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
How the Management and Environmental Conditions Affect the Weed Vegetation in Canary Grass (Phalaris canariensis L.) Fields
Agronomy 2024, 14(6), 1169; https://doi.org/10.3390/agronomy14061169 (registering DOI) - 29 May 2024
Abstract
Canary grass (Phalaris canariensis L.) is a versatile crop with global significance; it is primarily cultivated for its small elliptical seeds, which are used as bird feed and for human consumption. This crop is adapted to various climates and soils, so it
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Canary grass (Phalaris canariensis L.) is a versatile crop with global significance; it is primarily cultivated for its small elliptical seeds, which are used as bird feed and for human consumption. This crop is adapted to various climates and soils, so it can be grown successfully in Hungary. However, challenges such as weed control, climate change impacts, and soil factors require strategic management for sustained success in canary grass cultivation. Our study investigated the impact of management and environmental (as seasonal and soil) factors on pre-harvest weed vegetation in canary grass fields in Southeast Hungary between 2017 and 2020. In addition to showing the weed vegetation of the canary grass, the aim of our work was to promote more effective weed management of canary grass by revealing correlations between soil, seasonality, and management variables, influencing weed diversity and coverage. Using the analysis of covariance (ANCOVA) and correlation tests, we tested significant variables, providing insights into the complex interactions affecting weed composition. A redundancy analysis (RDA) further unveiled the relationships between explanatory variables and weed species’ composition. The findings offer valuable information for effective weed management strategies in canary grass cultivation. Our comprehensive study on canary grass fields in Southeast Hungary sheds light on significant factors influencing weed composition and abundance. The average weed coverage was 10.8%, with summer annuals and creeping perennials being the most prevalent life forms. Echinochloa crus-galli, Cirsium arvense, Xanthium italicum, and Setaria viridis were among the dominant species. ANCOVAs revealed the impact of soil, management, and seasonal factors on weed cover, species richness, diversity, and yield levels. Soil properties like texture, pH, and nitrogen content showed varying effects on weed parameters. The vintage effect, tillage systems, and farming practices also played crucial roles. The redundancy analysis highlighted the influence of the year, soil sulfur content, and winter preceding crops on weed composition. In conclusion, the herbaceous vegetation in the studied area is dominated by summer germinating and creeping perennial species. Despite slight differences in average coverage and occurrence, a well-defined set of significant species is evident. Multicollinearity among variables suggests limitations to further increase the number of variables that can be included in the analysis. The ANCOVAs showed that the soil, seasonal, and farming variables significantly influence overall weed vegetation and crop yield, with a lesser impact on species richness and diversity. The reduced RDA model highlights the strong influence of the year on species’ composition, emphasizing the inherent factors during canary grass cultivation that are challenging to modify through farming practices.
Full article
(This article belongs to the Section Grassland and Pasture Science)
Open AccessArticle
Effects of Exogenous Brassinosteroid and Reduced Leaf Source on Source–Sink Relationships and Boll Setting in Xinjiang Cotton
by
Shanwei Lou, Hui Jiang, Jie Li, Liwen Tian, Mingwei Du, Tengfei Ma, Lizhen Zhang and Pengzhong Zhang
Agronomy 2024, 14(6), 1168; https://doi.org/10.3390/agronomy14061168 (registering DOI) - 29 May 2024
Abstract
Xinjiang cotton is characterized by high-density planting, which easily leads to competition between the source and sink, the shading of leaves and reproductive organs, and yield reduction. Balancing the relationship between source and sink can promote high and stable cotton yield. In this
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Xinjiang cotton is characterized by high-density planting, which easily leads to competition between the source and sink, the shading of leaves and reproductive organs, and yield reduction. Balancing the relationship between source and sink can promote high and stable cotton yield. In this study, field experiments were conducted by combining the exogenous application of brassinosteroid with a reduction in leaf source to study their effects on the physiological and yield attributes of cotton. The results indicate that brassinosteroid application increased the yield, with a maximum yield increase of 6.3%. The number of bolls per plant increased by 1.3 nos. The photosynthetic rate and dry matter accumulation were enhanced, and the proportion of reproductive organs in the dry matter increased by >4%. Under the reduced leaf source, brassinosteroid application increased the number of new leaves by 20%, delayed the shedding of reproductive organs by 5–10 days, and reduced the average shedding rate by 8.9%. Additionally, the number of bolls increased in the middle and upper parts and at the edge of the plant. The number of bolls increased by 19.4% on the 4th–8th fruiting branches and 60.7% at the edge. Under leaf reduction treatment, brassinosteroid application could generally increase yield. After brassinosteroid application and removing half the leaves of fruiting branches and all leaves of the vegetive branches, the yield was higher than that of the control. Thus, brassinosteroid application could improve the efficiency of the leaf source and promote dry matter accumulation in sinks. Moreover, it could optimize boll distribution and increase yield by reducing reproductive organ shedding. Under the high-density planting of cotton in Xinjiang, leaf source is a slight surplus, and a moderate reduction in plant density is conducive to increasing yield.
