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
Spatial Distribution of Soil Macroelements, Their Uptake by Plants, and Green Pea Yield under Strip-till Technology
Agronomy 2024, 14(4), 711; https://doi.org/10.3390/agronomy14040711 (registering DOI) - 28 Mar 2024
Abstract
Using conservation tillage to grow crops that enhance soil quality, such as legumes, seems to be one of the best solutions for sustainable agriculture. The field study was conducted to identify the effect of soil cultivation technology and fertilization, via strip-tilling (reduced) vs.
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Using conservation tillage to grow crops that enhance soil quality, such as legumes, seems to be one of the best solutions for sustainable agriculture. The field study was conducted to identify the effect of soil cultivation technology and fertilization, via strip-tilling (reduced) vs. plowing (conventional), on the availability and uptake of NPK and Mg, as well as on the growth of shoots and roots and yield of green peas (Pisum sativum L.). The research was carried out in central Poland (53°05′16.8″ N, 19°06′14.4″ E) over two growing seasons of green peas in 2016 and 2017. Our study has shown that the spatial distribution of macroelements in the soil is influenced by the tillage method. The availability and nutrient uptake by green peas, their growth parameters, and yield were also influenced by the tillage system. However, the effect was observed mainly in the first year of the study, which had less precipitation and higher temperatures. In general, in our study, the strip-till has a positive impact on the nutrient uptake by plants, contributing to longer shoots and roots and higher biomass accumulation, especially in the first part of the growing season. In 2016, with less rainfall, green peas under strip-tilling produced more pods per plant and the yield was higher than under plowing (by 13.8%). In 2017, with higher precipitation, an increase in yield under strip-tilling compared to plowing was also observed (by 9.1%), but this difference was not statistically significant. To sum up, strip-tillage seems to have a positive impact on the spatial distribution of macroelements, growth parameters, and yield of green peas, and can be recommended as a technology for the sustainable production of this crop.
Full article
(This article belongs to the Special Issue Applied Research and Extension in Agronomic Soil Fertility Series II)
Open AccessArticle
Task Allocation of Multi-Machine Collaborative Operation for Agricultural Machinery Based on the Improved Fireworks Algorithm
by
Suji Zhu, Bo Wang, Shiqi Pan, Yuting Ye, Enguang Wang and Hanping Mao
Agronomy 2024, 14(4), 710; https://doi.org/10.3390/agronomy14040710 (registering DOI) - 28 Mar 2024
Abstract
Currently, the multi-machine collaboration of agricultural machinery is one of the international frontiers and a topic of research interest in the field of agricultural equipment. However, the multi-machine cooperative operation of agricultural machinery is mostly limited to the research on task goal planning
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Currently, the multi-machine collaboration of agricultural machinery is one of the international frontiers and a topic of research interest in the field of agricultural equipment. However, the multi-machine cooperative operation of agricultural machinery is mostly limited to the research on task goal planning and cooperative path optimization of a single operation. To address the mentioned shortcomings, this study addresses the problem of multi-machine cooperative operation of fertilizer applicators in fields with different fertility and fertilizer cooperative distribution of fertilizer trucks. The research uses the task allocation method of a multi-machine cooperative operation of applying fertilizer-transporting fertilizer. First, the problems of fertilizer applicator operation and fertilizer truck fertilizer distribution are defined, and the operating time and the distribution distance are used as optimization objectives to construct functions to establish task allocation mathematical models. Second, a Chaos–Cauchy Fireworks Algorithm (CCFWA), which includes a discretized decoding method, a population initialization with a chaotic map, and a Cauchy mutation operation, is developed. Finally, the proposed algorithm is verified by tests in an actual scenario of fertilizer being applied in the test area of Jimo District, Qingdao City, Shandong Province. The results show that compared to the Fireworks Algorithm, Genetic Algorithm, and Particle Swarm Optimization, the proposed CCFWA can address the problem of falling into a local optimum while guaranteeing the convergence speed. Also, the variance of the CCFWA is reduced by more than 48% compared with the other three algorithms. The proposed method can realize multi-machine cooperative operation and precise distribution of seeds and fertilizers for multiple seeding-fertilizer applicators and fertilizer trucks.
Full article
(This article belongs to the Special Issue Precision Operation Technology and Intelligent Equipment in Farmland—2nd Edition)
Open AccessArticle
Effect of Cucumber Continuous Monocropping on Traditional Chinese Medicine Residue through Analysis of Physicochemical Characteristics and Microbial Diversity
by
Qingsong Zhao, Jingjing Dong, Zhiyong Yan, Ling Xu and Ake Liu
Agronomy 2024, 14(4), 709; https://doi.org/10.3390/agronomy14040709 (registering DOI) - 28 Mar 2024
Abstract
The use of traditional Chinese medicine (TCM) residue as a crop culture substrate has unique advantages in alleviating the obstacles associated with continuous monocropping, such as increasing production, improving quality and alleviating pests and diseases. However, the effect of TCM residue application on
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The use of traditional Chinese medicine (TCM) residue as a crop culture substrate has unique advantages in alleviating the obstacles associated with continuous monocropping, such as increasing production, improving quality and alleviating pests and diseases. However, the effect of TCM residue application on substrates in continuous monocropping practices has not been determined. In this study, the cucumber variety “Jinyou No. 10” was used as the material, and fermented TCM residue, vermiculite and perlite were used as organic substrates (3:1:1). The cucumbers were cultivated on substrates for different durations of continuous monocropping, which were the first cropping cycle (A1), second cropping cycle (A2), third cropping cycle (A3) and fourth cropping cycle (A4). The control (A0) was the substrate sample without any crop planted in it. After the cucumbers were harvested, substrate samples (areas around the cucumber roots) were collected. The physiochemical properties of the cultivated substrates were determined, and the microbial community structures were analyzed through 16S rRNA and ITS sequencing. The physiochemical indices of the substrates with different durations of continuous monocropping (A1–A4) were significantly different than those of the control (A0) substrate. Moreover, the continuous cropping of cucumber had greater effects on fungal communities than on bacterial communities. Bacterial community structure analysis revealed a greater proportion of important bacterial taxa (Proteobacteria, Chloroflexi, and Nitrospirae) in the continuous monocropping substrates than in the A0 substrate. For the fungal community, Ascomycota accounted for the largest percentage of the fungal community in all the samples. The diversity of the microbial community was found to be influenced primarily by electrical conductivity, organic matter content, pH and total potassium content according to the correlation analysis of physicochemical properties and relative abundance of the microbial community. Our study would provide a basis for addressing persistent challenges in continuous cropping and for obtaining the utmost benefit from using TCM organic residue waste.
