Journal Description
Agriculture
Agriculture
is an international, scientific peer-reviewed open access journal published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubAg, AGRIS, RePEc, and other databases.
- Journal Rank: JCR - Q1 (Agronomy) / CiteScore - Q2 (Plant Science)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.7 days after submission; acceptance to publication is undertaken in 2.4 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journals for Agriculture include: Poultry, Grasses and Crops.
Impact Factor:
3.6 (2022);
5-Year Impact Factor:
3.6 (2022)
Latest Articles
A Study on the Effect Mechanism of Pectin Modification on the Carrot Cell Wall’s Texture Formation under Ultrasonic and Infrared Drying
Agriculture 2024, 14(6), 803; https://doi.org/10.3390/agriculture14060803 (registering DOI) - 22 May 2024
Abstract
Abstract: The carrot has a high water content, and dehydration is an important means to extend its edible period and reduce storage and transportation costs. In the case of infrared (IR) drying, the porosity of the product is low and the structure is
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Abstract: The carrot has a high water content, and dehydration is an important means to extend its edible period and reduce storage and transportation costs. In the case of infrared (IR) drying, the porosity of the product is low and the structure is compact; the textural properties of the product are improved by using combined ultrasound and infrared (US-IR) drying; however, there is a lack of reports on the mechanism of this. Pectin has an important influence on the formation of the textural properties of fruit and vegetable tissues. In order to investigate the mechanism of the change in endogenous pectin properties in the carrot cell wall under US-IR drying on the improvement of the textural properties of the product, different fractions of pectins (water-soluble pectin, chelating pectin, alkali-soluble pectin) of the carrot were extracted, separated, and analysed. The thermal stability, component and content changes, Fourier infrared (FTIR), X-ray diffraction (XRD), esterification degree, molecular weight, monosaccharide composition, Ca ion content, and atomic force microscopy (AFM) of the pectins were determined. The results showed that the changes in the contents and properties of the carrot pectins under US-IR conditions had a positive effect on the improvement of the textural properties of the carrot tissues.
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(This article belongs to the Special Issue Agricultural Products Processing and Quality Detection)
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Amino Acid Changes during Maturation in Solanum Fruit
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Jūratė Staveckienė, Brigita Medveckienė, Viktorija Vaštakaitė-Kairienė, Jurgita Kulaitienė and Elvyra Jarienė
Agriculture 2024, 14(6), 802; https://doi.org/10.3390/agriculture14060802 (registering DOI) - 22 May 2024
Abstract
The goal of this study is to assess the impacts of ripening stage of four Solanum fruit species: (Solanum melanocerasum, Solanum nigrum, Solanum villosum, and Solanum retroflexum) on the content of amino acids and protein. Our objective is
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The goal of this study is to assess the impacts of ripening stage of four Solanum fruit species: (Solanum melanocerasum, Solanum nigrum, Solanum villosum, and Solanum retroflexum) on the content of amino acids and protein. Our objective is to enhance comprehension of the maturity process, with a particular focus on Solanum fruits, in order to determine the most advantageous time for harvesting. Amino acids play a crucial role in human nutrition by serving as building blocks for various primary and secondary metabolites. They are either a source of nutraceutical substances or important dietary components. The studied Solanum fruit’s amino acid profiles were found essential and nonessential amino acids. Our findings showed that dominant amino acids were nonessential amino acids. Depending on the ripening stage, the nonessential amino acid content of the Solanum melanocerasum fruits varied from 8.22 to 9.25 g 100 g−1, Solanum villosum from 5.34 to 6.60 g 100 g−1, Solanum nigrum from 6.12 to 8.73 g 100 g−1, and Solanum retroflexum from 8.27 to 9.75 g 100 g−1. A differentiated level of total protein is found in Solanum fruits at different ripening stages. The interval was from 10.62 to 28.06 g 100 g−1 depending on species or ripening stages.
