Journal Description
Agronomy
Agronomy
is an international, peer-reviewed, open access journal on agronomy and agroecology published monthly online by MDPI. The Spanish Society of Plant Physiology (SEFV) is affiliated with Agronomy and their members receive discounts on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubAg, AGRIS, and other databases.
- Journal Rank: JCR - Q1 (Agronomy) / CiteScore - Q1 (Agronomy and Crop Science)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.8 days after submission; acceptance to publication is undertaken in 2.4 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journals for Agronomy include: Seeds, Agrochemicals, Grasses and Crops.
Impact Factor:
3.7 (2022);
5-Year Impact Factor:
4.0 (2022)
Latest Articles
Comparing Regression and Classification Models to Estimate Leaf Spot Disease in Peanut (Arachis hypogaea L.) for Implementation in Breeding Selection
Agronomy 2024, 14(5), 947; https://doi.org/10.3390/agronomy14050947 (registering DOI) - 30 Apr 2024
Abstract
Late leaf spot (LLS) is an important disease of peanut, causing global yield losses. Developing resistant varieties through breeding is crucial for yield stability, especially for smallholder farmers. However, traditional phenotyping methods used for resistance selection are laborious and subjective. Remote sensing offers
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Late leaf spot (LLS) is an important disease of peanut, causing global yield losses. Developing resistant varieties through breeding is crucial for yield stability, especially for smallholder farmers. However, traditional phenotyping methods used for resistance selection are laborious and subjective. Remote sensing offers an accurate, objective, and efficient alternative for phenotyping for resistance. The objectives of this study were to compare between regression and classification for breeding, and to identify the best models and indices to be used for selection. We evaluated 223 genotypes in three environments: Serere in 2020, and Nakabango and Nyankpala in 2021. Phenotypic data were collected using visual scores and two handheld sensors: a red–green–blue (RGB) camera and GreenSeeker. RGB indices derived from the images, along with the normalized difference vegetation index (NDVI), were used to model LLS resistance using statistical and machine learning methods. Both regression and classification methods were also evaluated for selection. Random Forest (RF), the artificial neural network (ANN), and k-nearest neighbors (KNNs) were the top-performing algorithms for both regression and classification. The ANN (R2: 0.81, RMSE: 22%) was the best regression algorithm, while the RF was the best classification algorithm for both binary (90%) and multiclass (78% and 73% accuracy) classification. The classification accuracy of the models decreased with the increase in classification classes. NDVI, crop senescence index (CSI), hue, and greenness index were strongly associated with LLS and useful for selection. Our study demonstrates that the integration of remote sensing and machine learning can enhance selection for LLS-resistant genotypes, aiding plant breeders in managing large populations effectively.
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(This article belongs to the Section Pest and Disease Management)
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Active Soil Organic Carbon Pools Decrease with Increased Time since Land-Use Transition from Rice Paddy Cultivation to Areca Nut Plantations under the Long-Term Application of Inorganic Fertilizer
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Yunxing Wan, Qilin Zhu, Lijun Liu, Shuirong Tang, Yanzheng Wu, Xiaoqian Dan, Lei Meng, Qiuxiang He, Ahmed S. Elrys and Jinbo Zhang
Agronomy 2024, 14(5), 946; https://doi.org/10.3390/agronomy14050946 (registering DOI) - 30 Apr 2024
Abstract
Many croplands in the tropics of China have been converted over the last decades into areca nut plantations due to their high economic returns. This land-use transition was accompanied by changes in agricultural practices such as soil moisture regimes and fertilizer inputs, which
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Many croplands in the tropics of China have been converted over the last decades into areca nut plantations due to their high economic returns. This land-use transition was accompanied by changes in agricultural practices such as soil moisture regimes and fertilizer inputs, which may affect soil organic carbon (SOC) and its fractions, especially in tropical soils with low fertility and high nitrogen loss. Yet, how the time since land-use transition from rice paddy cultivation to areca nut plantations affects soil carbon dynamics and their underlying mechanisms in the tropics of China remains elusive. Here, areca nut plantation soils with different ages (2, 5, 10, 14, and 17 years) and paddy fields in the tropical region of China were investigated. The study result indicates that the contents of dissolved organic carbon (DOC), particulate organic carbon (POC), easily oxidized organic carbon (EOC), light organic carbon (LFOC), and microbial biomass carbon (MBC) decreased significantly with increased time since land-use transition from rice paddy cultivation to areca nut plantations. Similarly, the ratios of DOC/SOC, MBC/SOC, POC/SOC, LFOC/SOC, and EOC/SOC decreased significantly with increased time since land-use transition. Compared with the paddy soil, the carbon pool management index decreased by 36.6–76.7% under the areca nut plantations, concluding that increasing the time since land-use transition from rice paddy cultivation to areca nut plantations with high application rates of chemical fertilizers resulted in reduced soil active carbon fractions and SOC supply capacity. Therefore, agricultural practices such as the use of organic fertilizers should be applied to improve the soil’s ability to supply organic carbon in managed plantation ecosystems in the tropics of China.