Full article
(This article belongs to the Special Issue Rhizosphere Microorganisms—Volume II)
Open AccessArticle
Trend Prediction of Vegetation and Drought by Informer Model Based on STL-EMD Decomposition of Ha Cai Tou Dang Water Source Area in the Maowusu Sandland
by
Hexiang Zheng, Hongfei Hou, Ruiping Li and Changfu Tong
Agronomy 2024, 14(4), 708; https://doi.org/10.3390/agronomy14040708 (registering DOI) - 28 Mar 2024
Abstract
To accurately forecast the future development trend of vegetation in dry areas, it is crucial to continuously monitor phenology, vegetation health indices, and vegetation drought indices over an extended period. This is because drought caused by high temperatures significantly affects vegetation. This study
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To accurately forecast the future development trend of vegetation in dry areas, it is crucial to continuously monitor phenology, vegetation health indices, and vegetation drought indices over an extended period. This is because drought caused by high temperatures significantly affects vegetation. This study thoroughly investigated the spatial and temporal variations in phenological characteristics and vegetation health indices in the abdominal part of Maowusu Sandland in China over the past 20 years. Additionally, it established a linear correlation between vegetation health and temperature indices in the arid zone. To address the issue of predicting long-term trends in vegetation drought changes, we have developed a method that combines the Informer deep learning model with seasonal and Seasonal Trend decomposition using Loess (STL) and empirical mode decomposition (EMD). Additionally, we have utilized the linearly correlated indices of vegetation health and meteorological data spanning 20 years to predict the Normalized Difference Vegetation Index (NDVI) and Temperature Vegetation Dryness Index (TVDI). The study’s findings indicate that over the 20-year observation period, there was an upward trend in NDVI, accompanied by a decrease in both the frequency and severity of droughts. Additionally, the STL-EMD-Informer model successfully predicted the mean absolute percentage error (MAPE = 1.16%) of the future trend in vegetation drought changes for the next decade. This suggests that the overall health of vegetation is expected to continue improving during that time. This work examined the plant growth circumstances in dry locations from several angles and developed a complete analytical method for predicting long-term droughts. The findings provide a strong scientific basis for ecological conservation and vegetation management in arid regions.
Full article
(This article belongs to the Special Issue Crop Models for Agricultural Yield Prediction under Climate Change)
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Open AccessArticle
A Novel Three-Segment Model to Describe the Entire Soil–Water Characteristic Curve
by
Chunming Chi, Changwei Zhao and Jinhu Zhi
Agronomy 2024, 14(4), 707; https://doi.org/10.3390/agronomy14040707 (registering DOI) - 28 Mar 2024
Abstract
This study aims to accurately describe the soil–water characteristic curve (SWCC) across the full range from saturation to oven dryness. We propose a smooth, continuous three-segmented SWCC model that divides the saturation range into wet, air-dried, and oven-dried segments. The two model junction
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This study aims to accurately describe the soil–water characteristic curve (SWCC) across the full range from saturation to oven dryness. We propose a smooth, continuous three-segmented SWCC model that divides the saturation range into wet, air-dried, and oven-dried segments. The two model junction points are anchored at matric suctions of 104.5 and 106.5 cm, respectively. The soil water content at 104.5 cm represents the maximum soil hygroscopy, reflecting the maximum water content in air-dried soil, while the soil water content at 106.5 cm characterizes the minimum soil water content. This imbues the junction points with specific physical significance regarding soil moisture content and matric potential. The model was tested with the water retention data of nine soils across the SWCC and compared with three existing SWCC models based on the adjusted coefficient of determination (adjR2) and root mean square error (RMSE). The results indicated that the proposed model accurately described the entire SWCC. The three-segmented model yielded an adjR2 of >0.99 and an RMSE of ≤0.022 cm3 cm−3, outperforming other models. We also introduce a new method for predicting soil water data in air-dried and oven-dried segments. The results showed that the predicted soil water content values were accurate.
