Agronomy

27 pages, 3862 KiB

Open AccessArticle

Research on Remote Sensing Monitoring of Key Indicators of Corn Growth Based on Double Red Edges

by Ying Yin, Chunling Chen, Zhuo Wang, Jie Chang, Sien Guo, Wanning Li, Hao Han, Yuanji Cai and Ziyi Feng

Agronomy 2025, 15(2), 447; https://doi.org/10.3390/agronomy15020447 (registering DOI) - 12 Feb 2025

Abstract

The variation in crop growth provides critical insights for yield estimation, crop health diagnosis, precision field management, and variable-rate fertilization. This study constructs key monitoring indicators (KMIs) for corn growth based on satellite remote sensing data, along with inversion models for these growth [...] Read more.

The variation in crop growth provides critical insights for yield estimation, crop health diagnosis, precision field management, and variable-rate fertilization. This study constructs key monitoring indicators (KMIs) for corn growth based on satellite remote sensing data, along with inversion models for these growth indicators. Initially, the leaf area index (LAI) and plant height were integrated into the KMI by calculating their respective weights using the entropy weight method. Eight vegetation indices derived from Sentinel-2A satellite remote sensing data were then selected: the Normalized Difference Vegetation Index (NDVI), Perpendicular Vegetation Index (PVI), Soil-Adjusted Vegetation Index (SAVI), Red-Edge Inflection Point (REIP), Inverted Red-Edge Chlorophyll Index (IRECI), Pigment Specific Simple Ratio (PSSRa), Terrestrial Chlorophyll Index (MTCI), and Modified Chlorophyll Absorption Ratio Index (MCARI). A comparative analysis was conducted to assess the correlation of these indices in estimating corn plant height and LAI. Through recursive feature elimination, the most highly correlated indices, REIP and IRECI, were selected as the optimal dual red-edge vegetation indices. A deep neural network (DNN) model was established for estimating corn plant height, achieving optimal performance with an R² of 0.978 and a root mean square error (RMSE) of 2.709. For LAI estimation, a DNN model optimized using particle swarm optimization (PSO) was developed, yielding an R² of 0.931 and an RMSE of 0.130. KMI enables farmers and agronomists to monitor crop growth more accurately and in real-time. Finally, this study calculated the KMI by integrating the inversion results for plant height and LAI, providing an effective framework for crop growth assessment using satellite remote sensing data. This successfully enables remote sensing-based growth monitoring for the 2023 experimental field in Haicheng, making the precise monitoring and management of crop growth possible. Full article

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

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24 pages, 7672 KiB

Open AccessReview

Turning Waste Wool into a Circular Resource: A Review of Eco-Innovative Applications in Agriculture

by Francesca Camilli, Marco Focacci, Aldo Dal Prà, Sara Bortolu, Francesca Ugolini, Enrico Vagnoni and Pierpaolo Duce

Agronomy 2025, 15(2), 446; https://doi.org/10.3390/agronomy15020446 - 11 Feb 2025

Abstract

Agriculture significantly impacts the environment in terms of greenhouse gas emissions, soil nutrient depletion, water consumption, and pollution and waste produced by intensive farming. Wool has great potential and can be a valuable resource for agriculture due to its high nitrogen, carbon, and [...] Read more.

Agriculture significantly impacts the environment in terms of greenhouse gas emissions, soil nutrient depletion, water consumption, and pollution and waste produced by intensive farming. Wool has great potential and can be a valuable resource for agriculture due to its high nitrogen, carbon, and sulfur content and good water absorption and retention properties, benefiting soil carbon storage and fertility, as well as decreasing the risk of water contamination due to the slow decomposition and nitrogen release. This review aims to provide an overview of bio-based solutions that can benefit agroecosystems as a circular bioeconomy practice. Raw wool and wool hydrolysate are the most common applications, but also wool pellets, wool compost, and wool mats are interesting treatments for plant growing. Waste wool showed positive effects on soil fertility by primarily increasing nitrogen and sulfur content. Improved water retention capacity and microbial activity were also recorded in several studies. The use of wool as mulching is effective for weed control. Attention to the plant species tested aimed at identifying the most promising cultivations in terms of treatment efficiency, possibly lowering environmental impact on the agroecosystem. To eco-design and scale-up processes that strengthen the circular use of wool into widespread practices, further research should be encouraged in conjunction with environmental impact assessments and economic evaluations. Full article

(This article belongs to the Special Issue Organic Improvement in Agricultural Waste and Byproducts)

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19 pages, 6474 KiB

Open AccessArticle

Improved Lightweight YOLOv8 Model for Rice Disease Detection in Multi-Scale Scenarios

by Jinfeng Wang, Siyuan Ma, Zhentao Wang, Xinhua Ma, Chunhe Yang, Guoqing Chen and Yijia Wang

Agronomy 2025, 15(2), 445; https://doi.org/10.3390/agronomy15020445 - 11 Feb 2025

Abstract

In response to the challenges of detecting rice pests and diseases at different scales and the difficulties associated with deploying and running models on embedded devices with limited computational resources, this study proposes a multi-scale rice pest and disease recognition model (RGC-YOLO). Based [...] Read more.

In response to the challenges of detecting rice pests and diseases at different scales and the difficulties associated with deploying and running models on embedded devices with limited computational resources, this study proposes a multi-scale rice pest and disease recognition model (RGC-YOLO). Based on the YOLOv8n network, which includes an SPPF layer, the model introduces a structural reparameterization module (RepGhost) to achieve implicit feature reuse through reparameterization. GhostConv layers replace some standard convolutions, reducing the model’s computational cost and improving inference speed. A Hybrid Attention Module (CBAM) is incorporated into the backbone network to enhance the model’s ability to extract important features. The RGC-YOLO model is evaluated for accuracy and inference time on a multi-scale rice pest and disease dataset, including bacterial blight, rice blast, brown spot, and rice planthopper. Experimental results show that RGC-YOLO achieves a precision (P) of 86.2%, a recall (R) of 90.8%, and a mean average precision at Intersection over Union 0.5(mAP50) of 93.2%. In terms of model size, the parameters are reduced by 33.2%, and GFLOPs decrease by 29.27% compared to the base YOLOv8n model. Finally, the RGC-YOLO model is deployed on an embedded Jetson Nano device, where the inference time per image is reduced by 21.3% compared to the base YOLOv8n model, reaching 170 milliseconds. This study develops a multi-scale rice pest and disease recognition model, which is successfully deployed on embedded field devices, achieving high-accuracy real-time monitoring and providing valuable reference for intelligent equipment in unmanned farms. Full article

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

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17 pages, 1495 KiB

Open AccessArticle

Optimized Phosphorus Application Under Water Stress Enhances Photosynthesis, Physiological Traits, and Yield in Soybean During Flowering Stage

by Qu Chen, Tangzhe Nie, Yang Li, Hao Li, Yubo Sun, Yuzhe Wu, Yuxian Zhang and Mengxue Wang

Agronomy 2025, 15(2), 444; https://doi.org/10.3390/agronomy15020444 - 11 Feb 2025

Abstract

Phosphorus application is widely regarded as a key measure for improving crop resistance to drought. This study investigated the effect of appropriate phosphorus fertilization on photosynthesis, physiological traits, and yield under water stress during the soybean flowering stage and selected the drought-sensitive soybean [...] Read more.

Phosphorus application is widely regarded as a key measure for improving crop resistance to drought. This study investigated the effect of appropriate phosphorus fertilization on photosynthesis, physiological traits, and yield under water stress during the soybean flowering stage and selected the drought-sensitive soybean variety “Sui Nong 26” as the pot experiment object under a completely randomized design. The experiment was designed with three irrigation lower limits, corresponding to 70%, 60%, and 50% of the field capacity (FC), referred to as T1, T2, and T3. Four phosphorus fertilizer applications were also included: 0, 40, 50, and 60 mg·kg (designated as P0, P1, P2, and P3), resulting in a total of 12 treatments. Photosynthetic parameters, antioxidant enzyme activities, membrane lipid peroxidation, osmotic adjustment substances, yield, and yield components were measured to assess the effects of phosphorus fertilization on drought resistance. Results showed that under water stress, moderate phosphorus application (P1 and P2) enhanced photosynthetic capacity, antioxidation, osmotic adjustment, and yield, particularly by scavenging excess reactive oxygen species, protecting cells from oxidative damage, and maintaining metabolic balance, leading to increased yield. The average net photosynthetic rate and yield per plant under P1 and P2 levels increased by 33.53% and 37.67%, and 20.7% and 15.6%, respectively, compared to P0. In contrast, excessive phosphorus application (P3) improved the above parameters but had a significantly lower effect than moderate phosphorus application. Thus, appropriate phosphorus application is crucial for soybeans under water stress. Moderate application not only alleviates drought stress but also boosts soybean yield. This study highlights the importance of appropriate phosphorus use for mitigating water stress, offering scientific evidence for its practical application in agriculture. At the same time, with the increasing severity of climate change and water scarcity, phosphorus fertilizer application strategies under varying water conditions provide critical support for the application of precision agriculture technologies and ensuring food security. Full article

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18 pages, 5797 KiB

Open AccessArticle

Prediction and Impact Analysis of Soil Nitrogen and Salinity Under Reclaimed Water Irrigation: A Case Study

by Zeyu Liu, Kai Fang, Xiaoqin Sun, Yandong Wang, Zhuo Tian, Jing Liu, Liying Bai and Qilin He

Agronomy 2025, 15(2), 443; https://doi.org/10.3390/agronomy15020443 - 11 Feb 2025

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Abstract

Reclaimed water irrigation is increasingly being applied to address global water scarcity, yet its long-term effects on soil nitrogen cycling and salinity dynamics, particularly in agricultural and agroforestry systems, remain complex and insufficiently understood. Understanding these impacts is crucial for developing sustainable practices [...] Read more.

Reclaimed water irrigation is increasingly being applied to address global water scarcity, yet its long-term effects on soil nitrogen cycling and salinity dynamics, particularly in agricultural and agroforestry systems, remain complex and insufficiently understood. Understanding these impacts is crucial for developing sustainable practices that optimize resource use while ensuring the long-term health and viability of agricultural and agroforestry systems. This study employs genetic-algorithm-optimized random forest models (GA-RF1 and GA-RF2) to examine the dynamics of nitrogen indicators (NO₃⁻-N, NH₄⁺-N, and TN) and salinity indicators (EC and Cl⁻) under reclaimed water irrigation. The models achieved high predictive accuracy, with NSE values of 0.918, 0.946, 0.936, 0.967, and 0.887 for NO₃⁻-N, NH₄⁺-N, TN, EC, and Cl⁻, respectively, demonstrating their robustness. Key drivers of nitrogen indicators were identified as irrigation duration (years), fecal coliform levels, and soil depth, while salinity indicators were primarily influenced by land use type and the chemical composition of reclaimed water, including chemical oxygen demand, total phosphorus, and total nitrogen. Spatial analysis revealed significant nitrogen and salinity accumulation in surface soils with extended irrigation, particularly in farmland, where NO₃⁻-N and NH₄⁺-N peaked at 25 mg/kg and 15 mg/kg, respectively. EC exceeded 700 µS/cm during early irrigation stages but remained within crop tolerance levels. Conversely, grassland and woodland exhibited minimal nitrogen and salinity accumulation. These findings underscore the need for targeted management strategies to mitigate nitrogen and salinity buildup, particularly in farmland, to ensure long-term soil health and productivity under reclaimed water irrigation systems. Full article

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

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18 pages, 1804 KiB

Open AccessArticle

Effects of Biostimulant Foliar Applications on Morphological Traits, Yield, Antioxidant Capacity, and Essential Oil Composition of Thymus vulgaris L. Under Field Conditions

by Loriana Cardone, Flavio Polito, Michele Denora, Donato Casiello, Donato Castronuovo, Nunzia Cicco, Michele Perniola, Vincenzo De Feo and Vincenzo Candido

Agronomy 2025, 15(2), 442; https://doi.org/10.3390/agronomy15020442 - 11 Feb 2025

Viewed by 121

Abstract

Plant biostimulants are used to promote plant growth by increasing tolerance to abiotic stressors and improving the efficiency of natural resource use. In the present two-year research (2022–2023 and 2023–2024), the effects of biostimulant foliar applications on the morphological parameters, fresh and dry [...] Read more.