Full article
(This article belongs to the Section Agroecology Innovation: Achieving System Resilience)
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Open AccessArticle
Cereal-Legume Mixed Residue Addition Increases Yield and Reduces Soil Greenhouse Gas Emissions from Fertilized Winter Wheat in the North China Plain
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Md Raseduzzaman, Gokul Gaudel, Md Razzab Ali, Arbindra Timilsina, Fiston Bizimana, Stephen Okoth Aluoch, Xiaoxin Li, Yuming Zhang and Chunsheng Hu
Agronomy 2024, 14(6), 1167; https://doi.org/10.3390/agronomy14061167 (registering DOI) - 29 May 2024
Abstract
Incorporating crop residues into the soil is an effective method for improving soil carbon sequestration, fertility, and crop productivity. Such potential benefits, however, may be offset if residue addition leads to a substantial increase in soil greenhouse gas (GHG) emissions. This study aimed
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Incorporating crop residues into the soil is an effective method for improving soil carbon sequestration, fertility, and crop productivity. Such potential benefits, however, may be offset if residue addition leads to a substantial increase in soil greenhouse gas (GHG) emissions. This study aimed to quantify the effect of different crop residues with varying C/N ratios and different nitrogen (N) fertilizers on GHG emissions, yield, and yield-scaled emissions (GHGI) in winter wheat. The field experiment was conducted during the 2018–2019 winter wheat season, comprising of four residue treatments (no residue, maize residue, soybean residue, and maize-soybean mixed residue) and four fertilizer treatments (control, urea, manure, and manure + urea). The experiment followed a randomized split-plot design, with N treatments as the main plot factor and crop residue treatments as the sub-plot factor. Except for the control, all N treatments received 150 kg N ha−1 season−1. The results showed that soils from all treatments acted as a net source of N2O and CO2 fluxes but as a net sink of CH4 fluxes. Soybean residue significantly increased soil N2O emissions, while mixed residue had the lowest N2O emissions among the three residues. However, all residue amendments significantly increased soil CO2 emissions. Furthermore, soybean and mixed residues significantly increased grain yield by 24% and 21%, respectively, compared to no residue amendment. Both soybean and mixed residues reduced GHGI by 25% compared to maize residue. Additionally, the urea and manure + urea treatments exhibited higher N2O emissions among the N treatments, but they contributed to significantly higher grain yields and resulted in lower GHGI. Moreover, crop residue incorporation significantly altered soil N dynamics. In soybean residue-amended soil, both NH4+ and NO3− concentrations were significantly higher (p < 0.05). Conversely, soil NO3− content was notably lower in the maize-soybean mixed residue amendment. Overall, our findings contribute to a comprehensive understanding of how different residue additions from different cropping systems influence soil N dynamics and GHG emissions, offering valuable insights into effective agroecosystems management for long-term food security and soil sustainability while mitigating GHG emissions.
Full article
(This article belongs to the Special Issue Nutrient Cycling and Environmental Effects on Farmland Ecosystems)
Open AccessArticle
Can Nitrogen Fertilization and Intercropping Modify the Quality and Nutrient Yield of Barley–Field Bean Forage?
by
Francesco Giovanni Salvo Angeletti, Silvia Pampana, Iduna Arduini, Sergio Saia and Marco Mariotti
Agronomy 2024, 14(6), 1166; https://doi.org/10.3390/agronomy14061166 (registering DOI) - 29 May 2024
Abstract
Barley (Hordeum vulgare L.) and field bean (Vicia faba L. var minor) are often used for forage production in the Mediterranean environment. Their bromatological and productive characteristics are known when cultivated as sole crops, but if grown simultaneously in intercropping,
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Barley (Hordeum vulgare L.) and field bean (Vicia faba L. var minor) are often used for forage production in the Mediterranean environment. Their bromatological and productive characteristics are known when cultivated as sole crops, but if grown simultaneously in intercropping, the changes in their morphological and physiological characteristics could affect the quality and the nutrient yield of the resulting forages. In a two-year field research in Central Italy, we determined the bromatological traits and nutrient yields of barley and field bean, grown as sole crops or intercrops in a 1:1 additive design harvested at the heading and early dough stage with five nitrogen (N) rates (i.e., from 0 to 200 kg ha−1). Both intercropping and N fertilization increased the concentration of crude protein and fiber but decreased the general quality of the forage. However, the effects on nutrient yields were more marked; those of crude protein and total digestible nutrients increased by 46 and 29% with intercropping and by 49 and 46% with 150 kg N ha−1. Thus, we concluded that N fertilization should not exceed 50 kg ha−1 to maximize the relative feed value, while 150 kg ha−1 are suitable to boost nutrient yields.
Full article
(This article belongs to the Section Innovative Cropping Systems)
Open AccessArticle
Prediction Model of Nitrogen, Phosphorus, and Potassium Fertilizer Application Rate for Greenhouse Tomatoes under Different Soil Fertility Conditions
by
Xiaoyu Yu, Yuzhu Luo, Bing Bai, Xin Chen, Caiyan Lu and Xiuyuan Peng
Agronomy 2024, 14(6), 1165; https://doi.org/10.3390/agronomy14061165 (registering DOI) - 29 May 2024
Abstract
To reach the target yield of crops, nutrient management is essential. Selecting the appropriate prediction model and adjusting the nutrient supply based on the actual situation can effectively improve the nutrient utilization efficiency, crop yield, and product quality. Therefore, a prediction model of
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To reach the target yield of crops, nutrient management is essential. Selecting the appropriate prediction model and adjusting the nutrient supply based on the actual situation can effectively improve the nutrient utilization efficiency, crop yield, and product quality. Therefore, a prediction model of the NPK fertilizer application rate for greenhouse tomatoes under the target yield was studied in this study. Under low, medium, and high soil fertility conditions, a neural network prediction model based on the sparrow search algorithm (SSA-NN), a neural network prediction model based on the improved sparrow search algorithm (ISSA-NN), and a neural network prediction model based on the hybrid algorithm (HA-NN) were used to predict the NPK fertilizer application rate for greenhouse tomatoes. The experimental results indicated that the evaluation indexes (i.e., the mean square error (MSE), explained variance score (EVS), and coefficient of determination (R2)) of the HA-NN prediction model proposed in this study were superior than the SSA-NN and ISSA-NN prediction models under three different soil fertility conditions. Under high soil fertility, compared with the SSA-NN prediction model, the MSE of the ISSA-NN and HA-NN prediction models decreased to 0.007 and 0.005, respectively; the EVS increased to 0.871 and 0.908, respectively; and the R2 increased to 0.862 and 0.899, respectively. This study showed that the HA–NN prediction model was superior in predicting the NPK fertilizer application rate for greenhouse tomatoes under three different soil fertility conditions. Due to the significance of NPK fertilizer application rate prediction for greenhouse tomatoes, this technique is expected to bring benefits to agricultural production management and decision support.