Full article
(This article belongs to the Section Agricultural Product Quality and Safety)
Open AccessArticle
Design and Discrete-Element-Method Simulation of Helix Hole-Forming Machine for Deep Planting with Large Holes
by
Lihua Yu, Le Zheng, Pengfei Liang, Xuemei Wu, Fugui Zhang and Limei Zhao
Agriculture 2024, 14(6), 801; https://doi.org/10.3390/agriculture14060801 - 22 May 2024
Abstract
This study centered around the practical problem that there is no machine available for deep planting with large holes in hilly and mountainous areas of China. According to the principle of spiral lifting, a conical, double-spiral hole-forming machine was innovatively designed. The structural
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This study centered around the practical problem that there is no machine available for deep planting with large holes in hilly and mountainous areas of China. According to the principle of spiral lifting, a conical, double-spiral hole-forming machine was innovatively designed. The structural design and parameter calculation were completed. By discrete-element-method (DEM) simulation, the optimal lead and rotation speed of the hole former were obtained, and the hole-forming mechanisms of soil cutting, soil lifting, soil discharging, soil extruding, and soil returning were further revealed. The field test results indicated that the prototype had the advantages of convenient operation and good performance, and the formed holes met the agronomic requirements, with a qualification rate of 88.5%. In addition, it was found that the soil moisture content has a great influence on the formation of holes. Under the condition of low moisture content, the residence time at the bottom of a hole should be appropriately increased to improve the qualification rate of the holes formed. Our research results provided theoretical guidance and technical support for the design, optimization, popularization, and application of a hole-forming machine for deep planting with large holes (DPLH).
Full article
(This article belongs to the Special Issue Application of Modern Agricultural Equipment in Crop Cultivation)
Open AccessArticle
Research on a Multi-Lens Multispectral Camera for Identifying Haploid Maize Seeds
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Xiantao He, Jinting Zhu, Pinxuan Li, Dongxing Zhang, Li Yang, Tao Cui, Kailiang Zhang and Xiaolong Lin
Agriculture 2024, 14(6), 800; https://doi.org/10.3390/agriculture14060800 - 22 May 2024
Abstract
Haploid breeding can shorten the breeding period of new maize varieties and is an important means to increase maize yield. In the breeding program, a large number of haploid seeds need to be screened, and this step is mainly achieved manually, which hinders
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Haploid breeding can shorten the breeding period of new maize varieties and is an important means to increase maize yield. In the breeding program, a large number of haploid seeds need to be screened, and this step is mainly achieved manually, which hinders the industrialization of haploid maize breeding. This article aims to develop a multispectral camera to identify the haploid seeds automatically. The camera was manufactured by replacing narrow-band filters of the ordinary CCD camera, and the RGB, 405 nm, 980 nm and 1050 nm images of haploid or diploid seeds were simultaneously captured (the characteristic wavelengths were determined according to color and high-oil markers of maize). The performance was tested using four maize varieties with the two genetic markers. The results show that the developed multispectral camera significantly improved the recognition accuracy of haploid maize seeds to 92.33%, 97.33%, 97% and 93.33% for the TYD1903, TYD1904, TYD1907 and TYD1908 varieties, respectively. The cameras in the near-infrared region (wavelengths of 980 nm and 1050 nm) achieved better performance for the varieties of high-oil marker, with an increase of 0.84% and 1.5%, respectively. These results demonstrate the strong potential of the multispectral imaging technology in the haploid seed identification of maize.