Plant biostimulants are used to promote plant growth by increasing tolerance to abiotic stressors and improving the efficiency of natural resource use. In the present two-year research (2022–2023 and 2023–2024), the effects of biostimulant foliar applications on the morphological parameters, fresh and dry yields, antioxidant capacity, total phenolic content, and chemical composition of the essential oil of thyme (Thymus vulgaris L.) were studied. For this purpose, four commercial biostimulants, Biostimol Plus + Peptamin-V Plus^®, Acadian MPE^®, Megafol^®, and BlueN^®, were evaluated on thyme cultivated in field conditions. The experiment was laid out in a randomized block design with five treatments and with three replications. During the second growing season, the plants treated with BlueN^®, composed of the bacteria Methylobacterium symbioticum SB23, showed the highest plant weight (152.1 g plant⁻¹), fresh biomass yield (501.9 g m⁻²), and dry yield (172.2 g m⁻²). BlueN^® was the biostimulant that also obtained the highest essential oil yield in both years (0.47 and 0.53%), and for all biostimulants, the amount of thymol and carvacrol increased in the second year, especially with Megafol^® (63.75 and 3.16%). The antioxidant capacity was enhanced in the second year by all biostimulants, according to the ABTS assay, but in particular, by BlueN^® and BPPVP (26.97 μmol/g and 25.01 μmol/g), while the phenolic content was higher in the first year, especially with BlueN^® (65.98 mg GAE/g Extract). The other biostimulants had less intense effects. In conclusion, the biostimulants influenced some characteristics of the essential oil, but the greatest influencers were BlueN^®, Megafol^®, and BPPVP. Full article

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

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17 pages, 6994 KiB

Open AccessArticle

Integrative Transcriptomic and Metabolomic Analysis Reveals Regulatory Networks and Metabolite Dynamics in Gastrodia elata Flower Development

by Hongyu Chen, Ying Yu, Jiehong Zhao and Jian Zhang

Agronomy 2025, 15(2), 441; https://doi.org/10.3390/agronomy15020441 - 11 Feb 2025

Viewed by 104

Abstract

Flower development, a vital phase in the plant life cycle, involves intricate physiological and morphogenetic processes driven by dynamic molecular and metabolic processes. However, the specific molecular mechanisms and metabolite accumulation patterns during Gastrodia elata flower development remain largely unknown. This study utilized [...] Read more.

Flower development, a vital phase in the plant life cycle, involves intricate physiological and morphogenetic processes driven by dynamic molecular and metabolic processes. However, the specific molecular mechanisms and metabolite accumulation patterns during Gastrodia elata flower development remain largely unknown. This study utilized Illumina’s next-generation sequencing to analyze the G. elata flower transcriptome across three critical developmental stages, capturing gene expression changes, particularly those related to transcription factors that regulate flower formation and metabolite accumulation. FPKM analysis showed significant transcriptomic changes during G. elata flower development, while targeted metabolomics identified key metabolites with stage-specific variations via widely targeted metabolic profiling. Here, integrative transcriptome and metabolome analyses were performed to investigate floral genes and compounds in G. elata flowers at three different developmental stages. The differentially expressed genes (DEGs) and significant changes in metabolites (SCMs) involved in key biological pathways were identified. This approach aimed to identify functional genes or pathways jointly enriched in metabolites, thereby defining pathways linked to crucial biological phenotypes. By mapping DEGs and SCMs to KEGG pathways, the comprehensive network was constructed, uncovering functional relationships between gene expression and metabolite accumulation. This study proposes dynamic models of transcriptomic and metabolite changes, revealing key regulatory networks that govern G. elata flower development and potential applications. Full article

(This article belongs to the Special Issue Progress in Horticultural Crops—from Genotype to Phenotype—2nd Edition)

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Flower and petal phenotypes at three developmental stages: Stage 1 (TMH1) (A), Stage 2 (TMH2) (B), and Stage 3 (TMH3) (C). Full article ">Figure 2
The DEG analysis in G. elata. (A) PCA of 9 samples. (B) Correlation analysis of 9 samples. The scale bar represents the size of the correlation. (C) The box plot of expressed genes in 9 samples. (D) Hierarchical clustering of transcripts in G. elata. (E) Analysis of GO terms for DEGs in G. elata. (F) Analysis of KEGG enrichment for DEGs in G. elata. Full article ">Figure 3
Metabolic pathways analysis in G. elata. (A) PCA of 9 samples. (B) Heatmap and cluster analysis of metabolite profiles, illustrating variations at the metabolome level. (C) The scatter of SCMs in G. elata of Stage 2 vs. Stage 1. (D) The scatter of SCMs in G. elata of Stage 3 vs. Stage 2. (E) KEGG enrichment of SCMs in G. elata of Stage 2 vs. Stage 1. (F) KEGG enrichment of SCMs in G. elata of Stage 3 vs. Stage 2. Full article ">Figure 4
Integrative transcriptome and metabolome analysis in G. elata. (A) KEGG pathway enrichment analysis comparing Stage 2 vs. Stage 1. (B) KEGG pathway enrichment analysis comparing Stage 3 vs. Stage 2. (C) Heatmap and cluster analysis of Stage 2 vs. Stage 1. (D) Heatmap and cluster analysis of Stage 3 vs. Stage 2. Full article ">Figure 5
Integrative analysis of transcriptome and metabolome data for the flavonoid biosynthesis pathway (ko00941) in G. elata. (A) Mapping of enriched DEGs between Stage 2 and Stage 1, with red and blue boxes representing upregulated and downregulated genes, respectively, and red and blue dots indicating metabolites with increased and decreased accumulation, respectively. (B) Mapping of enriched DEGs between Stage 3 and Stage 2. (C) Expression patterns of DEGs involved in flavonoid biosynthesis across the three flower developmental stages. Full article ">Figure 6
Integrative analysis of transcriptome and metabolome data for the plant hormone signal transduction pathway (ko04075) in G. elata. (A) Mapping of enriched DEGs between Stage 3 and Stage 2. Red and blue boxes represent genes with upregulated and downregulated expression, respectively, and red and blue dots represent metabolites with increased and decreased accumulation, respectively. (B) Expression pattern of DEGs involved in plant hormone signal transduction across the three flower developmental stages. Full article ">

20 pages, 1829 KiB

Open AccessArticle

Selenium Biofortification with Se-Enriched Urea and Se-Enriched Ammonium Sulfate Fertilization in Different Common Bean Genotypes

by Filipe Aiura Namorato, Patriciani Estela Cipriano, Stefânia Barros Zauza, Pedro Antônio Namorato Benevenute, Suellen Nunes de Araújo, Raphael Felipe Rodrigues Correia, Ivan Célio Andrade Ribeiro, Everton Geraldo de Morais, Fábio Aurélio Dias Martins, Maria Ligia de Souza Silva and Luiz Roberto Guimarães Guilherme

Agronomy 2025, 15(2), 440; https://doi.org/10.3390/agronomy15020440 - 11 Feb 2025

Viewed by 132

Abstract

Common beans are an essential food source worldwide, particularly in developing countries, and are grown in soils poor in selenium (Se), a mineral essential for human health. Adding Se to fertilizers is a promising technique; however, more studies are needed on the efficacy [...] Read more.

Common beans are an essential food source worldwide, particularly in developing countries, and are grown in soils poor in selenium (Se), a mineral essential for human health. Adding Se to fertilizers is a promising technique; however, more studies are needed on the efficacy of this technique on common beans. This study aimed to evaluate the biofortification utilizing Se-enriched nitrogen fertilizers on common bean seeds’ agronomic, physiological, and nutritional characteristics. The pot experiment used a randomized block design with five treatments (urea, Se-enriched urea, ammonium sulfate, Se-enriched ammonium sulfate, and without N and Se), four genotypes (BRS Cometa, BRS Estilo, BRSMG Madrepérola and Pérola), and three replicates. The highest seed yield was 28.31 g pot⁻¹ with Pérola genotype fertilized Se-enriched ammonium sulfate. Photosynthetic rates ranged from 30.37 to 39.06 µmol m⁻² s⁻¹ for Pérola and BRSMG Madrepérola, both with Se-enriched ammonium sulfate. The highest seed Se concentration was 11.17 µg g⁻¹, with BRSMG Madrepérola fertilized with Se-enriched urea being 22.02%, 17.64%, and 22.47% higher than BRS Cometa, BRS Estilo, and Pérola, respectively. Se-enriched nitrogen fertilizers boost seed yield and alter physiological responses based on genotypes and Se-fertilizer interactions. Se-enriched fertilizers applied to soil can increase the Se concentration in common beans. Full article

(This article belongs to the Special Issue Agronomic Biofortification Practices on Crops)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
Seeds of the carioca genotypes were used: BRS Cometa (A), BRS Estilo (B), BRSMG Madrepérola (C), and Pérola (D). Full article ">Figure 2
SPAD index (A), ΔF/Fm—maximum quantum efficiency of the photochemical activity of photosystem II (B), A—net assimilation rate, μmol CO2 m−2 s−1 (C); gsw—stomatal conductance to water vapor, mol H2O m−2 s−1 (D); E—transpiration, mol H2O m−2 s−1 (E) and WUE—instantaneous water use efficiency, (μmol CO2/mmol H2O) × 100 (F). The lowercase letter group compares the fertilizer sources in each genotype, and the uppercase letter group compares the genotypes in each fertilizer source by the Scott–Knott test (p < 0.05). Where: U = urea, U + Se = Se-enriched urea, AS = ammonium sulfate, and AS + Se = Se-enriched ammonium sulfate. Full article ">Figure 3
Common bean genotypes seed yield (A), selenium (B), and sulfur (C) concentration in seeds of common bean genotypes subjected to sources of top-dressing fertilization. The lowercase letter group compares the fertilizer sources in each genotype, and the uppercase letter group compares the genotypes in each fertilizer source by the Scott–Knott test (p < 0.05). Where: U = urea, U + Se = Se-enriched urea, AS = ammonium sulfate, and AS + Se = Se-enriched ammonium sulfate. Full article ">Figure 4
Analysis of centesimal seed composition of common bean genotypes subjected to top-dressing fertilization sources. Crude protein (A); TC (total carbohydrates) (B); ash (C); and lipids (D). The lowercase letter group compares the fertilizer sources in each genotype, and the uppercase letter group compares the genotypes in each fertilizer source by the Scott–Knott test (p < 0.05). Where: U = urea, U + Se = Se-enriched urea, AS = ammonium sulfate, and AS + Se = Se-enriched ammonium sulfate. Full article ">

12 pages, 228 KiB

Open AccessArticle

The Effects of Organic Fertilizer Applications on the Nutrient Elements Content of Eggplant Seeds

by Sevinç Başay, Saliha Dorak and Barış Bülent Aşik

Agronomy 2025, 15(2), 439; https://doi.org/10.3390/agronomy15020439 - 11 Feb 2025

Viewed by 112

Abstract

This research was carried out to investigate the effectiveness of using organic fertilizers in improving the organic seed production process and increasing the seed quality needed in organic agriculture production. The experiment was established with organic fertilizers (farmyard manure—FYM, leonardite—L, vermicompost—VC) and the [...] Read more.

This research was carried out to investigate the effectiveness of using organic fertilizers in improving the organic seed production process and increasing the seed quality needed in organic agriculture production. The experiment was established with organic fertilizers (farmyard manure—FYM, leonardite—L, vermicompost—VC) and the eggplant plant ’Pala-49’ variety and conducted for two years. As a result of the study, vegetative growth height varied between 52.65 and 68.06 cm, plant diameter width ranged from 51.85 to 61.20 cm, fruit height ranged from 14.67 to 21.90 cm, and fruit diameter varied between 4.73 and 6.73 cm. These differences were observed among farmyard manure (FYM), leonardite (L), and vermicompost (VC) organic fertilizer applications. In general, it was determined that the first year gave better results. In terms of parameters, the best result in all parameters was obtained from farmyard manure (FYM) organic fertilizer application. In addition, the nutrient element contents of the seed samples were found to be statistically significant. Organic applications significantly increased the nutrient element content of the seed samples according to the control. The nitrogen content varied between 0.242% and 0.271%, and the phosphorus content ranged between 0.274% and 0.456%. The highest K content was determined in farmyard manure (FYM) application in both years (0.272% and 0.309%). In contrast, Fe, Zn, and Mn contents were 35.1 mg kg⁻¹, 63.7 mg kg⁻¹, and 200.7 mg kg⁻¹ in vermicompost (VC) application in the second year, respectively. The effect of the treatments on soil available nutrient content was also found to be significant. The amount of soil available for plant nutrients was higher in the second year. Full article

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

16 pages, 1673 KiB

Open AccessArticle

The Effects of Dried Apple Pomace on Fermentation Quality and Proteolysis of Alfalfa Silages

by Tongtong Dai, Jiangyu Long, Guanjun Zhang, Xianjun Yuan and Zhihao Dong

Agronomy 2025, 15(2), 438; https://doi.org/10.3390/agronomy15020438 - 11 Feb 2025

Viewed by 111

Abstract

This work aimed to evaluate the effects of dried apple pomace (DAP) on the fermentation characteristics and proteolysis of alfalfa silages. The alfalfa was ensiled with (1) no additives (control), (2) 5% DAP, (3) 10% DAP and (4) 15% DAP based on fresh [...] Read more.