Full article
(This article belongs to the Section Agroecology Innovation: Achieving System Resilience)
Open AccessArticle
Influence of Nitrogen Application Rate on Wheat Grain Protein Content and Composition in China: A Meta-Analysis
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Hao-Yuan An, Jing-Jing Han, Qian-Nan He, Yi-Lin Zhu, Peng Wu, Yue-Chao Wang, Zhi-Qiang Gao, Tian-Qing Du and Jian-Fu Xue
Agronomy 2024, 14(6), 1164; https://doi.org/10.3390/agronomy14061164 - 29 May 2024
Abstract
The nitrogen application rate (NAR) has a significant effect on the contents of wheat grain protein and its composition. There is still no consensus regarding the appropriate NAR, given the differences in studied conditions and influence of factors such as geographical location, climate,
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The nitrogen application rate (NAR) has a significant effect on the contents of wheat grain protein and its composition. There is still no consensus regarding the appropriate NAR, given the differences in studied conditions and influence of factors such as geographical location, climate, and soil nutrient contents. In this study, 66 papers related to wheat grain protein and its composition published from 1984 to 2021 were selected for meta-analysis in comprehensively evaluating the response of wheat grain protein content and composition to NAR in China. The results reveal that NAR significantly increased total protein content by 9.49–28.6%, gliadin by 9.13–30.5%, glutenin by 12.9–45.4%, albumin by 5.06–15.8%, and globulin by 8.52–24.0% of wheat grain in China, respectively, compared to no nitrogen application. The optimal NAR is 240–300 kg ha−1 when specific planting conditions are not being considered. Under different growing conditions, the NAR that provided the greatest increase in wheat grain protein and its composition varied as follows: 180–240 kg ha−1 in Northwest China and at >100 m altitudes; >300 kg ha−1 in North China and at <100 m altitudes and lower soil base nutrient levels; 240–300 kg ha−1 in Southeast China, with higher soil nutrients levels and for all average annual temperatures and precipitation ranges. In conclusion, the results of the present study reveal that it is feasible to systematically enhance the contents of wheat grain protein and its related fractions by appropriate NAR under different cropping conditions.
Full article
(This article belongs to the Special Issue Sustainable Management and Tillage Practice in Agriculture)
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Open AccessArticle
Inversion of Glycyrrhiza Chlorophyll Content Based on Hyperspectral Imagery
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Miaomiao Xu, Jianguo Dai, Guoshun Zhang, Wenqing Hou, Zhengyang Mu, Peipei Chen, Yujuan Cao and Qingzhan Zhao
Agronomy 2024, 14(6), 1163; https://doi.org/10.3390/agronomy14061163 - 29 May 2024
Abstract
Glycyrrhiza is an important medicinal crop that has been extensively utilized in the food and medical sectors, yet studies on hyperspectral remote sensing monitoring of glycyrrhiza are currently scarce. This study analyzes glycyrrhiza hyperspectral images, extracts characteristic bands and vegetation indices, and constructs
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Glycyrrhiza is an important medicinal crop that has been extensively utilized in the food and medical sectors, yet studies on hyperspectral remote sensing monitoring of glycyrrhiza are currently scarce. This study analyzes glycyrrhiza hyperspectral images, extracts characteristic bands and vegetation indices, and constructs inversion models using different input features. The study obtained ground and unmanned aerial vehicle (UAV) hyperspectral images and chlorophyll content (called Soil and Plant Analyzer Development (SPAD) values) from sampling sites at three growth stages of glycyrrhiza (regreening, flowering, and maturity). Hyperspectral data were smoothed using the Savitzky–Golay filter, and the feature vegetation index was selected using the Pearson Correlation Coefficient (PCC) and Recursive Feature Elimination (RFE). Feature extraction was performed using Competitive Adaptive Reweighted Sampling (CARS), Genetic Algorithm (GA), and Successive Projections Algorithm (SPA). The SPAD values were then inverted using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), and the results were analyzed visually. The results indicate that in the ground glycyrrhiza inversion model, the GA-XGBoost model combination performed best during the regreening period, with R2, RMSE, and MAE values of 0.95, 0.967, and 0.825, respectively, showing improved model accuracy compared to full-spectrum methods. In the UAV glycyrrhiza inversion model, the CARS-PLSR combination algorithm yielded the best results during the maturity stage, with R2, RMSE, and MAE values of 0.83, 1.279, and 1.215, respectively. This study proposes a method combining feature selection techniques and machine learning algorithms that can provide a reference for rapid, nondestructive inversion of glycyrrhiza SPAD at different growth stages using hyperspectral sensors. This is significant for monitoring the growth of glycyrrhiza, managing fertilization, and advancing precision agriculture.
