity and rice development. It underscores the necessity of considering additional ecological factors, such as carbon dynamics, greenhouse gas emissions, and plant resilience to environmental stressors. Full article

(This article belongs to the Special Issue Advancements in Fertilization Strategies and Soil Health for Rice and Wheat Cultivation)

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17 pages, 10579 KiB

Open AccessArticle

Multiple Transcriptomic Networks Regulate the Callus Development Process in Panax ginseng

by Jaewook Kim, Jung-Woo Lee and Ick-Hyun Jo

Agronomy 2025, 15(5), 1244; https://doi.org/10.3390/agronomy15051244 - 20 May 2025

Abstract

Callus induction is one of the most important techniques in plant-based industries. Important features in the use of callus induction are the maintenance of pluripotency and the proliferation of cells. Although the importance of callus induction is also understood in ginseng, there are [...] Read more.

Callus induction is one of the most important techniques in plant-based industries. Important features in the use of callus induction are the maintenance of pluripotency and the proliferation of cells. Although the importance of callus induction is also understood in ginseng, there are no studies on the genetic modules associated with callus induction and growth regulation. Panax ginseng embryo tissue was wounded and cultured in callus-inducing media, and its time-course physiology was observed. Time-course callus samples were collected for total RNA extraction and RNA-Seq analysis using the Illumina HiSeq X Ten platform. P. ginseng embryo tissue was wounded and treated with varying amounts of gamma radiation in callus-inducing media, and samples were also collected for total RNA extraction and RNA-Seq analysis. A combinatory analysis of various network analyses was used to reveal the regulatory network underlying callus development. We were able to determine the time-course physiology of callus development and the dose-dependent effect of gamma radiation on callus development. Network analysis revealed two networks correlated with callus induction and two networks correlated with callus growth. Our research provides a regulatory network illustrating how callus is induced and growth is regulated in P. ginseng. This result would be helpful in the development of a cell culture system or clonal propagation protocol in P. ginseng. Full article

(This article belongs to the Special Issue Application of In Vitro Technology to Improving the Yield and Quality of Common and Alternative Crops)

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23 pages, 6826 KiB

Open AccessArticle

Digestate Application on Grassland: Effects of Application Method and Rate on GHG Emissions and Forage Performance

by Petr Šařec, Václav Novák, Oldřich Látal, Martin Dědina and Jaroslav Korba

Agronomy 2025, 15(5), 1243; https://doi.org/10.3390/agronomy15051243 - 20 May 2025

Abstract

The application of digestate as a fertilizer offers a promising alternative to synthetic inputs on permanent grasslands, with benefits for productivity and environmental performance. This four-year study evaluated the impact of two digestate application methods—disc injection (I) and band spreading (S)—combined with four [...] Read more.

The application of digestate as a fertilizer offers a promising alternative to synthetic inputs on permanent grasslands, with benefits for productivity and environmental performance. This four-year study evaluated the impact of two digestate application methods—disc injection (I) and band spreading (S)—combined with four dose variants (0, 20, 40, and 80 m³·ha⁻¹), including split-dose strategies. Emissions of ammonia (NH₃), carbon dioxide (CO₂), and methane (CH₄) were measured using wind tunnel systems immediately after application. Vegetation status was assessed via Sentinel-2-derived Normalized Difference Vegetation Index, Normalized Difference Water Index, and Modified Soil Adjusted Vegetation Index, and agronomic performance through dry matter yield (DMY), net energy for lactation (NEL), and relative feed value (RFV). NH₃ and CO₂ emissions increased proportionally with digestate dose, while CH₄ responses suggested a threshold effect, but considering solely the disc injection, CH₄ flux did not increase markedly with higher application rates. Disc injection resulted in significantly lower emissions of the monitored fluxes than band spreading. The split-dose I_40+40 variant achieved the highest DMY (3.57 t·ha⁻¹) and improved forage quality, as indicated by higher NEL values. The control variant (C, no fertilization) had the lowest yield and NEL. These results confirm that subsurface digestate incorporation in split doses can reduce emissions while supporting yield and forage quality. Based on the findings, disc injection at 40+40 m³·ha⁻¹ is recommended as an effective option for reducing emissions and maintaining productivity in managed grasslands. Full article

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

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23 pages, 8589 KiB

Open AccessArticle

A Deep Learning-Based Approach to Apple Tree Pruning and Evaluation with Multi-Modal Data for Enhanced Accuracy in Agricultural Practices

by Tong Hai, Wuxiong Wang, Fengyi Yan, Mingyu Liu, Chengze Li, Shengrong Li, Ruojia Hu and Chunli Lv

Agronomy 2025, 15(5), 1242; https://doi.org/10.3390/agronomy15051242 - 20 May 2025

Abstract

A deep learning-based tree pruning evaluation system is proposed in this study, which integrates hyperspectral images, sensor data, and expert system rules. The system aims to enhance the accuracy and robustness of tree pruning tasks through multimodal data fusion and online learning strategies. [...] Read more.