Full article
(This article belongs to the Section Agricultural Biosystem and Biological Engineering)
Open AccessArticle
Evaluation of the Coupled Coordination of the Water–Energy–Food–Ecology System Based on the Sustainable Development Goals in the Upper Han River of China
by
Nan Fu, Dengfeng Liu, Hui Liu, Baozhu Pan, Guanghui Ming and Qiang Huang
Agronomy 2024, 14(4), 706; https://doi.org/10.3390/agronomy14040706 (registering DOI) - 28 Mar 2024
Abstract
Water, energy, food, and ecology are essential for achieving sustainable development in a region, and in order to achieve the Sustainable Development Goals, their security is also essential at a river basin scale. This study investigated the interrelationships among the water system, food
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Water, energy, food, and ecology are essential for achieving sustainable development in a region, and in order to achieve the Sustainable Development Goals, their security is also essential at a river basin scale. This study investigated the interrelationships among the water system, food system, energy system, and ecosystem in China’s Upper Han River, in alignment with Goals 2, 6, 7, and 15 of the United Nations’ Sustainable Development Goals (SDGs). To evaluate the achievement of the SDGs in the Upper Han River, this water–energy–food–ecology system was evaluated by a thorough evaluation index system according to Goals 2, 6, 7, and 15, and the weights of the indices were given using a combination of the CRITIC weighting method and entropy approach. The level of coupling coordination of the system from 2000 to 2021 was quantitatively evaluated by using a coupling coordination degree model. The autoregressive integrated moving average model was built to forecast the process of the indices from 2022 to 2041, and the predicted processes of the system were evaluated by the coupling coordination degree model. The degree of coupling coordination improved from 0.396 to 0.845, and the comprehensive assessment development index increased by 113% from 2000 to 2021, demonstrating that it was a stable development period in general. The fragile support capacity of the water system for the energy system, food system, and ecosystem had a great impact on the overall comprehensive evaluation index. SDG2 (food system), SDG6 (water system), SDG7 (energy system), and SDG15 (ecosystem) all have higher levels of internal conflict. These bi-directional dynamics tended to converge in the sufficiency development mode in the future period as well as the historical period. The analysis of the relationship showed that there were inherent connections and interactions between the four goals, as presented by the high level of coupling that persisted between SDG2, SDG6, SDG7, and SDG15. In the process of promoting the achievement of these goals, the coupling degree also tends to be coordinated from 2022 to 2041. The results offer a view for the river basin’s sustainable development and management.
Full article
(This article belongs to the Section Agricultural Biosystem and Biological Engineering)
Open AccessArticle
Storage Temperature and Grain Moisture Effects on Phenolic Compounds as a Driver of Seed Coat Darkening in Red Lentil
by
Bhawana Bhattarai, James G. Nuttall, Minhao Li, Hafiz A. R. Suleria, Ashley J. Wallace, Glenn J. Fitzgerald and Cassandra K. Walker
Agronomy 2024, 14(4), 705; https://doi.org/10.3390/agronomy14040705 (registering DOI) - 28 Mar 2024
Abstract
The biochemistry underlying seed coat darkening of lentil due to extended storage is limited. This study investigated the relationship between seed coat darkening over time during storage and changes in concentration of phenolic compounds (total phenolic compounds, total condensed tannins, proanthocyanidins and anthocyanins)
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The biochemistry underlying seed coat darkening of lentil due to extended storage is limited. This study investigated the relationship between seed coat darkening over time during storage and changes in concentration of phenolic compounds (total phenolic compounds, total condensed tannins, proanthocyanidins and anthocyanins) in two red lentil cultivars (PBA Hallmark and PBA Jumbo2), stored at two grain moisture contents (10 and 14%, w/w) and two temperatures (4 and 35 °C) for 360 days. Seed coat darkening was only significant (p = 0.05) at high temperatures (35 °C) but not at low temperatures (4 °C), irrespective of grain moisture content and cultivar. The concentration of all phenolic compounds tested in this study reduced significantly (p = 0.05) throughout the study period, regardless of temperature and grain moisture treatments. The changes in seed coat brightness and redness followed a linear pattern, except for yellowness, where phenolic compounds initially reduced linearly and then remained constant thereafter. Darkening of seedcoat was only associated with the reduction in phenolic compounds tested in this study at 35 °C, and not at 4 °C. This suggests that seed coat darkening due to extended storage may not be directly linked to broad reductions in the groups of phenolic compounds or individual compounds assessed in this study. This information prompts further research to identify the actual biochemical processes that cause the darkening of seed coats during storage and assist in developing cultivars with stable seed coat colour by selecting and modifying such processes.
Full article
(This article belongs to the Section Plant-Crop Biology and Biochemistry)
Open AccessArticle
Transcriptomic and Metabolomic Analyses Reveal the Response to Short-Term Drought Stress in Bread Wheat (Triticum aestivum L.)
by
Xiaoyi Fu, Zhilian Liu, Xiong Du, Huijun Duan, Wenchao Zhen, Yuechen Zhang, Zhanliang Shi, Mingqi He and Ruiqi Li
Agronomy 2024, 14(4), 704; https://doi.org/10.3390/agronomy14040704 (registering DOI) - 28 Mar 2024
Abstract
Drought stress, a major abiotic stress, significantly affects wheat (Triticum aestivum L.) production globally. To identify genes and metabolic pathways crucial for responding to short-term drought stress, we conducted transcriptomic and metabolomic analyses of winter wheat cultivar Jimai 418 at four developmental
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Drought stress, a major abiotic stress, significantly affects wheat (Triticum aestivum L.) production globally. To identify genes and metabolic pathways crucial for responding to short-term drought stress, we conducted transcriptomic and metabolomic analyses of winter wheat cultivar Jimai 418 at four developmental stages: jointing (GS31), booting (GS45), anthesis (GS65), and 8 days after anthesis (DAA8). Transcriptomic analysis identified 14,232 differentially expressed genes (DEGs) under drought stress compared to the control. Specifically, 1387, 4573, 7380, and 892 DEGs were identified at the four developmental stages, respectively. Enriched pathways associated with these DEGs included plant hormone signal transduction, mitogen-activated protein kinase (MAPK) signaling, galactose metabolism, and starch and sucrose metabolism. Totals of 222, 633, 358, and 38 differentially accumulated metabolites (DAMs) were identified at the four stages, respectively. Correlation analysis of both datasets revealed DEGs and DAMs associated with plant hormone signal transduction, arginine and proline metabolism, ABC transporters, and amino acid biosynthesis. These findings offer significant insights into Jimai 418’s molecular response to short-term drought stress. The identified DEGs, DAMs, and enriched pathways contribute to our understanding of wheat drought tolerance. This research will facilitate further investigations into drought tolerance mechanisms and guide the breeding of wheat varieties with enhanced drought resistance.