Full article
(This article belongs to the Special Issue Exploring the Potential for Crop Productivity by Applying Novel Agrochemicals, including Fertilizers, Biochar, Biostimulants, and Plant Nutrition Regulators)
Open AccessArticle
Influence of EMR–Phosphogypsum–Biochar Mixtures on Sudan Grass: Growth Dynamics and Heavy Metal Immobilization
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Yang Luo, Fang Liu, Xuqiang Luo, Jun Ren, Jinmei Guo and Jinxin Zhang
Agronomy 2024, 14(5), 945; https://doi.org/10.3390/agronomy14050945 (registering DOI) - 30 Apr 2024
Abstract
This study investigates the growth dynamics and heavy metal immobilization in Sudan grass cultivated on substrates composed of electrolytic manganese residue (EMR), phosphogypsum, and chili straw biochar. Pot experiments revealed that a substrate with phosphogypsum constituting 75% of the mix hinders Sudan grass
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This study investigates the growth dynamics and heavy metal immobilization in Sudan grass cultivated on substrates composed of electrolytic manganese residue (EMR), phosphogypsum, and chili straw biochar. Pot experiments revealed that a substrate with phosphogypsum constituting 75% of the mix hinders Sudan grass seed germination. Compared with sole EMR utilization, the composite substrates notably enhanced plant growth, evidenced by increases in plant height and fresh weight. The integration of these substrates led to a significant elevation in total chlorophyll content (up to 54.39%) and a reduction in malondialdehyde (MDA) levels (up to 21.66%), indicating improved photosynthetic activity and lower oxidative stress. The addition of biochar reduced the content of Zn, Cd, and Mn in the roots of Sudan grass by up to 25.92%, 20.00%, and 43.17%, respectively; and reduced the content of Pb, Mn, and Cr in the shoot by up to 33.72%, 17.53%, and 26.32%, respectively. Fuzzy membership function analysis identified the optimal substrate composition as 75% EMR and 25% phosphogypsum, with 5% chili straw biochar, based on overall performance metrics. This study adopts the concept of “to treat waste with waste”. The approach is to fully consider the fertility characteristics of EMR, phosphogypsum, and biochar, underscoring the potential for utilizing waste-derived materials in cultivating Sudan grass and offering a sustainable approach to plant growth and heavy metal management.
Full article
(This article belongs to the Special Issue Evaluate the Functional Value of Agroecosystem under Different Management Scenarios)
Open AccessArticle
Optimizing Maize Yield and Resource Efficiency Using Surface Drip Fertilization in Huang-Huai-Hai: Impact of Increased Planting Density and Reduced Nitrogen Application Rate
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Liqian Wu, Guoqiang Zhang, Zhenhua Yan, Shang Gao, Honggen Xu, Jiaqiang Zhou, Dianjun Li, Yi Liu, Ruizhi Xie, Bo Ming, Jun Xue, Peng Hou, Shaokun Li and Keru Wang
Agronomy 2024, 14(5), 944; https://doi.org/10.3390/agronomy14050944 (registering DOI) - 30 Apr 2024
Abstract
Improving crop yield and resource utilization efficiency is essential for agricultural productivity. In the Huang-Huai-Hai maize region of China, optimizing planting density, nitrogen (N) application, and fertilization methods are key strategies for enhancing maize yield and N use efficiency. However, traditional approaches have
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Improving crop yield and resource utilization efficiency is essential for agricultural productivity. In the Huang-Huai-Hai maize region of China, optimizing planting density, nitrogen (N) application, and fertilization methods are key strategies for enhancing maize yield and N use efficiency. However, traditional approaches have often hindered these improvements. To address this issue, we conducted a study in Baoding, Hebei, from 2022 to 2023, focusing on planting density, the N application rate, and the fertilization method on grain yield, N use efficiency, water use efficiency (WUE), and economic benefits. The trial involved two planting densities: 6.0 × 104 plants ha−1 (D1, typical local density) and 9.0 × 104 plants ha−1 (D2). Five N application rates were tested: 0 (N0), 120 kg ha−1 (N1), 180 kg ha−1 (N2), 240 kg ha−1 (N3), and 300 kg ha−1 (N4). The control treatment (D1N4) utilized the local planting density and traditional fertilization methods. Our findings revealed a positive correlation between the maize yield and N application rate, with the maximum yields (13.78–13.88 t ha−1), high WUE (24.42–29.85 kg m−3), agronomic efficiency of N (AEN) (18.11–19.00 kg kg−1), and economic benefits (2.44 × 104–2.47 × 104 CNY ha−1) observed with D2N3 and surface drip fertilization. This was significantly higher than the yield and resource efficiency of traditional fertilization methods and saved fertilizer and production costs. Therefore, adopting surface drip fertilization, adjusting planting density, and optimizing N application rates proved effective in enhancing maize yield and resource utilization efficiency in the Huang-Huai-Hai maize region.