A deep learning-based tree pruning evaluation system is proposed in this study, which integrates hyperspectral images, sensor data, and expert system rules. The system aims to enhance the accuracy and robustness of tree pruning tasks through multimodal data fusion and online learning strategies. Various models, including Mask R-CNN, SegNet, Tiny-Segformer, Box2Mask, CS-Net, SVM, MLP, and Random Forest, were used in the experiments to perform tree segmentation and pruning evaluation, with comprehensive performance assessments conducted. The experimental results demonstrate that the proposed model excels in the tree segmentation task, achieving a precision of 0.94, recall of 0.90, F1 score of 0.92, and mAP@50 and mAP@75 of 0.91 and 0.90, respectively, outperforming other comparative models. These results confirm the effectiveness of multimodal data fusion and dynamic optimization strategies in improving the accuracy of tree pruning evaluation. The experiments also highlight the critical role of sensor data in pruning evaluation, particularly when combined with the online learning strategy, as the model can progressively optimize pruning decisions and adapt to environmental changes. Through this work, the potential and prospects of the deep learning-based tree pruning evaluation system in practical applications are demonstrated. Full article

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

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29 pages, 4070 KiB

Open AccessArticle

Impact of Digestate-Derived Nitrogen on Nutrient Content Dynamics in Winter Oilseed Rape Before Flowering

by Remigiusz Łukowiak, Witold Szczepaniak and Dominik Młodecki

Agronomy 2025, 15(5), 1241; https://doi.org/10.3390/agronomy15051241 - 20 May 2025

Abstract

The increase in biogas production has caused a simultaneous increase in the production of digestate, which is a valuable carrier of nutrients in crop plant production. Digestate-derived nitrogen ensures the optimal nutritional status of winter oilseed plants at critical stages of yield formation. [...] Read more.

The increase in biogas production has caused a simultaneous increase in the production of digestate, which is a valuable carrier of nutrients in crop plant production. Digestate-derived nitrogen ensures the optimal nutritional status of winter oilseed plants at critical stages of yield formation. This hypothesis was verified in field experiments with winter oilseed rape (WOSR) conducted in the 2015/2016, 2016/2017, and 2017/2018 growing seasons. The experiment consisted of three nitrogen fertilization systems (FSs)—mineral ammonium nitrate (AN) (AN-FS), digestate-based (D-FS), and ²/₃ digestate + ¹/₃ AN (DAN-FS)—and five N_f doses: 0, 80, 120, 160, and 240 kg N ha⁻¹. Plants fertilized with digestate had higher yields than those fertilized with AN. The highest seed yield (SY) was recorded in the DAN-FS, which was 0.56 t ha⁻¹ higher than that in the M-FS. The nitrogen fertilizer replacement value (NFRV), averaged over N doses, was 104% for the D-FS and reached 111% for the mixed DAN-FS system. The N content in WOSR leaves, which was within the range of 41–48 g kg⁻¹ DM at the rosette stage and within 34–44 g kg⁻¹ DM at the beginning of flowering, ensured optimal plant growth and seed yield. In WOSR plants fertilized with digestate, the nitrogen (N) content was significantly lower compared to that in plants fertilized with AN, but this difference did not have a negative impact on the seed yield (SY). The observed positive effect of the digestate on plant growth in the pre-flowering period of WOSR growth and on SY resulted from the impact of Mg, which effectively controlled Ca, especially in the third growing season (which was dry). Mg had a significant effect on the biomass of rosettes and on SY, but only when its content in leaves exceeded 2.0 g kg⁻¹ DM. It is necessary to emphasize the specific role of the digestate, which significantly reduced the Ca content in the indicator WOSR organs. Increased Ca content during the vegetative period of WOSR growth reduced leaf N and Zn contents, which ultimately led to a decrease in SY. Therefore, the rosette phase of WOSR growth should be considered a reliable diagnostic phase for both the correction of plants’ nutritional status and the prediction of SY. It can be concluded that the fertilization value of digestate-derived N was the same as that of ammonium nitrate. This means that the mineral fertilizer can be replaced by digestate in WOSR production. Full article

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

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18 pages, 3607 KiB

Open AccessArticle

Research on Monitoring Nitrogen Content of Soybean Based on Hyperspectral Imagery

by Yakun Zhang, Mengxin Guan, Libo Wang, Xiahua Cui, Yafei Wang, Peng Li, Shaukat Ali and Fu Zhang

Agronomy 2025, 15(5), 1240; https://doi.org/10.3390/agronomy15051240 - 20 May 2025

Abstract

In order to analyze the relationship between hyperspectral image and soybean canopy nitrogen content in the field, and to establish a prediction model for soybean canopy nitrogen content with few parameters and a simple structure, hyperspectral image data and corresponding nitrogen content data [...] Read more.

