Agronomy

16 pages, 6657 KiB

Open AccessEditor’s ChoiceArticle

DCF-Yolov8: An Improved Algorithm for Aggregating Low-Level Features to Detect Agricultural Pests and Diseases

by Lijuan Zhang, Gongcheng Ding, Chaoran Li and Dongming Li

Agronomy 2023, 13(8), 2012; https://doi.org/10.3390/agronomy13082012 - 29 Jul 2023

Cited by 12 | Viewed by 3507

The invasion of agricultural diseases and insect pests is a huge difficulty for the growth of crops. The detection of diseases and pests is a very challenging task. The diversity of diseases and pests in terms of shapes, colors, and sizes, as well [...] Read more.

The invasion of agricultural diseases and insect pests is a huge difficulty for the growth of crops. The detection of diseases and pests is a very challenging task. The diversity of diseases and pests in terms of shapes, colors, and sizes, as well as changes in the lighting environment, have a massive impact on the accuracy of the detection results. We improved the C2F module based on DenseBlock and proposed DCF to extract low-level features such as the edge texture of pests and diseases. Through the sensitivity of low-level features to the diversity of pests and diseases, the DCF module can better cope with complex detection tasks and improve the accuracy and robustness of the detection. The complex background environment of pests and diseases and different lighting conditions make the IP102 data set have strong nonlinear characteristics. The Mish activation function is selected to replace the CBS module with the CBM, which can better learn the nonlinear characteristics of the data and effectively solve the problems of gradient disappearance in the algorithm training process. Experiments show that the advanced Yolov8 algorithm has improved. Comparing with Yolov8, our algorithm improves the MAP50 index, Precision index, and Recall index by 2%, 1.3%, and 3.7%. The model in this paper has higher accuracy and versatility. Full article

(This article belongs to the Special Issue AI, Sensors and Robotics for Smart Agriculture)

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18 pages, 4374 KiB

Open AccessEditor’s ChoiceArticle

Influence of Different Nitrogen, Phosphorus, and Potassium Fertilizer Ratios on the Agronomic and Quality Traits of Foxtail Millet

by Guofang Xing, Junwei Ma, Xiaojie Liu, Biao Lei, Guo Wang, Siyu Hou and Yuanhuai Han

Agronomy 2023, 13(8), 2005; https://doi.org/10.3390/agronomy13082005 - 28 Jul 2023

Cited by 4 | Viewed by 1822

Abstract

Foxtail millet is highly valued in China; however, its optimal fertilization parameters are unknown. This study investigated the effects of nitrogen (N), phosphorus (P), and potassium (K) fertilizer combinations on foxtail millet agronomic traits, photosynthetic characteristics, yield, and quality to promote rational fertilizer [...] Read more.

Foxtail millet is highly valued in China; however, its optimal fertilization parameters are unknown. This study investigated the effects of nitrogen (N), phosphorus (P), and potassium (K) fertilizer combinations on foxtail millet agronomic traits, photosynthetic characteristics, yield, and quality to promote rational fertilizer application. Pot experiments were conducted using the “3414” fertilizer effect scheme and the representative crop variety was JG21, containing four NPK levels and 20 replicates per treatment, individually. The effects of N, P, and K levels on agronomic traits were analyzed during the jointing, heading, and filling stages. JG21 performed optimally under treatment with N₁₆₀P₉₀K₁₅₀ (T₆); the yield and fat content increased by 49.32% and 13% compared to the control. Correlation analysis revealed that N was significantly positively (negatively) correlated with the protein (amylose) content. P was significantly positively correlated with the fat and moisture content and K was correlated with the moisture, fat, and protein content, but was negatively with the amylose content. Overall, rational ratios of NPK fertilization improved foxtail millet yield and quality. Based on fuzzy comprehensive evaluation, the T₆ treatment (N₁₆₀P₉₀K₁₅₀) demonstrated the highest comprehensive effect among 13 NPK fertilizer combinations. Rational application of NPK in foxtail millet may improve agronomic performance by enhancing leaf photosynthetic efficiency and aboveground biomass accumulation. Full article

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

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19 pages, 1530 KiB

Open AccessEditor’s ChoiceArticle

Interaction of ZnO Nanoparticles with Metribuzin in a Soil–Plant System: Ecotoxicological Effects and Changes in the Distribution Pattern of Zn and Metribuzin

by Concepción García-Gómez, Rosa Ana Pérez, Beatriz Albero, Ana Obrador, Patricia Almendros and María Dolores Fernández

Agronomy 2023, 13(8), 2004; https://doi.org/10.3390/agronomy13082004 - 28 Jul 2023

Cited by 4 | Viewed by 1290

Abstract

The use of zinc oxide nanoparticles (ZnO NPs), applied as a possible micronutrient source, in conjunction with organic pesticides in agricultural soils has the potential to alter the environmental behavior and toxicity of these chemicals to soil biota. This research examines the joint [...] Read more.

The use of zinc oxide nanoparticles (ZnO NPs), applied as a possible micronutrient source, in conjunction with organic pesticides in agricultural soils has the potential to alter the environmental behavior and toxicity of these chemicals to soil biota. This research examines the joint effects of ZnO NPs and the herbicide metribuzin (MTZ) on phytotoxicity to plants, toxicity to soil microorganisms, and the accumulation of Zn and MTZ in plants. After 23 days, effects on growth, photosynthetic pigment content, and oxidative stress biomarkers in bean plants (Phaseolus vulgaris) and soil enzymatic activities were evaluated. Additionally, the amounts of Zn and MTZ (and the latter’s main metabolites) in soil and plant tissues were quantified. ZnO NPs reduced ammonium oxidase activity and growth among MTZ-stressed plants while reducing photosynthetic pigment levels and enhancing antioxidant enzymatic activities. MTZ had a marginal impact on the availability and accumulation of Zn in plant tissues, although significant effects were observed in some specific cases. In turn, ZnO NPs drastically affected MTZ degradation in soil and influenced MTZ accumulation/metabolization in the bean plants. Our findings indicate that the indirect effects of ZnO NPs, through their interaction with commonly used organic pesticides, may be relevant and should be taken into account in agricultural soils. Full article

(This article belongs to the Special Issue Importance of Zn Fertilization: Biofortification of Food Crops and Soil Zn Status)

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17 pages, 25331 KiB

Open AccessEditor’s ChoiceArticle

A Grape Dataset for Instance Segmentation and Maturity Estimation

by Achilleas Blekos, Konstantinos Chatzis, Martha Kotaidou, Theocharis Chatzis, Vassilios Solachidis, Dimitrios Konstantinidis and Kosmas Dimitropoulos

Agronomy 2023, 13(8), 1995; https://doi.org/10.3390/agronomy13081995 - 27 Jul 2023

Cited by 6 | Viewed by 2517

Abstract

Grape maturity estimation is vital in precise agriculture as it enables informed decision making for disease control, harvest timing, grape quality, and quantity assurance. Despite its importance, there are few large publicly available datasets that can be used to train accurate and robust [...] Read more.

Grape maturity estimation is vital in precise agriculture as it enables informed decision making for disease control, harvest timing, grape quality, and quantity assurance. Despite its importance, there are few large publicly available datasets that can be used to train accurate and robust grape segmentation and maturity estimation algorithms. To this end, this work proposes the CERTH grape dataset, a new sizeable dataset that is designed explicitly for evaluating deep learning algorithms in grape segmentation and maturity estimation. The proposed dataset is one of the largest currently available grape datasets in the literature, consisting of around 2500 images and almost 10 k grape bunches, annotated with masks and maturity levels. The images in the dataset were captured under various illumination conditions and viewing angles and with significant occlusions between grape bunches and leaves, making it a valuable resource for the research community. Thorough experiments were conducted using a plethora of general object detection methods to provide a baseline for the future development of accurate and robust grape segmentation and maturity estimation algorithms that can significantly advance research in the field of viticulture. Full article

(This article belongs to the Special Issue Computer Vision for Intelligent Crop Identification and Crop Protection)

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16 pages, 10041 KiB

Open AccessEditor’s ChoiceArticle

Pseudomonas fluorescens RB5 as a Biocontrol Strain for Controlling Wheat Sheath Blight Caused by Rhizoctonia cerealis

by Yanjie Yi, Zhipeng Hou, Yu Shi, Changfu Zhang, Lijuan Zhu, Xinge Sun, Rumeng Zhang and Zichao Wang

Agronomy 2023, 13(8), 1986; https://doi.org/10.3390/agronomy13081986 - 27 Jul 2023

Cited by 2 | Viewed by 1028

Abstract

Wheat sheath blight is a soil-borne fungal disease caused by Rhizoctonia cerealis and is a serious threat to wheat worldwide. A microbial fungicide is a promising alternative to a chemical fungicide for wheat disease control. In this study, strain RB5 against R. cerealis [...] Read more.

Wheat sheath blight is a soil-borne fungal disease caused by Rhizoctonia cerealis and is a serious threat to wheat worldwide. A microbial fungicide is a promising alternative to a chemical fungicide for wheat disease control. In this study, strain RB5 against R. cerealis was isolated from wheat rhizosphere soil, which was identified as Pseudomonas fluorescens according to physiological, biochemical, and 16S rRNA gene sequence analyses. For improving the antifungal activity of RB5, the response surface methodology (RSM) was used to optimize the culture conditions for strain RB5, and the optimal culture conditions are 8.7 g/L of cassava, 5.2 g/L of soybean meal, pH 6.8, a 218 r/min speed, a 31.5 °C temperature, and 54 h of culture time. The inhibition rate of the culture filtrate obtained under this culture condition was up to 79.06%. The investigation of action mechanism showed strain RB5 could produce protease, chitinase, and siderophore, and its culture filtrate disrupted the mycelial morphology and inhibited the activities of three cell-wall-degrading enzymes of R. cerealis. Furthermore, the pot experiment exhibited that RB5 significantly controlled the wheat sheath blight with an efficacy of 71.22%. The evaluation of toxicological safety on an animal indicated that the culture filtrate was safe on mice. Overall, the culture filtrate of RB5 is a very promising microbial fungicide for the control of wheat sheath blight. Full article

(This article belongs to the Special Issue Phytoalexins, Resistance Inducers, and Sustainable Control Measures in Crop Protection Strategies)

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20 pages, 7716 KiB

Open AccessEditor’s ChoiceArticle

Optimizing Nitrogen Regime Improves Dry Matter and Nitrogen Accumulation during Grain Filling to Increase Rice Yield

by Shenqi Zhou, Kun Liu, Xinxin Zhuo, Weilu Wang, Weiyang Zhang, Hao Zhang, Junfei Gu, Jianchang Yang and Lijun Liu

Agronomy 2023, 13(8), 1983; https://doi.org/10.3390/agronomy13081983 - 27 Jul 2023

Cited by 2 | Viewed by 1049

Abstract

Nitrogen (N) fertilizer is a critical element that affects rice yield. However, its effects on dry matter accumulation (DMA), N accumulation, and their physiological mechanisms with grain yield and N utilization efficiency still lack in-depth study. Three large-scale japonica rice varieties—Jinxiangyu 1, Nanjing [...] Read more.

Nitrogen (N) fertilizer is a critical element that affects rice yield. However, its effects on dry matter accumulation (DMA), N accumulation, and their physiological mechanisms with grain yield and N utilization efficiency still lack in-depth study. Three large-scale japonica rice varieties—Jinxiangyu 1, Nanjing 46, and Huaidao 5—were used in two field experiments with varying N fertilizer application rates to examine grain yield and N utilization efficiency. The results showed that: (1) In the range of 0~360 kg ha⁻¹ total N application rate (TNAR), the rice yields of the three cultivars were maximum under the TNAR at 270 kg ha⁻¹. The optimal TNAR for the highest yield of Jinxiangyu 1, Nanjing 46, and Huaidao 5 were calculated based on quadratic regressions with values of 305.5 kg ha⁻¹, 307.6 kg ha⁻¹, and 298.0 kg ha⁻¹, and the corresponding yields were 10.3 t ha⁻¹, 10.6 t ha⁻¹ and 10.2 t ha⁻¹, respectively. The N utilization efficiency decreased gradually with the increase in TNAR, and the recovery efficiency decreased from 35.7~38.19% to 29.61~31.59%. (2) The yield was significantly positively correlated with DMA and N accumulation from the heading stage (HD) to the maturity stage (MA). The DMA and N accumulation of HD-MA were significantly positively correlated with leaf photosynthetic rate, non-structural carbohydrate (NSC) accumulation in stems, root oxidation activity, zeatin (Z) + zeatin riboside (ZR) contents in roots, and nitrate reductase (NR) and glutamate synthase (GOGAT) activity in HD. (3) In the range of 0~216 kg ha⁻¹ panicle N application rate (PNAR), the rice yield was maximum under the PNAR at 108 kg ha⁻¹. The optimal PNAR for the highest yield of Jinxiangyu 1 was calculated based on the quadratic regression with values of 139.5 kg ha⁻¹, and the highest yield was 9.72 t ha⁻¹. The leaf photosynthetic rate, NSC accumulation in stems, root oxidation activity, Z + ZR contents in roots, and NR activity in leaves in rice were higher under 108 kg ha⁻¹ PNAR. Excessive application of panicle fertilizer reduced the above physiological indicators and rice yield. The above results showed that optimizing N fertilizer could increase the leaf photosynthetic rate, NSC accumulation in stems, root oxidation activity, Z + ZR contents in roots, and NR activity from HD to MA, which was beneficial to improving DMA and N uptake during HD-MA, thus improving grain yield and N utilization efficiency in rice. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Phenology and Nitrogen Fertilizer under Global Climate Change)

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16 pages, 1320 KiB

Open AccessEditor’s ChoiceReview

Fungi Parasitizing Powdery Mildew Fungi: Ampelomyces Strains as Biocontrol Agents against Powdery Mildews

by Márk Z. Németh, Diána Seress and Teruo Nonomura

Agronomy 2023, 13(8), 1991; https://doi.org/10.3390/agronomy13081991 - 27 Jul 2023

Viewed by 1927

Abstract

Among the mycoparasites, Ampelomyces strains are studied in detail, particularly regarding their use as biocontrol agents (BCAs) of powdery mildew (PM) fungi, including their potential to replace conventional agrochemicals. Ampelomyces strains are characterized morphologically; their ribosomal DNA internal transcribed spacer (rDNA-ITS) regions and [...] Read more.