Full article
(This article belongs to the Special Issue Innovation of Intelligent Detection and Pesticide Application Technology for Horticultural Crops)
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Open AccessArticle
Biochar Combined with Garbage Enzyme Enhances Nitrogen Conservation during Sewage Sludge Composting: Evidence from Microbial Community and Enzyme Activities Related to Ammoniation
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Jishao Jiang, Huilin Cui, Parag Bhople, Caspar C. C. Chater, Fuqiang Yu and Dong Liu
Agronomy 2024, 14(6), 1162; https://doi.org/10.3390/agronomy14061162 - 29 May 2024
Abstract
Nitrogen loss is an unavoidable problem during composting processes, and the ammonia oxidation process significantly affects nitrogen transformation. The objective of this study was to evaluate nitrogen transformation when garbage enzyme (GE), biochar (BC), pelelith (PL) and combinations thereof were added during sewage
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Nitrogen loss is an unavoidable problem during composting processes, and the ammonia oxidation process significantly affects nitrogen transformation. The objective of this study was to evaluate nitrogen transformation when garbage enzyme (GE), biochar (BC), pelelith (PL) and combinations thereof were added during sewage sludge composting. Meanwhile, the succession of ammonia-oxidizing bacteria (AOB) and archaea (AOA) were also explored via quantitative polymerase chain reaction and high-throughput sequencing. The results showed that GE + BC and GE + PL treatments decreased ammonia (NH3) formation by 23.8% and 8.3%, and that of nitrous oxide (N2O) by 25.7% and 26.3% relative to the control, respectively. Simultaneously, the GE, GE + BC, and GE + PL treatments boosted the succession of AOA and AOB, and increased the activities of ammonia monooxygenase (AMO) and hydroxylamine oxidoreductase (HAO) activities and the gene copies of AOA and AOB. The AMO activities, NH4-N, NO3-N, and C/N, significantly affect AOA and AOB community structures. The network analysis predicted that the AMO and HAO were secreted mainly by the unclassified_Archaea and norank_Crenarchaeota, whereas it also showed that the GE + BC improved microbial associations with AOA, enzymatic activity, and environmental factors. Thus, the addition of garbage enzyme and biochar appears to be a promising mitigation strategy to reduce nitrogen losses during the composting process.
Full article
(This article belongs to the Special Issue Recycling of Organic Wastes in Agriculture: Serving for Sustainable Agriculture)
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Open AccessArticle
Challenges in Mapping Soil Variability Using Apparent Soil Electrical Conductivity under Heterogeneous Topographic Conditions
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István Mihály Kulmány, László Bede, Dávid Stencinger, Sándor Zsebő, Péter Csavajda, Renátó Kalocsai, Márton Vona, Gergely Jakab, Viktória Margit Vona and Ákos Bede-Fazekas
Agronomy 2024, 14(6), 1161; https://doi.org/10.3390/agronomy14061161 - 29 May 2024
Abstract
Site-specific management requires the identification of treatment areas based on homogeneous characteristics. This study aimed to determine whether soil mapping based on apparent soil electrical conductivity (ECa) is suitable for mapping soil properties of fields with topographic heterogeneity. Research was conducted
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Site-specific management requires the identification of treatment areas based on homogeneous characteristics. This study aimed to determine whether soil mapping based on apparent soil electrical conductivity (ECa) is suitable for mapping soil properties of fields with topographic heterogeneity. Research was conducted on two neighbouring fields in Fejér county, Hungary, with contrasting topographic heterogeneity. To characterise the spatial variability of soil attributes, ECa was measured and supplemented by obtaining soil samples and performing soil profile analysis. The relationship between ECa and soil physical and chemical properties was analysed using correlation, principal component, and regression analyses. The research revealed that the quality and strength of the relationship between ECa and soil remarkably differed in the two studied fields. In homogeneous topographic conditions, ECa was weakly correlated with elevation as determined by soil physical texture and nutrient content in a strong (R2 = 0.72) linear model. On the other hand, ECa was significantly determined by elevation in heterogeneous topographic conditions in a moderate (R2 = 0.47) linear model. Consequently, ECa-based soil mapping can only be used to characterise the soil, thus delineating management zones under homogeneous topographic conditions.
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(This article belongs to the Special Issue Advances in Soil Fertility, Plant Nutrition and Nutrient Management)
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Open AccessArticle
Response Surface Methodology for Development of Nutrient Solution Formula for Hydroponic Lettuce Based on the Micro-Elements Fertilizer Requirements at Different Growth Stages
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Binbin Gong, Xiaowei Ren, Wenyu Hao, Jingrui Li, Shenglin Hou, Kun Yang, Xiaolei Wu and Hongbo Gao
Agronomy 2024, 14(6), 1160; https://doi.org/10.3390/agronomy14061160 - 29 May 2024
Abstract
In order to precisely obtain the impact of nutritional elements on lettuce yield and quality, in the present study, a nutrient solution formula suitable for lettuce hydroponic production was development using response surface methodology based on the determination for micro-elements in three growth
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In order to precisely obtain the impact of nutritional elements on lettuce yield and quality, in the present study, a nutrient solution formula suitable for lettuce hydroponic production was development using response surface methodology based on the determination for micro-elements in three growth stages and taking the interaction between elements into account. Then, the formula was optimized and validated, aiming for the goal of improving lettuce yield and quality. The results showed that 200 healthy lettuce leaves contained similar amounts of macro-elements, and there was no significant difference in the unit content of micro-elements among the seedling, rosette, and harvest stages. Quadratic regression models between shoot fresh weight, SPAD value, soluble sugar content, Vc content, and nutrient content were established (R2 = 0.91, 0.95, 0.98, and 0.81, respectively). The optimal concentrations of P, K, Ca, and Mg obtained by multi-objective optimization of the quadratic regression models for fresh weight, SPAD value, soluble sugar content, and Vc content were 2.71 mmol·L−1, 6.42 mmol·L−1, 5.58 mmol·L−1, and 7.11 mmol·L−1, respectively. The nutrient solution formula (T1) was found to be the optimal nutrient solution formula for improving lettuce growth and quality. Overall, we developed a specific and targeted nutrient solution formulation for lettuce; this formulation not only meets lettuce’s demand for nutrients, but also improves lettuce yield and quality, providing more choices for lettuce production in a region with high salts and high pH in the irrigation water.