Full article
(This article belongs to the Section Crop Breeding and Genetics)
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Open AccessArticle
Evaluation of the Potential of Using Machine Learning and the Savitzky–Golay Filter to Estimate the Daily Soil Temperature in Gully Regions of the Chinese Loess Plateau
by
Wei Deng, Dengfeng Liu, Fengnian Guo, Lianpeng Zhang, Lan Ma, Qiang Huang, Qiang Li, Guanghui Ming and Xianmeng Meng
Agronomy 2024, 14(4), 703; https://doi.org/10.3390/agronomy14040703 (registering DOI) - 28 Mar 2024
Abstract
Soil temperature directly affects the germination of seeds and the growth of crops. In order to accurately predict soil temperature, this study used RF and MLP to simulate shallow soil temperature, and then the shallow soil temperature with the best simulation effect will
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Soil temperature directly affects the germination of seeds and the growth of crops. In order to accurately predict soil temperature, this study used RF and MLP to simulate shallow soil temperature, and then the shallow soil temperature with the best simulation effect will be used to predict the deep soil temperature. The models were forced by combinations of environmental factors, including daily air temperature (Tair), water vapor pressure (Pw), net radiation (Rn), and soil moisture (VWC), which were observed in the Hejiashan watershed on the Loess Plateau in China. The results showed that the accuracy of the model for predicting deep soil temperature proposed in this paper is higher than that of directly using environmental factors to predict deep soil temperature. In testing data, the range of MAE was 1.158–1.610 °C, the range of RMSE was 1.449–2.088 °C, the range of R2 was 0.665–0.928, and the range of KGE was 0.708–0.885 at different depths. The study not only provides a critical reference for predicting soil temperature but also helps people to better carry out agricultural production activities.
Full article
(This article belongs to the Special Issue The Applications of Deep Learning in Smart Agriculture)
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Open AccessArticle
Bio-Inoculation of Tomato (Solanum lycopersicum L.) and Jalapeño Pepper (Capsicum annuum L.) with Enterobacter sp. DBA51 Increases Growth and Yields under Open-Field Conditions
by
John Paul Délano-Frier, Alberto Flores-Olivas and José Humberto Valenzuela-Soto
Agronomy 2024, 14(4), 702; https://doi.org/10.3390/agronomy14040702 (registering DOI) - 28 Mar 2024
Abstract
The rhizobacterium Enterobacter sp. DBA51 (DBA51), isolated from the semi-desert in Coahuila, Mexico, was previously found to increase the vegetative growth of tomato and tobacco plants cultivated under greenhouse conditions. The present report describes the results obtained from two independent open-field experiments performed
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The rhizobacterium Enterobacter sp. DBA51 (DBA51), isolated from the semi-desert in Coahuila, Mexico, was previously found to increase the vegetative growth of tomato and tobacco plants cultivated under greenhouse conditions. The present report describes the results obtained from two independent open-field experiments performed with tomato and jalapeño pepper commercial crops inoculated with DBA51. Additionally, plants inoculated with Bacillus subtilis LPM1 (LPM1) and uninoculated plants were included as positive and negative controls, respectively. DBA51 and LPM1 significantly promoted growth at vegetative stages in the tomato plants; this effect was evident in the stem diameter (DBA51 with p < 0.0001 and LPM1 with p < 0.0001) and height (DBA51 with p < 0.0001 and LPM1 with p < 0.0001) of the tomato plants. However, no differences were detected in the jalapeño pepper plants. Additionally, DBA51 and LPM1 treatments increased tomato fruit production by 80% and 31%, respectively, compared to uninoculated plants. A similar increase in yield was recorded in DBA51- and LPM1-treated jalapeño pepper plants, which was 75% and 56% higher than uninoculated controls, respectively. These results strongly recommend the potential use of DBA51 as a biofertilizer in horticultural crops.