Among the mycoparasites, Ampelomyces strains are studied in detail, particularly regarding their use as biocontrol agents (BCAs) of powdery mildew (PM) fungi, including their potential to replace conventional agrochemicals. Ampelomyces strains are characterized morphologically; their ribosomal DNA internal transcribed spacer (rDNA-ITS) regions and actin gene (ACT) fragments were sequenced and their mycoparasitic activity was analyzed. In the interaction between Ampelomyces strains and PM fungi, the spores of the mycoparasites germinate on plant leaves, and their hyphae then penetrate the hyphae of PM fungi. Ampelomyces hyphae continue their growth internally, initiating the atrophy of PM conidiophores and eventually their complete collapse. Following the successful destruction of PM hyphae by Ampelomyces, the mycoparasite produces new intracellular pycnidia in PM conidiophores. The progeny spores released by mature pycnidia become the sources of subsequent infections of intact PM hyphae. As a result, the number of Ampelomyces-inoculated PM colonies gradually declines, and the conidial release of PM colonies is inhibited after the first treatment. Almost all conidiophores of 5- and 10-day-old Ampelomyces-inoculated PM colonies undergo complete atrophy or collapse. Methodological advances and in-depth analyses of the Ampelomyces–PM interaction were recently published. In this review, we summarize the genetic and phylogenetic diversity, the timing of mycoparasitism and pycnidiogenesis, the results of quantitative and visual analyses using electrostatic and digital microscopy technologies, the PM biocontrol potential of Ampelomyces, and the potential commercialization of the mycoparasites. The information provided herein can support further biocontrol and ecological studies of Ampelomyces mycoparasites. Full article

(This article belongs to the Special Issue New Insights into Pathogen, Insect Pest, and Weed Control in Field and Greenhouse Cropping Systems)

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22 pages, 9649 KiB

Open AccessEditor’s ChoiceArticle

Maize Nitrogen Grading Estimation Method Based on UAV Images and an Improved Shufflenet Network

by Weizhong Sun, Bohan Fu and Zhao Zhang

Agronomy 2023, 13(8), 1974; https://doi.org/10.3390/agronomy13081974 - 26 Jul 2023

Cited by 3 | Viewed by 1203

Abstract

Maize is a vital crop in China for both food and industry. The nitrogen content plays a crucial role in its growth and yield. Previous researchers have conducted numerous studies on the issue of the nitrogen content in single maize plants from a [...] Read more.

Maize is a vital crop in China for both food and industry. The nitrogen content plays a crucial role in its growth and yield. Previous researchers have conducted numerous studies on the issue of the nitrogen content in single maize plants from a regression perspective; however, partition management techniques of precision agriculture require plants to be divided by zones and classes. Therefore, in this study, the focus is shifted to the problems of plot classification and graded nitrogen estimation in maize plots performed based on various machine learning and deep learning methods. Firstly, the panoramic unmanned aerial vehicle (UAV) images of maize farmland are collected by UAV and preprocessed to obtain UAV images of each maize plot to construct the required datasets. The dataset includes three classes—low nitrogen, medium nitrogen, and high nitrogen, with 154, 94, and 46 sets of UAV images, respectively, in each class. The training set accounts for eighty percent of the entire dataset and the test set accounts for the other twenty percent. Then, the dataset is used to train models based on machine learning and convolutional neural network algorithms and subsequently the models are evaluated. Comparisons are made between five machine learning classifiers and four convolutional neural networks to assess their respective performances, followed by a separate assessment of the most optimal machine learning classifier and convolutional neural networks. Finally, the ShuffleNet network is enhanced by incorporating SENet and improving the kernel size of the Depthwise separable convolution. The findings demonstrate that the enhanced ShuffleNet network has the highest performance; its classification accuracy, precision, recall, and F1 scores were 96.8%, 97.0%, 97.1%, and 97.0%, respectively. The RegNet, the optimal model among deep learning models, achieved accuracy, precision, recall, and F1 scores of 96.4%, 96.9%, 96.5%, and 96.6%, respectively. In comparison, logistic regression, the optimal model among the machine learning classifiers, attained accuracy of 77.6%, precision of 79.5%, recall of 77.6%, and an F1 score of 72.6%. Notably, the logistic regression exhibited significant enhancements of 19.2% in accuracy, 17.5% in precision, 19.5% in recall, and 24.4% in the F1 score. In contrast, RegNet demonstrated modest improvements of 0.4% in accuracy, 0.1% in precision, 0.6% in recall, and 0.4% in the F1 score. Moreover, ShuffleNet-improvement boasted a substantially lower loss rate of 0.117, which was 0.039 lower than that of RegNet (0.156). The results indicated the significance of ShuffleNet-improvement in the nitrogen classification of maize plots, providing strong support for agricultural zoning management and precise fertilization. Full article

(This article belongs to the Special Issue AI, Sensors and Robotics for Smart Agriculture)

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8 pages, 1824 KiB

Open AccessEditor’s ChoiceBrief Report

Genotyping the High Protein Content Gene NAM-B1 in Wheat (Triticum aestivum L.) and the Development of a KASP Marker to Identify a Functional Haplotype

by Jin-Kyung Cha, Hyeonjin Park, Youngho Kwon, So-Myeong Lee, Ki-Won Oh and Jong-Hee Lee

Agronomy 2023, 13(8), 1977; https://doi.org/10.3390/agronomy13081977 - 26 Jul 2023

Cited by 3 | Viewed by 1139

Abstract

Protein content is one of the main factors determining the end-use quality of wheat. NO APICAL MERISTEM-B1 (NAM-B1) is a major gene regulating wheat grain protein content. The present study aimed to identify new genetic resources using the wild-type NAM-B1 allele [...] Read more.

Protein content is one of the main factors determining the end-use quality of wheat. NO APICAL MERISTEM-B1 (NAM-B1) is a major gene regulating wheat grain protein content. The present study aimed to identify new genetic resources using the wild-type NAM-B1 allele to breed high-protein-content wheat cultivars. We genotyped the HIGH GRAIN PROTEIN CONTENT-B1 (GPC-B1) locus and NAM-B1 allele in 165 wheat cultivars. A kompetitive allele-specific polymerase chain reaction (KASP) marker was designed for functional NAM-B1 allele screening. The results revealed that 41 out of 165 cultivars carried the GPC-B1 locus. Among the 41 GPC-B1-carrying cultivars, the wild-type NAM-B1 allele was identified in only 3 cultivars, none of which were Korean. The remaining 38 cultivars showed a 1-bp insertion in NAM-B1, resulting in a stop codon in the middle of the gene, rendering it nonfunctional. Overall, this study reveals that the utilization of the three selected cultivars possessing the wild-type NAM-B1 gene, in conjunction with the developed KASP assay, could increase the protein content in Korean wheat cultivars. Full article

(This article belongs to the Section Crop Breeding and Genetics)

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22 pages, 4262 KiB

Open AccessEditor’s ChoiceReview

The Role of Buildings in Rural Areas: Trends, Challenges, and Innovations for Sustainable Development

by Alessia Cogato, Leonardo Cei, Francesco Marinello and Andrea Pezzuolo

Agronomy 2023, 13(8), 1961; https://doi.org/10.3390/agronomy13081961 - 25 Jul 2023

Cited by 3 | Viewed by 2248

Abstract

Rural buildings represent the functional relationship between rural communities and agricultural land. Therefore, research on rural buildings has practical repercussions on environmental and socio-economic sustainability. Comprehensive state-of-the-art research on rural buildings may address research activities. We present a systematic review of the scientific [...] Read more.

Rural buildings represent the functional relationship between rural communities and agricultural land. Therefore, research on rural buildings has practical repercussions on environmental and socio-economic sustainability. Comprehensive state-of-the-art research on rural buildings may address research activities. We present a systematic review of the scientific research between 2000 and 2022 based on the PRISMA protocol. Five main topics were identified. The results showed that the primary research focus was production (25.1%) and environmental management issues (23.2%). However, construction and efficiency are rapidly taking centre stage (20.6%). Regarding sustainability (20.8%), life cycle assessment, green buildings, recycling and global warming should be the future research focus. Energy efficiency will benefit from studies on thermal energy. More research on engineering and technologies (10.3%), specifically remote and automatic detection and transport in rural areas, will increase cost efficiency. The results may help improve the global efficiency of rural buildings in a modern farming system. Full article

(This article belongs to the Special Issue Sustainable Agri-Tech Engineering: Building Resilience for Climate Change Adaptation)

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19 pages, 480 KiB

Open AccessEditor’s ChoiceReview

Digitization of Crop Nitrogen Modelling: A Review

by Luís Silva, Luís Alcino Conceição, Fernando Cebola Lidon, Manuel Patanita, Paola D’Antonio and Costanza Fiorentino

Agronomy 2023, 13(8), 1964; https://doi.org/10.3390/agronomy13081964 - 25 Jul 2023

Cited by 2 | Viewed by 1492

Abstract

Applying the correct dose of nitrogen (N) fertilizer to crops is extremely important. The current predictive models of yield and soil–crop dynamics during the crop growing season currently combine information about soil, climate, crops, and agricultural practices to predict the N needs of [...] Read more.

Applying the correct dose of nitrogen (N) fertilizer to crops is extremely important. The current predictive models of yield and soil–crop dynamics during the crop growing season currently combine information about soil, climate, crops, and agricultural practices to predict the N needs of plants and optimize its application. Recent advances in remote sensing technology have also contributed to digital modelling of crop N requirements. These sensors provide detailed data, allowing for real-time adjustments in order to increase nutrient application accuracy. Combining these with other tools such as geographic information systems, data analysis, and their integration in modelling with experimental approaches in techniques such as machine learning (ML) and artificial intelligence, it is possible to develop digital twins for complex agricultural systems. Creating digital twins from the physical field can simulate the impact of different events and actions. In this article, we review the state-of-the-art of modelling N needs by crops, starting by exploring N dynamics in the soil−plant system; we demonstrate different classical approaches to modelling these dynamics so as to predict the needs and to define the optimal fertilization doses of this nutrient. Therefore, this article reviews the currently available information from Google Scholar and ScienceDirect, using relevant studies on N dynamics in agricultural systems, different modelling approaches used to simulate crop growth and N dynamics, and the application of digital tools and technologies for modelling proposed crops. The cited articles were selected following the exclusion criteria, resulting in a total of 66 articles. Finally, we present digital tools and technologies that increase the accuracy of model estimates and improve the simulation and presentation of estimated results to the manager in order to facilitate decision-making processes. Full article

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

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15 pages, 11202 KiB

Open AccessEditor’s ChoiceArticle

Identification and Analysis of SOD Family Genes in Peanut (Arachis hypogaea L.) and Their Potential Roles in Stress Responses

by Shutao Yu, Chuantang Wang, Qi Wang, Quanxi Sun, Yu Zhang, Jingchao Dong, Yechao Yin, Shihang Zhang and Guoqing Yu

Agronomy 2023, 13(8), 1959; https://doi.org/10.3390/agronomy13081959 - 25 Jul 2023

Cited by 2 | Viewed by 1131

Abstract

Superoxide dismutases (SODs) are crucial in safeguarding plants against reactive oxygen species (ROS) toxicity caused by abiotic or biotic factors. Although recent research has revealed the involvement of the SOD gene family in plant biological processes, the understanding of the SOD gene family [...] Read more.