Full article
(This article belongs to the Special Issue Integration of Agronomic Practices for Sustainable Crop Production—2nd Edition)
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Endophytic Capacity of Entomopathogenic Fungi in a Pasture Grass and Their Potential to Control the Spittlebug Mahanarva spectabilis (Hemiptera: Cercopidae)
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Michelle O. Campagnani, Alexander Machado Auad, Rogério Martins Maurício, Ana Paula Madureira, Mauroni Alves Cangussú, Luiz Henrique Rosa, Marcelo Francisco A. Pereira, Mayco Muniz, Sebastião Rocha O. Souza, Natany Brunelli M. Silva, Ana Carolina Rios Silva and Wellington Garcia Campos
Agronomy 2024, 14(5), 943; https://doi.org/10.3390/agronomy14050943 (registering DOI) - 30 Apr 2024
Abstract
Pests in pastures have compromised the production of biomass for feeding livestock herds. Many strategies have been applied to sustainably solve this problem. One viable and innovative technique is the delivery of entomopathogenic fungi through endophytes. Therefore, this study aimed to (i) evaluate
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Pests in pastures have compromised the production of biomass for feeding livestock herds. Many strategies have been applied to sustainably solve this problem. One viable and innovative technique is the delivery of entomopathogenic fungi through endophytes. Therefore, this study aimed to (i) evaluate the endophytic capacity of two entomopathogenic fungi, Fusarium multiceps UFMGCB 11443 and Metarhizium anisopliae UFMGCB 11444, in Urochloa brizantha [(Hochst. ex A. Rich.) Stapf] (Poaceae) cultivar ‘Marundu’) via foliar inoculation or seed treatment, and (ii) measure their efficiency in controlling Mahanarva spectabilis Distant, 1909 (Hemiptera: Cercopidae) in U. brizantha. In the greenhouse, the fungi colonized the tissues of U. brizantha plants when inoculated via foliar spraying or seed treatment. The fungi F. multiceps and M. anisopliae caused 88% and 97.1% epizootic effects via seed inoculation, respectively, and 100% epizootic effects via foliar inoculation. In the field, the lowest fungal dose of 0.5 kg/ha had the same effect as a fourfold greater dose, with a >86% decrease in insect pest infestation observed. In summary, the fungi F. multiceps and M. anisopliae have endophytic effects and can effectively control M. spectabilis in U. brizantha pastures.
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(This article belongs to the Special Issue Biological Pest Control in Agroecosystems)
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Soil Organic Matter Input Promotes Coastal Topsoil Desalinization by Altering the Salt Distribution in the Soil Profile
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Jingsong Li, Weiliu Li, Xiaohui Feng, Xiaojing Liu, Kai Guo, Fengcui Fan, Shengyao Liu and Songnan Jia
Agronomy 2024, 14(5), 942; https://doi.org/10.3390/agronomy14050942 (registering DOI) - 30 Apr 2024
Abstract
Organic amendment is an effective method to reclaim salt-affected soil. However, in coastal land with shallow saline groundwater, it is limited known about the mechanism of organic amendment on soil desalinization. Thus, to examine the effect of topsoil organic matter content on soil
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Organic amendment is an effective method to reclaim salt-affected soil. However, in coastal land with shallow saline groundwater, it is limited known about the mechanism of organic amendment on soil desalinization. Thus, to examine the effect of topsoil organic matter content on soil water/salt transport and distribution, two-year field observations in Bohai coastal land, North China, and soil column experiments simulating salt accumulation and salt leaching were conducted, respectively. There were different organic fertilizer amendment rates in 0–20 cm topsoil, 0% (CK), 50% (OA 0.5), and 100% (OA 1.0) (w/w) for soil column experiments. Field observation showed that after organic amendment (OA), the soil’s physical structure was improved, and less of the increase in topsoil salt content was observed, with more salt accumulated in deep soil layers during the dry season. In addition, OA greatly promoted salt leaching during the rainy seasons. The results of the soil column tests further indicated that OA treatments significantly inhibited soil evaporation, with less salt accumulated in the topsoil. Although there was no difference in soil water distribution between the CK and OA 0.5 treatment, the topsoil EC for the OA 0.5 treatment was significantly lower than that for CK. During soil water infiltration, the OA 0.5 and OA 1.0 treatments significantly increased the infiltration rates, enhanced the wetting front, and promoted salt leaching to deeper soil layers, compared with CK. The improvement of soil organic amounts could make the soil more self-resistant to the coastal salinization. The findings of this study provide some insights into soil water/salt regulation in heterogeneous soil masses and on the permanent management of coastal saline farmland.
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(This article belongs to the Topic Agronomy, Soil Health and Climate Change: Challenges and Solutions)
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Rapeseed Seed Coat Color Classification Based on the Visibility Graph Algorithm and Hyperspectral Technique
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Chaojun Zou, Xinghui Zhu, Fang Wang, Jinran Wu and You-Gan Wang
Agronomy 2024, 14(5), 941; https://doi.org/10.3390/agronomy14050941 (registering DOI) - 30 Apr 2024
Abstract
Information technology and statistical modeling have made significant contributions to smart agriculture. Machine vision and hyperspectral technologies, with their non-destructive and real-time capabilities, have been extensively utilized in the non-destructive diagnosis and quality monitoring of crops and seeds, becoming essential tools in traditional
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Information technology and statistical modeling have made significant contributions to smart agriculture. Machine vision and hyperspectral technologies, with their non-destructive and real-time capabilities, have been extensively utilized in the non-destructive diagnosis and quality monitoring of crops and seeds, becoming essential tools in traditional agriculture. This work applies these techniques to address the color classification of rapeseed, which is of great significance in the field of rapeseed growth diagnosis research. To bridge the gap between machine vision and hyperspectral technology, a framework is developed that includes seed color calibration, spectral feature extraction and fusion, and the recognition modeling of three seed colors using four machine learning methods. Three categories of rapeseed coat colors are calibrated based on visual perception and vector-square distance methods. A fast-weighted visibility graph method is employed to map the spectral reflectance sequences to complex networks, and five global network attributes are extracted to fuse the full-band reflectance as model input. The experimental results demonstrate that the classification recognition rate of the fused feature reaches 0.943 under the XGBoost model, confirming the effectiveness of the network features as a complement to the spectral reflectance. The high recognition accuracy and simple operation process of the framework support the further application of hyperspectral technology to analyze the quality of rapeseed.