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(This article belongs to the Special Issue Precision Planting Technology and Equipment in Advanced Crop Cultivation)
Open AccessArticle
Optimisation of the Spraying Process of Strawberries under Varying Operational Conditions
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Beata Cieniawska, Katarzyna Pentoś, Piotr Komarnicki, Jasper Tembeck Mbah, Maciej Samelski and Marek Barć
Agriculture 2024, 14(6), 799; https://doi.org/10.3390/agriculture14060799 - 22 May 2024
Abstract
Effective spraying is essential for modern agricultural production, to ensure a high-quality and abundant harvest. Simultaneously, it is crucial to minimise the negative impact of crop protection products on the environment. To achieve this, it is necessary to implement the appropriate technical and
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Effective spraying is essential for modern agricultural production, to ensure a high-quality and abundant harvest. Simultaneously, it is crucial to minimise the negative impact of crop protection products on the environment. To achieve this, it is necessary to implement the appropriate technical and technological parameters for the treatment and to consider the conditions under which the treatment is carried out. The aim of this study was to determine the relationship between the speed of the sprayer, the pressure of the liquid, and the type of nozzles, as well as air temperature and wind speed, in terms of the degree of coverage of the sprayed surfaces. The degree of coverage was analysed by spraying water-sensitive papers placed on the artificial plant, positioned to obtain horizontal and vertical surfaces. The study found that standard single flat fan nozzles provided greater coverage on upper horizontal surfaces, while standard nozzles were more effective for vertical transverse approach surfaces at lower fluid pressures and travel speeds. Neural networks were used to develop models of the relationships studied. Models with high accuracy for the validation data set were obtained in the case of the coverage of the vertical transverse leaving surface and the upper level surface (R = 0.93 and R = 0.86). These models were used to determine the optimum values of the technical parameters of the spraying process under the selected weather conditions. The maximum spray coverage (41.49%) was predicted for the XR nozzle under the following conditions: pressure = 200 kPa, driving speed = 1.4 m·s−1, temperature = 21.73 °C and wind speed = 0.32 m·s−1. Based on the sensitivity analysis of the neural models it was found that the greatest effect on the coverage of the vertical transverse leaving surface was observed for temperature and the coverage of the upper level surface was mostly influenced by driving speed.
Full article
(This article belongs to the Section Agricultural Technology)
Open AccessArticle
The Relationship between Geographical Indication Products and Farmers’ Incomes Based on Meta-Analysis
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Chunyan Li, Qi Ban, Lanqing Ge, Liwen Qi and Chenchen Fan
Agriculture 2024, 14(6), 798; https://doi.org/10.3390/agriculture14060798 - 22 May 2024
Abstract
Geographical indication (GI) products serve as one of the significant instruments for increasing farmers’ income. While most studies affirmatively indicate that GI products contribute to boosting farmers’ income growth, it is noteworthy that their relationship does not consistently demonstrate a positive correlation. The
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Geographical indication (GI) products serve as one of the significant instruments for increasing farmers’ income. While most studies affirmatively indicate that GI products contribute to boosting farmers’ income growth, it is noteworthy that their relationship does not consistently demonstrate a positive correlation. The academic discourse on this issue remains inconclusive. This study employs a meta-analysis method to reanalyze 140 effect sizes from 32 independent research samples across diverse global contexts. The findings reveal that the development of GI products significantly promotes farmer income growth, showing a high positive correlation (r = 0.348, CI = [0.104, 0.540]). Specifically, there exists a high positive correlation between GI products and per capita disposable income (r = 0.389) and a moderate positive correlation between GI products and agricultural product prices (r = 0.255). Further analysis indicates that factors at the sample level, literature level, and methodological level all exert moderating effects on the relationship between GI products and farmers’ income. This study not only provides a scientific response to the debate surrounding the relationship between GI products and farmers’ income but also delves into the underlying mechanisms. It holds significant importance for advancing the rational optimization of agricultural resources and enhancing agricultural competitiveness.
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(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Integration of Image and Sensor Data for Improved Disease Detection in Peach Trees Using Deep Learning Techniques
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Kuiheng Chen, Jingjing Lang, Jiayun Li, Du Chen, Xuaner Wang, Junyu Zhou, Xuan Liu, Yihong Song and Min Dong
Agriculture 2024, 14(6), 797; https://doi.org/10.3390/agriculture14060797 - 22 May 2024
Abstract
An innovative framework for peach tree disease recognition and segmentation is proposed in this paper, with the aim of significantly enhancing model performance in complex agricultural settings through deep learning techniques and data fusion strategies. The core innovations include a tiny feature attention
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An innovative framework for peach tree disease recognition and segmentation is proposed in this paper, with the aim of significantly enhancing model performance in complex agricultural settings through deep learning techniques and data fusion strategies. The core innovations include a tiny feature attention mechanism backbone network, an aligned-head module, a Transformer-based semantic segmentation network, and a specially designed alignment loss function. The integration of these technologies not only optimizes the model’s ability to capture subtle disease features but also improves the efficiency of integrating sensor and image data, further enhancing the accuracy of the segmentation tasks. Experimental results demonstrate the superiority of this framework. For disease detection, the proposed method achieved a precision of 94%, a recall of 92%, and an accuracy of 92%, surpassing classical models like AlexNet, GoogLeNet, VGGNet, ResNet, and EfficientNet. In lesion segmentation tasks, the proposed method achieved a precision of 95%, a recall of 90%, and an mIoU of 94%, significantly outperforming models such as SegNet, UNet, and UNet++. The introduction of the aligned-head module and alignment loss function provides an effective solution for processing images lacking sensor data, significantly enhancing the model’s capability to process real agricultural image data. Through detailed ablation experiments, the study further validates the critical role of the aligned-head module and alignment loss function in enhancing model performance, particularly in the attention-head ablation experiment where the aligned-head configuration surpassed other configurations across all metrics, highlighting its key role in the overall framework. These experiments not only showcase the theoretical effectiveness of the proposed method but also confirm its practical value in agricultural disease management practices.