Superoxide dismutases (SODs) are crucial in safeguarding plants against reactive oxygen species (ROS) toxicity caused by abiotic or biotic factors. Although recent research has revealed the involvement of the SOD gene family in plant biological processes, the understanding of the SOD gene family in peanut remains inadequate. This study comprehensively characterizes the SOD gene family in the peanut genome. A total of 25 AhSOD genes were identified and subsequently categorized into three subfamilies: sixteen AhCSDs, six AhFSDs, and three AhMSDs according to the phylogenetic tree. A comprehensive analysis revealed that the AhSOD genes underwent segmental duplications. The majority of AhSOD genes exhibited conserved exon–intron and motif structures within the same subfamily. The examination of cis-acting elements within the promoter regions of SOD genes revealed that the expression of AhSOD was subject to regulation by plant hormones, as well as responses to defense and stress. RNA-seq analysis showed expression diversity of AhSOD genes in various tissues and cold, drought, and salt stresses. Furthermore, the regulation of AhSOD gene expression is anticipated to involve numerous transcription factors. The gene ontology annotation results validate the role of AhSOD genes in various stress stimuli, SOD activity, reactive oxygen species metabolic processes, and cellular oxidant detoxification processes. This investigation serves as the initial genome-wide analysis of the AhSOD gene family, providing a basis for comprehending the function of the AhSOD gene family and enhancing plant tolerance to cold, drought, and salt stresses. Full article

(This article belongs to the Special Issue Advances in the Industrial Crops)

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13 pages, 3836 KiB

Open AccessEditor’s ChoiceArticle

Unattended Electric Weeder (UEW): A Novel Approach to Control Floor Weeds in Orchard Nurseries

by Yoshinori Matsuda, Koji Kakutani and Hideyoshi Toyoda

Agronomy 2023, 13(7), 1954; https://doi.org/10.3390/agronomy13071954 - 24 Jul 2023

Cited by 2 | Viewed by 964

Abstract

This study developed an unattended electric weeder (UEW) to control floor weeds in an orchard greenhouse. The UEW was a motor-driven dolly equipped with a spark exposer. The spark exposer was constructed by applying an alternating voltage (10 kV) to a conductor net [...] Read more.

This study developed an unattended electric weeder (UEW) to control floor weeds in an orchard greenhouse. The UEW was a motor-driven dolly equipped with a spark exposer. The spark exposer was constructed by applying an alternating voltage (10 kV) to a conductor net (expanded metal net). The charged conductor net (C-CN) discharged into the surrounding space. Wild oat and white clover were used as test weed species. Weed seedlings growing on the floor were grounded by the biological conductor and were subjected to a spark from the C-CN when they reached the discharge space. The spark-exposed seedlings were singed and shrunk instantaneously. In the present experiment, the UEW was remotely controlled to move on the soil-cover metal nets, which were laid on the floor to make a flat surface, in a stop-and-go manner, and to eject a spark to the weed seedlings that emerged from the floor. All of the mono- and dicotyledonous weed seedlings, which had been artificially sown on the floor, were completely eradicated using this method. Thus, this study provides an experimental basis for developing an unattended technique for controlling floor weeds in an orchard greenhouse. Full article

(This article belongs to the Special Issue New Insights into Pathogen, Insect Pest, and Weed Control in Field and Greenhouse Cropping Systems)

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16 pages, 1242 KiB

Open AccessEditor’s ChoiceArticle

Quantitative and Qualitative Traits of Duckweed (Lemna minor) Produced on Growth Media with Pig Slurry

by Marcin Sońta, Justyna Więcek, Ewa Szara, Anna Rekiel, Anna Zalewska and Martyna Batorska

Agronomy 2023, 13(7), 1951; https://doi.org/10.3390/agronomy13071951 - 24 Jul 2023

Viewed by 2448

Abstract

Duckweed is a plant with high phytoremediation abilities, which is why it is used in the process of cleaning the aquatic environment. The present study aimed to determine the effect of various concentrations of pig slurry added to the growth media used to [...] Read more.

Duckweed is a plant with high phytoremediation abilities, which is why it is used in the process of cleaning the aquatic environment. The present study aimed to determine the effect of various concentrations of pig slurry added to the growth media used to produce duckweed (Lemna minor) (laboratory Warsaw University of Life Sciences—SGGW) (experimental groups 1–9, pig slurry concentration (%): 1—2.00, 2—1.50, 3—1.00, 4—0.75, 5—0.50, 6—0.25, 7—0.12, 8—0.06, 9—0.03, control group 0—0.00). The contents of nutrients in the growth media could be classified as high (gr. 1–3), optimal (gr. 4–6), and deficient (gr. 7–9). Analyses were conducted for duckweed yield and growth medium parameters (pig slurry concentration, pH, salinity, temperature, TDS, and EC) on days 0, 10, 20, and 30 of the experiment. No growth or poor growth of duckweed were noted in groups 1, 6–9, and 0. In turn, satisfactory yields of duckweed green mass were recorded in groups 3–5, which allowed choosing them for further observations and analyses, including proximate composition (including protein content); contents of Ca, Mg, K, Na, Zn, Cu, Cd, Pb, Al, Cr, and α-tocopherol; and carotenoids—β-carotene, α-carotene, violaxanthin, zeaxanthin, lutein, amino acids, fatty acids as well as N-NH₄ and N-NO₃. The plant material had an acceptable proximate composition and nutritionally safe analyzed component contents. Appropriate, stable growth medium conditions allowed the production of satisfactory duckweed yields. The study results allowed us to conclude that it is feasible to obtain feed material meeting basic quality standards by maintaining a closed circuit of duckweed culture, and use in the agricultural environment is possible through harnessing pig slurry for its production and ensuring its optimal growth conditions. Full article

(This article belongs to the Special Issue Agricultural Waste Management in a Circular Economy Perspective)

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13 pages, 3663 KiB

Open AccessEditor’s ChoiceArticle

Evaluating Critical Nitrogen Dilution Curves for Assessing Maize Nitrogen Status across the US Midwest

by Hui Shao, Yuxin Miao, Fabián G. Fernández, Newell R. Kitchen, Curtis J. Ransom, James J. Camberato, Paul R. Carter, Richard B. Ferguson, David W. Franzen, Carrie A. M. Laboski, Emerson D. Nafziger, John E. Sawyer and John F. Shanahan

Agronomy 2023, 13(7), 1948; https://doi.org/10.3390/agronomy13071948 - 23 Jul 2023

Cited by 1 | Viewed by 1386

Abstract

Plant N concentration (PNC) has been commonly used to guide farmers in assessing maize (Zea mays L.) N status and making in-season N fertilization decisions. However, PNC varies based on the development stage. Therefore, a relationship between biomass and N concentration is [...] Read more.

Plant N concentration (PNC) has been commonly used to guide farmers in assessing maize (Zea mays L.) N status and making in-season N fertilization decisions. However, PNC varies based on the development stage. Therefore, a relationship between biomass and N concentration is needed (i.e., critical N dilution curve; CNDC) to better understand when plants are N deficient. A few CNDCs have been developed and used for plant N status diagnoses but have not been tested in the US Midwest. The objective of this study was to evaluate under highly diverse soil and weather conditions in the US Midwest the performance of CNDCs developed in France and China for assessing maize N status. Maize N rate response trials were conducted across eight US Midwest states over three years. This analysis utilized plant and soil measurements at V9 and VT development stages and final grain yield. Results showed that the French CNDC (y = 34.0x^−0.37, where y is critical PNC, and x is aboveground biomass) was better with a 91% N status classification accuracy compared to only 62% with the Chinese CNDC (y = 36.5x^−0.48). The N nutrition index (NNI), which is the quotient of the measured PNC and the calculated critical N concentration (Nc) based on the French CNDC was significantly related to soil nitrate-N content (R² = 0.38–0.56). Relative grain yield on average reached a plateau at NNI values of 1.36 at V9 and 1.21 at VT but for individual sites ranging from 0.80 to 1.41 at V9 and from 0.62 to 1.75 at VT. The NNI threshold values or ranges optimal for crop biomass production may not be optimal for grain yield production. It is concluded that the CNDC developed in France is suitable as a general diagnostic tool for assessing maize N status in US Midwest. However, the threshold values of NNI for diagnosing maize N status and guiding N applications vary significantly across the region, making it challenging to guide specific on-farm N management. More studies are needed to determine how to effectively use CNDC to make in-season N recommendations in the US Midwest. Full article

(This article belongs to the Special Issue The Importance of Soil Spatial Variability in Precision Agriculture)

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36 pages, 3264 KiB

Open AccessEditor’s ChoiceReview

Methodologies for Water Accounting at the Collective Irrigation System Scale Aiming at Optimizing Water Productivity

by Antónia Ferreira, João Rolim, Paula Paredes and Maria do Rosário Cameira

Agronomy 2023, 13(7), 1938; https://doi.org/10.3390/agronomy13071938 - 22 Jul 2023

Cited by 4 | Viewed by 2499

Abstract

To improve water use efficiency and productivity, particularly in irrigated areas, reliable water accounting methodologies are essential, as they provide information on the status and trends in irrigation water availability/supply and consumption/demand. At the collective irrigation system level, irrigation water accounting (IWA) relies [...] Read more.

To improve water use efficiency and productivity, particularly in irrigated areas, reliable water accounting methodologies are essential, as they provide information on the status and trends in irrigation water availability/supply and consumption/demand. At the collective irrigation system level, irrigation water accounting (IWA) relies on the quantification of water fluxes from the diversion point to the plants, at both the conveyance and distribution network and the irrigated field level. Direct measurement is the most accurate method for IWA, but in most cases, there is limited metering of irrigation water despite the increasing pressure on both groundwater and surface water resources, hindering the water accounting procedures. However, various methodologies, tools, and indicators have been developed to estimate the IWA components, depending on the scale and the level of detail being considered. Another setback for the wide implementation of IWA is the vast terminology used in the literature for different scales and levels of application. Thus, the main objectives of this review, which focuses on IWA for collective irrigation services, are to (i) demonstrate the importance of IWA by showing its relationship with water productivity and water use efficiency; (ii) clarify the concepts and terminology related to IWA; and (iii) provide an overview of various approaches to obtain reliable data for the IWA, on the demand side, both at the distribution network and on-farm systems. From the review, it can be concluded that there is a need for reliable IWA, which provides a common information base for all stakeholders. Future work could include the development of user-friendly tools and methodologies to reduce the bridge between the technology available to collect and process the information on the various water accounting components and its effective use by stakeholders. Full article

(This article belongs to the Special Issue Improving Agricultural Water Productivity to Ensure Food Security under Changing Environments)

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32 pages, 664 KiB

Open AccessEditor’s ChoiceReview

Complementary Use of Ground-Based Proximal Sensing and Airborne/Spaceborne Remote Sensing Techniques in Precision Agriculture: A Systematic Review

by Angelos Alexopoulos, Konstantinos Koutras, Sihem Ben Ali, Stefano Puccio, Alessandro Carella, Roberta Ottaviano and Athanasios Kalogeras

Agronomy 2023, 13(7), 1942; https://doi.org/10.3390/agronomy13071942 - 22 Jul 2023

Cited by 9 | Viewed by 3959

Abstract

As the global population continues to increase, projected to reach an estimated 9.7 billion people by 2050, there will be a growing demand for food production and agricultural resources. Transition toward Agriculture 4.0 is expected to enhance agricultural productivity through the integration of [...] Read more.

As the global population continues to increase, projected to reach an estimated 9.7 billion people by 2050, there will be a growing demand for food production and agricultural resources. Transition toward Agriculture 4.0 is expected to enhance agricultural productivity through the integration of advanced technologies, increase resource efficiency, ensure long-term food security by applying more sustainable farming practices, and enhance resilience and climate change adaptation. By integrating technologies such as ground IoT sensing and remote sensing, via both satellite and Unmanned Aerial Vehicles (UAVs), and exploiting data fusion and data analytics, farming can make the transition to a more efficient, productive, and sustainable paradigm. The present work performs a systematic literature review (SLR), identifying the challenges associated with UAV, Satellite, and Ground Sensing in their application in agriculture, comparing them and discussing their complementary use to facilitate Precision Agriculture (PA) and transition to Agriculture 4.0. Full article

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

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13 pages, 2391 KiB

Open AccessEditor’s ChoiceArticle

Effect of Different Ratios of Red and Blue Light on Maximum Stomatal Conductance and Response Rate of Cucumber Seedling Leaves

by Xue Li, Shiwen Zhao, Aiyu Lin, Yuanyuan Yang, Guanzhi Zhang, Peng Xu, Yongjun Wu and Zhenchao Yang

Agronomy 2023, 13(7), 1941; https://doi.org/10.3390/agronomy13071941 - 22 Jul 2023

Cited by 3 | Viewed by 1407

Abstract

Light can regulate leaf stomatal development and movement, but the effects of different red-to-blue light mass ratios on leaf stomatal morphology and openness are not fully understood. In this trial, five different red-to-blue light (R:B) ratio treatments were used to study the changes [...] Read more.

Light can regulate leaf stomatal development and movement, but the effects of different red-to-blue light mass ratios on leaf stomatal morphology and openness are not fully understood. In this trial, five different red-to-blue light (R:B) ratio treatments were used to study the changes in morphology, photosynthesis, and stomatal-related indexes of cucumber seedlings under fixed light intensity (200 μmol·m⁻²·s⁻¹). The results showed that the thickness of spongy tissue and stomatal size (SZ) of cucumber seedling leaves decreased, and the photosynthetic potential, stomatal density (SD), maximum stomatal conductance and stomatal responsiveness increased with decreasing R:B content. The experimental results showed that when R:B = is 1:9, cucumber seedlings had the greatest stomatal density and the fastest response rate, and the stomatal opening rate was accelerated with the increase in the proportion of blue light; when R:B = is 3:7, the stomatal conductance was the greatest and the net photosynthetic rate was the highest. This trial provides some implications for changing plant light quality and thus affecting stomatal development and movement. Full article

(This article belongs to the Section Horticultural and Floricultural Crops)

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15 pages, 2888 KiB

Open AccessEditor’s ChoiceArticle

A Comprehensive Approach to Assessing Yield Map Quality in Smart Agriculture: Void Detection and Spatial Error Mapping

by John Byabazaire, Gregory M. P. O’Hare, Rem Collier, Chamil Kulatunga and Declan Delaney

Agronomy 2023, 13(7), 1943; https://doi.org/10.3390/agronomy13071943 - 22 Jul 2023

Cited by 2 | Viewed by 1370

Abstract

Smart agriculture relies on accurate yield maps as a crucial tool for decision-making. Many yield maps, however, suffer from spatial errors that can compromise the quality of their data, while several approaches have been proposed to address some of these errors, detecting voids [...] Read more.

