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
Water, Salt, and Ion Transport and Its Response to Water-Saving Irrigation in the Hetao Irrigation District Based on the SWAT-Salt Model
Agronomy 2024, 14(5), 953; https://doi.org/10.3390/agronomy14050953 (registering DOI) - 02 May 2024
Abstract
Soil salinization is one of the main hazards affecting the sustainable development of agriculture in the Hetao Irrigation District (HID) of Inner Mongolia. To grasp the water and salt transport patterns and spatial–temporal distribution characteristics of the HID at the regional scale, the
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Soil salinization is one of the main hazards affecting the sustainable development of agriculture in the Hetao Irrigation District (HID) of Inner Mongolia. To grasp the water and salt transport patterns and spatial–temporal distribution characteristics of the HID at the regional scale, the improved Soil and Water Assessment Tool with a salinity module (SWAT-Salt) model was used to establish the distributed water and salt transport model for the watershed in this study. The results demonstrated that the modified model could more accurately represent the process of water and salt changes in the HID. The coefficient of determination (R2) in the simulation of streamflow and discharge salt loading was 0.83 and 0.86, respectively, and the Nash–Sutcliffe efficiency (NSE) was 0.80 and 0.74, respectively. Based on this, different hydrological processes (surface runoff, lateral flow, groundwater, soil seepage) as well as spatial–temporal distribution characteristics of water salinity in groundwater and soil were analyzed in the HID. Differences in groundwater and soil salinity in different land uses and soil types were also compared. Of these, surface runoff and lateral flow salt discharge loading are concentrated in the southwestern portion of the basin, while groundwater salt discharge loading is concentrated in the eastern as well as southwestern portions of the basin. The salt discharge loading from groundwater accounts for about 98.7% of the total salt discharge loading from all hydrological pathways and is the major contributing part of salt discharge from the irrigation area. Soil salinity increases gradually from west to east. Groundwater salinity (2946 mg/L) and soil water electrical conductivity (0.309 dS/m) were minimized in the cropland. Meanwhile, rational allocation of irrigation water can appropriately increase the amount of salt discharge loading. In conclusion, the model could provide a reference for the investigation of soil salinization and water–salt management measures in irrigation areas.
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(This article belongs to the Section Soil and Plant Nutrition)
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Composting Dairy Manure with Biochar: Compost Characteristics, Aminopyralid Residual Concentrations, and Phytotoxicity Effects
by
Annesly Netthisinghe, Paul Woosley, William Strunk, Getahun Agga and Karamat Sistani
Agronomy 2024, 14(5), 952; https://doi.org/10.3390/agronomy14050952 (registering DOI) - 01 May 2024
Abstract
Aminopyralid (2-pyridine carboxylic acid, 4-amino-3, 6-dichloro-2-pyridine carboxylic acid) is an auxin herbicide that has been used widely to control broadleaf weeds in pasture and hay fields. With no post-application withdrawal time, aminopyralid absorbed into forage material can contaminate compost feed stocks such as
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Aminopyralid (2-pyridine carboxylic acid, 4-amino-3, 6-dichloro-2-pyridine carboxylic acid) is an auxin herbicide that has been used widely to control broadleaf weeds in pasture and hay fields. With no post-application withdrawal time, aminopyralid absorbed into forage material can contaminate compost feed stocks such as hay, grass bedding material, and manure. Composts derived from such feed stocks raises concerns about after-effect injuries to sensitive crops by residual aminopyralids. Biochar (BC) additive may affect the composting process and immobilizes organic pollutants. This study examined the effect of composting dairy manure/sawdust 1:1 mixture containing 10 ppb (wet) of aminopyralid with 0%, 2%, 4%, and 10% (w/w) BC levels on chemical and biological characteristics of compost, residual aminopyralid concentration, and intensity of plant injury to tomato (Lycopersicon esculentum L.) plants after composting in 140 L plastic rotary drum reactors for two 6-month cycles. Biochar addition decreased organic matter degradation and intensified reduction in residual aminopyralid levels in a dose-dependent manner. Composting with BC concentrated more N, P, and K, caused mild plant injuries, and increased the above ground biomass compared to the no BC incorporation. Addition of BC for composting aminopyralid-contaminated dairy manure can increase the phyto safety level of compost while enhancing the key fertilizer values.