This work aimed to evaluate the effects of dried apple pomace (DAP) on the fermentation characteristics and proteolysis of alfalfa silages. The alfalfa was ensiled with (1) no additives (control), (2) 5% DAP, (3) 10% DAP and (4) 15% DAP based on fresh weight (FW) for 1, 3, 7, 14, 30 and 60 days, respectively. With the increasing proportion of DAP, lactic acid bacteria (LAB) count, lactic acid (LA) and dry matter (DM) content linearly (p < 0.05) increased, while the pH, the content of acetic acid (AA), propionic acid (PA), butyric acid (BA) and ammonia nitrogen (NH₃-N) linearly (p < 0.05) decreased during ensiling. The 10% and 15% DAP silages had significantly (p < 0.05) lower aerobic bacteria (AB), yeast and enterobacteria counts than the control during ensiling. The contents of nonprotein nitrogen (NPN), peptide nitrogen (peptide-N) and free amino acid nitrogen (FAA-N) and activities of carboxypeptidase, aminopeptidase and acid proteinase linearly (p < 0.05) decreased as DAP proportion increased during ensiling. On day 60, the addition of DAP significantly (p < 0.05) decreased the contents of tryptamine, phenylethylamine, putrescine, cadaverine, histamine, tyramine, spermidine, spermine and total biogenic amines compared with the control. As the DAP ratio increased, the contents of threonine, valine, isoleucine, leucine, phenylalanine, lysine, histidine, arginine, aspartic acid, serine, glutamic, total amino acids, crude protein (CP) and water-soluble carbohydrates (WSCs) linearly (p < 0.05) increased, while the contents of glycine, alanine, cysteine, and proline linearly (p < 0.05) decreased on day 60. Overall, the addition of 15% DAP was optimal as indicated by better fermentation quality and less proteolysis than other treatments. Full article

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

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14 pages, 5789 KiB

Open AccessArticle

Simulating Pasture Yield Under Alternative Environments and Grazing Management in Wisconsin, USA

by Elissa Chasen, Eric Booth and Claudio Gratton

Agronomy 2025, 15(2), 437; https://doi.org/10.3390/agronomy15020437 - 11 Feb 2025

Viewed by 164

Abstract

Pasture yield is crucial to the economic viability of grass-based livestock enterprises, yet the difficulty in predicting yields under various environmental and management conditions prevents effective planning. We used USDA-SSURGO data to create a random forest model that predicts pasture yield potential based [...] Read more.

Pasture yield is crucial to the economic viability of grass-based livestock enterprises, yet the difficulty in predicting yields under various environmental and management conditions prevents effective planning. We used USDA-SSURGO data to create a random forest model that predicts pasture yield potential based on soil properties for the state of Wisconsin (USA). This model is highly accurate (RMSE = 0.11 tons/acre, or 4% of the average yield), predicting pasture yields in Wisconsin grasslands to range from 1.0 to5.3 tons/acre, with an average yield of 2.6 tons/acre. We then integrated this model with guidelines from a USDA-NRCS grazing planning tool to adjust pasture yield potential for different levels of grazing intensity. The adjustments were multiplied to the random forest model output and ranged from 0.65 for continuously grazed pasture to 1.2 for pastures rotated more than once per day. The model is available to use within an online decision support tool through an R-shiny interface and can be easily replicated for other states in the Midwest US. The tool is easy to use and can support farmer analysis of the costs and benefits of grass-based agriculture. Full article

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

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Number of grass yield observations from SSURGO database used in model fitting in Wisconsin counties. Full article ">Figure 2
Average grassland yield (tons/ac) by county in the SSURGO database. Full article ">Figure 3
Final model fit of random forest model using soil SSURGO data to predict pasture yield. (a) Predicted yield from final fitted model (Y-axis) compared to observed SSURGO representative yield (X-axis). Red line represents a 1:1 relationship between the two. RMSE = 0.11, R2 = 0.99. (b) Map of root mean square error (RMSE) of grass yield model across Wisconsin showing the range of model fits by county. Full article ">Figure 4
Permutation-based variable importance plot. Values are the change in RMSE after 1000 permutations in which the model is run with a resampled value from the empirical distribution of each variable. Larger values reflect more important variables. Full article ">Figure 5
Partial dependance plots of the 4 most important variables in the random forest model: (a) grass species, (b) soil-associated variation in saturated hydraulic conductivity (ksat), (c) available water capacity (AWC), and (d) percent slope. Gray lines represent individual yield predictions for a soil in which all parameters are held constant except the parameter on the x-axis. Black dotted line represents the average predicted yield across the range of the variable. Full article ">Figure 6
Demonstration of yield potential of a single soil across grazing management and grass species, as displayed in the R-shiny web application figure. Full article ">Figure 7
Average yield for grass species from University of Wisconsin Extension variety trials, 1983–2017 (circles), and soil survey yield averages (triangles), along with resulting yield groupings (blue = low-yielding, green = medium-yielding, and red = high-yielding species). Species abbreviations: IR = Italian ryegrass; KB = Kentucky bluegrass; MF = meadow fescue; QG = quackgrass; SB = smooth bromegrass; PR = perennial ryegrass; TM = timothy grass; RC = reed canarygrass; MB = meadow bromegrass; FE = festulolium; OG = orchardgrass; TF = tall fescue. Full article ">

13 pages, 4064 KiB

Open AccessArticle

High-Throughput Sequence Analysis of Microbial Communities of Soybean in Northeast China

by Yuanyuan Wang, Qingyao Bai, Fanqi Meng, Wei Dong, Haiyan Fan, Xiaofeng Zhu, Yuxi Duan and Lijie Chen

Agronomy 2025, 15(2), 436; https://doi.org/10.3390/agronomy15020436 - 10 Feb 2025

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Abstract

Soybean, an essential oil crop in China, has witnessed accelerated seed transfer domestically and abroad in recent years. Seed carriage has emerged as a major route for the dissemination of soybean diseases. In this study, 14 soybean cultivars from three northeastern provinces were [...] Read more.

Soybean, an essential oil crop in China, has witnessed accelerated seed transfer domestically and abroad in recent years. Seed carriage has emerged as a major route for the dissemination of soybean diseases. In this study, 14 soybean cultivars from three northeastern provinces were collected and examined for seed-borne microorganisms using traditional detection technology and high-throughput sequencing technology. Through traditional detection techniques, a total of six genera of bacteria and seventeen genera of fungi were isolated from the test varieties. The quantity and types of microorganisms on the seed surface were greater than those on the seed coat and within the seed, while the seed coat and internal seed contained fewer microorganisms. The dominant fungal genera were Cladosporium, Fusarium, Aspergillus, and Alternaria, accounting for 21.23%, 17.45%, 15.57%, and 11.56% of the genera, respectively. The dominant bacterial genera were Pseudomonas, Sphingomonas, and Pantoea, accounting for 37.46%, 17.29%, and 15.27% of the genera, respectively. The dominant genera obtained through traditional seed-carrying assay techniques were also dominant in high-throughput sequencing. However, some dominant genera obtained through high-throughput sequencing were not isolated by traditional methods. High-throughput sequencing analysis revealed that soybean seeds from Jilin Province had the highest abundance of seed-borne fungi, followed by seeds from Liaoning Province and Heilongjiang Province. Jilin Province also had the highest abundance of seed-borne bacteria, followed by Heilongjiang Province and Liaoning Province. The isolation and identification of microorganisms on soybean seeds provide a scientific basis for seed quarantine treatment and disease control, which is of great significance for soybean production in China. Full article

(This article belongs to the Special Issue Recent Advances in Legume Crop Protection)

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13 pages, 1382 KiB

Open AccessArticle

Evaluating the Level of Total Mercury Present in the Soils of a Renowned Tea Production Region

by Jinghua Xu, Ruijia Xie, Liping Liu and Zhiqun Huang

Agronomy 2025, 15(2), 435; https://doi.org/10.3390/agronomy15020435 - 10 Feb 2025

Viewed by 292

Abstract

Total mercury pollution in oolong tea garden soils was comprehensively investigated in this study. Soil samples were collected from 146 villages in a famous oolong tea production area. The total mercury content in the soils ranged from 0.025 to 0.296 mg/kg, with a [...] Read more.

Total mercury pollution in oolong tea garden soils was comprehensively investigated in this study. Soil samples were collected from 146 villages in a famous oolong tea production area. The total mercury content in the soils ranged from 0.025 to 0.296 mg/kg, with a median of 0.105 mg/kg. According to the Soil Accumulation Index Method, 67.81% of samples were pollution-free, 31.51% had pollution levels from none to moderate, and 0.68% were moderately polluted. The PMF model revealed that natural geochemical processes were the main mercury source, contributing 72.4%, with some from transportation, coal combustion, and industrial activities. Most values were below the HQ threshold, suggesting low non-carcinogenic risk from mercury in most soils. Further research is needed to understand mercury’s bioaccumulation in tea leaves and assess short- and long-term exposure risks for a better understanding of its long-term impacts on the tea industry and human health. Full article

(This article belongs to the Special Issue Heavy Metal Pollution and Prevention in Agricultural Soils)

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15 pages, 9987 KiB

Open AccessArticle

Characterizing Optimum N Rate in Waterlogged Maize (Zea mays L.) with Unmanned Aerial Vehicle (UAV) Remote Sensing

by Bhawana Acharya, Syam Dodla, Brenda Tubana, Thanos Gentimis, Fagner Rontani, Rejina Adhikari, Dulis Duron, Giulia Bortolon and Tri Setiyono

Agronomy 2025, 15(2), 434; https://doi.org/10.3390/agronomy15020434 - 10 Feb 2025

Viewed by 267

Abstract

High soil moisture due to frequent excessive precipitation can lead to reductions in maize grain yields and increased nitrogen (N) loss. The traditional methods of computing N status in crops are destructive and time-consuming, especially in waterlogged fields. Therefore, in this study, we [...] Read more.

High soil moisture due to frequent excessive precipitation can lead to reductions in maize grain yields and increased nitrogen (N) loss. The traditional methods of computing N status in crops are destructive and time-consuming, especially in waterlogged fields. Therefore, in this study, we used unmanned aerial vehicle (UAV) remote sensing to evaluate the status of maize under different N rates and excessive soil moisture conditions. The experiment was performed using a split plot design with four replications, with soil moisture conditions as main plots and different N rates as sub-plots. The artificial intelligence SciPy (version 1.5.2) optimization algorithm and spherical function were used to estimate the economically optimum N rate under the different treatments. The computed EONR for CRS 2022 was 157 kg N ha⁻¹ for both treatments, with the maximum net return to N of USD 1203 ha⁻¹. In 2023, the analysis suggested a lower maximum attainable yield in excessive water conditions, with EONR pushed up to 197 kg N ha⁻¹ as compared to 185 kg N ha⁻¹ in the control treatment, resulting in a lower maximum net return to N of USD 884 ha⁻¹ as compared to USD 1019 ha⁻¹ in the control treatment. This study reveals a slight reduction of the fraction of NDRE at EONR to maximum NDRE under excessive water conditions, highlighting the need for addressing such abiotic stress circumstances when arriving at an N rate recommendation based on an N-rich strip concept. This study confirms the importance of sensing technology for N monitoring in maize, particularly in supporting decision making in nutrient management under adverse weather conditions. Full article

(This article belongs to the Special Issue Revolutionizing Crop Management: Integrating UAV Technology for Precision Agriculture)