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(This article belongs to the Special Issue Comprehensive Application and Prospects of New Technologies for Plant Protection)
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Effective Biotic Elicitors for Augmentation of Secondary Metabolite Production in Medicinal Plants
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Divya Jain, Shiwali Bisht, Anwar Parvez, Kuldeep Singh, Pranav Bhaskar and Georgios Koubouris
Agriculture 2024, 14(6), 796; https://doi.org/10.3390/agriculture14060796 - 22 May 2024
Abstract
Plants are an essential component of our daily diet, and their nutritional value has been thoroughly studied for many years. The ability of plants to adapt to changing environmental conditions through signaling systems is an essential component of their survival. Plants undergo an
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Plants are an essential component of our daily diet, and their nutritional value has been thoroughly studied for many years. The ability of plants to adapt to changing environmental conditions through signaling systems is an essential component of their survival. Plants undergo an array of physiological alterations to respond to stress from biotic sources. Secondary compounds frequently accumulate in crops that are sensitive to stress, particularly those with several eliciting agents or signaling molecules. Plants contain various types of bioactive compounds, including phytosterols, alkaloids, glycosides, and polyphenols, which make them valuable for the food and pharmaceutical industries. The increased production of secondary metabolites via elicitation has opened up a new field of study with the potential to provide substantial financial gains for the pharmaceutical and nutraceutical industries. These elicitors are pharmacological compounds that activate specific transcription factors and up-regulate genes to activate metabolic pathways. Thus, the current review discusses the mechanism of biotic elicitation and various elicitation techniques using biotic (proteins, carbohydrates, rhizobacteria, fungi, and hormones) elicitors that may increase the yield of secondary metabolites, particularly in medicinal plants, which is advantageous to the agrochemical and therapeutic industries.
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(This article belongs to the Special Issue Advanced Research of Secondary Metabolites in Medicinal and Industrial Plants)
Open AccessArticle
Does Addressing Rural Energy Poverty Contribute to Achieving Sustainable Agricultural Development?
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Jingyi Wang, Xiaolong Sun, Shuai Zhang and Xuebiao Zhang
Agriculture 2024, 14(6), 795; https://doi.org/10.3390/agriculture14060795 - 22 May 2024
Abstract
Promoting sustainable agricultural development is pivotal to realizing sustainable development goals. This study initially constructs a comprehensive indicator to delineate the landscape of agricultural sustainable development (ASD) across China. While ASD in China demonstrates an upward trajectory, it remains relatively low and exhibits
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Promoting sustainable agricultural development is pivotal to realizing sustainable development goals. This study initially constructs a comprehensive indicator to delineate the landscape of agricultural sustainable development (ASD) across China. While ASD in China demonstrates an upward trajectory, it remains relatively low and exhibits disparities across regions. Ensuring food security with minimal energy consumption in agriculture is particularly critical for China, and fostering access to affordable and clean energy services in rural areas is essential for expediting the transition to sustainable agriculture. This study investigates the impact of rural energy poverty (REP) on ASD across 30 Chinese provinces from 2000 to 2017, revealing that the eradication of REP yields tangible benefits for ASD. Furthermore, considering regional disparities, the elimination of REP significantly enhances ASD, particularly in non-major grain-producing areas compared to major grain-producing regions. These findings underscore the imperative of integrating efforts to alleviate energy poverty with initiatives aimed at advancing ASD. Such integration is indispensable for driving the overarching transition toward sustainable agriculture.