Full article
(This article belongs to the Special Issue Composting as a Key Driver for Sustainable Agricultural Scenarios—Volume II)
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Maize/Peanut Intercropping Affects Legume Nodulation in Semi-Arid Conditions
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Chen Feng, Guijuan Du, Yue Zhang, Liangshan Feng, Lili Zhang, Qi Wang, Wuyan Xiang, Wei Bai, Qian Cai, Tianran Sun, Zhanxiang Sun and Lizhen Zhang
Agronomy 2024, 14(5), 951; https://doi.org/10.3390/agronomy14050951 (registering DOI) - 01 May 2024
Abstract
Maize/peanut intercropping is practiced widely to increase land productivity and considered a sustainable way for using and saving resources through peanut’s complementary N source via biological N2 fixation. Our study aims to understand how maize/peanut intercropping affects the nodulation of peanuts under
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Maize/peanut intercropping is practiced widely to increase land productivity and considered a sustainable way for using and saving resources through peanut’s complementary N source via biological N2 fixation. Our study aims to understand how maize/peanut intercropping affects the nodulation of peanuts under water-limiting conditions and different nitrogen inputs. A two-year micro-plot experiment in 2015–2016 and a two-year field experiment in 2017–2018 were conducted to quantify nodulation in maize/peanut intercropping and sole peanut cropping under four N fertilization rates (N-free, low, medium, and high N) in rain-fed water-limited conditions. In the micro-plot experiment, intercropped peanuts increased nodule biomass compared to sole peanuts. The nodule number of intercropped peanuts was 51.6% (p = 0.001) higher than that of sole cropped peanuts, while nodule weights did not differ at high N fertilization rates and were lower in the no-N fertilization control. However, the results were different in the field experiment. Both the nodule number and single weight of the sole cropped peanut were 48.7% (p = 0.020) and 58.9% (p = 0.014) higher than that of the intercropped peanut. The ratio of the nodule weight to aboveground dry matter at the beginning peg in the dry year of 2017 was lower in intercropping than sole cropping, especially at low N fertilization rates. The potential increase in nodulation found in a well-controlled micro-plot environment might be limited by strong water and light competitions in field conditions. The results could contribute to the understanding of interspecific interactions in cereal/legume intercropping.
Full article
(This article belongs to the Special Issue Integration of Agronomic Practices for Sustainable Crop Production—2nd Edition)
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Impact of Photosynthetic Efficiency on Watermelon Cultivation in the Face of Drought
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Dayane Mércia Ribeiro Silva, Allan Cunha Barros, Ricardo Barros Silva, Wesley de Oliveira Galdino, José Wilker Germano de Souza, Isabelly Cristina da Silva Marques, Jadielson Inácio de Sousa, Viviane da Silva Lira, Alan Fontes Melo, Lucas da Silva de Abreu, Elias de Oliveira Albuquerque Júnior, Luana do Nascimento Silva Barbosa, Antônio Lucrécio dos Santos Neto, Valdevan Rosendo dos Santos, Francisco Gilvan Borges Ferreira Freitas Júnior, Fernanda Nery Vargens, João Henrique Silva da Luz, Elizabeth Orika Ono and João Domingos Rodrigues
Agronomy 2024, 14(5), 950; https://doi.org/10.3390/agronomy14050950 (registering DOI) - 01 May 2024
Abstract
Water availability is a limiting factor for plant production, especially in Brazilian semi-arid regions. The main aim of the study was to investigate the physiological effects of drought during the fruiting stage of watermelon cultivation. A completely randomized block design with four replications
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Water availability is a limiting factor for plant production, especially in Brazilian semi-arid regions. The main aim of the study was to investigate the physiological effects of drought during the fruiting stage of watermelon cultivation. A completely randomized block design with four replications and six treatments varied by the number of lateral drip tapes (1 or 2) and the duration of drought stress (0, 4, and 8 days) was used. The following parameters were evaluated: relative chlorophyll content, relative leaf water content, electrolyte leakage, CO2 assimilation (A), stomatal conductance (gs), internal CO2 concentration, leaf temperature, transpiration (E), water use efficiency (WUE), carboxylation efficiency (CE), yield, thickness, diameter, length, and fruit °brix, at 4 and 8 days of drought. Drought negatively affected photosynthesis, particularly in treatments with a single dripper and 4 days of drought, resulting in reductions of up to 60% in A, 68% in gs, 44% in E, 58% in WUE, and 59% in CE, but did not have a significant effect on watermelon yield after 4 or 8 days of irrigation. It was concluded that drought influences the physiological responses of watermelon plants, mainly in reducing photosynthesis, but does not drastically affect fruit productivity in short periods of stress.