Full article
(This article belongs to the Section Agricultural Biosystem and Biological Engineering)
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Open AccessArticle
Estimation of Millet Aboveground Biomass Utilizing Multi-Source UAV Image Feature Fusion
by
Zhongyu Yang, Zirui Yu, Xiaoyun Wang, Wugeng Yan, Shijie Sun, Meichen Feng, Jingjing Sun, Pengyan Su, Xinkai Sun, Zhigang Wang, Chenbo Yang, Chao Wang, Yu Zhao, Lujie Xiao, Xiaoyan Song, Meijun Zhang and Wude Yang
Agronomy 2024, 14(4), 701; https://doi.org/10.3390/agronomy14040701 (registering DOI) - 28 Mar 2024
Abstract
Aboveground biomass (AGB) is a key parameter reflecting crop growth which plays a vital role in agricultural management and ecosystem assessment. Real-time and non-destructive biomass monitoring is essential for accurate field management and crop yield prediction. This study utilizes a multi-sensor-equipped unmanned aerial
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Aboveground biomass (AGB) is a key parameter reflecting crop growth which plays a vital role in agricultural management and ecosystem assessment. Real-time and non-destructive biomass monitoring is essential for accurate field management and crop yield prediction. This study utilizes a multi-sensor-equipped unmanned aerial vehicle (UAV) to collect remote sensing data during critical growth stages of millet, including spectral, textural, thermal, and point cloud information. The use of RGB point cloud data facilitated plant height extraction, enabling subsequent analysis to discern correlations between spectral parameters, textural indices, canopy temperatures, plant height, and biomass. Multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) models were constructed to evaluate the capability of different features and integrated multi-source features in estimating the AGB. Findings demonstrated a strong correlation between the plant height derived from point cloud data and the directly measured plant height, with the most accurate estimation of millet plant height achieving an R2 of 0.873 and RMSE of 7.511 cm. Spectral parameters, canopy temperature, and plant height showed a high correlation with the AGB, and the correlation with the AGB was significantly improved after texture features were linearly transformed. Among single-factor features, the RF model based on textural indices showcased the highest accuracy in estimating the AGB (R2 = 0.698, RMSE = 0.323 kg m−2, and RPD = 1.821). When integrating two features, the RF model incorporating textural indices and canopy temperature data demonstrated optimal performance (R2 = 0.801, RMSE = 0.253 kg m−2, and RPD = 2.244). When the three features were fused, the RF model constructed by fusing spectral parameters, texture indices, and canopy temperature data was the best (R2 = 0.869, RMSE = 0.217 kg m−2, and RPD = 2.766). The RF model based on spectral parameters, texture indices, canopy temperature, and plant height had the highest accuracy (R2 = 0.877, RMSE = 0.207 kg m−2, and RPD = 2.847). In this study, the complementary and synergistic effects of multi-source remote sensing data were leveraged to enhance the accuracy and stability of the biomass estimation model.
Full article
(This article belongs to the Section Precision and Digital Agriculture)
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Open AccessArticle
Partial Substitution of Chemical N with Solid Cow Manure Improved Soil Ecological Indicators and Crop Yield in a Wheat–Rice Rotation System
by
Jintao Yu, Chun Zhang, Xuan Wang, Hongchuan Li, Yusef Kianpoor Kalkhajeh and Hongxiang Hu
Agronomy 2024, 14(4), 700; https://doi.org/10.3390/agronomy14040700 (registering DOI) - 28 Mar 2024
Abstract
Alternative fertilizers are essential to minimizing the deteriorating effects of chemical fertilizers on soil and water quality/health. Accordingly, the present work investigated the effects of combined organic–inorganic fertilization (COIF) on wheat and rice yields, soil nutrients, and soil Cd accumulation. Hence, seven different
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Alternative fertilizers are essential to minimizing the deteriorating effects of chemical fertilizers on soil and water quality/health. Accordingly, the present work investigated the effects of combined organic–inorganic fertilization (COIF) on wheat and rice yields, soil nutrients, and soil Cd accumulation. Hence, seven different treatments were set up: control (CK); conventional fertilization (CF); adequate fertilization (OF); organic fertilizer replacing 25% (T1) and 50% (T2) of OF; and organic nitrogen (N) replacing 25% (M1) and 50% (M2) of OF-N. Overall, significant increases occurred in the yields of COIF crops. Compared with the CF, the highest wheat and rice yields happened in the M1 treatment (with a difference of approximately 18.5%) (p < 0.05). COIF slightly alleviated soil acidification, and improved the cation exchange capacity (CEC) of the study soils. Furthermore, COIF treatments significantly increased the contents of total phosphorus, total potassium, available phosphorus, and available potassium by 6.35 to 16.9%, 3.17 to 10.9%, 5.53 to 28.7%, and 2.6 to 12%, respectively (p < 0.05). Nevertheless, negligible increases took place in the Cd content of COIF soils compared with that of the CK. Altogether, our results concluded that 25% replacement of OF-N by organic N (M1) effectively improved the fertility/ecological sustainability of the study soils.
Full article
(This article belongs to the Section Plant-Crop Biology and Biochemistry)
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Open AccessArticle
A Method for Analyzing the Phenotypes of Nonheading Chinese Cabbage Leaves Based on Deep Learning and OpenCV Phenotype Extraction
by
Haobin Xu, Linxiao Fu, Jinnian Li, Xiaoyu Lin, Lingxiao Chen, Fenglin Zhong and Maomao Hou
Agronomy 2024, 14(4), 699; https://doi.org/10.3390/agronomy14040699 (registering DOI) - 28 Mar 2024
Abstract
Nonheading Chinese cabbage is an important leafy vegetable, and quantitative identification and automated analysis of nonheading Chinese cabbage leaves are crucial for cultivating new varieties with higher quality, yield, and resistance. Traditional leaf phenotypic analysis relies mainly on phenotypic observation and the practical
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Nonheading Chinese cabbage is an important leafy vegetable, and quantitative identification and automated analysis of nonheading Chinese cabbage leaves are crucial for cultivating new varieties with higher quality, yield, and resistance. Traditional leaf phenotypic analysis relies mainly on phenotypic observation and the practical experience of breeders, leading to issues such as time consumption, labor intensity, and low precision, which result in low breeding efficiency. Considering these issues, a method for the extraction and analysis of phenotypes of nonheading Chinese cabbage leaves is proposed, targeting four qualitative traits and ten quantitative traits from 1500 samples, by integrating deep learning and OpenCV image processing technology. First, a leaf classification model is trained using YOLOv8 to infer the qualitative traits of the leaves, followed by the extraction and calculation of the quantitative traits of the leaves using OpenCV image processing technology. The results indicate that the model achieved an average accuracy of 95.25%, an average precision of 96.09%, an average recall rate of 96.31%, and an average F1 score of 0.9620 for the four qualitative traits. From the ten quantitative traits, the OpenCV-calculated values for the whole leaf length, leaf width, and total leaf area were compared with manually measured values, showing RMSEs of 0.19 cm, 0.1762 cm, and 0.2161 cm2, respectively. Bland–Altman analysis indicated that the error values were all within the 95% confidence intervals, and the average detection time per image was 269 ms. This method achieved good results in the extraction of phenotypic traits from nonheading Chinese cabbage leaves, significantly reducing the personpower and time costs associated with genetic resource analysis. This approach provides a new technique for the analysis of nonheading Chinese cabbage genetic resources that is high-throughput, precise, and automated.