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(This article belongs to the Special Issue Current Research on Hyperspectral and Multispectral Imaging and Their Applications in Precision Agriculture Ⅱ)
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QTL Mapping for Agronomic Important Traits in Well-Adapted Wheat Cultivars
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Jingxian Liu, Danfeng Wang, Mingyu Liu, Meijin Jin, Xuecheng Sun, Yunlong Pang, Qiang Yan, Cunzhen Liu and Shubing Liu
Agronomy 2024, 14(5), 940; https://doi.org/10.3390/agronomy14050940 (registering DOI) - 30 Apr 2024
Abstract
Wheat (Triticum aestivum L.) is one of the most important food crops worldwide and provides the staple food for 40% of the world’s population. Increasing wheat production has become an important goal to ensure global food security. The grain yield of wheat
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Wheat (Triticum aestivum L.) is one of the most important food crops worldwide and provides the staple food for 40% of the world’s population. Increasing wheat production has become an important goal to ensure global food security. The grain yield of wheat is a complex trait that is usually influenced by multiple agronomically important traits. Thus, the genetic dissection and discovery of quantitative trait loci (QTL) of wheat-yield-related traits are very important to develop high-yield cultivars to improve wheat production. To analyze the genetic basis and discover genes controlling important agronomic traits in wheat, a recombinant inbred lines (RILs) population consisting of 180 RILs derived from a cross between Xinong822 (XN822) and Yannong999 (YN999), two well-adapted cultivars, was used to map QTL for plant height (PH), spike number per spike (SNS), spike length (SL), grain number per spike (GNS), spike number per plant (SN), 1000- grain weight (TGW), grain length (GL), grain width (GW), length/width of grain (GL/GW), perimeter of grain (Peri), and surface area of grains (Sur) in three environments. A total of 64 QTL were detected and distributed on all wheat chromosomes except 3A and 5A. The identified QTL individually explained 2.24–38.24% of the phenotypic variation, with LOD scores ranging from 2.5 to 29. Nine of these QTL were detected in multiple environments, and seven QTL were associated with more than one trait. Additionally, Kompetitive Allele Specific PCR (KASP) assays for five major QTL QSns-1A.2 (PVE = 6.82), QPh-2D.1 (PVE = 37.81), QSl-2D (PVE = 38.24), QTgw-4B (PVE = 8.78), and QGns-4D (PVE = 13.54) were developed and validated in the population. The identified QTL and linked markers are highly valuable in improving wheat yield through marker-assisted breeding, and the large-effect QTL can be fine-mapped for further QTL cloning of yield-related traits in wheat.
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(This article belongs to the Special Issue The Stress of Crop Adversity: The Mechanisms and Pathways of Stress Resistance)
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Establishment of a Reference Evapotranspiration Forecasting Model Based on Machine Learning
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Puyi Guo, Jiayi Cao and Jianhui Lin
Agronomy 2024, 14(5), 939; https://doi.org/10.3390/agronomy14050939 (registering DOI) - 30 Apr 2024
Abstract
Water scarcity is a global problem. Deficit irrigation (DI) reduces evapotranspiration, improving water efficiency in agriculture. Reference evapotranspiration is an important factor in determining DI. forecasting predicts field water consumption and enables proactive irrigation decisions,
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Water scarcity is a global problem. Deficit irrigation (DI) reduces evapotranspiration, improving water efficiency in agriculture. Reference evapotranspiration is an important factor in determining DI. forecasting predicts field water consumption and enables proactive irrigation decisions, offering guidance for water resource management. However, implementation of forecasting faces challenges due to complex calculations and extensive meteorological data requirements. This project aims to develop a machine learning system for forecasting. The project involves studying methods and identifying required meteorological parameters. Historical meteorological data and weather forecasts were obtained from meteorological websites and analyzed for accuracy after preprocessing. A machine learning-based model was created to forecast reference crop evapotranspiration. The model’s input parameters were selected through path analysis before it was optimized using Bayesian optimization to reduce overfitting and improve accuracy. Three forecasting models were developed: one based on historical meteorological data, one based on weather forecasts, and one that corrects the weather forecasts. All three models achieved good accuracy, with root mean square errors ranging from 0.52 to 0.81 mm/day. Among them, the model based on weather forecast had the highest accuracy; the RMSE six days before the forecast period was between 0.52 and 0.75 mm/day, and the RMSE on the seventh day of the forecast period was 1.12 mm/day. In summary, this project has established a mathematical model of prediction based on machine learning, which can achieve more accurate predictions for within a few days.
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(This article belongs to the Section Water Use and Irrigation)
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Integrating CEDGAN and FCNN for Enhanced Evaluation and Prediction of Plant Growth Environments in Urban Green Spaces
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Ying Wang, Zhansheng Mao, Hexian Jin, Abbas Shafi, Zhenyu Wang and Dan Liu
Agronomy 2024, 14(5), 938; https://doi.org/10.3390/agronomy14050938 (registering DOI) - 30 Apr 2024
Abstract
Conducting precise evaluations and predictions of the environmental conditions for plant growth in green spaces is crucial for ensuring their health and sustainability. Yet, assessing the health of urban greenery and the plant growth environment represents a significant and complex challenge within the
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Conducting precise evaluations and predictions of the environmental conditions for plant growth in green spaces is crucial for ensuring their health and sustainability. Yet, assessing the health of urban greenery and the plant growth environment represents a significant and complex challenge within the fields of urban planning and environmental management. This complexity arises from two main challenges: the limitations in acquiring high-density, high-precision data, and the difficulties traditional methods face in capturing and modeling the complex nonlinear relationships between environmental factors and plant growth. In light of the superior spatial interpolation capabilities of CEDGAN (conditional encoder–decoder generative adversarial neural network), notwithstanding its comparative lack of robustness across different subjects, and the excellent ability of FCNN (fully connected neural network) to fit multiple nonlinear equation models, we have developed two models based on these network structures. One model performs high-precision spatial attribute interpolation for urban green spaces, and the other predicts and evaluates the environmental conditions for plant growth within these areas. Our research has demonstrated that, following training with various samples, the CEDGAN network exhibits satisfactory performance in interpolating soil pH values, with an average pixel error below 0.03. This accuracy in predicting both spatial distribution and feature aspects improves with the increase in sample size and the number of controlled sampling points, offering an advanced method for high-precision spatial attribute interpolation in the planning and routine management of urban green spaces. Similarly, FCNN has shown commendable performance in predicting and evaluating plant growth environments, with prediction errors generally less than 0.1. Comparing different network structures, models with fewer hidden layers and nodes yielded superior training outcomes.