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(This article belongs to the Special Issue Crop and Vegetable Physiology under Environmental Stresses)
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Selectivity of the Premixtures Flufecanet, Diflufenican and Flufenacet, Diflufenican, Metribuzin on Bread Wheat (Triticum aestivum) and Barley (Hordeum vulgare) and Efficacy on ALS/ACCase-Resistant Populations of Lolium rigidum L.
by
Thomas Gitsopoulos, Ioannis Georgoulas, Eirini Vazanelli and Despoina Botsoglou
Agronomy 2024, 14(5), 949; https://doi.org/10.3390/agronomy14050949 (registering DOI) - 01 May 2024
Abstract
The premixtures flufenacet plus diflufenican and flufenacet plus diflufenican plus metribuzin are two herbicides recently registered in Greece for weed control in bread wheat and barley with application early post-emergence to the crop (1st–3rd leaf growth stage). To evaluate the selectivity of these
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The premixtures flufenacet plus diflufenican and flufenacet plus diflufenican plus metribuzin are two herbicides recently registered in Greece for weed control in bread wheat and barley with application early post-emergence to the crop (1st–3rd leaf growth stage). To evaluate the selectivity of these new herbicides, pot experiments were conducted by applying flufenacet plus diflufenican at 240 + 120 g ai ha−1 and flufenacet plus diflufenican plus metribuzin at 119.7 + 119.7 + 44.8 g ai ha−1 to bread wheat and barley, at 1st (BBCH-11), 2nd (BBCH-12) and 3rd (BBCH-13) leaf growth stage. The efficacy of the herbicides at the above-mentioned rates in pre-emergence application was also tested on three ALS/ACCase herbicide-resistant populations of Lolium rigidum L. in comparison with the pre-emergence herbicides prosulfocarb and chlorotoluron plus diflufenican at 3200 g ai ha−1 and 1380 + 92 g ai ha−1, respectively. The results revealed decreased selectivity of both premixtures when applied at BBCH-11 for both winter cereals, with flufenacet plus diflufenican being less selective compared to flufenacet plus diflufenican plus metribuzin. Both herbicides highly controlled the three herbicide-resistant L. rigidum populations. The results indicated that both premixtures are effective chemical options for the management of herbicide resistant L. rigidum. To ensure crop safety and optimize efficacy, application at BBCH-12 is recommended.
Full article
(This article belongs to the Special Issue Herbicides and Chemical Control of Weeds)
Open AccessEditorial
Analysis of Complex Traits and Molecular Selection in Annual Crops
by
Chao Shen
Agronomy 2024, 14(5), 948; https://doi.org/10.3390/agronomy14050948 (registering DOI) - 01 May 2024
Abstract
Annual crops, which include staple crops like rice [...]
Full article
(This article belongs to the Special Issue Analysis of Complex Traits and Molecular Selection in Annual Crops)
Open AccessArticle
Comparing Regression and Classification Models to Estimate Leaf Spot Disease in Peanut (Arachis hypogaea L.) for Implementation in Breeding Selection
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Ivan Chapu, Abhilash Chandel, Emmanuel Kofi Sie, David Kalule Okello, Richard Oteng-Frimpong, Robert Cyrus Ongom Okello, David Hoisington and Maria Balota
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.
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(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.
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(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
by
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.
Full article
(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.
Full article
(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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Open AccessArticle
QTL Mapping for Agronomic Important Traits in Well-Adapted Wheat Cultivars
by
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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Open AccessArticle
Establishment of a Reference Evapotranspiration Forecasting Model Based on Machine Learning
by
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
by
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
by
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)
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Phosphorus Distribution within Aggregates in Long-Term Fertilized Black Soil: Regulatory Mechanisms of Soil Organic Matter and pH as Key Impact Factors
by
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
by
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
by
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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