Smart agriculture relies on accurate yield maps as a crucial tool for decision-making. Many yield maps, however, suffer from spatial errors that can compromise the quality of their data, while several approaches have been proposed to address some of these errors, detecting voids or holes in the maps remains challenging. Additionally, the quality of yield datasets is typically evaluated based on root mean squared errors after interpolation. This evaluation method relies on weighbridge data, which can occasionally be inaccurate, impacting the quality of decisions made using the datasets. This paper introduces a novel algorithm designed to identify voids in yield maps. Furthermore, it maps three types of spatial errors (GPS errors, yield surges, and voids) to two standard data quality dimensions (accuracy and completeness). Doing so provides a quality score that can be utilized to assess the quality of yield datasets, eliminating the need for weighbridge data. The paper carries out three types of evaluations: (1) evaluating the algorithm’s efficacy by applying it to a dataset containing fields with and without voids; (2) assessing the benefits of integrating void detection and other spatial error identification techniques into the yield data processing chain; and (3) examining the correlation between root mean squared error and the proposed quality score before and after filtering out spatial errors. The results of the evaluations demonstrate that the proposed algorithm achieves a 100% sensitivity, 91% specificity, and 82% accuracy in identifying yield maps with voids. Additionally, there is a decrease in the root mean squared error when various spatial errors, including voids after applying the proposed data pre-processing chain. The inverse correlation observed between the root mean squared error and the proposed quality score (−0.577 and −0.793, before and after filtering spatial errors, respectively) indicates that the quality score can effectively assess the quality of yield datasets. This assessment enables seamless integration into real-time big data quality assessment solutions based on various data quality dimensions. Full article

(This article belongs to the Special Issue “Smart Agriculture” Information Technology and Agriculture Cross-Discipline Research and Development)

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17 pages, 4203 KiB

Open AccessEditor’s ChoiceArticle

Nitrogen, Phosphorus, and Potassium Uptake in Rain-Fed Corn as Affected by NPK Fertilization

by Ravinder Singh, Steven Kyle Sawatzky, Matthew Thomas, Samuel Akin, Hailin Zhang, William Raun and Daryl Brian Arnall

Agronomy 2023, 13(7), 1913; https://doi.org/10.3390/agronomy13071913 - 20 Jul 2023

Cited by 1 | Viewed by 3014

Abstract

Effective nutrient management requires understanding nutrient uptake at various growth stages and nutrient removal by the harvested portion. Information on nutrient accumulation was provided by some older literature, and a few researchers have focused on this issue in this modern period with modern [...] Read more.

Effective nutrient management requires understanding nutrient uptake at various growth stages and nutrient removal by the harvested portion. Information on nutrient accumulation was provided by some older literature, and a few researchers have focused on this issue in this modern period with modern hybrids and improved corn cultivation practices. While almost all the studies were conducted in northern states of the US, information for the Southern Great Plains is still limited. To bridge this knowledge gap, a 2-year field study was conducted in a rain-fed corn production system. The study aimed to evaluate the impact of nitrogen (N), phosphorus (P) and potassium (K) fertilization on N, P, and K contents in aboveground plants at different growth stages. Pre-plant application of N (0, 67, 133 kg N ha⁻¹), P (0 and 20 kg ha⁻¹) and K (0 and 60 kg ha⁻¹) fertilizers was done. Results from our study revealed that nutrient uptake values, pattern and dynamics depend on environmental conditions, soil type and management practices. N concentration in plants showed a linear response to N application rate while P and K concentrations were unaffected by NPK fertilization rates. Total N, P and K uptake was primarily driven by N application rate, showing a linear increase with higher N rates. Co-application of P and K with N did not significantly affect nutrient concentration and uptake. Full article

(This article belongs to the Topic Plants Nutrients)

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15 pages, 6107 KiB

Open AccessEditor’s ChoiceArticle

YOLOv5-ASFF: A Multistage Strawberry Detection Algorithm Based on Improved YOLOv5

by Yaodi Li, Jianxin Xue, Mingyue Zhang, Junyi Yin, Yang Liu, Xindan Qiao, Decong Zheng and Zezhen Li

Agronomy 2023, 13(7), 1901; https://doi.org/10.3390/agronomy13071901 - 19 Jul 2023

Cited by 7 | Viewed by 2283

Abstract

The smart farm is currently a hot topic in the agricultural industry. Due to the complex field environment, the intelligent monitoring model applicable to this environment requires high hardware performance, and there are difficulties in realizing real-time detection of ripe strawberries on a [...] Read more.

The smart farm is currently a hot topic in the agricultural industry. Due to the complex field environment, the intelligent monitoring model applicable to this environment requires high hardware performance, and there are difficulties in realizing real-time detection of ripe strawberries on a small automatic picking robot, etc. This research proposes a real-time multistage strawberry detection algorithm YOLOv5-ASFF based on improved YOLOv5. Through the introduction of the ASFF (adaptive spatial feature fusion) module into YOLOv5, the network can adaptively learn the fused spatial weights of strawberry feature maps at each scale as a way to fully obtain the image feature information of strawberries. To verify the superiority and availability of YOLOv5-ASFF, a strawberry dataset containing a variety of complex scenarios, including leaf shading, overlapping fruit, and dense fruit, was constructed in this experiment. The method achieved 91.86% and 88.03% for mAP and F1, respectively, and 98.77% for AP of mature-stage strawberries, showing strong robustness and generalization ability, better than SSD, YOLOv3, YOLOv4, and YOLOv5s. The YOLOv5-ASFF algorithm can overcome the influence of complex field environments and improve the detection of strawberries under dense distribution and shading conditions, and the method can provide technical support for monitoring yield estimation and harvest planning in intelligent strawberry field management. Full article

(This article belongs to the Special Issue AI, Sensors and Robotics for Smart Agriculture)

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20 pages, 3951 KiB

Open AccessEditor’s ChoiceReview

Perspectives and Advances in Organic Formulations for Agriculture: Encapsulation of Herbicides for Weed Control

by Francisco J. Rodríguez-Mejías, Aurelio Scavo, Nuria Chinchilla, José M. G. Molinillo, Stefan Schwaiger, Giovanni Mauromicale and Francisco A. Macías

Agronomy 2023, 13(7), 1898; https://doi.org/10.3390/agronomy13071898 - 18 Jul 2023

Cited by 2 | Viewed by 1699

Abstract

This article offers a critical analysis of the evolution of encapsulation methods for herbicides and natural products, with a main focus on organic formulations. It extols the possibilities presented by these micro- and nanomaterials, such as their slow release, stability, bioavailability, water solubility, [...] Read more.

This article offers a critical analysis of the evolution of encapsulation methods for herbicides and natural products, with a main focus on organic formulations. It extols the possibilities presented by these micro- and nanomaterials, such as their slow release, stability, bioavailability, water solubility, and stability for classical and natural herbicides from their origins to the present. Full article

(This article belongs to the Special Issue Cropping Systems and Agronomic Management Practices of Field Crops)

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16 pages, 3865 KiB

Open AccessEditor’s ChoiceArticle

Formulation of Matrine Oil-Based Suspension Concentrate for Improving the Wetting of Droplets and Spraying Performance

by Meng Li, Zhen Wang, Huanwen Meng, Baozhu Dong, Xile Deng and Hongyou Zhou

Agronomy 2023, 13(7), 1895; https://doi.org/10.3390/agronomy13071895 - 18 Jul 2023

Cited by 2 | Viewed by 1900

Abstract

Matrine is an efficient, low-toxicity, and environmentally friendly botanical pesticide; however, it is mainly applied as a soluble concentrate (SL) with a limited utilization rate that is unsuitable for ultra-low-volume spraying and unmanned aerial vehicles. Therefore, a matrine formulation (such as an oil-based [...] Read more.

Matrine is an efficient, low-toxicity, and environmentally friendly botanical pesticide; however, it is mainly applied as a soluble concentrate (SL) with a limited utilization rate that is unsuitable for ultra-low-volume spraying and unmanned aerial vehicles. Therefore, a matrine formulation (such as an oil-based suspension concentrate, OD) is more effective. In this study, matrine ODs were prepared with three kinds of emulsifiers (VO/02N, VO/03, and VO/01). The storage stability, suspensibility, viscosity, surface tension, contact angle, droplet density, fraction of coverage, maximum retention, indoor control, effect of adhesion tension, and adhesion work of matrine ODs were studied. All three types of matrine ODs had favorable stability, and the wetting and spraying performance of the matrine ODs were more effective than those of the matrine SLs. Among the three types of matrine ODs, the viscosity, wettability, spray performance, and maximum retention of the suspension made with emulsifier VO/03 were superior to those of the other two emulsifiers, and they were more effective in controlling Spodoptera frugiperda. Increasing the solution concentration improved the spreading velocity of the droplets on the solid surface and the wettability. The matrine OD prepared from emulsifier VO/03 had the most effective wettability and spraying properties, and it can be used for ultra-low-volume spraying and aerial application. This study offers new insights into the efficient use of plant-based pesticides. Full article

(This article belongs to the Special Issue Application of Nanomaterials in Pesticides: Pesticide Controlled Release Formulations)

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12 pages, 281 KiB

Open AccessEditor’s ChoiceArticle

Effect of Poultry Litter Application Method and Rainfall and Delayed Wrapping on Warm-Season Grass Baleage

by Christine C. Nieman, Wayne K. Coblentz, Philip A. Moore, Jr. and Matthew S. Akins

Agronomy 2023, 13(7), 1896; https://doi.org/10.3390/agronomy13071896 - 18 Jul 2023

Cited by 3 | Viewed by 1026

Abstract

Poultry litter is a widely available fertilizer in the southeast USA and subsurface application of litter can increase both forage production and nutritive value. Frequent rainfall events and high humidity often limit time available for hay curing; baled silage techniques can increase harvest [...] Read more.

Poultry litter is a widely available fertilizer in the southeast USA and subsurface application of litter can increase both forage production and nutritive value. Frequent rainfall events and high humidity often limit time available for hay curing; baled silage techniques can increase harvest time flexibility. Unfortunately, rainfall events can still occur without forecast during harvest events, resulting in delayed baling or wrapping. The objective of this study was to evaluate poultry litter amendment methods, subsurface (SUB) and surface (SURF), and the effect of no rain (NR) on bales with wrapping after 2 h compared with rained-on bales with 17 h delayed wrapping (RDW) on warm-season grass baleage fermentation and nutritive value. Data were analyzed as a randomized complete block design with two amendment treatments and two post-baling treatments. Crude protein (CP) was greater (p < 0.01) and neutral detergent fiber (NDF) was lower (p < 0.01) in both pre- and post-ensiled bales with subsurface-applied poultry litter. Rain and delayed wrapping resulted in lower pH (p = 0.03), starch (p < 0.01), and water-soluble carbohydrates (p < 0.01) in pre-ensiled bales, compared to those that did not receive rain and were wrapped within 2 h, while post-ensiled bales only differed in lower (p < 0.01) starch and slightly greater (p < 0.01) NDF in RDW. Lactic acid (p < 0.01), acetic acid (p < 0.01), and total acids (p = 0.03) were greater in SUB, while butyric acid tended to be greater (p = 0.09), and alcohols (p = 0.05) were greater in SURF. Bales from RDW and NR only differed by greater (p < 0.01) propionic acid concentrations in NR. Under the conditions of this experiment, subsurface application of poultry litter increased final nutritive value, while rainfall and delayed wrapping of 17 h had few effects on the final nutritive value of warm-season grass baleage. Full article

(This article belongs to the Special Issue Prospects for the Development of Silage and Green Fodder)

15 pages, 3215 KiB

Open AccessEditor’s ChoiceArticle

Frequency of Outcrossing and Isolation Distance in Faba Beans (Vicia faba L.)

by Kedar N. Adhikari, Lucy Burrows, Abdus Sadeque, Christopher Chung, Brian Cullis and Richard Trethowan

Agronomy 2023, 13(7), 1893; https://doi.org/10.3390/agronomy13071893 - 17 Jul 2023

Viewed by 1180

Abstract

Faba beans (Vicia faba L.) constitute a partially outcrossing species requiring an isolation distance to maintain genetic purity when more than one variety is grown in field conditions. This information is crucial for seed growers and faba bean breeders. A study was [...] Read more.