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(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Deep Learning for Multi-Source Data-Driven Crop Yield Prediction in Northeast China
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Jian Lu, Jian Li, Hongkun Fu, Xuhui Tang, Zhao Liu, Hui Chen, Yue Sun and Xiangyu Ning
Agriculture 2024, 14(6), 794; https://doi.org/10.3390/agriculture14060794 - 22 May 2024
Abstract
The accurate prediction of crop yields is crucial for enhancing agricultural efficiency and ensuring food security. This study assesses the performance of the CNN-LSTM-Attention model in predicting the yields of maize, rice, and soybeans in Northeast China and compares its effectiveness with traditional
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The accurate prediction of crop yields is crucial for enhancing agricultural efficiency and ensuring food security. This study assesses the performance of the CNN-LSTM-Attention model in predicting the yields of maize, rice, and soybeans in Northeast China and compares its effectiveness with traditional models such as RF, XGBoost, and CNN. Utilizing multi-source data from 2014 to 2020, which include vegetation indices, environmental variables, and photosynthetically active parameters, our research examines the model’s capacity to capture essential spatial and temporal variations. The CNN-LSTM-Attention model integrates Convolutional Neural Networks, Long Short-Term Memory, and an attention mechanism to effectively process complex datasets and manage non-linear relationships within agricultural data. Notably, the study explores the potential of using kNDVI for predicting yields of multiple crops, highlighting its effectiveness. Our findings demonstrate that advanced deep-learning models significantly enhance yield prediction accuracy over traditional methods. We advocate for the incorporation of sophisticated deep-learning technologies in agricultural practices, which can substantially improve yield prediction accuracy and food production strategies.
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(This article belongs to the Section Digital Agriculture)
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Research and Preliminary Evaluation of Key Technologies for 3D Reconstruction of Pig Bodies Based on 3D Point Clouds
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Kaidong Lei, Xiangfang Tang, Xiaoli Li, Qinggen Lu, Teng Long, Xinghang Zhang and Benhai Xiong
Agriculture 2024, 14(6), 793; https://doi.org/10.3390/agriculture14060793 - 22 May 2024
Abstract
In precision livestock farming, the non-contact perception of live pig body measurement data is a critical technological branch that can significantly enhance breeding efficiency, improve animal welfare, and effectively prevent and control diseases. Monitoring pig body measurements allows for accurate assessment of their
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In precision livestock farming, the non-contact perception of live pig body measurement data is a critical technological branch that can significantly enhance breeding efficiency, improve animal welfare, and effectively prevent and control diseases. Monitoring pig body measurements allows for accurate assessment of their growth and production performance. Currently, traditional sensing methods rely heavily on manual measurements, which not only have large errors and high workloads but also may cause stress responses in pigs, increasing the risk of African swine fever, and its costs of prevention and control. Therefore, we integrated and developed a system based on a 3D reconstruction model that includes the following contributions: 1. We developed a non-contact system for perceiving pig body measurements using a depth camera. This system, tailored to the specific needs of laboratory and on-site pig farming processes, can accurately acquire pig body data while avoiding stress and considering animal welfare. 2. Data preprocessing was performed using Gaussian filtering, mean filtering, and median filtering, followed by effective estimation of normals using methods such as least squares, principal component analysis (PCA), and random sample consensus (RANSAC). These steps enhance the quality and efficiency of point cloud processing, ensuring the reliability of 3D reconstruction tasks. 3. Experimental evidence showed that the use of the RANSAC method can significantly speed up 3D reconstruction, effectively reconstructing smooth surfaces of pigs. 4. For the acquisition of smooth surfaces in 3D reconstruction, experimental evidence demonstrated that the RANSAC method significantly improves the speed of reconstruction. 5. Experimental results indicated that the relative errors for chest girth and hip width were 3.55% and 2.83%, respectively. Faced with complex pigsty application scenarios, the technology we provided can effectively perceive pig body measurement data, meeting the needs of modern production.