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(This article belongs to the Special Issue Applications of Machine Learning and Remote Sensing in Crop and Vegetation Monitoring)
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Open AccessEditorial
Agricultural Environment and Intelligent Plant Protection Equipment
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Xiongkui He, Fuzeng Yang and Baijing Qiu
Agronomy 2024, 14(5), 937; https://doi.org/10.3390/agronomy14050937 (registering DOI) - 30 Apr 2024
Abstract
Intelligent plant protection equipment utilizes advanced sensor technology and data analysis algorithms to achieve real-time monitoring and precise management of crop growth status, pest and disease situations, and environmental parameters [...]
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(This article belongs to the Special Issue Agricultural Environment and Intelligent Plant Protection Equipment)
Open AccessArticle
Phosphorus Distribution within Aggregates in Long-Term Fertilized Black Soil: Regulatory Mechanisms of Soil Organic Matter and pH as Key Impact Factors
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Naiyu Zhang, Qiong Wang, Yanhua Chen, Shuxiang Zhang, Xianmei Zhang, Gu Feng, Hongjun Gao, Chang Peng and Ping Zhu
Agronomy 2024, 14(5), 936; https://doi.org/10.3390/agronomy14050936 (registering DOI) - 30 Apr 2024
Abstract
Understanding soil phosphorus (P) distribution and its key drivers is fundamental for sustainable P management. In this study, a 21-year fertilization experiment on black soil was carried out, setting up five fertilization treatments: unfertilized control (CK), nitrogen and potassium (NK), nitrogen, P and
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Understanding soil phosphorus (P) distribution and its key drivers is fundamental for sustainable P management. In this study, a 21-year fertilization experiment on black soil was carried out, setting up five fertilization treatments: unfertilized control (CK), nitrogen and potassium (NK), nitrogen, P and potassium (NPK), NPK plus straw (NPKS), and NPK plus manure (NPKM). The distribution and effecting factors of P pools within soil aggregates were investigated. Compared to CK, the NK and NPK treatments decreased calcium-associated P concentration in all aggregate fractions. Meanwhile, the NPK treatment significantly increased the organic P extracted from NaOH in unaggregated particles (<0.053 mm). This was mainly due to the reduction in soil pH. The NPKS and NPKM treatments increased almost all P forms in aggregates, especially Ca-P. For the NPKM treatment, inorganic P extracted from resin, NaHCO3, and NaOH increased as aggregate size increased. This was mainly because straw or manure addition promoted soil organic carbon (SOC) storage in aggregates, creating more sorption sites via association with amorphous metallic minerals, and, thus, facilitating P accumulation. In conclusion, decreasing soil pH by chemical fertilizers is an effective strategy for mobilizing soil P, whereas increasing SOC by straw or manure facilitates P accumulation.
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(This article belongs to the Section Soil and Plant Nutrition)
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Phosphorus Release Dynamics from Ashes during a Soil Incubation Study: Effect of Feedstock Characteristics and Combustion Conditions
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Berta Singla Just, Pablo Martín Binder, Nagore Guerra-Gorostegi, Laura Díaz-Guerra, Rosa Vilaplana, Nicola Frison, Erik Meers, Laia Llenas and Ana Robles Aguilar
Agronomy 2024, 14(5), 935; https://doi.org/10.3390/agronomy14050935 (registering DOI) - 30 Apr 2024
Abstract
Recovering phosphorus (P) through combustion from waste streams, like wastewater sludge and animal manure, offers a promising solution. This research explores the P release patterns in different ashes derived from secondary raw materials, using a long-term soil incubation lasting 160 days. The study
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Recovering phosphorus (P) through combustion from waste streams, like wastewater sludge and animal manure, offers a promising solution. This research explores the P release patterns in different ashes derived from secondary raw materials, using a long-term soil incubation lasting 160 days. The study evaluated the P release dynamics in five types of ashes from enhanced biological phosphorus removal (EBPR) systems and pig slurry burned at different temperatures. According to the results, a primary effect was observed on P bioavailability during the initial incubation period. All tested ashes release more than 50% of the total P applied between days 5 and 10. Ashes from EBPR exhibited higher P release than those from pig manure, indicating ash origin as a key factor in P release. Additionally, combustion temperature was crucial, with higher temperatures resulting in increased P release rates. Furthermore, the Pearson correlation revealed a strong relationship between the characteristics of the ashes and the amount of P release. Overall, these findings suggest that ashes could be a valuable P-source for agriculture avoiding the process of wet chemical P extraction, thus reducing both economic and environmental costs.