Full article
(This article belongs to the Special Issue Computer Vision and Deep Learning Technology in Agriculture: Volume II)
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Open AccessReview
The Fate and Challenges of the Main Nutrients in Returned Straw: A Basic Review
by
Huandi Li, Jiang Li, Xiyun Jiao, Hongzhe Jiang, Yong Liu, Xinglang Wang and Chao Ma
Agronomy 2024, 14(4), 698; https://doi.org/10.3390/agronomy14040698 (registering DOI) - 28 Mar 2024
Abstract
Due to containing an abundance of essential nutrients, straw has significant potential to mitigate carbon (C), nitrogen (N), phosphorus (P), and potassium (K) deficits in soil. However, a lack of comprehensive and systematic reviews on C, N, P, and K release and conversion
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Due to containing an abundance of essential nutrients, straw has significant potential to mitigate carbon (C), nitrogen (N), phosphorus (P), and potassium (K) deficits in soil. However, a lack of comprehensive and systematic reviews on C, N, P, and K release and conversion from straw and on the impact of available nutrients in soils supplemented using straw-returning (SR) practices is noticeable in the literature. Therefore, we investigated straw decomposition, its nutrient release characteristics, and the subsequent fate of nutrients in soils. At early stages, straw decomposes rapidly and then gradually slows down at later stages. Nutrient release rates are generally in the K > P > C > N order. Nutrient fate encompasses fractions mineralized to inorganic nutrients, portions which supplement soil organic matter (SOM) pools, and other portions which are lost via leaching and gas volatilization. In future research, efforts should be made to quantitatively track straw nutrient release and fate and also examine the potential impact of coordinated supply-and-demand interactions between straw nutrients and plants. This review will provide a more systematic understanding of SR’s effectiveness in agriculture.
Full article
(This article belongs to the Topic Agronomy, Soil Health and Climate Change: Challenges and Solutions)
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Open AccessArticle
ODN-Pro: An Improved Model Based on YOLOv8 for Enhanced Instance Detection in Orchard Point Clouds
by
Yaoqiang Pan, Xvlin Xiao, Kewei Hu, Hanwen Kang, Yangwen Jin, Yan Chen and Xiangjun Zou
Agronomy 2024, 14(4), 697; https://doi.org/10.3390/agronomy14040697 (registering DOI) - 28 Mar 2024
Abstract
In an unmanned orchard, various tasks such as seeding, irrigation, health monitoring, and harvesting of crops are carried out by unmanned vehicles. These vehicles need to be able to distinguish which objects are fruit trees and which are not, rather than relying on
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In an unmanned orchard, various tasks such as seeding, irrigation, health monitoring, and harvesting of crops are carried out by unmanned vehicles. These vehicles need to be able to distinguish which objects are fruit trees and which are not, rather than relying on human guidance. To address this need, this study proposes an efficient and robust method for fruit tree detection in orchard point cloud maps. Feature extraction is performed on the 3D point cloud to form a two-dimensional feature vector containing three-dimensional information of the point cloud and the tree target is detected through the customized deep learning network. The impact of various feature extraction methods such as average height, density, PCA, VFH, and CVFH on the detection accuracy of the network is compared in this study. The most effective feature extraction method for the detection of tree point cloud objects is determined. The ECA attention module and the EVC feature pyramid structure are introduced into the YOLOv8 network. The experimental results show that the deep learning network improves the precision, recall, and mean average precision by 1.5%, 0.9%, and 1.2%, respectively. The proposed framework is deployed in unmanned orchards for field testing. The experimental results demonstrate that the framework can accurately identify tree targets in orchard point cloud maps, meeting the requirements for constructing semantic orchard maps.