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(This article belongs to the Special Issue Application of Sensor Technologies in Livestock Farming)
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The Residue Chemistry Transformation Linked to the Fungi Keystone Taxa during Different Residue Tissues Incorporation into Mollisols in Northeast China
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Qilin Zhang, Xiujun Li, Guoshuang Chen, Nana Luo, Jing Sun, Ezemaduka Anastasia Ngozi and Xinrui Lu
Agriculture 2024, 14(6), 792; https://doi.org/10.3390/agriculture14060792 - 21 May 2024
Abstract
Managing carbon input from crop straw in cropland ecosystems could increase soil organic carbon (SOC) sequestration to achieve C neutrality and mitigate climate change. The complexity of the chemical structures of crop residue largely affects SOC sequestration. Fungi communities play an important role
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Managing carbon input from crop straw in cropland ecosystems could increase soil organic carbon (SOC) sequestration to achieve C neutrality and mitigate climate change. The complexity of the chemical structures of crop residue largely affects SOC sequestration. Fungi communities play an important role in the degradation of crop residues. However, the relationship between the fungal community composition and the chemical structures of crop residues remains unclear and requires further investigation. Therefore, a 120-day incubation experiment was conducted in Mollisols in Northeast China to investigate the decomposition processes and dynamics of maize straw stem (ST), leaf (LE) and sheath (SH) residues using 13C-NMR spectroscopy. Additionally, the microbiomes associated with these residues were analyzed through high-throughput sequencing to explore their relationship. Our results showed that the alkyl C contents in all treatments exhibited increases ranging from 15.1% to 49.1%, while the O-alkyl C contents decreased, ranging from 0.02% to 11.2%, with the incubation time. The A/OA ratios of ST, LE and SH treatments were increased by 23.7%, 43.4% and 49.3% with incubation time, respectively. During the early stages of straw decomposition, Ascomycota dominated, and in the later stage, Basidiomycota were predominant. The class of Sordariomycetes played a key role in the chemistry transformation of straw tissues during decomposition. The keystone taxa abundances, Fusarium_kyushuense, and Striatibotrys_eucylindrospora, showed strong negative correlations with di-O-alkyl C and carbonyl-C content and positive correlations with the β-glucosidase and peroxidase enzyme activity, respectively. In conclusion, our study demonstrated that the keystone taxa play a significant role in regulating the chemical structures of straw tissues, providing a better understanding of the influence of residue quality on SOC sequestration.
Full article
(This article belongs to the Special Issue Monitoring, Modelling and Management of Agricultural Air Pollutants and Greenhouse Gases)
Open AccessArticle
A Study of Farmers’ Behavior in Classifying Domestic Waste Based on the Participants Intellectual Decision Model
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Jing Wang, Nan Zhao, Dongjian Li and Shiping Li
Agriculture 2024, 14(6), 791; https://doi.org/10.3390/agriculture14060791 - 21 May 2024
Abstract
The farmers’ deep participation in the classification of domestic waste plays a crucial role in reducing the amount of waste out of the village from the source, lowering the cost of waste treatment, and realizing the sustainable development of rural waste resocialization, reduction,
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The farmers’ deep participation in the classification of domestic waste plays a crucial role in reducing the amount of waste out of the village from the source, lowering the cost of waste treatment, and realizing the sustainable development of rural waste resocialization, reduction, and harmlessness. This paper aims to identify the key factors and logical structure that influence the farmers’ behavior in classifying domestic waste and provide recommendations for improving it. Based on the Participants’ Intellectual Decision (PID) Model, we constructed a theoretical analysis framework for farmers’ decision-making on domestic waste classification, and the PID model was further extended by combining with the practice of rural domestic waste management in China and proposing the research hypothesis that factors, such as community attributes, rules of operation, the status of the participants, and the situation of external actions, have a significant impact on the farmers’ behavior in classifying domestic waste. Empirical analyses were carried out with the help of the ordered logistic model and the DEMATEL-ISM using 939 research data of farmers in Jiangsu and Gansu provinces of China. The results show the following: (1) classification of domestic waste by farmers in the sample area was predominantly unclassified (34.40%) and two-classified (40.58%); (2) 17 factors, including regional disparity, Party affiliation, organizational support perception, environmental emotions, conscious governance attitudes, trust in village cadres, social reference norms, and expected outcomes, have a significant impact on the farmers’ behavior in classifying domestic waste; (3) trust in village cadres, organizational support perception, and environmental emotion are superficial direct factors; incentive measures, fee level, waste transport situation, difficulty perception, self-consciousness perception, social reference norms, and expected outcomes are middle indirect factors; whether or not it is a demonstration village, Party membership and regional disparity are deep root factors affecting farmers to classify their domestic waste.