Faba beans (Vicia faba L.) constitute a partially outcrossing species requiring an isolation distance to maintain genetic purity when more than one variety is grown in field conditions. This information is crucial for seed growers and faba bean breeders. A study was conducted at the University of Sydney’s Plant Breeding Institute, Narrabri, over two years to examine the extent of natural outcrossing using a creamy white flower characteristic as a morphological marker, which is controlled by a single recessive gene. The white-flowered genotype (IX225c) was grown in paired rows of 150 m length in four directions from a central 480 m² plot of the normal flowered genotype PBA Warda. A beehive was placed in the central plot at the flowering time and natural pollination was allowed. At maturity, seed samples were taken from the white-flowered genotype at designated intervals along each axis and 100 seeds from each sample were grown in the glasshouse/birdcage to the 4–5 leaf stage and the proportion of plants displaying a stipule spot pigmentation (normal flower color and spotted stipule are linked) was used to determine the percentage of outcrossing. Maximum outcrossing of 2.28% occurred where both genotypes were grown side by side (0 m) and the degree of outcrossing decreased as the distance along each axis from the central plot increased. At a 6 m distance, the outcrossing was less than 1%; however, on occasion, it increased to 1% beyond a distance of 100 m, indicating the volatile and unpredictable nature of bee flights. Distance had a major effect on outcrossing but the direction and its interaction had no effect. The results suggest that to limit outcrossing to below 0.5%, a distance of more than 150 m between plots of different faba beans cultivars would be required. It also indicated that Australian faba bean genotypes are mostly self-fertile and a relatively narrow isolation distance will ensure self-fertilization in seed production and breeding programs. Full article

(This article belongs to the Section Crop Breeding and Genetics)

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12 pages, 681 KiB

Open AccessEditor’s ChoiceArticle

Effects of Phosphate Fertilizer Application on the Growth and Yield of Tartary Buckwheat under Low-Nitrogen Condition

by Qiuyue Zhou, Jingang Tang, Changmin Liu, Kaifeng Huang and Xiaoyan Huang

Agronomy 2023, 13(7), 1886; https://doi.org/10.3390/agronomy13071886 - 17 Jul 2023

Cited by 8 | Viewed by 1631

Abstract

This study aimed to clarify the effect of phosphorus fertilizer on the senescence and yield of Tartary buckwheat under low-nitrogen treatment. A two-year field experiment to investigate the characteristics was conducted on Tartary buckwheat (Qianku 5) under four phosphorus fertilizer application rates, 0(CK), [...] Read more.

This study aimed to clarify the effect of phosphorus fertilizer on the senescence and yield of Tartary buckwheat under low-nitrogen treatment. A two-year field experiment to investigate the characteristics was conducted on Tartary buckwheat (Qianku 5) under four phosphorus fertilizer application rates, 0(CK), 40(LP), 80(MP), and 120 kg·ha⁻¹ (HP), in the absence of nitrogen treatment. Compared with CK, MP treatment increased the plant height, node number of main stem, branch number of main stem, root-morphology items, root activity, enzyme activity related to root nitrogen metabolism, leaf chlorophyll content, and antioxidant enzyme activity by an average of 27.82%, 36.00%, 31.76%, 70.63%, 103.16%, 45.63%, 19.42%, and 45.48%, respectively. MP treatment significantly decreased the malondialdehyde content by 23.54% compared with that of CK. Among all treatments, the HP treatment had the highest content. The grain number per plant, grain weight per plant, and yield under MP treatment were 1.54, 1.65, and 1.53 times those of CK, respectively. In summary, the appropriate phosphate fertilizer treatment (80 kg·ha⁻¹) can delay senescence, promote the growth, and increase the yield of Tartary buckwheat at low nitrogen levels. Such treatment is recommended for use in production to jointly achieve the high yield and high nitrogen conservation of Tartary buckwheat. Full article

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

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15 pages, 1624 KiB

Open AccessEditor’s ChoiceArticle

Greenhouse Gas Emissions from Double-Season Rice Field under Different Tillage Practices and Fertilization Managements in Southeast China

by Tong Yang, Zhi Yang, Chunchun Xu, Fengbo Li, Fuping Fang and Jinfei Feng

Agronomy 2023, 13(7), 1887; https://doi.org/10.3390/agronomy13071887 - 17 Jul 2023

Cited by 4 | Viewed by 2062

Abstract

To better understand the effects of tillage practice and fertilization management on greenhouse gas emissions and yields, a four-year field experiment was conducted to assess the effects of tillage practices (rotary tillage (RT) and no tillage (NT)) on the emissions of methane (CH [...] Read more.

To better understand the effects of tillage practice and fertilization management on greenhouse gas emissions and yields, a four-year field experiment was conducted to assess the effects of tillage practices (rotary tillage (RT) and no tillage (NT)) on the emissions of methane (CH₄) and nitrous oxide (N₂O) and rice yield under four fertilization management strategies (no fertilizer without straw (CK), inorganic fertilizer without straw (F), inorganic fertilize with biochar (FB), and inorganic fertilizer with straw (FS)). The results showed that NT significantly reduced CH₄ emissions by 21.1% and 52.6% compared to RT in early and late rice, respectively. Conversely, NT led to a significant increase in N₂O emissions by 101.0%, 79.0%, and 220.8% during the early rice, late rice, and fallow periods. Nevertheless, global warming potential (GWP) and greenhouse gas intensity (GHGI) were significantly mitigated, respectively, by 36.4% and 35.9% in NT, compared to RT treatment. There were significant interactions between tillage practice and fertilization management. Compared with CK, the F and FB treatments significantly reduced the GWP, respectively, by 40.4% and 53.8%, as well as the GHGI, respectively, by 58.2% and 69.9% in the RT condition; however, no significant difference was found under the NT condition. In contrast, the FS treatment significantly increased GWP and GHGI in both the RT and NT conditions. Overall, FB treatment had the same significantly low GHGI rating, with a value of 0.44 kg CO₂-eq kg⁻¹ yield year⁻¹ in RT and NT. Thus, the conversion of straw to biochar and its application to rice fields is a potentially sustainable agricultural strategy for mitigating GHG emissions and increasing yields. This study provides theoretical and practical support for double-season rice production in climate-smart agriculture. Full article

(This article belongs to the Special Issue Agricultural Management for Climate Change Adaptation, Greenhouse Gas Mitigation, and Agricultural Productivity)

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20 pages, 767 KiB

Open AccessEditor’s ChoiceReview

Ecophysiological Responses of Rice (Oryza sativa L.) to Drought and High Temperature

by Romesh Kumar Salgotra and Bhagirath Singh Chauhan

Agronomy 2023, 13(7), 1877; https://doi.org/10.3390/agronomy13071877 - 16 Jul 2023

Cited by 8 | Viewed by 4122

Abstract

Global rice crop production is being threatened by a frequent rise in high temperatures and drought. Drought and heat stresses adversely affect the morphological, physiological, and biochemical characteristics of rice, resulting in reduced crop productivity. Heat and drought stresses entail physiological changes in [...] Read more.

Global rice crop production is being threatened by a frequent rise in high temperatures and drought. Drought and heat stresses adversely affect the morphological, physiological, and biochemical characteristics of rice, resulting in reduced crop productivity. Heat and drought stresses entail physiological changes in rice plants, such as stomata closure, reduced photosynthesis, loss of turgor adjustment, and reduction in crop productivity. These stresses also cause metabolic changes by increasing the activities of antioxidative enzymes, phytohormones, abscisic acid, reactive oxygen species, and reactive stress metabolites. Among the different growth stages of rice, the reproductive stage is the most sensitive stage to high temperature and drought, resulting in low seed setting and grain yield. Genetic improvement and development of drought and heat-stress-tolerant rice varieties increase seed setting and enhance yield production even under stress conditions. Because of the multigenic nature of traits, the development of drought and high-temperature-tolerant varieties through genetic improvement is the best approach. Here, we summarized the effects of heat and drought stresses on the physiological traits of rice. We focused on different approaches to managing high-temperature and drought stresses, such as an adjustment in cultural practices, genetic improvement through molecular breeding, and the development of transgenics and chemical spray from an agricultural practice perspective. Full article

(This article belongs to the Special Issue Advances in Rice Physioecology and Sustainable Cultivation)

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11 pages, 796 KiB

Open AccessEditor’s ChoiceArticle

Looking beyond Glyphosate for Site-Specific Fallow Weed Control in Australian Grain Production

by Angus Malmo, John C. Broster and Michael J. Walsh

Agronomy 2023, 13(7), 1878; https://doi.org/10.3390/agronomy13071878 - 16 Jul 2023

Cited by 1 | Viewed by 980

Abstract

Summer annual weed species in northern Australian summer fallows are frequently present at low densities and, increasingly, are glyphosate-resistant, creating the need for alternative herbicides for site-specific weed control. Alternative non-selective herbicide treatments are effective on problematic summer fallow weeds; however, many are [...] Read more.

Summer annual weed species in northern Australian summer fallows are frequently present at low densities and, increasingly, are glyphosate-resistant, creating the need for alternative herbicides for site-specific weed control. Alternative non-selective herbicide treatments are effective on problematic summer fallow weeds; however, many are yet to be evaluated as site-specific (spot spraying) treatments. This study aimed to identify herbicides that could be used in place of glyphosate to control larger/mature Chloris virgata and Sonchus oleraceus plants. The response of these weed species to 12 herbicide treatments was evaluated in pot experiments conducted over summer/autumn 2022. Despite herbicide treatments not being consistently effective across both species, there were instances where control was achieved by some herbicide treatments. S. oleraceus was controlled (i.e., ≤10% plant survival) by glufosinate-ammonium, paraquat and also with protoporphyrinogen-oxidase (PPO)-inhibiting herbicides saflufenacil, tiafenacil and trifludimoxazin. However, these results were not consistent in repeated studies or for C. virgata. Glyphosate was the only herbicide that controlled C. virgata. A glyphosate replacement as a spot-spraying treatment was not identified, and until further studies are more successful, alternative approaches are needed to preserve the ongoing effectiveness of this herbicide. Full article

(This article belongs to the Special Issue Herbicides and Chemical Control of Weeds)

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21 pages, 4514 KiB

Open AccessEditor’s ChoiceArticle

Effects of Bacillus subtilis HS5B5 on Maize Seed Germination and Seedling Growth under NaCl Stress Conditions

by Peng Song, Biao Zhao, Xingxin Sun, Lixiang Li, Zele Wang, Chao Ma and Jun Zhang

Agronomy 2023, 13(7), 1874; https://doi.org/10.3390/agronomy13071874 - 15 Jul 2023

Cited by 5 | Viewed by 1732

Abstract

Salinity is one of the most important factors limiting agricultural productivity. The positive effects of an inoculation with Bacillus subtilis HS5B5 on maize (Zea mays L.) seed germination and seedling growth under saline conditions were elucidated in this study. Maize plants were [...] Read more.

Salinity is one of the most important factors limiting agricultural productivity. The positive effects of an inoculation with Bacillus subtilis HS5B5 on maize (Zea mays L.) seed germination and seedling growth under saline conditions were elucidated in this study. Maize plants were treated with four NaCl concentrations (0, 100, 200, and 300 mmol·L⁻¹) under hydroponic conditions and the plants inoculated with B. subtilis HS5B5 were compared with the non-inoculated plants in terms of key morphophysiological leaf and root traits. Maize seed germination and seedling growth were inhibited by NaCl stress. The inoculation with B. subtilis HS5B5 significantly increased the germination rate, germination potential, shoot length, and root length under NaCl stress conditions. Moreover, the plant height, biomass, root to shoot weight ratio, above-ground fresh weight, and below-ground fresh weight were higher for the inoculated maize seedlings than for the non-inoculated plants under saline conditions. Additionally, B. subtilis HS5B5 alleviated the salt-induced damage to maize by increasing the chlorophyll content, altering the abundance of osmoregulatory substances, and increasing antioxidant enzyme activities, while decreasing the malondialdehyde content. After the NaCl treatment, the Na⁺ content in the leaves and roots of maize plants inoculated with B. subtilis HS5B5 decreased significantly, while the K⁺ content increased. Thus, the inhibitory effect of NaCl stress on maize seed germination and seedling growth was mitigated by B. subtilis HS5B5, suggesting the utility of this microorganism for improving crop cultivation under saline conditions. Full article

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12 pages, 4836 KiB

Open AccessEditor’s ChoiceArticle

Using Fluorescence Spectroscopy to Assess Compost Maturity Degree during Composting

by Yao-Tsung Chang, Chia-Hsing Lee, Chi-Ying Hsieh, Ting-Chien Chen and Shih-Hao Jien

Agronomy 2023, 13(7), 1870; https://doi.org/10.3390/agronomy13071870 - 15 Jul 2023

Cited by 6 | Viewed by 1995

Abstract

Uncertainty remains over composting time and maturity degree for compost production. The objectives of this study were to establish maturity indicators for composting based on spectral and chemical components and to provide a reference for future composting management. Several indicators of composting were [...] Read more.