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Figure 1
Maize flooding and N rate study sites. Fields H and C were used for the study trials at LSU AgCenter Red River Research Station (RRS) in 2022 and 2023, respectively. Fields 31 and 33 were used for the study trials at LSU AgCenter Central Research Station (CRS) in 2022 and 2023, respectively. Designation letters and numbers for the fields of interest and the surrounding ones are shown in the station maps. Full article ">Figure 2
Water input (mm) and mean daily temperature (°C) during the maize growing season at the study sites. CRS: LSU AgCenter Central Research Station. RRS: LSU AgCenter Red River Station. Full article ">Figure 3
Yield response to N in this study and N response curve as described with spherical function for the CRS site and the corresponding EONR and net return to N, comparing the control (W1) and excessive water conditions (W2). CRS: LSU AgCenter Central Research Station. RRS: LSU AgCenter Red River Research Station. EONR: Economically Optimum N Rate. Full article ">Figure 4
Maps of normalized difference red edge (NDRE) and (a) normalized difference vegetation index (NDVI) (b) sensed on 23 June 2022 for field H LSU AgCenter Red River Research Station (RRS) in Bossier City, LA, USA. The corresponding NDRE and NDVI maps for the sensing date of 12 June 2023 for field C in this site are shown in panels (c) and (d), respectively. The associated water and N rate treatments for the given plot designation in these maps are listed in <a href="#agronomy-15-00434-t004" class="html-table">Table 4</a>. Full article ">Figure 5
Maps of normalized difference red edge (NDRE) and (a) normalized difference vegetation index (NDVI) (b) sensed on 9 June 2022 for field 31 LSU Central Research Station (CRS), Baton Rouge, LA, USA. The corresponding NDRE and NDVI maps for the sensing data of 8 July 2023 for field 33 in this site are shown in panels (c) and (d), respectively. The associated water and N rate treatments for the given plot designation in these maps are listed in <a href="#agronomy-15-00434-t004" class="html-table">Table 4</a>. Full article ">Figure 6
Relationship of vegetation indices (NDRE and NDVI) collected in mid-May (CRS 2022 and 2023, and RRS 2022), early June (CRS 2022 and 2023, and RRS 2022 and 2023), and late June (RRS 2022 and 2023, and CRS 2023) with maize yield. CRS: LSU AgCenter Central Research Station. NDRE: normalized difference red edge. NDVI: normalized difference vegetation index. Blue lines represent fitted regression lines. Grey areas surrounding the blue lines indicate 95% confidence interval for the regression. Full article ">Figure 7
Quadratic (fitted) relationship of early-June NDRE with N rate in this study. For the CRS site, the graphs include the estimated NDRE values at the computed EONR, comparing the control and excessive water conditions. Additional data points between N rates of 70 to 130 were based on reconstructed data using the yield VI relationship in <a href="#agronomy-15-00434-f006" class="html-fig">Figure 6</a> and yield response to N rate in <a href="#agronomy-15-00434-f003" class="html-fig">Figure 3</a>. Full article ">

17 pages, 2143 KiB

Open AccessReview

Contamination of Phthalic Acid Esters in China’s Agricultural Soils: Sources, Risk, and Control Strategies

by Jin Han, Zhenying Jiang, Pengfei Li, Jian Wang and Xian Zhou

Agronomy 2025, 15(2), 433; https://doi.org/10.3390/agronomy15020433 - 10 Feb 2025

Viewed by 253

Abstract

Phthalic acid esters (PAEs), as an emergent pollutant in China’s agricultural environment, have raised significant environmental and health concerns due to their persistence, bioaccumulation, and potential risks. This review explores the sources, distribution, ecological impacts, and human health risks associated with PAEs in [...] Read more.

Phthalic acid esters (PAEs), as an emergent pollutant in China’s agricultural environment, have raised significant environmental and health concerns due to their persistence, bioaccumulation, and potential risks. This review explores the sources, distribution, ecological impacts, and human health risks associated with PAEs in agricultural soils and crop systems across China. PAEs primarily originate from agricultural plastic materials, wastewater irrigation, and agrochemical additives, leading to widespread contamination. Concentrations of PAEs vary significantly by region, with hotspots identified in areas with intensive agriculture and industrial activities. The transfer of PAEs from soil to crops is a critical pathway for human exposure, particularly through vegetables and grains, posing carcinogenic and non-carcinogenic risks. The review highlights the fate and transformation processes of PAEs, including adsorption, migration, volatilization, and microbial degradation, which influence their environmental behavior and risks. Effective risk control measures, such as microbial remediation and advancements in biodegradation technologies, offer sustainable solutions to mitigate PAE contamination. This study emphasizes the critical need for comprehensive monitoring systems, stringent regulatory frameworks, and the implementation of sustainable agricultural practices to effectively reduce PAE concentrations in soils, thereby safeguarding soil health, ensuring food safety, and protecting human health. Full article

(This article belongs to the Special Issue Risk and Management of Organic Contaminants in the Agricultural Environment)

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26 pages, 12201 KiB

Open AccessArticle

MPG-YOLO: Enoki Mushroom Precision Grasping with Segmentation and Pulse Mapping

by Limin Xie, Jun Jing, Haoyu Wu, Qinguan Kang, Yiwei Zhao and Dapeng Ye

Agronomy 2025, 15(2), 432; https://doi.org/10.3390/agronomy15020432 - 10 Feb 2025

Viewed by 244

Abstract

The flatness of the cut surface in enoki mushrooms (Flammulina filiformis Z.W. Ge, X.B. Liu & Zhu L. Yang) is a key factor in quality classification. However, conventional automatic cutting equipment struggles with deformation issues due to its inability to adjust the [...] Read more.

The flatness of the cut surface in enoki mushrooms (Flammulina filiformis Z.W. Ge, X.B. Liu & Zhu L. Yang) is a key factor in quality classification. However, conventional automatic cutting equipment struggles with deformation issues due to its inability to adjust the grasping force based on individual mushroom sizes. To address this, we propose an improved method that integrates visual feedback to dynamically adjust the execution end, enhancing cut precision. Our approach enhances YOLOv8n-seg with Star Net, SPPECAN (a reconstructed SPPF with efficient channel attention), and C2fDStar (C2f with Star Net and deformable convolution) to improve feature extraction while reducing computational complexity and feature loss. Additionally, we introduce a mask ownership judgment and merging optimization algorithm to correct positional offsets, internal disconnections, and boundary instabilities in grasping area predictions. Based on this, we optimize grasping parameters using an improved centroid-based region width measurement and establish a region width-to-PWM mapping model for the precise conversion from visual data to gripper control. Experiments in real-situation settings demonstrate the effectiveness of our method, achieving a mean average precision (mAP50:95) of 0.743 for grasping area segmentation, a 4.5% improvement over YOLOv8, with an average detection speed of 10.3 ms and a target width measurement error of only 0.14%. The proposed mapping relationship enables adaptive end-effector control, resulting in a 96% grasping success rate and a 98% qualified cutting surface rate. These results confirm the feasibility of our approach and provide a strong technical foundation for the intelligent automation of enoki mushroom cutting systems. Full article

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

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16 pages, 3141 KiB

Open AccessArticle

Optimizing Unmanned Aerial Vehicle Operational Parameters to Improve Pest Control Efficacy and Decrease Pesticide Dosage in Tea Gardens

by Mengtao Wu, Zhaoqun Li, Yuzhou Yang, Xiangfei Meng, Zongxiu Luo, Lei Bian, Chunli Xiu, Nanxia Fu, Zongmao Chen, Guochang Wang and Xiaoming Cai

Agronomy 2025, 15(2), 431; https://doi.org/10.3390/agronomy15020431 - 10 Feb 2025

Viewed by 285

Abstract

Labor shortages in the Chinese tea industry have accelerated the need for crop protection unmanned aerial vehicles (CP-UAVs), which can greatly improve working efficiency. However, CP-UAV operational parameters must be optimized for effective pest control. In this study, the spraying performance of two [...] Read more.

Labor shortages in the Chinese tea industry have accelerated the need for crop protection unmanned aerial vehicles (CP-UAVs), which can greatly improve working efficiency. However, CP-UAV operational parameters must be optimized for effective pest control. In this study, the spraying performance of two CP-UAVs (DJI T30 and T40) under different operational parameters were compared in tea gardens. Additionally, the utility of CP-UAVs for controlling tea leafhoppers was investigated. Droplet coverage and size increased as the spray volume increased for both T30 (from 30 L·ha⁻¹ to 90 L·ha⁻¹) and T40 (from 60 L·ha⁻¹ to 150 L·ha⁻¹). Under the same operational parameters, spray deposition at the surface and inner part of the tea canopy was 1.4- and 2.9 times higher, respectively, for T40 than for T30. For T40, droplet penetrability increased significantly following decreases in working height (from 5 to 2 m) and driving speed (from 5 to 3 m·s⁻¹). The spray performance and control effect of T40 were significantly greater under optimal operational parameters (driving speed of 3 m·s⁻¹, working height of 2.5 m, and spray volume of 120 L·ha⁻¹) than under conventional application parameters (driving speed of 5 m·s⁻¹, working height of 4.5 m, and spray volume of 45 L·ha⁻¹). Using T40 under the optimal operational parameters decreased the amount of pesticide required to control tea leafhoppers by 25%, relative to the amount required for traditional knapsack sprayers. Furthermore, pesticide residue levels were similar for T40 and the knapsack sprayer. These findings provide valuable insights into the application of CP-UAVs in tea gardens, which may be important for further developing a modern, intensive, and sustainable tea industry. Full article

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

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Deposition characteristics of T30 in different operational parameters. (A) Droplet coverage and its CV at the tea canopy surface; (B) droplet size (Dv50) and its CV at the tea canopy surface. The operational parameters of T30 in different treatments are shown in <a href="#agronomy-15-00431-t002" class="html-table">Table 2</a>. Data are presented as mean + standard error for droplet coverage and droplet size, and as mean for CV. Different letters above each column represent significant differences between treatments (Tukey’s test, p < 0.05). Full article ">Figure 2
Deposition characteristics of T40 in different operational parameters. (A) Droplet coverage and its CV at the tea canopy surface; (B) droplet coverage at 15 cm below the tea canopy surface; (C) droplet size (Dv50) and its CV at the tea canopy surface. The operational parameters of T40 in different treatments are shown in <a href="#agronomy-15-00431-t002" class="html-table">Table 2</a>. Data are presented as mean + standard error for droplet coverage and droplet size, and as mean for CV. Different letters above each column represent significant differences between treatments (Tukey’s test, p < 0.05). Full article ">Figure 3
Comparison of Allura Red deposition and its CV between T30 and T40 under the same spray volume (75 L·ha−1), driving speed (3 m·s−1) and working height (2.5 m). (A) At the tea canopy surface; (B) at 15 cm below the tea canopy surface. Data are presented as mean + or ± standard error. Different letters above each column represent significant differences between treatments (t-tests, p < 0.05). Full article ">Figure 4
Spraying quality comparison of T40 under the optimal and conventional application parameters in control effect experiment. (A,C) Droplet coverage at the tea canopy surface and its CV for tolfenpyrad and afidopyropen, respectively; (B,D) droplet coverage at 15 cm below the tea canopy surface for tolfenpyrad and afidopyropen, respectively. OP-75%, under the optimal application parameters and at 75% of the pesticide minimum dose; CP-75%, under the conventional application parameters and at 75% of the pesticide minimum dose. Data are presented as mean + standard error for droplet coverage, and as mean for CV. Different letters above each column represent significant differences between treatments (t-tests, p < 0.05). Full article ">Figure 5
Spraying quality comparison of T40 at two pesticide doses in control effect experiment. (A,C) Droplet coverage at the tea canopy surface and its CV for tolfenpyrad and afidopyropen, respectively; (B,D) droplet coverage at 15 cm below the tea canopy surface for tolfenpyrad and afidopyropen, respectively. OP-75%, under the optimal application parameters and at 75% of the pesticide minimum dose; OP-50%, under the optimal application parameters and at 50% of the pesticide minimum dose. Data are presented as mean + standard error for droplet coverage, and as mean for CV. Full article ">Figure 6
Control effect comparison of T40 under the optimal and conventional application parameters. (A) Tolfenpyrad; (B) afidopyropen. OP-75%, under the optimal application parameters and at 75% of the pesticide minimum dose; CP-75%, under the conventional application parameters and at 75% of the pesticide minimum dose. Data are presented as mean + standard error. Different letters above each column represent significant differences between treatments (t-tests, p < 0.05). Full article ">Figure 7
Control effect comparison between the knapsack sprayer and T40. (A) Tolfenpyrad; (B) afidopyropen. OP-75%, T40 under the optimal application parameters and at 75% of the pesticide minimum dose; OP-50%, T40 under the optimal application parameters and at 50% of the pesticide minimum dose; KS-100%, the knapsack sprayer at the pesticide minimum dose; KS-75%, the knapsack sprayer at 75% of the pesticide minimum dose. Data are presented as mean + standard error. Different letters above each column represent significant differences between treatments (Tukey’s test, p < 0.05). Full article ">Figure 8
Pesticide residue level comparison between the knapsack sprayer and T40. (A) Tolfenpyrad; (B) afidopyropen. OP-75%, T40 under the optimal application parameters and at 75% of the pesticide minimum dose; KS-100%, the knapsack sprayer at the pesticide minimum dose. Data are presented as mean + standard error. Full article ">

18 pages, 8822 KiB

Open AccessArticle

Microbial Selenium-Enriched Bacterial Fertilizer: Biofortification Technology to Boost Pea Sprout Quality and Selenium Content

by Yaqi Wang, Ying Li, Yu Wu, Yang Liu, Yadong Chen, Yanlong Zhang and Xiangqian Jia

Agronomy 2025, 15(2), 430; https://doi.org/10.3390/agronomy15020430 - 9 Feb 2025

Viewed by 453

Abstract

Selenium-enriched vegetables are a safe way to combat selenium deficiency in humans. Here, a new microbial selenium-enriched bacterial fertilizer (named “HJ”) was prepared and studied by dipping, and then its application strategy was optimized and compared with other commercially available selenium fertilizers. The [...] Read more.