In order to analyze the relationship between hyperspectral image and soybean canopy nitrogen content in the field, and to establish a prediction model for soybean canopy nitrogen content with few parameters and a simple structure, hyperspectral image data and corresponding nitrogen content data of soybean canopy at different growth periods under different fertilization treatments were acquired. Three spectral characteristic variables selection methods, including correlation coefficient analysis, stepwise regression, and spectral index analysis, were used to determine the spectral characteristic variables that are closely related to the soybean canopy nitrogen content. The predictive models for soybean canopy nitrogen content based on spectral characteristic variables were established using a multiple linear regression algorithm. On this basis, the established prediction models for soybean canopy nitrogen content were compared and analyzed, and the optimal prediction model for soybean canopy nitrogen content was determined. To verify the applicability of prediction models for soybean canopy nitrogen content, a spatial distribution map of soybean canopy nitrogen content at the regional scale was drawn based on unmanned aerial vehicle (UAV) hyperspectral imaging data at the flowering and seed filling stages of soybean in the experimental area, and the spatial distribution of soybean nitrogen content was statistically analyzed. The results show the following: (1) Soybean canopy spectral reflectance was highly significantly negatively correlated with soybean canopy nitrogen content in the range of 450–729 nm, and highly significantly positively correlated in the range of 756–774 nm, with the largest positive correlation coefficient of 0.2296 at 765 nm and the largest absolute value of negative correlation coefficient of −0.8908 at 630 nm. (2) The predictive model for soybean canopy nitrogen content based on three optimal spectral indices, NDSI(R₅₅₂,R₅₅₅), RSI(R₅₃₇,R₅₇₃), and DSI(R₅₄₀,R₅₅₅), was optimal, with R² of 0.9063 and 0.91566 and RMSE of 3.3229 and 3.2219 for the calibration and prediction set, respectively. (3) Based on the established optimal prediction model for soybean canopy nitrogen content combined with the UAV hyperspectral image data, spatial distribution maps of soybean nitrogen content at the flowering and seed filling stages were generated, and the R² between soybean nitrogen content in the spatial distribution map and the ground measured value was 0.93906, the RMSE was 3.6476, and the average relative error was 9.5676%, which indicates that the model had higher prediction accuracy and applicability. (4) The overall results show that the optimal prediction model for soybean canopy nitrogen content established based on hyperspectral imaging data has the characteristics of few parameters, a simple structure, and strong applicability, which provides a new method for realizing rapid, dynamic, and non-destructive monitoring of soybean nutritional status on the regional scale and provides a decision-making basis for precision fertilization management during soybean growth. Full article

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

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18 pages, 2111 KiB

Open AccessArticle

Can Zinc Oxide Nanoparticles Alleviate the Adverse Effects of Salinity Stress in Coffea arabica?

by Jegnes Benjamín Meléndez-Mori, Yoiner K. Lapiz-Culqui, Eyner Huaman-Huaman, Marileydi Zuta-Puscan and Manuel Oliva-Cruz

Agronomy 2025, 15(5), 1239; https://doi.org/10.3390/agronomy15051239 - 20 May 2025

Abstract

Salinity is one of the main limiting factors for agricultural production worldwide. Nanotechnology has emerged as a possible tool to improve plant tolerance to salt stress. However, the application of zinc oxide (ZnO) nanoparticles in agriculture raises questions about their safety and long-term [...] Read more.

Salinity is one of the main limiting factors for agricultural production worldwide. Nanotechnology has emerged as a possible tool to improve plant tolerance to salt stress. However, the application of zinc oxide (ZnO) nanoparticles in agriculture raises questions about their safety and long-term impact. The objective of this study was to investigate the effects of foliar application of ZnO nanoparticles on the physiology and defense systems of coffee plants in the presence/absence of NaCl (150 mM). A foliar spray of ZnO-NPs (0, 50, and 100 mg L⁻¹) was applied to coffee plants individually and in combination with simulated stress conditions. The results showed that the application of ZnO-NPs to plants under salt stress had both positive and negative effects. An increase in proline content ranging from 33% to 77% was detected in stressed plants treated with ZnO-NPs, in contrast to stressed plants that did not receive the application. CAT activity increased by 69.4% to 152.8% with the application of ZnO-NPs compared to plants under salt stress that did not receive the treatment. Additionally, the application of ZnO-NPs decreased H₂O₂ levels by up to 18.7% with respect to the control group. On the other hand, 45% higher Na⁺ accumulation was observed in NaCl-stressed seedlings treated with ZnO-NPs (50 mg L⁻¹). MDA levels in stressed plants treated with ZnO-NPs increased by 3% to 50%. Furthermore, the combined effect of ZnO-NP (100 mg L⁻¹) and salt resulted in a significant reduction in carotenoids, limiting their photoprotective function. The results obtained indicate the complex interaction between the application of ZnO-NPs and various physiological processes in coffee plants, including photosynthesis, antioxidant enzyme activity, and the generation of reactive oxygen species. This phenomenon requires detailed analysis to fully understand the response of coffee plants to ZnO-NPs’ application. Full article

(This article belongs to the Special Issue Regulation of Nanomaterials in Crop Growth and Physiology Under Abiotic Stress)

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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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