Uncertainty remains over composting time and maturity degree for compost production. The objectives of this study were to establish maturity indicators for composting based on spectral and chemical components and to provide a reference for future composting management. Several indicators of composting were assessed for three commercial composts at 0, 7, 15, 30, 45, and 60 days during the germination of Chinese cabbage, including (1) central temperature, (2) moisture content, (3) pH, (4) electrical conductivity, (5) C/N ratio, (6) E4/E6 ratio, (7) fluorescence humification index (HIX), and (8) germination index (GI). We evaluated the optimal composting time using these indicators, reflecting the changes in hog manure, chicken manure, and agricultural by-product composts throughout their composting process to provide a basis for maturity time. The results showed that the E4/E6 ratio, C/N ratio, humic acid (HA), fulvic acid (FA), and germination rate, which reached a stable status after 30 days of composting, could be the indicators of “early-stage” maturity. In contrast, central temperature, electrical conductivity, HIX, and GI reached stable values after 45 days of composting and thus could be more suitable indicators of full maturity. Based on our results, we recommend a minimum composting time of 30 days to achieve primary maturity, while fully matured compost may be obtained after 45 days. Full article

(This article belongs to the Special Issue Composting as a Key Driver for Sustainable Agricultural Scenarios—Volume II)

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18 pages, 10829 KiB

Open AccessEditor’s ChoiceArticle

A Novel Approach for Predicting Heavy Metal Contamination Based on Adaptive Neuro-Fuzzy Inference System and GIS in an Arid Ecosystem

by Elsayed Said Mohamed, Mohamed E. M. Jalhoum, Abdelaziz A. Belal, Ehab Hendawy, Yara F. A. Azab, Dmitry E. Kucher, Mohamed. S. Shokr, Radwa A. El Behairy and Hasnaa M. El Arwash

Agronomy 2023, 13(7), 1873; https://doi.org/10.3390/agronomy13071873 - 15 Jul 2023

Cited by 5 | Viewed by 1463

Abstract

The issue of agricultural soil pollution is especially important as it directly affects the quality of food and the lives of humans and animals. Soil pollution is linked to human activities and agricultural practices. The main objective of this study is to assess [...] Read more.

The issue of agricultural soil pollution is especially important as it directly affects the quality of food and the lives of humans and animals. Soil pollution is linked to human activities and agricultural practices. The main objective of this study is to assess and predict soil contamination by heavy metals utilizing an innovative method based on the adaptive neuro-fuzzy inference system (ANFIS), an effective artificial intelligence technology, and GIS in a semiarid and dry environment. A total of 150 soil samples were randomly collected in the neighboring area of the Bahr El-Baqar drain. Ordinary kriging (OK) was employed to generate spatial pattern maps for the following heavy metals: chromium (Cr), iron (Fe), cadmium (Cd), and nickel (Ni). The adaptive neuro-fuzzy inference system (ANFIS), known as one of the most effective applications of artificial intelligence (AI), was utilized to predict soil contamination by the selected heavy metals (Cr, Fe, Cd, and Ni). In total 150 samples were used, 136 soil samples were used for training and 14 for testing. The ANFIS predicting results were compared with the experimental results; this comparison proved its effectiveness, as a root mean square error (RMSE) was 0.048594 in training, and 0.0687 in testing, which is an acceptable result. The results showed that both the exponential and spherical models were quite suitable for Cr, Fe, and Ni. The correlation values (R²) were close to one in training and test; however, the stable model performed well with Cd. The high concentration of heavy metals was the most prevalent, encompassing approximately 51.6% of the study area. Furthermore, the average concentration of heavy metals in this degree was 82.86 ± 15.59 mg kg⁻¹ for Cr, 20,963.84 ± 4447.83 mg kg⁻¹ for Fe, 1.46 ± 0.42 mg kg⁻¹ for Cd, and 48.71 ± 11.88 mg kg⁻¹ for Ni. The comparison clearly demonstrates that utilizing the ANFIS model is a superior option for predicting the level of soil pollution. Ultimately, these findings can serve as a foundation for decision-makers to develop acceptable measures for mitigating heavy metal contamination. Full article

(This article belongs to the Special Issue Evaluation and Management of Heavy Metal Contamination in Agricultural Soil and Water)

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23 pages, 10683 KiB

Open AccessEditor’s ChoiceArticle

Analyzing Characteristics of Grassland Gross Ecosystem Product to Inform Decision Making in the Karst Desertification Control

by Yongyao Li, Kangning Xiong, Wenfang Zhang, Shuzhen Song and Lu Luo

Agronomy 2023, 13(7), 1861; https://doi.org/10.3390/agronomy13071861 - 14 Jul 2023

Cited by 4 | Viewed by 1081

Abstract

Synergistically enhancing and realizing the value of grassland ecosystem services (ES) for economic activity is an important but challenging task for achieving sustainability in the karst desertification control (KDC). However, how to use grassland ES value characteristics in the KDC to make decisions [...] Read more.

Synergistically enhancing and realizing the value of grassland ecosystem services (ES) for economic activity is an important but challenging task for achieving sustainability in the karst desertification control (KDC). However, how to use grassland ES value characteristics in the KDC to make decisions on ES improvement, human well-being enhancement, and sustainable development remains unclear. In this paper, we took the contiguous region of karst desertification in Yunnan-Guangxi-Guizhou, China, a global hotspot, as the study area. Based on the valuation of the gross ecosystem product (GEP) and county economic intensity, we analyzed the structural and spatial characteristics of grassland GEP in the KDC using spatial analysis methods. We found that: (1) the grassland GEP in the KDC is mainly distributed in counties with low economic intensity (86.05% of the total number of counties) and vulnerable to losses caused by the livelihood of farmers; (2) the grassland GEP in the KDC is spatially small and scattered (the geographic concentration lies between 0.015 and 0.237), which makes it difficult to form industrial scale advantages; (3) the public product index (66.22–96.77%) and industry scale concentration (97.87–99.86%) of grassland GEP in the KDC are high, and most of the GEP is difficult to transform on the private market. Based on our findings, we proposed three corresponding recommendations for economic decision-making. The results of this study can provide a reference for economic decision-making regarding the management of grassland ES in karst areas with similar conditions and beyond. Full article

(This article belongs to the Special Issue Grassland and Pasture Ecological Management and Utilization)

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16 pages, 2664 KiB

Open AccessEditor’s ChoiceArticle

Edamame Yield and Quality Response to Nitrogen and Sulfur Fertilizers

by Keren Brooks, Mark Reiter, Bo Zhang and Joshua Mott

Agronomy 2023, 13(7), 1865; https://doi.org/10.3390/agronomy13071865 - 14 Jul 2023

Cited by 2 | Viewed by 1487

Abstract

As United States farmers adapt soybean (Glycine max) production methods from oilseed to vegetable (edamame), key management practices will need to be considered. The key objective of this study was to determine the optimal nitrogen (N) rate and N application timing [...] Read more.

As United States farmers adapt soybean (Glycine max) production methods from oilseed to vegetable (edamame), key management practices will need to be considered. The key objective of this study was to determine the optimal nitrogen (N) rate and N application timing for edamame in the mid-Atlantic coastal plain system. The study was conducted for three years in Painter, VA, USA on sandy loam soils. A factorial arrangement of four N rates was applied with two application timing strategies: at-planting, and split application. Leaf tissue samples were collected and analyzed at R1. At harvest, the Normalized Difference Vegetation Index (NDVI) was measured, whole pods were mechanically collected, and yield was recorded. Additionally, pod and bean physical and chemical quality were assessed. Nitrogen fertilization significantly increased pod yield in two out of three years. R1 leaf N and sulfur (S) concentrations correlated to the yield, and R1 leaf and R6 whole-plant N concentrations correlated to the total N uptake. None of the tested parameters indicated that N fertilizer decreased yield or quality. In conclusion, we found that N fertilizer applied at planting may aid edamame yield and profit for sandy loam soils in the mid-Atlantic, USA. Full article

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

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21 pages, 1938 KiB

Open AccessEditor’s ChoiceArticle

Using Remote and Proximal Sensing in Organic Agriculture to Assess Yield and Environmental Performance

by Johannes Schuster, Ludwig Hagn, Martin Mittermayer, Franz-Xaver Maidl and Kurt-Jürgen Hülsbergen

Agronomy 2023, 13(7), 1868; https://doi.org/10.3390/agronomy13071868 - 14 Jul 2023

Cited by 5 | Viewed by 1282

Abstract

Satellite and sensor-based systems of site-specific fertilization have been developed almost exclusively in conventional farming. Agronomic and ecological advantages can also be expected from these digital methods in organic farming. However, it has not yet been investigated whether the algorithms and models are [...] Read more.

Satellite and sensor-based systems of site-specific fertilization have been developed almost exclusively in conventional farming. Agronomic and ecological advantages can also be expected from these digital methods in organic farming. However, it has not yet been investigated whether the algorithms and models are also applicable under organic farming conditions. In this study, the digital data and systems tested in the years 2021 and 2022 in southern Germany were (a) reflectance measurements with a tractor-mounted multispectral sensor, calculation of the vegetation index REIP, and application of algorithms; (b) satellite data in combination with the plant growth model PROMET; and (c) determination of the vegetation index NDVI based on satellite data. They were used to determine plant parameters (crop yield, biomass potential) and to calculate nitrogen balances at a high spatial resolution (10 × 10 m). The digital systems were tested at two sites with different organic farming systems (arable farming and dairy farming). Validation of the digital methods was carried out with ground-truth data from manual biomass sampling and combine harvester yield measurement. The nitrate leaching risk from the crop rotations of the farms was analyzed via site-specific N balancing using multi-year satellite data. The N balances were validated by measuring nitrate concentrations in leakage water. Additionally, soil properties, such as soil organic carbon (SOC) and total nitrogen (TN), were measured at the sub-field level. Using geostatistics, plant data, soil properties, and nitrate measurements were transferred into grids of the same resolution to enable correlation analyses. The correlations between yield determined with digital systems and the validation data were up to r = 0.77. Site-specific N balancing showed moderately positive correlations with nitrate concentrations in leakage water (r = 0.50–0.66). The strongly positive influence of the soil properties SOC and TN on crop yields underlines the importance of soil organic matter on soil fertility and site-specific yield potentials. The results show that digital methods allow the spatially high-resolution determination of yields and nitrogen balances in organic farming. This can be the basis for new management strategies in organic farming, e.g., the targeted use of limited nutrients to increase yields. Further validations under differentiated soil, climate, and management conditions are required to develop remote and proximal sensing applications in organic farming. Full article

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

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21 pages, 2048 KiB

Open AccessEditor’s ChoiceArticle

Impact of the Farming System and Amino-Acid Biostimulants on the Content of Carotenoids, Fatty Acids, and Polyphenols in Alternative and Common Barley Genotypes

by Rafał Nowak, Małgorzata Szczepanek, Karolina Błaszczyk, Joanna Kobus-Cisowska, Anna Przybylska-Balcerek, Kinga Stuper-Szablewska, Jarosław Pobereżny, Mohammad Bagher Hassanpouraghdam and Farzad Rasouli

Agronomy 2023, 13(7), 1852; https://doi.org/10.3390/agronomy13071852 - 13 Jul 2023

Cited by 4 | Viewed by 1680

Abstract

Barley (Hordeum vulgare) grain stands out among other cereals due to its high nutritional value. It results mainly from the high content of fiber and antioxidants, such as phenolic compounds. Barley grains can also be an important source of unsaturated fatty [...] Read more.

Barley (Hordeum vulgare) grain stands out among other cereals due to its high nutritional value. It results mainly from the high content of fiber and antioxidants, such as phenolic compounds. Barley grains can also be an important source of unsaturated fatty acids and carotenoids that are beneficial to health. This study assessed the effect of the foliar application of an amino-acid biostimulant on the content of phenolic compounds, carotenoids, and the composition of fatty acids in the grain of alternative, black-grain barley genotypes, and the commonly used ‘Soldo’ cultivar, grown in conventional and organic farming systems. The dark-pigmented grains contained significantly more phenolic acids and flavonoids than the yellow seed of the traditional cultivar and were characterized by a significantly higher proportion of unsaturated fatty acids. The application of the biostimulant significantly increased the concentration of phenolic compounds in grains, especially of alternative genotypes. Full article

(This article belongs to the Special Issue Recent Insights in Sustainable Agriculture and Nutrient Management)

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15 pages, 7291 KiB

Open AccessEditor’s ChoiceArticle

Weed Identification in Maize Fields Based on Improved Swin-Unet

by Jiaheng Zhang, Jinliang Gong, Yanfei Zhang, Kazi Mostafa and Guangyao Yuan

Agronomy 2023, 13(7), 1846; https://doi.org/10.3390/agronomy13071846 - 13 Jul 2023

Cited by 9 | Viewed by 1917

Abstract

The maize field environment is complex. Weeds and maize have similar colors and may overlap, and lighting and weather conditions vary. Thus, many methods for the automated differentiation of maize and weeds achieve poor segmentation or cannot be used in real time. In [...] Read more.