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(This article belongs to the Section Precision and Digital Agriculture)
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Open AccessArticle
Optimizing Straw Mulching Methods to Control Soil and Water Losses on Loess Sloped Farmland
by
Xinkai Zhao, Xiaoyu Song, Danyang Wang, Lanjun Li, Pengfei Meng, Chong Fu, Long Wang, Wanyin Wei, Nan Yang, Yu Liu and Huaiyou Li
Agronomy 2024, 14(4), 696; https://doi.org/10.3390/agronomy14040696 (registering DOI) - 28 Mar 2024
Abstract
Straw mulching is a key method for controlling soil and water losses. Mulching costs may be reduced by applying it in strips rather than over entire areas. However, the effect of different straw mulching methods on the effectiveness of reducing soil erosion is
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Straw mulching is a key method for controlling soil and water losses. Mulching costs may be reduced by applying it in strips rather than over entire areas. However, the effect of different straw mulching methods on the effectiveness of reducing soil erosion is unclear. In this study, the effects of straw mulching strip length (covering 1/4, 1/2, 3/4, and 4/4 of the slope length) and coverage rate (0.2, 0.5, and 0.8 kg m−2) on interception, infiltration, runoff, and soil erosion were investigated at the plot scale using rainfall simulation experiments. The further complex correlations between these variables were analyzed using structural equation modeling (SEM). Bare slopes were used as a control group. The rainfall intensity was chosen to be 60 mm h−1. The results showed that (1) the modified Merriam interception model can describe the change in interception with time under straw mulching conditions well (R2 > 0.91, NSE > 0.75). (2) A total of 35.39–78.79% of the rainwater is converted into infiltration on straw-covered slopes, while this proportion is 36.75% on bare slopes. The proportion of rainwater converted to infiltration was greatest (78.79%) when the straw covered 3/4 of the slope length at a coverage rate of 0.5 kg m−2, which was the most conducive to rainwater harvesting on the slope. (3) Straw mulching protects the topsoil from the impact of raindrops and directly affects the sediment yield (direct effect = −0.44). Straw mulching can also indirectly affect sediment yield by increasing interception, reducing runoff, and decreasing the sediment carrying capacity of runoff (indirect effect = −0.83). Compared with bare slopes, straw covering at least 1/2 of the slope length can significantly reduce runoff yield, but straw covering only 1/4 of the slope length can significantly reduce sediment yield. Moreover, once the straw mulch slope length reaches 3/4 and the coverage rate reaches 0.5 kg m−2, further increases in mulch slope length and coverage rate will not significantly reduce the runoff and sediment yields. These results assessed the effectiveness of different straw mulching methods in controlling soil and water losses on sloping farmland.
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(This article belongs to the Section Farming Sustainability)
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Open AccessArticle
Allelopathic Activity of a Novel Compound and Two Known Sesquiterpene from Croton oblongifolius Roxb.
by
Seinn Moh Moh, Shunya Tojo, Toshiaki Teruya and Hisashi Kato-Noguchi
Agronomy 2024, 14(4), 695; https://doi.org/10.3390/agronomy14040695 (registering DOI) - 28 Mar 2024
Abstract
Plant extracts with allelopathic activity and their related compounds have been investigated for a long time as an eco-friendly approach to sustainable weed management. Croton oblongifolius (Roxb.) is a traditional medicinal plant valued for its diverse source of bioactive compounds that have been
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Plant extracts with allelopathic activity and their related compounds have been investigated for a long time as an eco-friendly approach to sustainable weed management. Croton oblongifolius (Roxb.) is a traditional medicinal plant valued for its diverse source of bioactive compounds that have been used to treat various diseases. C. oblongifolius leaf extract was previously described to involve a number of allelochemicals. Therefore, we conducted this research to explore more of the allelochemicals in the leaves of C. oblongifolius. The leaf extracts showed significant inhibitory activity against two test plants, Lolium multiflorum (monocot) and Medicago sativa (dicot). The bioassay-directed chromatographic purification of the leaf extracts yielded three compounds, including one novel compound, identified using spectral data, as follows: (1) alpinolide peroxide, (2) 6-hydroxy alpinolide, and (3) 3-hydroxy-5-isopropyl-3-methyl-2,3-dihydro-1H-inden-1-one (a novel sesquiterpene). These compounds considerably limited the growth of L. sativum. The compound concentrations affecting a 50% growth limitation (IC50) of L. sativum varied from 0.16 to 0.34 mM. Therefore, these characterized compounds may be allelopathic agents that cause the allelopathy of C. oblongifolius.
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(This article belongs to the Special Issue Extraction and Analysis of Bioactive Compounds in Crops - Series II)
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Unraveling the Tolerance of Moringa oleifera to Excess K through Increased N Absorption and Mg Use Efficiency
by
Bianca Cavalcante da Silva, Jairo Osvaldo Cazetta and Renato de Mello Prado
Agronomy 2024, 14(4), 694; https://doi.org/10.3390/agronomy14040694 (registering DOI) - 28 Mar 2024
Abstract
The tolerance of Moringa oleifera plants to excess K may be linked to nutritional mechanisms, but studies are lacking. The present study was conducted to analyze the tolerance of Moringa oleifera to nutritional imbalance and its importance in the growth of plants submitted
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The tolerance of Moringa oleifera plants to excess K may be linked to nutritional mechanisms, but studies are lacking. The present study was conducted to analyze the tolerance of Moringa oleifera to nutritional imbalance and its importance in the growth of plants submitted to high doses of K in the absence and presence of N. The experiment was conducted in pots with 9 dm3 of Oxisol in a 4 × 2 factorial scheme, with potassium doses of 0, 110, 190, and 265 mg dm−3 combined with nitrogen doses of 0 and 100 mg dm−3. The increase in K uptake by moringa is enhanced by N supply but decreases the uptake of Ca and Mg. Notwithstanding, this was of little importance as the soil cultivated had adequate Ca and Mg contents and was sufficient for adequate plant nutrition without impairing plant growth. The moringa plant is tolerant to nutritional imbalances when grown in environments with high K content probably because N favors an increase in Mg use efficiency, avoiding biological disturbances. The results of this study contributed to our understanding of how moringa induces nutritional mechanisms of action to deal with excess K in crops.