Selenium-enriched vegetables are a safe way to combat selenium deficiency in humans. Here, a new microbial selenium-enriched bacterial fertilizer (named “HJ”) was prepared and studied by dipping, and then its application strategy was optimized and compared with other commercially available selenium fertilizers. The results showed that the application of HJ selenium fertilizer to peas by soaking (Se concentration 10 μg/mL) and foliar application (Se concentration 8 μg/mL) could effectively enhance their growth, selenium enrichment ability, stress tolerance and nutritional quality. In particular, the selenium content of peas in the HJ-treated group exhibited a significant increase of 69.86% in comparison with the control group. Moreover, HJ treated pea sprouts demonstrated enhanced antioxidant activity, as well as elevated levels of vitamin C and protein, amongst other observations. The findings of this study offer novel insights into the development of eco-friendly selenium fertilizers and provide guidance for optimal fertilizer application techniques. Full article

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

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Schematic diagram of the experimental procedure. (A) Preparation of selenium-enriched bacterial fertilizer HJ. (B) Optimization of HJ application strategies in pea sprouts production. (C) Comparison of HJ with different selenium fertilizers. In the Figure, HA refers to humic acid, COS denotes chitosan oligosaccharides, and PEG400 denotes polyethylene glycol 400. HJ is the experimental group, and P1, P2 and P3 of the positive control group represent three kinds of commercial selenium fertilizers, “KaiJin”, “ZhenXi” and “SiJiFeng”. Full article ">Figure 2
Pea seeds soaking experiment. (A) Flow chart of the experiment, (B) Graph of the imbibing solution, (C) Results of seed germination potential and germination rate, and (D) Results of fresh weight and dry weight of pea sprouts after imbibition. Different lowercase letters indicate significant differences (p < 0.05) between the two groups. Full article ">Figure 3
Optimization of HJ application strategies in pea sprouts production. (A) Shoot length, (B) Root length, (C) Edibility rate, (D) Total Se content, (E) Soluble protein content, (F) Soluble sugar content, (G) Chlorophyll a content, (H) Chlorophyll b content, (I) Total chlorophyll content, and (J) Carotenoids content. Different lowercase letters indicate significant differences (p < 0.05) between the two groups. Full article ">Figure 4
Comparison of the effects of different application methods for selenium concentration of 8 μg/mL HJ. (A) Shoot length, (B) Root length, (C) Edibility rate, (D) Total Se content, (E) Soluble protein content, (F) Soluble sugar content, (G) Chlorophyll a content, (H) Chlorophyll b content, (I) Total chlorophyll content, and (J) Carotenoids content. Different lowercase letters indicate significant differences (p < 0.05) between the two groups. Full article ">Figure 5
Effects of various selenium fertilizers on growth indexes, total Se content and physiological indexes of pea sprouts. (A) Comparison of pea sprouts in each treatment group, (B) Shoot length, (C) Root length, (D) Edibility rate, (E) Fresh weight, (F) Dry weight, (G) Water content, (H) Total Se content, (I) Soluble protein content, (J) Soluble sugar content, (K) Chlorophyll b content, (L) Chlorophyll a content, (M) Total chlorophyll content, and (N) Carotenoids content. In the Figure, HJ is the experimental group and P1, P2 and P3 of the positive control group represent three kinds of commercial selenium fertilizers, “KaiJin”, “ZhenXi” and “SiJiFeng”. Different lowercase letters indicate significant differences (p < 0.05) between the two groups. Full article ">Figure 6
Effect of various selenium fertilizers on antioxidant enzyme activities and nutritional quality of pea sprouts. (A) SOD activity, (B) POD activity, (C) CAT activity, (D) MDA content, (E) free amino acids, (F) vitamin C, and (G) nitrate nitrogen. In the Figure, HJ is the experimental group and P1, P2 and P3 of the positive control group represent three kinds of commercial selenium fertilizers, “KaiJin”, “ZhenXi” and “SiJiFeng”. Different lowercase letters indicate significant differences (p < 0.05) between the two groups. Full article ">Figure 7
Radar analysis of the effects of different selenium fertilizers on various indicators of pea sprouts. In the Figure, HJ is the experimental group, and P1, P2 and P3 of the positive control group represent three kinds of commercial selenium fertilizers, “KaiJin”, “ZhenXi” and “SiJiFeng”. Full article ">

19 pages, 3424 KiB

Open AccessArticle

Organic Manure Amendment Fortifies Soil Health by Enriching Beneficial Metabolites and Microorganisms and Suppressing Plant Pathogens

by Buqing Wei, Jingjing Bi, Xueyan Qian, Chang Peng, Miaomiao Sun, Enzhao Wang, Xingyan Liu, Xian Zeng, Huaqi Feng, Alin Song and Fenliang Fan

Agronomy 2025, 15(2), 429; https://doi.org/10.3390/agronomy15020429 - 9 Feb 2025

Viewed by 377

Abstract

Soil health reflects the sustained capacity of soil to function as a vital living ecosystem, ensuring support for all forms of life. The evaluation of soil health relies heavily on physicochemical indicators. However, it remains unclear whether and how microbial traits are related [...] Read more.

Soil health reflects the sustained capacity of soil to function as a vital living ecosystem, ensuring support for all forms of life. The evaluation of soil health relies heavily on physicochemical indicators. However, it remains unclear whether and how microbial traits are related to soil health in soil with long-term organic manure amendment. This study aims to examine how detrimental and beneficial microbial traits change with soil health based on physicochemical indicators. This research measures the effects of 9-year manure supplementation on soil health using multiomics techniques. We found that, compared to 100% chemical fertilizers, the soil health index increased by 5.2%, 19.3%, and 72.6% with 25%, 50%, and 100% organic fertilizer amendments, respectively. Correspondingly, the abundance of beneficial microorganisms, including Actinomadura, Actinoplanes, Aeromicrobium, Agromyces, Azospira, Cryobacterium, Dactylosporangium, Devosia, Hyphomicrobium, Kribbella, and Lentzea, increased progressively, while the abundance of the pathogenic fungus Fusarium decreased with the organic manure application rate. In addition, the application of organic manure significantly increased the concentrations of soil metabolites, such as sugars (raffinose, trehalose, maltose, and maltotriose) and lithocholic acid, which promoted plant growth and soil aggregation. Moreover, the abundances of pathogens and beneficial microorganisms and the concentrations of beneficial soil metabolites were significantly correlated with the soil health index based on physicochemical indicators. We conclude that organic fertilizer can enhance soil health by promoting the increase in beneficial microorganisms while suppressing detrimental microorganisms, which can serve as potential indicators for assessing soil health. In agricultural production, substituting 25–50% of chemical fertilizers with organic fertilizers significantly helps improve soil health and promotes crop growth. Full article

(This article belongs to the Special Issue A Circular Economy: Chemical, Microbiological and Environmental Implications of Mineral and Organic Fertilizers Use in Soils)

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TN (a), SOM (b), C/N (c), available P (d), available K (e), pH (f), BG (g), NAG (h), AP (i), and LAP (j) in soils with different levels of organic amendment. Enzyme stoichiometry is characterized by BG/(LAP + NAG) and (LAP + NAG)/AP (k). Fertilizer treatments include M0 (full chemical fertilizer), M25 (25% organic fertilizer amendment), M50 (50% organic amendment), and M100 (100% organic amendment). According to the one-way ANOVA and Duncan’s test, different letters indicate significant differences among treatments for the same indicator (p < 0.05). The vertical bars represent standard errors (n = 3). Full article ">Figure 2
Bacterial (a) and fungal (b) α-diversity (Shannon index) under different levels of organic amendment. Pairwise comparisons between treatments were performed using the t-test. A PCoA based on Bray–Curtis distances shows differences in soil bacterial (c) and fungal (d) compositions under long-term organic amendment, with PERMANOVA using the Adonis function permutation test. The significance levels are as follows: ns, p > 0.05; *, p ≤ 0.05; **, p ≤ 0.01; and ***, p ≤ 0.001. Full article ">Figure 3
Phylogenetic tree of bacterial (a) and fungal (b) ASVs with abundances greater than 0.1%. The inner ring color represents the phylum of each ASV. The middle ring shows the relative abundance of each ASV in each treatment. The outer ring color represents the differential analysis results after FDR (<0.05) correction: purple, orange, and green indicate no significant difference, significant upregulation, or significant downregulation in M100 compared to M0, respectively. The shades of red and blue represent the absolute value of log2FC. The abundance of plant-beneficial microorganisms (c) and plant pathogenic microorganisms (d) changes with the organic amendment gradient, as obtained by querying and matching the PBB and FungalTraits databases. According to the one-way ANOVA and Duncan’s test, different letters indicate significant differences among treatments for the same indicator (p < 0.05). Full article ">Figure 4
The PCoA performed based on Bray–Curtis distances shows differences in the soil metabolite composition under long-term organic amendment (a), with PERMANOVA carried out using the Adonis function permutation test. The results of metabolites upregulated (b) and downregulated (c) in M25, M50, and M100 relative to M0, with FDR > 0.05 as the standard. The internal upset plot shows the number of differential metabolites in M25, M50, and M100 relative to M0. The middle circle shows the number of all differential metabolites in M25, M50, and M100. The outer circle shows the total number of differential metabolites in the M25, M50, and M100 treatments. The KEGG pathway enrichment analysis results for metabolites commonly upregulated in M25, M50, and M100 compared to M0 with FDR > 0.05 (d). The enrichment factor (Rich Factor) represents the number of differential metabolites enriched in the pathway divided by the number of background metabolites enriched in the pathway. The classification of metabolites commonly upregulated (e) and downregulated (f) in M25, M50, and M100 relative to M0 based on the HMDB database. Changes in maize plant height after adding 0.05 mM of lithocholic acid, maltotriose, maltose, trehalose, and raffinose to the nutrient solution (g) and photos of maize plants 10 days after adding metabolites (h). Changes in the proportion of <0.25 mm and >0.25 mm aggregates in soil after adding 1, 5, and 25 mmol/kg of trehalose and raffinose and culturing for three weeks (i). The significance levels are as follows: ns, p > 0.05; *, p ≤ 0.05; **, p ≤ 0.01; and ***, p ≤ 0.001. Different letters indicate significant differences among treatments for the same indicator (p < 0.05). Full article ">Figure 5
Soil health index calculated from TN, SOM, pH, available P, and available K (a). According to one-way ANOVA and Duncan’s test, different letters indicate significant differences among treatments for same indicator (p < 0.05). Vertical bars represent standard errors (n = 3). Variation partitioning and hierarchical partitioning assess individual effects of soil chemical and enzymatic indicators on microbiota and metabolite variation, explaining separate explanatory power of these indicators (b). Correlation analysis of soil health index, chemical properties, enzymatic activity, beneficial microbes, plant pathogens, and beneficial metabolites with screening threshold of p < 0.05, R > 0.8, or R < −0.8 (c). Full article ">

19 pages, 5141 KiB

Open AccessArticle

Exogenous Melatonin Application Delays Senescence and Improves Postharvest Antioxidant Capacity in Blueberries

by Jie Li, Ying Wang, Jinying Li, Yanan Li, Chunze Lu, Zihuan Hou, Haiguang Liu and Lin Wu

Agronomy 2025, 15(2), 428; https://doi.org/10.3390/agronomy15020428 - 9 Feb 2025

Viewed by 314

Abstract

Blueberries are highly prone to postharvest decay, resulting in significant nutrient loss and economic damage. Current research on the postharvest storage of blueberries primarily focuses on storage techniques, while the underlying mechanisms remain insufficiently explored. To further explore the role of exogenous melatonin [...] Read more.