Full article
(This article belongs to the Special Issue Advances in Data, Models, and Their Applications in Agriculture)
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Open AccessArticle
QTL Mapping for Bacterial Wilt Resistance in Eggplant via Bulked Segregant Analysis Using Genotyping by Sequencing
by
Xi’ou Xiao, Wenqiu Lin, Heng Nie, Zhe Duan and Ke Liu
Agronomy 2024, 14(6), 1159; https://doi.org/10.3390/agronomy14061159 - 29 May 2024
Abstract
The bacterial wilt disease caused by Ralstonia solanacearum is a significant threat to eggplant production. Breeding and promoting resistant varieties is one of the most effective methods to manage bacterial wilt. Conducting QTL (quantitative trait locus) mapping of resistant genes can substantially enhance
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The bacterial wilt disease caused by Ralstonia solanacearum is a significant threat to eggplant production. Breeding and promoting resistant varieties is one of the most effective methods to manage bacterial wilt. Conducting QTL (quantitative trait locus) mapping of resistant genes can substantially enhance the breeding of plant resistance to bacterial wilt. In this study, a population of 2200 F2 individuals derived from resistant and susceptible materials was utilized to establish extreme resistance and susceptibility pools. Following resequencing analysis of the parents and extreme pools, the QTL were examined using the DEEP-BSA software and QTLseqr R package (version 0.7.5.2). The results revealed that the detection of 10 QTL sites on chromosomes 5, 8, 9, and 11 by the five algorithms of the DEEP-BSA software. Additionally, the candidate region of 62 Mb–72 Mb on chromosome 5 was identified in all five algorithms of the DEEP-BSA software, as well as by the QTLseqr R package. Subsequent gene annotation uncovered 276 genes in the candidate region of 62 Mb–72 Mb on chromosome 5. Additionally, RNA-seq results indicated that only 13 genes had altered expression levels following inoculation with R. solanacearum in the resistant materials. Based on the expression levels, SMEL4_05g015980.1 and SMEL4_05g016110.1 were identified as candidate genes. Notably, SNP annotation identified a non-synonymous mutation in the exonic region of SMEL4_05g015980.1 and a variant in the promoter region of SMEL4_05g016110.1. The research findings have practical significance for the isolation of bacterial wilt resistance genes in eggplant and the development of resistance to bacterial wilt varieties in eggplant.
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(This article belongs to the Section Crop Breeding and Genetics)
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Open AccessArticle
Genetic Diversity of HMW-GS and the Correlation of Grain Quality Traits in Bread Wheat (Triticum aestivum L.) in Hubei Province, China
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Xiaofang Wang, Yue An, Junpeng Chen, Mengwei Wang, Chengyang Wang, Wei Hua, Qifei Wang, Song Gao, Daorong Zhang, Dong Ling, Xifeng Ren and Jinghuan Zhu
Agronomy 2024, 14(6), 1158; https://doi.org/10.3390/agronomy14061158 - 29 May 2024
Abstract
High-molecular-weight glutenin subunits (HMW-GS) are an important component of total cereal proteins in wheat. It is closely related to the processing quality of flour. Here, we analyzed allelic variations at the Glu-1 locus in 163 wheat accessions from Hubei Province, China with SDS-PAGE.
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High-molecular-weight glutenin subunits (HMW-GS) are an important component of total cereal proteins in wheat. It is closely related to the processing quality of flour. Here, we analyzed allelic variations at the Glu-1 locus in 163 wheat accessions from Hubei Province, China with SDS-PAGE. Among the 15 alleles detected, alleles 1, 7+8, and 2+12 were the major alleles, and 7, 6+8, and 2+10 were rare alleles. The breeding lines had higher genetic diversity than the commercial varieties. Alleles 7 and 6+8 significantly reduced the grain protein content and wet gluten content of wheat. The “1, 7+9, 5+10” and “1, 14+15, and 2+12” allelic combinations significantly increased the grain protein content, hardness index, test weight, and wet gluten content of wheat. Alleles 7+9, 14+15, and 5+10 were identified as alleles related to high wheat quality. The “1, 7, 5+10”, “1, 6+8, 5+10”, “null, 7+9, 2+12”, “1, 14+15, 2+12”, and “1, 7+9, 5+10” allelic combinations had greater effects on wheat grain quality traits. These results demonstrate the effects of HMW-GS on wheat grain quality traits and provide a reference for the genetic improvement of wheat quality.