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(This article belongs to the Section Plant-Crop Biology and Biochemistry)
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Implementation of Proximal and Remote Soil Sensing, Data Fusion and Machine Learning to Improve Phosphorus Spatial Prediction for Farms in Ontario, Canada
by
Abdelkrim Lachgar, David J. Mulla and Viacheslav Adamchuk
Agronomy 2024, 14(4), 693; https://doi.org/10.3390/agronomy14040693 - 27 Mar 2024
Abstract
One of the challenges in site-specific phosphorus (P) management is the substantial spatial variability in plant available P across fields. To overcome this barrier, emerging sensing, data fusion, and spatial predictive modeling approaches are needed to accurately reveal the spatial heterogeneity of P.
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One of the challenges in site-specific phosphorus (P) management is the substantial spatial variability in plant available P across fields. To overcome this barrier, emerging sensing, data fusion, and spatial predictive modeling approaches are needed to accurately reveal the spatial heterogeneity of P. Seven spatially variable fields located in Ontario, Canada are clustered into two zones; four fields are located in eastern Ontario and three others are located in western Ontario. This study compares Bayesian Additive Regression Trees (BART), Support Vector Machine regressor (SVM), and Ordinary Kriging (OK), along with novel data fusion concepts, to analyze integrated high-density spatial data layers related to spatial variability in soil available P. Feature selection and interaction detection using BART variable selection and Recursive Feature Elimination (RFE) for SVM were applied to 42 predictors, including soil-vegetation indices derived from PlanetScope multispectral imagery, high-density apparent soil electrical conductivity (ECa), and high-resolution topographic attributes derived from DUALEM-21S and a Real-Time Kinematic (RTK) global navigation satellite systems (GNSS) receiver, respectively. Modeling spatial heterogeneity of soil available P with BART showed higher accuracy than SVM and OK in both zones of this study when trained and tested on ground truth data from clusters of farms. A BART variable selection approach resulted in six auxiliary predictors of soil available P in the eastern zone, while only four predictors were selected to predict P in the western zone. RFE for SVM resulted in models with 15 and 12 auxiliary predictors in the eastern and western Ontario zones. Topographic elevation was the most influential predictor of soil available P in both zones. Compared with the SVM and OK methods, BART exhibited lower average RMSE values for individual fields of 1.86 ppm and 3.58 ppm across the eastern and western Ontario zones, respectively, along with higher R2 values of 0.85 and 0.83, respectively. In contrast, SVM had RMSE values for individual fields in the eastern and western Ontario zones, respectively, averaging 5.04 ppm and 7.51 ppm and R2 values of 0.27 and 0.43. RMSE values for soil available P in individual fields across the eastern and western Ontario zones averaged 4.77 ppm and 7.81 ppm, respectively, with the OK method, while R2 values averaged 0.19 and 0.44. The selection of suitable auxiliary predictors and data fusion, combined with BART spatial machine learning algorithms, have potential to be a useful tool to accurately estimate spatial patterns in soil available P for agricultural fields in Ontario, Canada.
Full article
(This article belongs to the Special Issue The Importance of Soil Spatial Variability in Precision Agriculture)
Open AccessArticle
Enhancing Drought Resistance and Yield of Wheat through Inoculation with Streptomyces pactum Act12 in Drought Field Environments
by
Bin Yang, Hongwei Wen, Shanshan Wang, Jinhui Zhang, Yuzhi Wang, Ting Zhang, Kai Yuan, Lahu Lu, Yutao Liu, Quanhong Xue and Hao Shan
Agronomy 2024, 14(4), 692; https://doi.org/10.3390/agronomy14040692 - 27 Mar 2024
Abstract
Drought stress is the primary abiotic factor affecting wheat growth, development, and yield formation. The application of plant growth-promoting rhizobacteria (PGPR) represents an environmentally sustainable approach to mitigate the impacts of drought stress on wheat. This study conducted field experiments using two winter
[...] Read more.
Drought stress is the primary abiotic factor affecting wheat growth, development, and yield formation. The application of plant growth-promoting rhizobacteria (PGPR) represents an environmentally sustainable approach to mitigate the impacts of drought stress on wheat. This study conducted field experiments using two winter wheat varieties, the drought-sensitive variety Jimai 22 and the drought-resistant variety Chang 6878, aiming to investigate the effects of Streptomyces pactum Act12 inoculation on photosynthetic characteristics, physiological parameters, and yield traits during the jointing, heading, and middle-filling stages under drought stress. The results revealed that drought stresses significantly reduced chlorophyll content, leaf area, biomass, and yield in wheat, while Act12 inoculation significantly increased chlorophyll content, photosynthetic efficiency, antioxidant enzyme activity such as superoxide dismutase (SOD) and peroxidase (POD), osmolyte content (proline and soluble proteins), and decreased malondialdehyde (MDA) content. These combined effects alleviated drought stress, resulting in increased biomass and yield in wheat. Under drought stress, an increase in leaf proline content of 13.53% to 53.23% (Jimai 22) and 17.17% to 43.08% (Chang 6878) was observed upon Act12 inoculation. Moreover, a decrease in MDA content was recorded of 15.86% to 53.61% (Jimai 22) and 13.47% to 26.21% (Chang 6878). Notably, there was a corresponding increase in yield of 11.78% (Jimai 22) and 13.55% (Chang 6878). In addition, grain quality analysis revealed a significant improvement in grain hardness with Act12 inoculation. Therefore, Act12 demonstrates the potential for enhancing the sustainable development of wheat production in arid and semi-arid regions.
Full article
(This article belongs to the Section Farming Sustainability)
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