Blueberries are highly prone to postharvest decay, resulting in significant nutrient loss and economic damage. Current research on the postharvest storage of blueberries primarily focuses on storage techniques, while the underlying mechanisms remain insufficiently explored. To further explore the role of exogenous melatonin (MT) in delaying the senescence of blueberry fruit, this study treated fruits with sterile water (control) and 300 μmol·L⁻¹ MT during the pink fruit stage. After maturation, the fruits were stored at 4 °C for 30 days, and we investigated the effects of exogenous MT on postharvest blueberry quality, reactive oxygen species (ROS) metabolism, antioxidant enzyme activities, and the expression of related genes. The results showed that, compared to the control, 300 μmol·L⁻¹ MT effectively delayed the increase in fruit decay rate and the decline in firmness, while enhancing the total soluble solids (TSS) content and ascorbic acid (AsA) levels. It also reduced the accumulation of malondialdehyde (MDA), hydrogen peroxide (H₂O₂), and the production rate of superoxide anion (O₂⁻), while maintaining higher activities of ascorbate peroxidase (APX), superoxide dismutase (SOD), and catalase (CAT). Furthermore, MT treatment upregulated the expression of antioxidant enzyme-related genes VcSOD1, VcSOD2, and VcAPX3. These findings indicate that treating blueberries with 300 μmol·L⁻¹ MT at the pink fruit stage improves postharvest quality, alleviates oxidative damage, and delays senescence. This study provides a theoretical foundation and practical reference for blueberry storage and preservation, laying the groundwork for further understanding the regulatory mechanisms of exogenous MT in postharvest fruit senescence. Full article

(This article belongs to the Special Issue Applications of Pre- and Post-harvest Techniques in Horticultural Products)

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19 pages, 2556 KiB

Open AccessArticle

The Effect of Silicon–Melatonin Nanoparticles on Improving Germination Parameters and Reducing Salinity Toxicity by Maintaining Ion Homeostasis in Cyamopsis tetragonoloba L. Seedlings

by Mozhgan Alinia, Seyed Abdolreza Kazemeini, Samad Sabbaghi, Shima Sayahi, Alireza Abolghasemi and Behnam Asgari Lajayer

Agronomy 2025, 15(2), 427; https://doi.org/10.3390/agronomy15020427 - 8 Feb 2025

Viewed by 465

Abstract

The salinity of water and soil is a constraint that has an extreme effect on germination and the establishment of crops. Therefore, it is pivotal to boost crop salt tolerance in global semi-arid regions. By mixing Si in an ME medium, a new [...] Read more.

The salinity of water and soil is a constraint that has an extreme effect on germination and the establishment of crops. Therefore, it is pivotal to boost crop salt tolerance in global semi-arid regions. By mixing Si in an ME medium, a new complex of nanoparticles (Si-CTS-HPC-ME NPs) was synthesized, and we investigated the role of Si-CTS-HPC-ME NPs on Cyamopsis tetragonoloba germination and tolerance against salinity stress. Thus, this study examined the influence of Si-CTS-HPC-ME NPs at different concentrations (N₁: 0, N₂: 40 and N₃: 80 mg L⁻¹) on some germination and seedling growth parameters and the ion homeostasis of Cyamopsis tetragonoloba L. (cluster bean) seedlings under three salinity levels (S₁: 0, S₂: 6 and S₃: 12 dS m⁻¹). With increasing salinity, the energy of germination (GE), index of germination (GI), index of vitality (VI), seedling vigor index (SVI), fresh weight (SFW) and dry (SDW) weight of seedlings, plumule length (PL), and radicle length (RL) parameters gradually decreased, while the mean germination time (MGT) and coefficient of velocity of germination (CVG) increased in salt-stressed cluster bean seedlings in comparison to the control. However, the usage of Si-CTS-HPC-ME NPs was effective in enhancing cluster bean tolerance to salinity by enhancing total phenols and flavonoids and improving K⁺, Si, and Ca⁺² uptake, thus reducing lipid peroxidation, decreasing sodium ion uptake and potassium leakage, and promoting germination parameters compared with non-NP-treated seedlings. Meanwhile, 40 mg L⁻¹ Si-CTS-HPC-ME NPs exhibited an effective response in saline conditions compared with the other NP treatments. Consequently, the application of Si-CTS-HPC-ME NPs in salt-stressed cluster bean seedlings can serve as an effective technique to enhance salinity tolerance in saline conditions under arid and semi-arid climatic conditions. Full article

(This article belongs to the Special Issue Plant Ecophysiology Under Anthropogenic and Natural Stresses)

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Characterization of Si-CTS-HPC-ME NPs. (a) XRD pattern of melatonin, sodium metasilicate and Si-CTS-HPC-ME NPs, (b) SEM images, (c) EDS and (d) FTIR peaks. Full article ">Figure 2
The effect of Si-CTS-HPC-ME NPs application (N1: 0, N2: 40 and N3: 80 mg L−1) and salinity stress (S1: 0, S2: 6 and S3: 12 dS m−1) on the phenotypic changes of cluster bean seedlings. Full article ">Figure 3
The effect of Si-CTS-HPC-ME NPs application (N1: 0, N2: 40 and N3: 80 mg L−1) and salinity stress (S1: 0, S2: 6 and S3: 12 dS m−1) on the percentage of germination (GP) (a), index of germination (GI) (b), energy of germination (GE) (c), mean germination time (MGT) (d), index of seedling vigor (SVI) (e), index of vitality (VI) (f) and coefficient of velocity of germination (CVG) (g) of cluster bean seedlings. Different letters in the columns show significant differences (p < 0.05) according to the LSD test. Full article ">Figure 4
The effect of Si-CTS-HPC-ME NPs application (N1: 0, N2: 40 and N3: 80 mg L−1) and salinity stress (S1: 0, S2: 6 and S3: 12 dS m−1) on the fresh (SFW) (a) and dry (SDW) (b) weight, length of plumule (PL) (c) and length of radicle (RL) (d) of cluster bean seedlings. Different letters in the columns show significant differences (p < 0.05) according to the LSD test. Full article ">Figure 5
The effect of Si-CTS-HPC-ME NPs application (N1: 0, N2: 40 and N3: 80 mg L−1) and salinity stress (S1: 0, S2: 6 and S3: 12 dS m−1) on the total phenol (a) and total flavonoid (b) of cluster bean seedlings. Different letters in the columns show significant differences (p < 0.05) according to the LSD test. Full article ">Figure 6
The effect of Si-CTS-HPC-ME NP application (N1: 0, N2: 40 and N3: 80 mg L−1) and salinity stress (S1: 0, S2: 6 and S3: 12 dS m−1) on the MDA content of cluster bean seedlings. Different letters in the columns show significant differences (p < 0.05) according to the LSD test. Full article ">Figure 7
The effect of Si-CTS-HPC-ME NP application (N1: 0, N2: 40 and N3: 80 mg L−1) and salinity stress (S1: 0, S2: 6 and S3: 12 dS m−1) on K+ (a), Na+ (b), Si (c) and Ca+2 (d) of cluster bean seedlings. Different letters in the columns show significant differences (p < 0.05) according to the LSD test. Full article ">Figure 8
Pearson’s correlation coefficient combined with heat maps (a) and the principal component analysis (PCA) (b) for the measured parameters of the related Si-CTS-HPC-ME NP-treated cluster seedlings exposed to salinity stress. Plumule length (PL), radicle length (RL), fresh (SFW) and dry (SDW) weight of seedling, malondialdehyde (MDA), total phenol (Ph) and flavonoid (FLV), sodium (Na+), potassium (K+), silicate (Si) and calcium (Ca+2) concentrations of cluster bean seedlings. Full article ">

31 pages, 1957 KiB

Open AccessArticle

Overcoming Barriers to the Adoption of Decision Support Systems in Integrated Pest Management in Some European Countries

by Jurij Marinko, Vladimir Kuzmanovski, Mark Ramsden and Marko Debeljak

Agronomy 2025, 15(2), 426; https://doi.org/10.3390/agronomy15020426 - 8 Feb 2025

Viewed by 559

Abstract

Decision support systems (DSSs) can improve decision making in integrated pest management (IPM), but are still underutilised despite proven environmental and economic benefits. To overcome the barriers to DSS adoption, this study analyses survey data from 31 farmers and 94 farm advisors, researchers [...] Read more.

Decision support systems (DSSs) can improve decision making in integrated pest management (IPM), but are still underutilised despite proven environmental and economic benefits. To overcome the barriers to DSS adoption, this study analyses survey data from 31 farmers and 94 farm advisors, researchers and developers across 11 European countries. Using machine learning techniques, respondents were first categorised into clusters based on their responses to the questionnaire. The clusters were then explained using classification trees. For each cluster, customised approaches were proposed to overcome the barriers to DSS adoption. For farmers, these include building trust through co-development, offering free trials, organising practical workshops and providing clear instructions for use. For farm advisors and researchers, involvement in the development of DSS and giving them access to information about the characteristics of the DSS is crucial. IPM DSS developers should focus on 14 key recommendations to improve trust and the ease of use, increase the transparency of DSS descriptions and validation, and extend development to underserved sectors such as viticulture and vegetable farming. These recommendations aim to increase the uptake of DSSs to ultimately improve the implementation of IPM practises and help reduce the risk and use of pesticides across Europe despite the ever-growing challenges in agriculture. Full article

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

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Distribution of points representing (a) farmers (n = 31) and (b) farm advisors, researchers and DSS developers (ARDs), (n = 94) in a two-dimensional plane and the clusters they form (two clusters of farmers and three clusters of ARDs). For farmers, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) was used with at least five points in the cluster, while for ARDs K-means with three clusters was used. Full article ">Figure 2
Model for classification of farmers into two corresponding clusters. The model correctly classified 96.8% of the 31 instances. Numbers in front of attributes correspond to question number. All questions and response options can be found in <a href="#app1-agronomy-15-00426" class="html-app">Appendix A</a>. Full article ">Figure 3
Model for classification of farm advisors, researchers and DSS developers (ARDs) into three corresponding clusters. The model correctly classified 94.7% of the 94 instances. Numbers in front of attributes correspond to question number. All questions and response options can be found in <a href="#app1-agronomy-15-00426" class="html-app">Appendix A</a>. Full article ">Figure 4
Respondents’ answers to the section of the questionnaire relating to the characteristics of DSSs. Responses were obtained from 31 farmers and 93 farm advisors, researchers and decision support systems (DSS) developers (ARDs). Positive responses are coloured in shades of green, while the answer “No” is coloured orange. All questions and response options can be found in <a href="#app1-agronomy-15-00426" class="html-app">Appendix A</a>, <a href="#agronomy-15-00426-t0A5" class="html-table">Table A5</a>. Full article ">

15 pages, 2494 KiB

Open AccessArticle

High-Throughput Field Screening of Cassava Brown Streak Disease Resistance for Efficient and Cost-Saving Breeding Selection

by Mouritala Sikirou, Najimu Adetoro, Samar Sheat, Eric Musungayi, Romain Mungangan, Miafuntila Pierre, Kayode Fowobaje, Ibnou Dieng, Zoumana Bamba, Ismail Rabbi, Hapson Mushoriwa and Stephan Winter

Agronomy 2025, 15(2), 425; https://doi.org/10.3390/agronomy15020425 - 8 Feb 2025

Viewed by 247

Abstract

Cassava brown streak disease (CBSD) remains the most severe threat to cassava production in the Great Lakes region and Southern Africa. Screening for virus resistance by subjecting cassava to high virus pressure in the epidemic zone (hotspots) is a common but lengthy process [...] Read more.