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(This article belongs to the Section Crop Breeding and Genetics)
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Open AccessArticle
Effect of Plant Density on Growth and Bioactive Compounds in Salvia miltiorrhiza
by
Zhiheng Xing, Guihong Bi, Tongyin Li, Qianwen Zhang and Patricia R. Knight
Agronomy 2024, 14(6), 1157; https://doi.org/10.3390/agronomy14061157 - 29 May 2024
Abstract
Danshen (Salvia miltiorrhiza) is an herbaceous plant widely used in the pharmaceutical industry. However, the majority of medicinal plants utilized in the US are imported, posing challenges such as fluctuations in bioactive compound concentrations and insufficient supply to meet demand. Determining
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Danshen (Salvia miltiorrhiza) is an herbaceous plant widely used in the pharmaceutical industry. However, the majority of medicinal plants utilized in the US are imported, posing challenges such as fluctuations in bioactive compound concentrations and insufficient supply to meet demand. Determining the optimal plant density is a key management decision for danshen production. This study aimed to investigate the effects of different plant densities on the growth and bioactive compound content of danshen cultivated in Mississippi. A field experiment was conducted to investigate the effects of different plant densities on individual plant growth, photosynthesis, and the content of bioactive components in danshen in 2020 and 2021. Six plant densities were designed: 30 × 20 cm (between row spacing × within row spacing), 30 × 30 cm, 30 × 40 cm, 45 × 20 cm, 45 × 30 cm, or 45 × 40 cm. A plant density of 45 × 40 cm resulted in danshen plants exhibiting the highest Plant Growth Index (PGI), SPAD, root number, shoot number, shoot fresh and dry weight, maximum root diameter, maximum root length, net photosynthesis, intracellular CO2 concentration, tanshinone I, and cryptotanshinone, regardless of year. Plants spaced at 45 × 30 cm had similar root fresh weight, root dry weight, and tanshinone IIA and salvianolic acid B levels compared with plants grown at the 45 × 40 cm spacing, and both were significantly higher than other densities.
Full article
(This article belongs to the Special Issue Medicinal and Aromatic Plants: Cultivation, Chemistry and Promising Applications)
Open AccessArticle
Isolation, Identification and Characterization of Leptosphaerulina trifolii, the Causative Agent of Alfalfa Leptosphaerulina Leaf Spot in Inner Mongolia, China
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Hongli Huo, Jiuru Huangfu, Peiling Song, Dongmei Zhang, Zhidan Shi, Lili Zhao, Ziqin Li and Hongyou Zhou
Agronomy 2024, 14(6), 1156; https://doi.org/10.3390/agronomy14061156 - 28 May 2024
Abstract
Leptosphaerulina leaf spot, caused by Leptosphaerulina trifolii, is a major disease of alfalfa (Medicago sativa), leading to noticeable losses. From 2022 to 2023, we collected samples of alfalfa with symptoms of the disease from different locations in Inner Mongolia, China. Nine
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Leptosphaerulina leaf spot, caused by Leptosphaerulina trifolii, is a major disease of alfalfa (Medicago sativa), leading to noticeable losses. From 2022 to 2023, we collected samples of alfalfa with symptoms of the disease from different locations in Inner Mongolia, China. Nine fungal isolates recovered from these samples were identified through morphological traits and a maximum likelihood phylogeny based on concatenated partial sequences of ITS, 28S, and rpb2. A pathogenicity test on alfalfa confirmed the pathogenicity of the isolates on alfalfa. Analysis of physiological traits of L. trifolii revealed optimal mycelium growth at 20 °C and a pH range of 5 to 7, with soluble starch as the preferred carbon source and yeast extract as the optimal nitrogen source. The pathogen thrived in V8-juice agar and oat agar media. This study confirms L. trifolii as the causative agent of Leptosphaerulina leaf spot of alfalfa in Inner Mongolia and provides valuable insights into its optimal growth conditions. These findings enhance the understanding and management of this disease in alfalfa fields.
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(This article belongs to the Special Issue Diseases of Herbaceous Plants)
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Taoism-Net: A Fruit Tree Segmentation Model Based on Minimalism Design for UAV Camera
by
Yanheng Mai, Jiaqi Zheng, Zefeng Luo, Chaoran Yu, Jianqiang Lu, Caili Yu, Zuanhui Lin and Zhongliang Liao
Agronomy 2024, 14(6), 1155; https://doi.org/10.3390/agronomy14061155 - 28 May 2024
Abstract
The development of precision agriculture requires unmanned aerial vehicles (UAVs) to collect diverse data, such as RGB images, 3D point clouds, and hyperspectral images. Recently, convolutional networks have made remarkable progress in downstream visual tasks, while often disregarding the trade-off between accuracy and
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The development of precision agriculture requires unmanned aerial vehicles (UAVs) to collect diverse data, such as RGB images, 3D point clouds, and hyperspectral images. Recently, convolutional networks have made remarkable progress in downstream visual tasks, while often disregarding the trade-off between accuracy and speed in UAV-based segmentation tasks. The study aims to provide further valuable insights using an efficient model named Taoism-Net. The findings include the following: (1) Prescription maps in agricultural UAVs requires pixel-level precise segmentation, with many focusing solely on accuracy at the expense of real-time processing capabilities, being incapable of satisfying the expectations of practical tasks. (2) Taoism-Net is a refreshingly segmented model, overcoming the challenges of complexity in deep learning, based on minimalist design, which is used to generate prescription maps through pixel level classification mapping of geodetic coordinates (the lychee tree aerial dataset in Guangdong is used for experiments). (3) Compared with mainstream lightweight models or mature segmentation algorithms, Taoism-Net achieves significant improvements, including an improvement of at least 4.8% in mIoU, and manifested a superior performance in the accuracy–latency curve. (4) “The greatest truths are concise” is a saying widely spread by ancient Taoism, indicating that the most fundamental approach is reflected through the utmost minimalism; moreover, Taoism-Net expects to a build bridge between academic research and industrial deployment, for example, UAVs in precision agriculture.