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(This article belongs to the Special Issue Bio-Based Fertilizers in Agriculture: New Opportunities and Challenges)
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Research on a Biofilter for a Typical Application Scenario in China: Treatment of Pesticide Residue Wastewater in Orchards
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Jin Zeng, Quanchun Yuan, Wenzhi Xu, Hailong Li, Menghui Li, Xiaohui Lei, Wei Wang, Qiang Lin, Xue Li, Rui Xu and Xiaolan Lyu
Agronomy 2024, 14(5), 934; https://doi.org/10.3390/agronomy14050934 (registering DOI) - 30 Apr 2024
Abstract
To reduce pesticide pollution and promote sustainable agricultural development in China, we designed a pilot-scale biofilter system to treat residual imidacloprid wastewater in an orchard. The biofilter system demonstrated a high rate of removal of imidacloprid from the biodegradation wastewater, with removal rates
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To reduce pesticide pollution and promote sustainable agricultural development in China, we designed a pilot-scale biofilter system to treat residual imidacloprid wastewater in an orchard. The biofilter system demonstrated a high rate of removal of imidacloprid from the biodegradation wastewater, with removal rates from the outlet exceeding 99% at different concentrations of pesticides. Among environmental factors, imidacloprid concentration at the inlet and biomixture significantly affected the activity of imidacloprid-degrading bacteria. The dominant microbial communities during the stable operation of the biofilter system included Firmicutes, Actinobacteria, Proteobacteria, and Bacteroidetes at the phylum level and Bacillus, Methylobacter, and unclassified_f__Microbacteriaceae at the genus level. In future initiatives to improve biofilter performance and applicability, increasing attention should be paid to the dominant microbial communities, the number of biofilter units, and important environmental factors. Orchard workers in China should improve the existing treatment of residual pesticide wastewater to mitigate agricultural non-point source pollution.
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(This article belongs to the Special Issue Novel Studies in High-Performance and Precision Plant Protection Products Application)
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Molecular Identification and Phylogenetic Analysis of Cymbidium Species (Orchidaceae) Based on the Potential DNA Barcodes matK, rbcL, psbA-trnH, and Internal Transcribed Spacer
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Zhenming Chen, Ling Gao, Huizhong Wang and Shangguo Feng
Agronomy 2024, 14(5), 933; https://doi.org/10.3390/agronomy14050933 (registering DOI) - 29 Apr 2024
Abstract
Numerous Cymbidium species have significant commercial value globally due to their exotic ornamental flowers. Identifying Cymbidium species is challenging due to their similar shapes, which hinders their rational use and the conservation of germplasm resources. In the present study, firstly, four plastid loci
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Numerous Cymbidium species have significant commercial value globally due to their exotic ornamental flowers. Identifying Cymbidium species is challenging due to their similar shapes, which hinders their rational use and the conservation of germplasm resources. In the present study, firstly, four plastid loci (matK, rbcL, psbA-trnH, and atpF-atpH) and a nuclear locus (internal transcribed spacer, ITS) were initially examined to identify Cymbidium species. Secondly, we inferred the interspecific phylogeny of Cymbidium species using ITS sequences. All of these DNA regions, with the exception of atpF-atpH, could be readily amplified from Cymbidium, and the corresponding DNA sequences can be successfully obtained by sequencing. Our research demonstrated that ITS exhibited the highest intra- and interspecific divergences, the greatest barcoding gap, and the highest proportion of species identification. The phylogenetic analysis of Cymbidium species based on the ITS regions primarily corroborated the results obtained using traditional morphological methods. A comparative analysis of candidate DNA barcodes has shown that the ITS can be used not only for barcoding Cymbidium species but also for the phylogenetic analysis of Cymbidium.
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(This article belongs to the Special Issue Plant Genetic Resources and Biotechnology)
Open AccessArticle
Effect of Phosphorus Application on Subcellular Distribution and Chemical Morphology of Cadmium in Eggplant Seedlings under Cadmium Stress
by
Qinghui Meng, Wenhua Fan, Fenwu Liu, Gailing Wang and Xiaoying Di
Agronomy 2024, 14(5), 932; https://doi.org/10.3390/agronomy14050932 (registering DOI) - 29 Apr 2024
Abstract
Soil cadmium (Cd) contamination poses a serious threat to ecosystems, and the application of phosphorus fertilizers can reduce Cd toxicity. However, the specific effects of different phosphorus fertilizers on the subcellular distribution and chemical morphology of Cd in eggplant grown in calcareous Cd-contaminated
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Soil cadmium (Cd) contamination poses a serious threat to ecosystems, and the application of phosphorus fertilizers can reduce Cd toxicity. However, the specific effects of different phosphorus fertilizers on the subcellular distribution and chemical morphology of Cd in eggplant grown in calcareous Cd-contaminated soil remain unclear. This study examined the impact of various types and levels of phosphate fertilizers on the subcellular distribution and chemical morphology of cadmium in eggplant seedlings using a two-factor analysis. The investigation was conducted via a pot experiment utilizing a two-factor analysis. The application of 0.35 g kg−1 dicalcium phosphate significantly decreased the Cd content in the subcellular distribution and induced notable alterations in the chemical morphology of Cd in eggplant roots. Specifically, the ethanol-extracted Cd state decreased by 65.45%, and the sodium chloride-extracted Cd state decreased by 64.65%. Conversely, Cd extracted by deionized water, acetic acid, hydrochloric acid, and the residue state increased by 6.20%, 4.01%, 20.87%, and 17.85%, respectively. The application of 0.35 g kg−1 dicalcium phosphate resulted in the most significant reduction in Cd content in eggplant and modification of subcellular Cd distribution and chemical morphology in roots.