Cassava brown streak disease (CBSD) remains the most severe threat to cassava production in the Great Lakes region and Southern Africa. Screening for virus resistance by subjecting cassava to high virus pressure in the epidemic zone (hotspots) is a common but lengthy process because of unpredictable and erratic virus infections requiring multiple seasons for disease evaluation. This study investigated the feasibility of graft-infections to provide a highly controlled infection process that is robust and reproducible to select and eliminate susceptible cassava at the early stages and to predict the resistance of adapted and economically valuable varieties. To achieve this, a collection of cassava germplasm from the Democratic Republic of Congo and a different set of breeding trials comprising two seed nurseries and one preliminary yield trial were established. The cassava varieties OBAMA and NAROCASS 1 infected with CBSD were planted one month after establishment of the main trials in a 50 m² plot to serve as the source of the infection and to provide scions to graft approximately 1 ha. Grafted plants were inspected for virus symptoms and additionally tested by RT-qPCR for sensitive detection of the viruses. The incidence and severity of CBSD and cassava mosaic disease (CMD) symptoms were scored at different stages of plant growth and fresh root yield determined at harvesting. The results from the field experiments proved that graft-infection with infected plants showed rapid symptom development in susceptible cassava plants allowing instant exclusion of those lines from the next breeding cycle. High heritability, with values ranging from 0.63 to 0.97, was further recorded for leaf and root symptoms, respectively. Indeed, only a few cassava progenies were selected while clones DSC260 and two species of M. glaziovii (Glaziovii20210005 and Glaziovii20210006) showed resistance to CBSD. Taken together, grafting scions from infected cassava is a highly efficient and cost-effective method to infect cassava with CBSD even under rugged field conditions. It replaces an erratic infection process with a controlled method to ensure precise screening and selection for virus resistance. The clones identified as resistant could serve as elite donors for introgression, facilitating the transfer of resistance to CBSD. Full article

(This article belongs to the Special Issue Advanced Research on Diagnosis and Biological Control of Crop Diseases)

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16 pages, 2614 KiB

Open AccessArticle

Enhancing Soil Physical Quality with Diatomite Amendments

by Tomasz Głąb and Krzysztof Gondek

Agronomy 2025, 15(2), 424; https://doi.org/10.3390/agronomy15020424 - 8 Feb 2025

Viewed by 281

Abstract

Climate change poses significant challenges to agricultural productivity due to reduced water availability and increased temperatures. Developing innovative techniques to enhance soil water retention has emerged as a crucial strategy to mitigate these challenges. This study investigates the effects of diatomite addition type, [...] Read more.

Climate change poses significant challenges to agricultural productivity due to reduced water availability and increased temperatures. Developing innovative techniques to enhance soil water retention has emerged as a crucial strategy to mitigate these challenges. This study investigates the effects of diatomite addition type, particle size, and application rate on the physical quality of sandy soil, focusing specifically on water retention characteristics. The experiment involved three particle size fractions of diatomite mixed with additives (biochar, dolomite, and bentonite) at different rates. Soil water retention characteristics and differential porosity were evaluated. Results showed that diatomite application increased soil bulk density but improved water retention capabilities, especially when supplemented with additives. Bentonite addition with diatomite resulted in the highest available water capacity, while dolomite had minimal effect on water retention. Biochar supplementation significantly enhanced water retention characteristics, leading to higher field capacity and plant-available water capacity. The study revealed that the particle size of diatomite did not have a substantial effect on soil physical properties, except for its influence on available water capacity. Diatomite application did not lead to water repellency of soil. These findings highlight the potential of diatomite and additives to improve soil water retention, providing valuable insights for sustainable agriculture. Full article

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

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Particle size distribution of diatomites used in the experiment. Diatomite particle size classes: DT1, 0–1.0 mm; DT05 0–0.5 mm; DT001, 0–0.01 mm. Full article ">Figure 2
Relationship between soil bulk density (BD) and diatomite rate. DT, diatomite without additives; DT + BC, diatomite with biochar; DT + DL, diatomite with dolomite, DT + BN diatomite with bentonite. Full article ">Figure 3
Pore size distribution (determined by SWRC model) of soil porosity after different diatomite treatment. CTR: control (soil without diatomite); DT: diatomite; BC: biochar; DL: dolomite; BN: bentonite; DT001: 0–0.01 mm; DT05-1: 0–0.5 mm; DT1: 0–1.0 mm diatomite particle size. Full article ">Figure 4
The soil water retention curves (SWRC) based on van Genuchten model for the investigated soil after different diatomite treatments. CTR: control (soil without diatomite); DT: diatomite; BC: biochar; DL: dolomite; BN: bentonite; DT001: 0.005–0.01 mm; DT05-1: 0–0.5 mm; DT1: 0–1.0 mm diatomite particle size. Full article ">Figure 4 Cont.
The soil water retention curves (SWRC) based on van Genuchten model for the investigated soil after different diatomite treatments. CTR: control (soil without diatomite); DT: diatomite; BC: biochar; DL: dolomite; BN: bentonite; DT001: 0.005–0.01 mm; DT05-1: 0–0.5 mm; DT1: 0–1.0 mm diatomite particle size. Full article ">Figure 5
Relationship between field water capacity (FC) and plant wilting point (PWP) and diatomite rate. DT, diatomite without additives; DT + BC, diatomite with biochar; DT + DL, diatomite with dolomite, DT + BN diatomite with bentonite. Full article ">Figure 6
Relationship between available water retention (AWC) and productive water retention (PWC) and diatomite rate (x) and particle diameter (y). Full article ">

19 pages, 10945 KiB

Open AccessArticle

Assessment of Fishery By-Products for Immobilization of Arsenic and Heavy Metals in Contaminated Soil and Evaluation of Heavy Metal Uptake in Crops

by Se Hyun Park, Sang Hyeop Park and Deok Hyun Moon

Agronomy 2025, 15(2), 423; https://doi.org/10.3390/agronomy15020423 (registering DOI) - 7 Feb 2025

Viewed by 198

Abstract

The contamination of soil with arsenic (As) and heavy metal is an increasing global environmental concern. The objective of this study was to rehabilitate soil contaminated with As, Pb, and Zn using fishery by-products as stabilizers to achieve both soil restoration and waste [...] Read more.

The contamination of soil with arsenic (As) and heavy metal is an increasing global environmental concern. The objective of this study was to rehabilitate soil contaminated with As, Pb, and Zn using fishery by-products as stabilizers to achieve both soil restoration and waste resource recycling. Cockle shells (CS) and manila clam shells (MC), selected as fishery by-product stabilizers, were processed into −#10-mesh and −#20-mesh materials. Additionally, a −#10-mesh material was calcined at a high temperature to produce calcined cockle shells (CCS) and calcined manila clam shells (CMC). Contaminated soil was treated with 2–10 wt% of these stabilizers and subjected to wet incubation for 1–4 weeks. Subsequently, the concentrations of As, Pb, and Zn eluted by 0.1 M HCl were evaluated. Additionally, lettuce was grown in stabilized soil to evaluate the reduction in contaminant mobility. The stabilization treatment results indicated that the concentrations of eluted As, Pb, and Zn were significantly reduced when treated with the −#10-mesh and −#20-mesh CS and MC, and they were rarely detected when treated with the calcined materials (CCS and CMC). The Pb concentration in lettuce grown in the contaminated soil pot exceeded the criterion for leafy vegetables (0.3 mg/kg); however, Pb was not detected in lettuce from the stabilized soil pot. An X-ray diffraction (XRD) analysis revealed that CaCO₃, the main component of CS and MC, was converted to CaO after calcination. Scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX) and SEM elemental dot map analyses revealed that the immobilization of As was related to Ca–As precipitation and the immobilization of Pb and Zn to the pozzolanic reaction. Thus, recycling and processing CS and MC as stabilizers for contaminated soil can restore the agricultural value of the soil by immobilizing As, Pb, and Zn into safe forms, thus effectively preventing their uptake by crops. Full article

(This article belongs to the Topic Environmental Pollution in Modern Agriculture: Causes, Effect, and Control)

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16 pages, 2778 KiB

Open AccessArticle

Characterization of Neopestalotiopsis Species Associated with Strawberry Crown Rot in Italy

by Greta Dardani, Ilaria Martino, Francesco Aloi, Cristiano Carli, Roberto Giordano, Davide Spadaro and Vladimiro Guarnaccia

Agronomy 2025, 15(2), 422; https://doi.org/10.3390/agronomy15020422 - 7 Feb 2025

Viewed by 249

Abstract

Different Pestalotiopsis-like species have been reported in strawberry worldwide, as agents of leaf spot, root rot, and crown rot. The identification of Pestalotiopsis-like fungi is based on both molecular and morphological analyses to discriminate between species and clarify phylogenetic relationships. Recent [...] Read more.

Different Pestalotiopsis-like species have been reported in strawberry worldwide, as agents of leaf spot, root rot, and crown rot. The identification of Pestalotiopsis-like fungi is based on both molecular and morphological analyses to discriminate between species and clarify phylogenetic relationships. Recent studies have provided robust multi-locus analyses, which reclassified most Pestalotiopsis-like isolates associated with strawberry root and crown rot diseases as Neopestalotiopsis spp. Numerous disease outbreaks have been observed in strawberry fields in Italy in recent years, showing that Neopestalotiopsis is an emerging pathogen. A survey was conducted in Northern Italy during 2022–2023 to investigate the diversity and distribution of Neopestalotiopsis species. Morphological and phylogenetic characterization, based on ITS, tef1 and tub2, led to the identification of four species: Neopestalotiopsis rosae, N. iranensis, N. hispanica (syn. vaccinii) and N. scalabiensis. Based on our results from multi-locus phylogenetic analysis, N. hispanica and N. vaccinii were grouped in the same cluster; thus, they were confirmed to be the same species. Pathogenicity tests with representative isolates of each species were conducted on strawberry ‘Portola’ transplants. All isolates were shown to be wound pathogens in strawberry, causing crown rot, and were successfully re-isolated. Neopestalotiopsis rosae was confirmed to be the most dominant and virulent species associated with these symptoms, as well as the most dominant among the obtained isolates. To the best of our knowledge, this work represents the first report of N. scalabiensis being associated with the crown rot of strawberry in Italy and the first report of N. iranensis in association with the crown rot of strawberry worldwide. Full article

(This article belongs to the Special Issue Enhancing Crop Production: Unveiling the Vital Role of Plant Roots and Their Dynamic Interplay with Soil and the Environment)

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(A) Strawberry plants cultivated in commercial field showing collapse and death associated with Neopestalotiopsis spp.; (B) longitudinal section of strawberry crown of naturally infected plant (on the left), showing crown rot associated with Neopestalotiopsis spp. compared to healthy plant (on the right); (C) symptoms of ‘Portola’ strawberry plant 8 weeks post-inoculation with N. rosae. Full article ">Figure 2
Phylogenetic tree for Neopestalotiopsis species, resulting from a Bayesian analysis of the combined ITS, tef1 and tub2 sequence alignment. Bayesian posterior probabilities (PPs) and maximum likelihood bootstrap support values (ML-BS) are indicated at the nodes (PP/ML-BS). Ex-type strains are indicated with *. Pestalotiopsis trachicarpicola (OP068) was used as an outgroup. Isolates in red were collected in this study. Full article ">Figure 3
Neopestalotiopsis hispanica/vacciniii isolate CVG2369 (A–C) and isolate CVG2367 (D–F); Neopestalotiopsis iranensis isolate CVG2370 (G–I). (A,B,D,E,G,H) Colony on PDA (above and reverse). (C,F,I) Conidia. Full article ">Figure 4
Neopestalotiopsis rosae isolate CVG2376 (A–C) and isolate CVG2398 (D–F); Neopestalotiopsis scalabiensis isolate CVG2400 (G–I). (A,B,D,E,G,H) Colony on PDA (above and reverse). (C,F,I) Conidia. Full article ">Figure 5
Mean lesion lengths (A) for evaluating internal symptoms and (B) midpoint severity for external symptom evaluation. Vertical bars indicate standard errors. Data in each histogram accompanied by different letters are significantly different (p = 0.05). Full article ">

23 pages, 3547 KiB

Open AccessArticle

Classification of Garden Chrysanthemum Flowering Period Using Digital Imagery from Unmanned Aerial Vehicle (UAV)

by Jiuyuan Zhang, Jingshan Lu, Qimo Qi, Mingxiu Sun, Gangjun Zheng, Qiuyan Zhang, Fadi Chen, Sumei Chen, Fei Zhang, Weimin Fang and Zhiyong Guan

Agronomy 2025, 15(2), 421; https://doi.org/10.3390/agronomy15020421 - 7 Feb 2025

Viewed by 334

Abstract

Monitoring the flowering period is essential for evaluating garden chrysanthemum cultivars and their landscaping use. However, traditional field observation methods are labor-intensive. This study proposes a classification method based on color information from canopy digital images. In this study, an unmanned aerial vehicle [...] Read more.