The maize field environment is complex. Weeds and maize have similar colors and may overlap, and lighting and weather conditions vary. Thus, many methods for the automated differentiation of maize and weeds achieve poor segmentation or cannot be used in real time. In this paper, a weed recognition model based on improved Swin-Unet is proposed. The model first performs semantic segmentation of maize seedlings and uses the resulting mask to identify weeds. U-Net acts as the semantic segmentation framework, and a Swin transformer module is introduced to improve performance. DropBlock regularization, which randomly hides some blocks in crop feature maps, is applied to enhance the generalization ability of the model. Finally, weed areas are identified and segmented with the aid of an improved morphological processing algorithm. The DeepLabv3+, PSANet, Mask R-CNN, original Swin-Unet, and proposed models are trained on a dataset of maize seedling images. The proposed Swin-Unet model outperforms the others, achieving a mean intersection over union of 92.75%, mean pixel accuracy of 95.57%, and inference speed of 15.1 FPS. Our model could be used for accurate, real-time segmentation of crops and weeds and as a reference for the development of intelligent agricultural equipment. Full article

(This article belongs to the Special Issue Precision Operation Technology and Intelligent Equipment in Farmland)

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16 pages, 3021 KiB

Open AccessEditor’s ChoiceArticle

Response of Rice Grain Yield and Soil Fertility to Fertilization Management under Three Rice-Based Cropping Systems in Reclaimed Soil

by Ping Liu, Tingyu Zhang, Guiliang Wang, Jing Ju, Wei Mao and Haitao Zhao

Agronomy 2023, 13(7), 1840; https://doi.org/10.3390/agronomy13071840 - 12 Jul 2023

Cited by 3 | Viewed by 1797

Abstract

Reasonable cropping systems and fertilizer management are vital for improving the quality of barren soil. The effectiveness of different crop rotation methods and fertilizers in soil improvement depends on various factors, including soil type, climate conditions, and crop type. In the present study, [...] Read more.

Reasonable cropping systems and fertilizer management are vital for improving the quality of barren soil. The effectiveness of different crop rotation methods and fertilizers in soil improvement depends on various factors, including soil type, climate conditions, and crop type. In the present study, based on three rice-based cropping systems, the effects of organic fertilizers combined with slow-release fertilizers on rice yield and soil fertility in reclaimed soil were analyzed. The results showed that the rice grain yield was highest under the rice-fallow rotation system (RF) with the application of rapeseed meal fertilizer. Available nutrients such as AN, N_NH₄⁺, TP, and AK showed a significant positive correlation with rice grain yield (p < 0.05). PCA and PERMANOVA analysis supported significant variation in CAZyme abundance among cropping systems (R² = 0.60, p = 0.001) and significant differences between slow-release fertilizer treatments and organic fertilizer treatments (p < 0.05), but not among the three organic fertilizer treatments. Network analysis indicated positive stronger correlations among all functional enzymes in organic fertilizer treatments compared to chemical fertilizer treatments. RDA and correlation heat map results showed that C/N ratios and N_NH₄⁺ were strongly related to CAZyme composition. PLS-PM analysis revealed that soil available nitrogen positively influenced several variables, while rice grain yield was negatively influenced by soil enzymes and TOC. These findings suggested that under appropriate cropping systems, partially substituting chemical fertilizers with organic fertilizers can effectively enhance the availability of nutrients in the soil, alter the activity of carbon-cycling microorganisms, and increase rice grain yield. Full article

(This article belongs to the Special Issue Applied Research and Extension in Agronomic Soil Fertility Series II)

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17 pages, 1122 KiB

Open AccessEditor’s ChoiceArticle

Appraisal of Soil Taxonomy and the World Reference Base for Soil Resources Applied to Classify Purple Soils from the Eastern Sichuan Basin, China

by Qian Meng, Song Li, Bin Liu, Jin Hu, Junyan Liu, Yangyang Chen and En Ci

Agronomy 2023, 13(7), 1837; https://doi.org/10.3390/agronomy13071837 - 11 Jul 2023

Cited by 3 | Viewed by 1335

Abstract

Purple soil is a type of global soil that is referred to by various names in different countries, which makes it difficult to understand, utilize, and ameliorate purple soil internationally. Soil Taxonomy (ST) and the World Reference Base for Soil Resources (WRB) are [...] Read more.

Purple soil is a type of global soil that is referred to by various names in different countries, which makes it difficult to understand, utilize, and ameliorate purple soil internationally. Soil Taxonomy (ST) and the World Reference Base for Soil Resources (WRB) are the most widely used soil classification systems in the world. The aim of this study was to clarify the classification of purple soil in ST and the WRB and to establish a reference between different classification systems of purple soil. Therefore, based on the current principles and methods of the ST and WRB systems, 18 typical purple soil profiles in the eastern Sichuan Basin were identified, retrieved, and classified. Then, the soil units of the WRB were compared with those of ST and the Chinese Soil Taxonomy (CST). The results revealed that the 18 typical purple soil profiles could be classified into three soil orders, four soil group orders, and seven soil subgroups in ST and four reference soil groups (RSGs) in the WRB; each profile had its own unique principal and supplementary qualifier combinations within the soil units. It was found that when compared with the ST system, the WRB and CST systems had stronger abilities to distinguish purple soil. In addition, the WRB system was able to more comprehensively consider soil characteristics such as soil layer thickness, ferric horizon, soil color, texture mutations, and carbonate through qualifiers. However, the CST system added diagnostic characteristics, such as the lithologic characteristics of purplish sandstones and shales and the ferric properties and alic properties at the soil group and subgroup levels, which enhanced the differentiation ability of the purple soil at the subgroup level. Full article

(This article belongs to the Special Issue Cultivated Land Sustainability in the Anthropocene)

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16 pages, 3014 KiB

Open AccessEditor’s ChoiceArticle

Method for Segmentation of Banana Crown Based on Improved DeepLabv3+

by Junyu He, Jieli Duan, Zhou Yang, Junchen Ou, Xiangying Ou, Shiwei Yu, Mingkun Xie, Yukang Luo, Haojie Wang and Qiming Jiang

Agronomy 2023, 13(7), 1838; https://doi.org/10.3390/agronomy13071838 - 11 Jul 2023

Cited by 2 | Viewed by 1435

Abstract

As the banana industry develops, the demand for intelligent banana crown cutting is increasing. To achieve efficient crown cutting of bananas, accurate segmentation of the banana crown is crucial for the operation of a banana crown cutting device. In order to address the [...] Read more.

As the banana industry develops, the demand for intelligent banana crown cutting is increasing. To achieve efficient crown cutting of bananas, accurate segmentation of the banana crown is crucial for the operation of a banana crown cutting device. In order to address the existing challenges, this paper proposed a method for segmentation of banana crown based on improved DeepLabv3+. This method replaces the backbone network of the classical DeepLabv3+ model with MobilenetV2, reducing the number of parameters and training time, thereby achieving model lightweightness and enhancing model speed. Additionally, the Atrous Spatial Pyramid Pooling (ASPP) module is enhanced by incorporating the Shuffle Attention Mechanism and replacing the activation function with Meta-ACONC. This enhancement results in the creation of a new feature extraction module, called Banana-ASPP, which effectively handles high-level features. Furthermore, Multi-scale Channel Attention Module (MS-CAM) is introduced to the Decoder to improve the integration of features from multiple semantics and scales. According to experimental data, the proposed method has a Mean Intersection over Union (MIoU) of 85.75%, a Mean Pixel Accuracy (MPA) of 91.41%, parameters of 5.881 M and model speed of 61.05 f/s. Compared to the classical DeepLabv3+ network, the proposed model exhibits an improvement of 1.94% in MIoU and 1.21% in MPA, while reducing the number of parameters by 89.25% and increasing the model speed by 47.07 f/s. The proposed method enhanced banana crown segmentation accuracy while maintaining model lightweightness and speed. It also provided robust technical support for relevant parameters calculation of banana crown and control of banana crown cutting equipment. Full article

(This article belongs to the Special Issue Computer Vision and Deep Learning Technology in Agriculture)

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18 pages, 2157 KiB

Open AccessEditor’s ChoiceReview

Genetics and Environmental Factors Associated with Resistance to Fusarium graminearum, the Causal Agent of Gibberella Ear Rot in Maize

by Andrea Magarini, Alessandro Passera, Martina Ghidoli, Paola Casati and Roberto Pilu

Agronomy 2023, 13(7), 1836; https://doi.org/10.3390/agronomy13071836 - 11 Jul 2023

Cited by 2 | Viewed by 1820

Abstract

Maize is one of the most important food and feed sources at the worldwide level. Due to this importance, all the pathogens that can infect this crop can harm both food safety and security. Fungi are the most important pathogens in cultivated maize, [...] Read more.

Maize is one of the most important food and feed sources at the worldwide level. Due to this importance, all the pathogens that can infect this crop can harm both food safety and security. Fungi are the most important pathogens in cultivated maize, and Fusarium spp. are one of the most important families. Reduction in yield and production of dangerous mycotoxins are the main effects of Fusarium spp. infection. Fusarium graminearum (part of the Fusarium graminearum species complex) is one the most important fungi that infect maize, and it is the causative agent of Gibberella ear rot (GER). The main characteristics of this species include its ability to infect various species and its varying infection pressures across different years. This fungus produces various harmful mycotoxins, such as deoxynivalenol, zearalenone, butanolide, and culmorin. Infection can start from silk channels or from ear wounds. In the first case, the environmental conditions are the most important factors, but in the second, a key role is played by the feeding action of lepidopteran larvae (in Europe, Ostrinia nubilalis). All these factors need to be taken into account to develop a successful management strategy, starting from cropping methods that can reduce the source of inoculum to the direct control of the fungus with fungicide, as well as insect control to reduce ear wounds. But, the most important factor that can reduce the effects of this fungus is the use of resistant hybrids. Different studies have highlighted different defensive methods developed by the plant to reduce fungal infections, like fast drying of silk and kernels, chemical compounds produced by the plant after infection, and mechanical protection from insects’ wounds. The aim of this paper is to review the scientific evidence of the most important management strategies against GER in maize and to highlight the genetic basis which is behind hybrid resistance to this disease, with a focus on genes and QTLs found in studies conducted across the world and with different types of maize from tropical cultivars to European flint. Full article

(This article belongs to the Special Issue Novel Studies in Crop Breeding for Promoting Agro-Biodiversity)

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23 pages, 2983 KiB

Open AccessEditor’s ChoiceReview

Secondary Metabolites, Other Prospective Substances, and Alternative Approaches That Could Promote Resistance against Phytophthora infestans

by Hana Dufková, Marie Greplová, Romana Hampejsová, Marharyta Kuzmenko, Ervín Hausvater, Břetislav Brzobohatý and Martin Černý

Agronomy 2023, 13(7), 1822; https://doi.org/10.3390/agronomy13071822 - 9 Jul 2023

Viewed by 1683

Abstract

Potato (Solanum tuberosum) is a valuable staple crop that provides nutrition for a large part of the human population around the world. However, the domestication process reduced its resistance to pests and pathogens. Phytophthora infestans, the causal agent of late [...] Read more.

Potato (Solanum tuberosum) is a valuable staple crop that provides nutrition for a large part of the human population around the world. However, the domestication process reduced its resistance to pests and pathogens. Phytophthora infestans, the causal agent of late blight disease, is the most destructive pathogen of potato plants. Considerable efforts have been made to develop late blight-resistant potato cultivars, but the success has been limited and present-day potato production requires the extensive use of fungicides. In this review, we summarize known sources of late blight resistance and obstacles in P. infestans control. We outline the problematic aspects of chemical treatment, the possible use of biological control, and available resources of natural resistance in wild Solanum accessions. We focus on prospective putative markers of resistance that are often overlooked in genome-centered studies, including secondary metabolites from alkaloid, phenylpropanoid, and terpenoid classes, lipids, proteins, and peptides. We discuss the suitability of these molecules for marker-assisted selection and the possibility of increasing the speed of conventional breeding of more resilient cultivars. Full article

(This article belongs to the Special Issue Advances in Molecular Technologies on Plant Disease Management)

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14 pages, 4900 KiB

Open AccessEditor’s ChoiceArticle

A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention

by Guoliang Yang, Jixiang Wang, Ziling Nie, Hao Yang and Shuaiying Yu

Agronomy 2023, 13(7), 1824; https://doi.org/10.3390/agronomy13071824 - 9 Jul 2023

Cited by 42 | Viewed by 12452

Abstract

A tomato automatic detection method based on an improved YOLOv8s model is proposed to address the low automation level in tomato harvesting in agriculture. The proposed method provides technical support for the automatic harvesting and classification of tomatoes in agricultural production activities. The [...] Read more.

A tomato automatic detection method based on an improved YOLOv8s model is proposed to address the low automation level in tomato harvesting in agriculture. The proposed method provides technical support for the automatic harvesting and classification of tomatoes in agricultural production activities. The proposed method has three key components. Firstly, the depthwise separable convolution (DSConv) technique replaces the ordinary convolution, which reduces the computational complexity by generating a large number of feature maps with a small amount of calculation. Secondly, the dual-path attention gate module (DPAG) is designed to improve the model’s detection precision in complex environments by enhancing the network’s ability to distinguish between tomatoes and the background. Thirdly, the feature enhancement module (FEM) is added to highlight the target details, prevent the loss of effective features, and improve detection precision. We built, trained, and tested the tomato dataset, which included 3098 images and 3 classes. The proposed algorithm’s performance was evaluated by comparison with the SSD, faster R-CNN, YOLOv4, YOLOv5, and YOLOv7 algorithms. Precision, recall rate, and mAP (mean average precision) were used for evaluation. The test results show that the improved YOLOv8s network has a lower loss and 93.4% mAP on this dataset. This improvement is a 1.5% increase compared to before the improvement. The precision increased by 2%, and the recall rate increased by 0.8%. Moreover, the proposed algorithm significantly reduced the model size from 22 M to 16 M, while achieving a detection speed of 138.8 FPS, which satisfies the real-time detection requirement. The proposed method strikes a balance between model size and detection precision, enabling it to meet agriculture’s tomato detection requirements. The research model in this paper will provide technical support for a tomato picking robot to ensure the fast and accurate operation of the picking robot. Full article

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

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19 pages, 3156 KiB

Open AccessEditor’s ChoiceArticle

Does Precision Technologies Adoption Contribute to the Economic and Agri-Environmental Sustainability of Mediterranean Wheat Production? An Italian Case Study

by Adele Finco, Deborah Bentivoglio, Matteo Belletti, Giulia Chiaraluce, Marco Fiorentini, Luigi Ledda and Roberto Orsini

Agronomy 2023, 13(7), 1818; https://doi.org/10.3390/agronomy13071818 - 8 Jul 2023

Cited by 1 | Viewed by 2466

Abstract

The European Green Deal has set a concrete strategic plan to increase farm sustainability. At the same time, the current global challenges, due to climate change and fuels and commodity market crises, combined with the COVID-19 pandemic and the ongoing war in Ukraine, [...] Read more.