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(This article belongs to the Special Issue New Trends in Agricultural UAV Application)
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Improvement of Climate Resource Utilization in the Southwestern Hilly Region through the Construction of a New Multi-Maturing Cropping System
by
Fanlei Kong, Tongliang Li, Wei Zhang, Pijiang Yin, Fan Liu, Tianqiong Lan, Dongju Feng, Xinglong Wang and Jichao Yuan
Agronomy 2024, 14(6), 1154; https://doi.org/10.3390/agronomy14061154 - 28 May 2024
Abstract
The construction of an appropriate cropping pattern is crucial for the improvement of regional agricultural economic efficiency and sustainable development. Despite previous efforts, there remains a gap in optimizing cropping patterns that fully leverage climate resources to enhance production efficiency. This study addresses
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The construction of an appropriate cropping pattern is crucial for the improvement of regional agricultural economic efficiency and sustainable development. Despite previous efforts, there remains a gap in optimizing cropping patterns that fully leverage climate resources to enhance production efficiency. This study addresses this gap by systematically comparing the differences in climate resource allocation, production efficiency and crop response among models by constructing four new triple-maturing cropping models at typical ecological sites in the hilly areas of southwest China. To solve the above problems, we constructed eight cropping patterns and classified them to three as follows: the Traditional Double Cropping System, the Traditional Triple Cropping System, the Novel Triple Cropping System. The results showed that the new multi-maturing planting pattern was significantly better than the traditional two-maturing netting pattern and the traditional three-maturing planting pattern in terms of light, temperature and water productivity. Compared with the traditional two-maturity net cropping model and the traditional three-maturity cropping model, the new cropping model increased light energy productivity by 97.88% and 50.00%, respectively; light energy use by an average of 0.48% and 0.31%; cumulative temperature productivity by an average of 84.70% and 49.14%; and rainfall productivity by an average of 101.04% and 49.61%. An assessment of the light, temperature and water meteorological resource use efficiency of the different treatments showed that the resource use efficiency of the new multi-maturing planting pattern was on average 111.58% and 74.78% higher than that of the traditional two-maturing net planting pattern and the traditional three-maturing planting pattern, with the T6 pattern having the highest resource use efficiency. The new multi-ripening cropping pattern has demonstrated production stability in response to changes in light, temperature and water resources, better adapting to weekly climate changes, stabilizing yields and improving efficiency. In summary, the results of this study can provide a theoretical basis for optimizing cropping patterns and promoting the use of climate resources in agriculture and sustainable development. Future research should focus on further refining these models, exploring their adaptability to various climatic conditions, and evaluating their long-term economic and environmental impacts.
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(This article belongs to the Section Innovative Cropping Systems)
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The Seasonal Response of N2O Emissions on Increasing Precipitation and Nitrogen Deposition and Its Driving Factors in Temperate Semi-Arid Grassland
by
Qin Peng, Yuchun Qi, Feihu Yin, Yu Guo, Yunshe Dong, Xingren Liu, Xiujin Yuan and Ning Lv
Agronomy 2024, 14(6), 1153; https://doi.org/10.3390/agronomy14061153 - 28 May 2024
Abstract
The accurate assessment of the rise in nitrous oxide (N2O) under global changes in grasslands has been hindered because of inadequate annual observations. To measure the seasonal response of N2O emissions to increased water and nitrogen (N) deposition, one
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The accurate assessment of the rise in nitrous oxide (N2O) under global changes in grasslands has been hindered because of inadequate annual observations. To measure the seasonal response of N2O emissions to increased water and nitrogen (N) deposition, one year round N2O emissions were investigated by chamber weekly in the growing season and every two weeks in the non-growing season in semi-arid temperate grasslands northern China. The results showed the temperate semi-arid grassland to be a source of N2O with greater variability and contribution during the non-growing season. The individual effects of water or N addition increased N2O emissions during the growing season, while the effects of water or N addition depended on the N application rates during the non-growing season. Soil properties, particularly soil temperature and water-filled pore space (WFPS), played key roles in regulating N2O emissions. Structural equation modeling revealed that these factors explained 71% and 35% of the variation in N2O fluxes during the growing and non-growing season, respectively. This study suggested that without observations during the non-growing season it is possible to misestimate the annual N2O emissions and the risk of N2O emissions increasing under global change. This would provide insights for future management strategies for mitigating greenhouse gas emissions.
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(This article belongs to the Special Issue Nutrient Cycling and Environmental Effects on Farmland Ecosystems)
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Diversification of Intensively Used Grassland: Resilience and Good Fodder Quality across Different Soil Types
by
Regine Albers and Dirk Carl Albach
Agronomy 2024, 14(6), 1152; https://doi.org/10.3390/agronomy14061152 - 28 May 2024
Abstract
In Central Europe, grasslands for dairy production are typically characterised by monocultures with high input rates of artificial fertilisers. However, it was suggested that biodiversity could reduce the need for anthropogenic inputs in functionally diversified grassland mixes while maintaining or enhancing yields and
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In Central Europe, grasslands for dairy production are typically characterised by monocultures with high input rates of artificial fertilisers. However, it was suggested that biodiversity could reduce the need for anthropogenic inputs in functionally diversified grassland mixes while maintaining or enhancing yields and fodder quality. To investigate this hypothesis, we developed five consecutively diversified grassland mixes consisting of ryegrass, legumes, a non-leguminous forb, and additional grass species for intensive fodder production, and tested them under regular agricultural conditions in a three-year experiment on sandy soil, marshland, and bog soil at one-hectare per mix and site. All mixtures produced similar high-quality forage in terms of utilisable crude protein content and net energy lactation rate, even under challenging climatic conditions. However, a high abundance of Dactylis glomerata can decrease these values, although factors such as seasonality and rainfall affect them to a greater degree. The seasonal composition changes between the functional groups, such as strong spring growth of grasses and strong summer growth of legumes, show complementarity rather than competition between the groups, resulting in consistent biomass production during the growth period. The results were consistent over the three soil types and provide the basis for further adaptation of mixes and breeding.