Monitoring the flowering period is essential for evaluating garden chrysanthemum cultivars and their landscaping use. However, traditional field observation methods are labor-intensive. This study proposes a classification method based on color information from canopy digital images. In this study, an unmanned aerial vehicle (UAV) with a red-green-blue (RGB) sensor was utilized to capture orthophotos of garden chrysanthemums. A mask region-convolutional neural network (Mask R-CNN) was employed to remove field backgrounds and categorize growth stages into vegetative, bud, and flowering periods. Images were then converted to the hue-saturation-value (HSV) color space to calculate eight color indices: R_ratio, Y_ratio, G_ratio, Pink_ratio, Purple_ratio, W_ratio, D_ratio, and Fsum_ratio, representing various color proportions. A color ratio decision tree and random forest model were developed to further subdivide the flowering period into initial, peak, and late periods. The results showed that the random forest model performed better with F1-scores of 0.9040 and 0.8697 on two validation datasets, requiring less manual involvement. This method provides a rapid and detailed assessment of flowering periods, aiding in the evaluation of new chrysanthemum cultivars. Full article

(This article belongs to the Special Issue New Trends in Agricultural UAV Application—2nd Edition)

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Overview of the experimental area. (a) Location of China; (b) Location of Nanjing, Jiangsu Province; (c) UAV orthophoto of the experimental field. Plant images from plots 2 and 3 were used to establish flowering-period classification dataset 1, while images from plots 1 and 4 were used to establish flowering-period classification dataset 2. Full article ">Figure 2
Pictures of garden chrysanthemums at different growth stages. (a,b) represent the vegetative growth period; (c,d) represent the budding period; (e–g) represent the flowering period. Full article ">Figure 3
(a) Flowering-period classification flowchart, Y means that the condition is met, and N means that the condition is unmet; (b) represents the initial flowering period; (c) represents the peak flowering period; (d) represents the late flowering period. Full article ">Figure 4
Color ratio decision tree flowering-period classification model. I represents the initial flowering period; P represents the peak flowering period; L represents the late flowering period; Y means that the condition is met, and N means that the condition is unmet. Full article ">Figure 5
Construction and application of the random forest flowering-period classification model. Full article ">Figure 6
Color information of misclassified plants. (a,b) are plants in the initial flowering period that have been mistakenly identified as late flowering period; (c–e) are plants in the late flowering period that were mistakenly classified as peak flowering. G_area represents the green area extracted from the canopy image based on the previously defined conditions, Fsum_area represents the inflorescence area, and D_area represents the deteriorated area. Full article ">Figure 7
The importance of color indices in random forest model. Full article ">Figure A1
Orthorectified RGB image of garden chrysanthemum populations. (a) 2023 orthophoto imagery of garden chrysanthemum populations; (b) the yellow arrow represents the ridge width, the orange arrow represents the row spacing, and the red arrow represents the plant spacing; (c) 2024 orthophoto imagery of garden chrysanthemum populations. Full article ">

14 pages, 1943 KiB

Open AccessArticle

Optimizing Nitrogen for Sustainable Yield and Efficiency: Insights from Shouguang Facility-Grown Tomatoes

by Xueying Wang, Jingchao Jia, Caiyan Lu, Huaihai Chen, Xin Chen, Xiuyuan Peng, Guangyu Chi, Qiaobo Song, Yanyu Hu and Jian Ma

Agronomy 2025, 15(2), 420; https://doi.org/10.3390/agronomy15020420 - 7 Feb 2025

Viewed by 326

Abstract

Facility-based agriculture has rapidly advanced due to its capacity for high-intensity and year-round crop cultivation. This study evaluated the effects of different nitrogen fertilizer application rates on the growth of greenhouse tomatoes, while utilizing ¹⁵N tracing technology to explore nitrogen utilization efficiency [...] Read more.

Facility-based agriculture has rapidly advanced due to its capacity for high-intensity and year-round crop cultivation. This study evaluated the effects of different nitrogen fertilizer application rates on the growth of greenhouse tomatoes, while utilizing ¹⁵N tracing technology to explore nitrogen utilization efficiency during the growth process of facility-grown tomatoes. The results indicate that nitrogen application rates within the range of N60–N80 (93–128 kg N ha⁻¹) can optimally balance yield, nitrogen-use efficiency, and crop growth. Application rates exceeding this range do not enhance yield and lead to reduced nitrogen-use efficiency. Tomato plants exhibited a low N requirement during the seedling stage, relying primarily on native soil N stocks during the flowering stage. Fertilizer-derived N use increased during the fruiting stage. These findings demonstrate that excessive N inputs lead to diminishing returns and potential nutrient imbalances, while fully utilizing soil N stocks during the seedling and flowering stages is essential. This study emphasizes the importance of adjusting nitrogen input according to the developmental stages of the crop to optimize yield and resource utilization. Full article

(This article belongs to the Special Issue Growth and Nutrient Management of Vegetables—2nd Edition)

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18 pages, 3489 KiB

Open AccessArticle

Plastic Film Residue Reshaped Protist Communities and Induced Soil Nutrient Deficiency Under Field Conditions

by Ge Wang, Qian Sun, Maolu Wei, Miaomiao Xie, Ting Shen and Dongyan Liu

Agronomy 2025, 15(2), 419; https://doi.org/10.3390/agronomy15020419 - 7 Feb 2025

Viewed by 351

Abstract

The use of plastic agricultural mulching films presents a “double-edged sword”: while these films enhance crop yields, they also lead to the accumulation of plastic film residues in the soil, creating new pollutants (microplastics). Our understanding of the “plastisphere”, a niche formed by [...] Read more.

The use of plastic agricultural mulching films presents a “double-edged sword”: while these films enhance crop yields, they also lead to the accumulation of plastic film residues in the soil, creating new pollutants (microplastics). Our understanding of the “plastisphere”, a niche formed by agricultural film residues in the soil, where unique microbial communities and soil conditions converge remains limited. This is particularly true for protists, which are recognized as key determinants of soil health. Therefore, this study simulated a field experiment to analyze the effects of long-term plastic film residues on the structure of protist microbial communities in the rhizosphere, bulk soil and plastisphere of oilseed rape as well as their effects on soil nutrients. The results revealed that the residual plastic films underwent significant structural and chemical degradations. Protist diversity and co-occurrence network complexity were markedly reduced in plastisphere soils. In addition, soil moisture content, inorganic nitrogen and available phosphorus levels declined, leading to deficiencies in soil nutrients. Functional shifts in consumer protists and phototrophs along with weakened network interactions, have been identified as key drivers of impaired nutrient turnover. Our study underscores the critical role of protist communities in maintaining soil nutrient cycling and highlights the profound adverse effects of plastic film residues on soil ecosystems. These findings provide valuable insights into mitigating plastic residue accumulation to preserve long-term soil fertility and ensure sustainable agricultural productivity. Full article

(This article belongs to the Special Issue The Impact of Mulching on Crop Production and Farmland Environment)

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Figure 1
Changes in the surface characteristics of plastic film residue before and after one year in soil. SEM images of film residues before (a) and after one year in soil (b). FTIR spectra and CI index of plastic film residues before and after one year in soil (c,d). Full article ">Figure 2
Chao1 (a) and Shannon (b) indices, PCoA analysis with PERMANOVA test based on Bray–Curtis distance (c), and relative abundance of the top five divisions (d) of the protist community in different treatments. Data are shown as mean ± SE (n = 6). An asterisk (*) indicates a significant difference according to the Kruskal–Wallis test followed by Dunn’s post hoc test. The significance levels are defined as follows: ** p < 0.01, *** p < 0.001, and ns indicates no significant difference. Full article ">Figure 3
Relative abundance of functional protist consumer (a), phototrophic (b), and parasitic (c) groups in different treatments. An asterisk (*) indicates a significant difference according to the Kruskal–Wallis test followed by Dunn’s post hoc test. The significance levels are defined as follows: ** p < 0.01, **** p < 0.0001, and ns indicates no significant difference. Full article ">Figure 4
PCoA analysis with PERMANOVA test based on Bray–Curtis distance of consumer (a), phototrophic (c), and parasitic (e) communities. The relative abundance of the top subdivision of consumer (b), phototrophic (d), and parasite (f) protist communities in different treatments. Data are shown as mean ± SE (n = 6). An asterisk (*) indicates a significant difference according to the Kruskal–Wallis test followed by Dunn’s post hoc test. The significance levels are defined as follows: * p < 0.05, ** p < 0.01, and ns indicates no significant difference. Full article ">Figure 5
Co-occurrence networks and topological attributes of soil protist communities in rhizosphere (a), bulk (b) and plastisphere (c) soils. Microbial network stability indices, including positive cohesion (d), negative cohesion (e) and network complexity (f) in soils with different treatments. An asterisk (*) indicates a significant difference according to the Kruskal–Wallis test followed by Dunn’s post hoc test, with significance levels defined as follows: * p < 0.05, and **** p < 0.0001, and ns indicates no significant difference. Full article ">Figure 6
Linear correlations between protist functional groups, network complexity and soil nutrient content in different treatments. (a) Correlations of phototrophic, parasitic, and consumer protists and network complexity with IN content. (b) Correlation between the same variables and AP content. IN, inorganic nitrogen; AP, available phosphorus. The dashed lines represent 95% confidence intervals. Full article ">Figure 7
Averaged models for each IN (a) and AP (b) content. Parameter estimates and variance are explained in the averaged model for each soil nutrient level. Parameters are classified into four groups: consumer, phototroph, parasite, and complexity. Full article ">

19 pages, 11739 KiB

Open AccessArticle

Exploring the Spatial Distribution Characteristics of Urban Soil Heavy Metals in Different Levels of Urbanization

by Jianwei Sun, Mengchan Chen, Jingrou Xiao, Gang Xu, Haitao Zhang, Ganlin Zhang, Fangqin Yang, Chang Zhao and Long Guo

Agronomy 2025, 15(2), 418; https://doi.org/10.3390/agronomy15020418 - 7 Feb 2025

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Abstract

With the development of urbanization and industrialization worldwide, soil heavy metal pollution has become a critical and pressing environmental problem in urban areas. Soil heavy metals exhibit complex and varying spatial aggregation and diffusion processes within diverse urban landscapes, especially in different urban [...] Read more.

With the development of urbanization and industrialization worldwide, soil heavy metal pollution has become a critical and pressing environmental problem in urban areas. Soil heavy metals exhibit complex and varying spatial aggregation and diffusion processes within diverse urban landscapes, especially in different urban areas with varying urbanization levels. However, many existing experimental methods and conventional models overlook the crucial aspects of spatial autocorrelation and heterogeneity between soil heavy metals and influencing factors. This neglect poses significant environmental concerns, as rapid monitoring of soil heavy metals and accurate identification of their determinants become imperative. This study investigated four environmentally sensitive and potentially harmful soil heavy metals, arsenic (As), cadmium (Cd), copper (Cu), and lead (Pb), in two urban areas in China with varying urbanization levels. Enshi (a prefecture-level city) and Wuhan (a provincial capital city) were selected for comparison of the spatially variable relationships between soil heavy metals and their influencing factors. We employed a global stepwise linear regression (STR) model and a local spatial model-geographically weighted regression (GWR) to map the spatial distribution of soil heavy metals based on 121 auxiliary variables, including terrain, geophysical, socioeconomic factors, and remote sensing data. Our results showed that: (1) soil heavy metals exhibited strong spatial aggregation in the prefecture-level city (Enshi) but, nonetheless, have strong spatial heterogeneity in the provincial capital city (Wuhan) due to elevated anthropogenic disturbances; (2) GWR accurately mapped the spatial distributions of As (r = 0.47 and 0.66), Cd (r = 0.74 and 0.53), Cu (r = 0.60 and 0.54), and Pb (r = 0.44 and 0.50) based on auxiliary variables in different cities and also can clearly reveal the spatially variable relationships with main influence factors; (3) human activities were the primary driving factors influencing As and Pb, while natural environment variables were identified as the main potential sources of Cd and Cu. This study demonstrates a methodology to explore spatially variable characteristics of soil heavy metals and their spatial varying relationships with influence factors. The comparative analysis between two cities provides insights that can greatly enhance quantitative source apportionment and support sustainable management strategies for controlling soil heavy metal pollution across varied urban environments. Full article

(This article belongs to the Section Farming Sustainability)

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Agronomy, Volume 15, Issue 2 (February 2025) – 195 articles

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