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(This article belongs to the Topic Soil Fertility and Plant Nutrition for Sustainable Agriculture)
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Open AccessArticle
Seedling-YOLO: High-Efficiency Target Detection Algorithm for Field Broccoli Seedling Transplanting Quality Based on YOLOv7-Tiny
by
Tengfei Zhang, Jinhao Zhou, Wei Liu, Rencai Yue, Mengjiao Yao, Jiawei Shi and Jianping Hu
Agronomy 2024, 14(5), 931; https://doi.org/10.3390/agronomy14050931 (registering DOI) - 28 Apr 2024
Abstract
The rapid and accurate detection of broccoli seedling planting quality is crucial for the implementation of robotic intelligent field management. However, existing algorithms often face issues of false detections and missed detections when identifying the categories of broccoli planting quality. For instance, the
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The rapid and accurate detection of broccoli seedling planting quality is crucial for the implementation of robotic intelligent field management. However, existing algorithms often face issues of false detections and missed detections when identifying the categories of broccoli planting quality. For instance, the similarity between the features of broccoli root balls and soil, along with the potential for being obscured by leaves, leads to false detections of “exposed seedlings”. Additionally, features left by the end effector resemble the background, making the detection of the “missed hills” category challenging. Moreover, existing algorithms require substantial computational resources and memory. To address these challenges, we developed Seedling-YOLO, a deep-learning model dedicated to the visual detection of broccoli planting quality. Initially, we designed a new module, the Efficient Layer Aggregation Networks-Pconv (ELAN_P), utilizing partial convolution (Pconv). This module serves as the backbone feature extraction network, effectively reducing redundant calculations. Furthermore, the model incorporates the Content-aware ReAssembly of Features (CARAFE) and Coordinate Attention (CA), enhancing its focus on the long-range spatial information of challenging-to-detect samples. Experimental results demonstrate that our Seedling-YOLO model outperforms YOLOv4-tiny, YOLOv5s, YOLOv7-tiny, and YOLOv7 in terms of speed and precision, particularly in detecting ‘exposed seedlings’ and ‘missed hills’-key categories impacting yield, with Average Precision (AP) values of 94.2% and 92.2%, respectively. The model achieved a mean Average Precision of 0.5 ([email protected]) of 94.3% and a frame rate of 29.7 frames per second (FPS). In field tests conducted with double-row vegetable ridges at a plant spacing of 0.4 m and robot speed of 0.6 m/s, Seedling-YOLO exhibited optimal efficiency and precision. It achieved an actual detection precision of 93% and a detection efficiency of 180 plants/min, meeting the requirements for real-time and precise detection. This model can be deployed on seedling replenishment robots, providing a visual solution for robots, thereby enhancing vegetable yield.
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(This article belongs to the Special Issue Precision Operation Technology and Intelligent Equipment in Farmland—2nd Edition)
Open AccessArticle
Unraveling Shikimate Dehydrogenase Inhibition by 6-Nitroquinazoline-2,4-diol and Its Impact on Soybean and Maize Growth
by
Aline Marengoni Almeida, Josielle Abrahão, Flavio Augusto Vicente Seixas, Paulo Sergio Alves Bueno, Marco Aurélio Schüler de Oliveira, Larissa Fonseca Tomazini, Rodrigo Polimeni Constantin, Wanderley Dantas dos Santos, Rogério Marchiosi and Osvaldo Ferrarese-Filho
Agronomy 2024, 14(5), 930; https://doi.org/10.3390/agronomy14050930 (registering DOI) - 28 Apr 2024
Abstract
The shikimate pathway is crucial for the biosynthesis of aromatic amino acids in plants and represents a promising target for developing new herbicides. This work aimed to identify inhibitors of shikimate dehydrogenase (SDH), a key enzyme of the shikimate pathway that catalyzes the
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The shikimate pathway is crucial for the biosynthesis of aromatic amino acids in plants and represents a promising target for developing new herbicides. This work aimed to identify inhibitors of shikimate dehydrogenase (SDH), a key enzyme of the shikimate pathway that catalyzes the conversion of 3-dehydroshikimate to shikimate. Virtual screening and molecular dynamic simulations were performed on the SDH active site of Arabidopsis thaliana (AtSDH), and 6-nitroquinazoline-2,4-diol (NQD) was identified as a potential inhibitor. In vitro assays showed that NQD decreased the activity of AtSDH by reducing Vmax while keeping KM unchanged, indicating non-competitive inhibition. In vivo, hydroponic experiments revealed that NQD reduced the root length of soybean and maize. Additionally, NQD increased the total protein content and certain amino acids. Soybean roots uptake NQD more efficiently than maize roots. Furthermore, NQD reduced shikimate accumulation in glyphosate-treated soybean roots, suggesting its potential to restrict the flow of metabolites along the shikimate pathway in soybean. The simultaneous treatment of maize seedlings with glyphosate and NQD accumulated gallic acid in the roots, indicating that NQD inhibits SDH in vivo. Overall, the data indicate that NQD inhibits SDH both in vitro and in vivo, providing valuable insights into the potential development of herbicides targeting SDH.