The European Green Deal has set a concrete strategic plan to increase farm sustainability. At the same time, the current global challenges, due to climate change and fuels and commodity market crises, combined with the COVID-19 pandemic and the ongoing war in Ukraine, affect the need for quality food and necessitate the reduction of negative external effects of agricultural production, with fair remuneration for the farmers. In response, precision agriculture has great potential to contribute to sustainable development. Precision agriculture is a farming management system that provides a holistic approach to managing the spatial and temporal crop and soil variability within a field to improve the farm’s performance and sustainability. However, farmers are still hesitant to adopt it. On these premises, the study aims to evaluate the impacts of precision agriculture technologies on farm economic, agronomic, and environmental management by farmers adopting (or not) these technologies, using the case study method. In detail, the work focuses on the period 2014–2022 for two farms that cultivate durum wheat in central Italy. The results suggest that the implementation of precision technologies can guarantee economic and agri-environmental efficiency. The results could serve as a basis for developing a program to start training in farms as well as to suggest policy strategies. Full article

(This article belongs to the Topic Novel Studies in Agricultural Economics and Sustainable Farm Management)

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10 pages, 1856 KiB

Open AccessEditor’s ChoiceArticle

Maize Grain Germination Is Accompanied by Acidification of the Environment

by Konrad Wellmann, Jens Varnskühler, Gerhard Leubner-Metzger and Klaus Mummenhoff

Agronomy 2023, 13(7), 1819; https://doi.org/10.3390/agronomy13071819 - 8 Jul 2023

Cited by 1 | Viewed by 1861

Abstract

Seed germination is a complex process involving several stages, starting with the imbibition of water and ending with the emergence of the radicle. In the current study, we address the observation of an unexpected pH shift during the imbibition of maize grains. We [...] Read more.

Seed germination is a complex process involving several stages, starting with the imbibition of water and ending with the emergence of the radicle. In the current study, we address the observation of an unexpected pH shift during the imbibition of maize grains. We used direct pH measurements of soak water, the pH indicator methyl red, and anatomical analysis to shed light on the acidification associated with maize (Zea mays L.) germination, a largely overlooked phenomenon. Our work shows that acidification during imbibition of maize grains is a two-step process: (i) early, rapid acidification (pH values up to 4.4), in which protons stored in the (dead) pericarp/testa are mobilised and rapidly diffuse into the surrounding medium, and (ii) late, delayed acidification (pH values just below 6), starting hours after contact of grains with water, representing an active transport process caused by living cells of the seed. We discuss the physiological mechanisms and ecological relevance of environmental acidification during maize grain germination. Full article

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

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17 pages, 11106 KiB

Open AccessEditor’s ChoiceArticle

Research on Apple Object Detection and Localization Method Based on Improved YOLOX and RGB-D Images

by Tiantian Hu, Wenbo Wang, Jinan Gu, Zilin Xia, Jian Zhang and Bo Wang

Agronomy 2023, 13(7), 1816; https://doi.org/10.3390/agronomy13071816 - 8 Jul 2023

Cited by 8 | Viewed by 1977

Abstract

The vision-based fruit recognition and localization system is the basis for the automatic operation of agricultural harvesting robots. Existing detection models are often constrained by high complexity and slow inference speed, which do not meet the real-time requirements of harvesting robots. Here, a [...] Read more.

The vision-based fruit recognition and localization system is the basis for the automatic operation of agricultural harvesting robots. Existing detection models are often constrained by high complexity and slow inference speed, which do not meet the real-time requirements of harvesting robots. Here, a method for apple object detection and localization is proposed to address the above problems. First, an improved YOLOX network is designed to detect the target region, with a multi-branch topology in the training phase and a single-branch structure in the inference phase. The spatial pyramid pooling layer (SPP) with serial structure is used to expand the receptive field of the backbone network and ensure a fixed output. Second, the RGB-D camera is used to obtain the aligned depth image and to calculate the depth value of the desired point. Finally, the three-dimensional coordinates of apple-picking points are obtained by combining two-dimensional coordinates in the RGB image and depth value. Experimental results show that the proposed method has high accuracy and real-time performance: F1 is 93%, mean average precision (mAP) is 94.09%, detection speed can reach 167.43 F/s, and the positioning errors in X, Y, and Z directions are less than 7 mm, 7 mm, and 5 mm, respectively. Full article

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

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15 pages, 4192 KiB

Open AccessEditor’s ChoiceArticle

Preparation of Polyclonal Antibody against ZmBT1 Protein and Its Application in Hormone-Regulated Starch Synthesis

by Lun Liu, Yun Qing, Noman Shoaib, Runze Di, Hanmei Liu, Yangping Li, Yufeng Hu, Yubi Huang and Guowu Yu

Agronomy 2023, 13(7), 1805; https://doi.org/10.3390/agronomy13071805 - 7 Jul 2023

Cited by 2 | Viewed by 1182

Abstract

In order to investigate the crucial role of ZmBT1 in starch accumulation during maize grain development and analyze the expression and distribution of ZmBT1 in various maize tissues, we prepared a polyclonal antibody. Specifically, we successfully expressed the recombinant plasmid pGEX-6p-ZmBT1-C (382-437aa) and [...] Read more.

In order to investigate the crucial role of ZmBT1 in starch accumulation during maize grain development and analyze the expression and distribution of ZmBT1 in various maize tissues, we prepared a polyclonal antibody. Specifically, we successfully expressed the recombinant plasmid pGEX-6p-ZmBT1-C (382-437aa) and purified Gst-ZmBT1-C as the antigen for antibody preparation. Our results confirmed that the ZmBT1 protein in maize tissues can be specifically recognized by the ZmBT1 antibody. Through Western blotting, we observed that the expression protein of ZmBT1 varied by tissues, with the highest content in the grain and endosperm. Furthermore, we employed a combination of Western blotting and quantitative real-time PCR to show that the expression level of ZmBT1 can be influenced by plant hormones. This finding suggests that ZmBT1 plays a critical role in the accumulation of starch and opens up new avenues for functional studies of this protein. Full article

(This article belongs to the Special Issue Physiology, Biochemistry and Molecular Biology of Plant Mineral Nutrition)

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14 pages, 2799 KiB

Open AccessEditor’s ChoiceArticle

Soil Organic Carbon Prediction Based on Different Combinations of Hyperspectral Feature Selection and Regression Algorithms

by Naijie Chang, Xiaowen Jing, Wenlong Zeng, Yungui Zhang, Zhihong Li, Di Chen, Daibing Jiang, Xiaoli Zhong, Guiquan Dong and Qingli Liu

Agronomy 2023, 13(7), 1806; https://doi.org/10.3390/agronomy13071806 - 7 Jul 2023

Cited by 3 | Viewed by 1187

Abstract

Cropland soil organic carbon (SOC) is crucial for global food security and mitigating the greenhouse effect. Accurate SOC prediction using hyperspectral data is essential for dynamic monitoring of soil carbon pools in croplands. However, effective methods to reduce hyperspectral data dimensionality and integrate [...] Read more.

Cropland soil organic carbon (SOC) is crucial for global food security and mitigating the greenhouse effect. Accurate SOC prediction using hyperspectral data is essential for dynamic monitoring of soil carbon pools in croplands. However, effective methods to reduce hyperspectral data dimensionality and integrate it with suitable regression algorithms for reliable prediction models are poorly understood. In this study, we analyzed 108 soil samples from Changting County, Fujian Province, China. Our objective was to evaluate the performance of various combinations of six feature selection methods and four regression algorithms for SOC prediction. Our findings are as follows: the combination of the Successive Projections Algorithm (SPA) and Partial Least Squares (PLS) yielded the most favorable results, with R² (0.61), RMSE (1.77 g/kg), and MAE (1.48 g/kg). Moreover, we determined the relative importance of variables, with the following ranking: 696 nm > 892 nm > 783 nm > 1641 nm > 1436 nm > 396 nm > 392 nm > 2239 nm > 2129 nm. Notably, 696 nm exhibited the highest importance in the SPA-PLS model, with the Variable Importance in Projection (VIP) value of 1.22. This study provides profound insights into feature selection methods and regression algorithms for SOC prediction, highlighting the superiority of SPA-PLS as the optimal combination. Full article

(This article belongs to the Special Issue Art of Spectra: At the Crossroad of Agriculture and Remote Sensing Disciplines)

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11 pages, 2628 KiB

Open AccessEditor’s ChoiceArticle

Advancing Soil Organic Carbon and Total Nitrogen Modelling in Peatlands: The Impact of Environmental Variable Resolution and vis-NIR Spectroscopy Integration

by Wanderson de Sousa Mendes and Michael Sommer

Agronomy 2023, 13(7), 1800; https://doi.org/10.3390/agronomy13071800 - 6 Jul 2023

Cited by 1 | Viewed by 1289

Abstract

Visible and near-infrared (vis-NIR) spectroscopy has proven to be a straightforward method for sample preparation and scaling soil testing, while the increasing availability of high-resolution remote sensing (RS) data has further facilitated the understanding of spatial variability in soil organic carbon (SOC) and [...] Read more.

Visible and near-infrared (vis-NIR) spectroscopy has proven to be a straightforward method for sample preparation and scaling soil testing, while the increasing availability of high-resolution remote sensing (RS) data has further facilitated the understanding of spatial variability in soil organic carbon (SOC) and total nitrogen (TN) across landscapes. However, the impact of combining vis-NIR spectroscopy with high-resolution RS data for SOC and TN prediction remains an open question. This study evaluated the effects of incorporating a high-resolution LiDAR-derived digital elevation model (DEM) and a medium-resolution SRTM-derived DEM with vis-NIR spectroscopy for predicting SOC and TN in peatlands. A total of 57 soil cores, comprising 262 samples from various horizons (<2 m), were collected and analysed for SOC and TN content using traditional methods and ASD Fieldspec^® 4. The 262 observations, along with elevation data from LiDAR and SRTM, were divided into 80% training and 20% testing datasets. By employing the Cubist modelling approach, the results demonstrated that incorporating high-resolution LiDAR data with vis-NIR spectra improved predictions of SOC (RMSE: 4.60%, RPIQ: 9.00) and TN (RMSE: 3.06 g kg⁻¹, RPIQ: 7.05). In conclusion, the integration of LiDAR and soil spectroscopy holds significant potential for enhancing soil mapping and promoting sustainable soil management. Full article

(This article belongs to the Special Issue Soil Sensing and Landscape Modeling for Agronomic Application)

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13 pages, 2114 KiB

Open AccessEditor’s ChoiceArticle

Different Functional and Taxonomic Composition of the Microbiome in the Rhizosphere of Two Purslane Genotypes

by Angel Carrascosa, Jose Antonio Pascual, Alvaro López-García, Maria Romo-Vaquero, Margarita Ros, Spyridon A. Petropoulos and Maria del Mar Alguacil

Agronomy 2023, 13(7), 1795; https://doi.org/10.3390/agronomy13071795 - 4 Jul 2023

Cited by 1 | Viewed by 1277

Abstract

Soil microbial communities have an important role in plant establishment and health. Particularly, the role of the soil microbiome in agriculture is of current interest. The study of microbial communities associated with purslane could open questions about the rational exploitation of the microbiota [...] Read more.

Soil microbial communities have an important role in plant establishment and health. Particularly, the role of the soil microbiome in agriculture is of current interest. The study of microbial communities associated with purslane could open questions about the rational exploitation of the microbiota for sustainable agricultural purposes. In this study, the composition of the fungal and bacterial communities and the bacterial metabolic functions, associated with the rhizospheres of two purslane genotypes (one commercially available and one collected from the wild in Spain) were evaluated. The results showed a clear effect of purslane genotype on fungal and bacterial community composition and functional profiles. The bacterial community of the commercial purslane rhizosphere was characterized by more numerous metabolic pathways, mainly pathways related to Terpenoids and Polyketides, Carbohydrate, Lipid, and Amino Acid metabolism. By contrast, the rhizosphere bacterial community of the Spanish (wild) genotype was characterized by the enrichment of functions related to cellular processes such as cell motility and transport. We hypothesize that these differences could be due to differential effects of root exudate composition on the microbial functional community composition. This finding points out the need to consider differences in the functional characteristics of plant genotypes when selecting the beneficial microorganisms to be used as biofertilizers aiming to maximize plant growth and resistance to environmental stressors. Full article

(This article belongs to the Special Issue Biochar, Fertilizers and Microbes Interactions for Soil Health and Crop Productivity)

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