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(This article belongs to the Special Issue Advances in Grassland Ecology and Grass Phenotypic Plasticity)
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Melatonin Affects Leymus chinensis Aboveground Growth and Photosynthesis by Regulating Rhizome Growth
by
Yufeng Fan, Lingling Li, Tao Ma and Xiangyang Hou
Agronomy 2024, 14(6), 1151; https://doi.org/10.3390/agronomy14061151 - 28 May 2024
Abstract
Leymus chinensis is a perennial rhizomatous clone plant. It exhibits strong rhizomatous tillering and clonal growth through asexual reproduction. The root system is interdependent with aboveground growth and root growth can regulate aboveground growth and photosynthesis. Melatonin has been shown to regulate root
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Leymus chinensis is a perennial rhizomatous clone plant. It exhibits strong rhizomatous tillering and clonal growth through asexual reproduction. The root system is interdependent with aboveground growth and root growth can regulate aboveground growth and photosynthesis. Melatonin has been shown to regulate root growth and promote photosynthesis. However, it remains unclear whether melatonin affects aboveground growth and photosynthesis by regulating rhizome growth. To address this gap, we studied nine Leymus chinensis from different geographical locations, all grown under the same conditions. We selected two materials with strong (LC19) and weak (LC2) rhizome growth abilities from nine materials and treated them with exogenous melatonin. We found there were significant positive correlations between stem length, plant height, leaf number and rhizome traits. Additionally, rhizome traits showed significant positive correlations with photosynthetic indices and chlorophyll content. Specifically, for LC2, treatment with 200 μmol/L melatonin significantly increased root length, the number of extravaginal ramets and rhizome clonal growth rate by 88.72%, 43.75% and 43.70%, respectively, resulting in significant increases in aboveground traits. Similarly, for LC19, 200 μmol/L melatonin treatment led to significant increases of 74.66%, 23.02%, 62.71% and 62.72% in four traits, respectively, along with aboveground trait improvements. Furthermore, around 300 μmol/L melatonin treatment promoted photosynthetic efficiency in LC2, while around 100 μmol/L melatonin treatment had the same effect in LC19. In conclusion, our study highlights the relationship between rhizome growth ability, aboveground growth and photosynthesis in Leymus chinensis. Additionally, it suggests that exogenous melatonin can enhance aboveground growth and photosynthesis by regulating rhizome growth.
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(This article belongs to the Section Plant-Crop Biology and Biochemistry)
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Monitoring Land Management Practices Using Vis–NIR Spectroscopy Provides Insights into Predicting Soil Organic Carbon and Limestone Levels in Agricultural Plots
by
Juan E. Herranz-Luque, Javier Gonzalez-Canales, Juan P. Martín-Sanz, Omar Antón, Ana Moreno-Delafuente, Mariela J. Navas-Vázquez, Rubén Ramos-Nieto, Ramón Bienes, Andrés García-Díaz, Maria Jose Marques and Blanca Sastre
Agronomy 2024, 14(6), 1150; https://doi.org/10.3390/agronomy14061150 - 28 May 2024
Abstract
This study aimed to establish sound relationships between soil properties of agricultural land in central Spain and their spectral attributes to contribute to deriving an indicator for sustainable farm management. Sixteen plots, managed under various conditions, eight with traditional tillage and eight with
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This study aimed to establish sound relationships between soil properties of agricultural land in central Spain and their spectral attributes to contribute to deriving an indicator for sustainable farm management. Sixteen plots, managed under various conditions, eight with traditional tillage and eight with other alternative managements, were selected to gather soil samples representing three predominant soil orders (Leptosols, Cambisols, and Luvisols). Soil sampling was conducted from depths ranging from 0 to 30 cm (0–50; 5–10, 10–20, and 20–30 cm), ensuring a broad spectrum of sample variability across different times and locations. The reflectance of soil 64 soil samples was measured within the range of 400 to 900 nm, and the corresponding concentrations of soil organic carbon and majoritarian minerals, calcium carbonate, quartz, phyllosilicates, K-Feldspar, and plagioclase were determined for each sample. Partial least squares analysis was employed to construct prediction models using a calibration dataset comprising 66% of randomly selected samples. The remaining 33% of samples were utilized for model validation. The prediction models for the measured soil chemical properties yielded R2 values ranging from 0.14 to 0.79. Only SOC and limestone provided accurate prediction models. These findings hold promise for developing a soil health indicator tailored for site-specific crop management. However, the complex composition of soil organic carbon and calcium carbonate in certain soils underscores the importance of careful interpretation and validation of remote sensing data, as well as the need for advanced modeling approaches that can account for the interactions between multiple soil constituents.
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(This article belongs to the Special Issue The Effect of Appropriate Agriculture Management on Soil and Sustainable Crop Productivity)
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