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(This article belongs to the Special Issue Application of Natural Products for Weed Control in Agricultural Systems)
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Identification of Candidate Genes for Salt Tolerance at Seedling Stage in Rice Using QTL-Seq and Chromosome Segment Substitution Line-Derived Population
by
Jiraporn Leawtrakun, Wanchana Aesomnuk, Srisawat Khanthong, Reajina Dumhai, Decha Songtoasesakul, Sunadda Phosuwan, Jiratchaya Nuanpirom, Varodom Charoensawan, Jonaliza L. Siangliw, Vinitchan Ruanjaichon, Theerayut Toojinda, Samart Wanchana, Meechai Siangliw and Siwaret Arikit
Agronomy 2024, 14(5), 929; https://doi.org/10.3390/agronomy14050929 (registering DOI) - 28 Apr 2024
Abstract
Rice is a staple food for more than half of the world’s population. However, the pervasive problem of salinity is severely undermining rice production, especially in coastal and low-lying areas where soil salinization is widespread. This stress, exacerbated by climate change, necessitates the
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Rice is a staple food for more than half of the world’s population. However, the pervasive problem of salinity is severely undermining rice production, especially in coastal and low-lying areas where soil salinization is widespread. This stress, exacerbated by climate change, necessitates the development of salt-tolerant rice varieties to ensure food security. In this study, an F2:3 population (n = 454) from a cross of KDML105 and its chromosome segment substitution line (CSSL) was used to identify genomic regions associated with salt tolerance at the seedling stage. Using the QTL-seq approach, a QTL significantly associated with salt tolerance was identified on chromosome 1. Annotation of candidate genes in this region revealed the potential regulators of salt tolerance, including MIKC-type MADS domain proteins, calmodulin-binding transcription factors, and NB-ARC domain-containing proteins. These and other identified genes provide insights into the genetic basis of salt tolerance. This study underscores the importance of using advanced genomics tools and CSSL populations in the study of complex traits such as salt tolerance in rice. Several candidate genes identified in this study could be used in further studies on molecular or physiological mechanisms related to the salt response and tolerance mechanism in rice. Additionally, these genes could also be utilized in plant breeding programs for salt tolerance.
Full article
(This article belongs to the Collection Abiotic Stress Tolerance in Plants: Towards a Sustainable Agriculture)
Open AccessArticle
Effects of Straw Returning on Soil Aggregates and Its Organic Carbon and Nitrogen Retention under Different Mechanized Tillage Modes in Typical Hilly Regions of Southwest China
by
Chengyi Huang, Huijuan Huang, Shengjie Huang, Weibo Li, Kairui Zhang, Yian Chen, Liu Yang, Ling Luo and Liangji Deng
Agronomy 2024, 14(5), 928; https://doi.org/10.3390/agronomy14050928 (registering DOI) - 28 Apr 2024
Abstract
Tillage modes and straw returning influence soil aggregate stability and the distribution of organic carbon (C) and nitrogen (N) in aggregates of different particle sizes. In the typical hilly regions of southwest China, the predominant soil type is purple soil, characterized by heavy
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Tillage modes and straw returning influence soil aggregate stability and the distribution of organic carbon (C) and nitrogen (N) in aggregates of different particle sizes. In the typical hilly regions of southwest China, the predominant soil type is purple soil, characterized by heavy texture and high stickiness, with relatively lower soil fertility compared to other soil types. The improper use of fertilizers and field management practices further exacerbates soil compaction. However, abundant straw resources in the region provide an opportunity for comprehensive straw utilization. The effective utilization of straw resources is of significant importance for stabilizing agricultural ecological balance, improving resource utilization efficiency, and alleviating ecological pressure. Previously, most studies have focused on the impact of different mechanized tillage systems on the physical and chemical properties of soil in hilly areas, while research on the preservation of water-stable aggregates’ organic C and N content remains limited. In this study, the soil properties of fields under a winter pea–summer corn rotation for two years were studied with regards to the effects of straw returning on its water-stable aggregate distribution, macroaggregate content (R0.25), mean weight diameter (MWD), geometric mean diameter (GMD), and the organic C and N content in soil aggregates of different particle sizes and at different depths. The effects of five different tillage modes were assessed, namely rotary tillage with straw mixed retention (RTM), conventional tillage with straw burial retention (CTB), no-tillage with straw covered retention (NTC), subsoiling with straw covered retention (STC), and no-tillage without straw retention (NT). Based on the study results, under different tillage modes, straw returning effectively enhanced the soil organic carbon (SOC) and total nitrogen (TN) reserves at the plow layer (0–30 cm), SOC increased by 17.2% to 88%, and TN increased by 8.6% to 85.9%. At the same time, the content of 0.25–2 mm aggregates increased under the straw-return treatments under different tillage patterns. The NT treatment had the lowest R0.25 and MWD and GMD values for soil aggregates at different depths, which were significantly different (p < 0.05) from the other treatment modes. The correlation coefficients between SOC and soil aggregate stability indices ranged from 0.68 to 0.90, with most of them showing highly significant positive correlations (p < 0.01). In conclusion, straw returning under different tillage systems has improved soil aggregate stability and promoted soil structure stability. Specifically, the STC treatment has shown more pronounced effects on soil improvement in the upper soil layer of the hilly regions in southwest China, while the RTM treatment is beneficial for improving the lower soil layer. Therefore, the comprehensive experimental results indicate that the combination of STC and RTM treatments represents the most promising mechanized tillage and straw returning practices for the hilly regions in southwest China.
Full article
(This article belongs to the Special Issue Tillage Systems and Fertilizer Application on Soil Health)
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