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(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Unstable State of Hydrologic Regime and Grain Yields in Northern Kazakhstan Estimated with Tree-Ring Proxies
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Irina P. Panyushkina, Altyn Shayakhmetova, Sergey Pashkov and Leonid I. Agafonov
Agriculture 2024, 14(6), 790; https://doi.org/10.3390/agriculture14060790 - 21 May 2024
Abstract
Changes in the hydrologic regime impose great challenges for grain production. We investigated the impact of dry and wet extremes on the recent losses of crops in Severo-Kazakhstanskaya Oblast (SKO), where 25% of Kazakhstan’s wheat is produced. We reconstructed the Palmer Drought Severity
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Changes in the hydrologic regime impose great challenges for grain production. We investigated the impact of dry and wet extremes on the recent losses of crops in Severo-Kazakhstanskaya Oblast (SKO), where 25% of Kazakhstan’s wheat is produced. We reconstructed the Palmer Drought Severity Index (June–August PDSI) and average grain yields (with an explained variance of 48% and 44%, respectively) using five tree ring width chronologies. The extended history of the moisture variability and yields of spring wheat, oats, and barley shows the strong impact of hydrology, rather than the heat, on the grain production. We defined three distinctive hydrologic regimes in SKO: (1) 1886–1942, (2) 1943–1977, (3) 1978–2023. The early regime had fewer drought events, including some that covered a single year. Their duration increased up to 3 years in the second period. The latest regime is an extreme mode of hydrologic variability with events abruptly switching from extremely dry to extremely wet conditions (called “whiplash”). The 21st century regime signifies that the intensified and prolonged decade-long drought transitioned into pluvial condition. The new regime created sizable instability for grain producers. This crop yield reconstruction denotes the potential of the tree-ring proxy for understanding the impact of climate change on the agriculture and food security of Central Asia.
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(This article belongs to the Section Crop Production)
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Open AccessArticle
Assessment of Resistance of Barley Varieties to Diseases in Polish Organic Field Trials
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Tomasz Lenartowicz, Henryk Bujak, Marcin Przystalski, Inna Mashevska, Kamila Nowosad, Krzysztof Jończyk and Beata Feledyn-Szewczyk
Agriculture 2024, 14(5), 789; https://doi.org/10.3390/agriculture14050789 - 20 May 2024
Abstract
Leaf rust and net blotch are two important fungal diseases of barley. Leaf rust is the most important rust disease of barley, whereas net blotch can result in significant yield losses and cause the deterioration of crop quality. The best and the most
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Leaf rust and net blotch are two important fungal diseases of barley. Leaf rust is the most important rust disease of barley, whereas net blotch can result in significant yield losses and cause the deterioration of crop quality. The best and the most environmentally friendly method to control diseases is to cultivate resistant varieties. The aim of the current study was to identify barley varieties with an improved resistance to leaf rust and net blotch in Polish organic post-registration trials conducted in the years 2020–2022. For this purpose, the cumulative link mixed model with several variance components was applied to model resistance to leaf rust and net blotch. It was found that the reference variety Radek was the most resistant to leaf rust, whereas variety Avatar outperformed the reference variety in terms of resistance to net blotch, although the difference between the two varieties was non-significant. In the present study, the use of the cumulative link mixed model framework made it possible to calculate cumulative probabilities or the probability of a given score for each variety and disease, which might be useful for plant breeders and crop experts. Both, the method of analysis and resistant varieties may be used in the breeding process to derive new resistant varieties suitable for the organic farming system.
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(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)
Open AccessArticle
A Standardized Treatment Model for Head Loss of Farmland Filters Based on Interaction Factors
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Zhenji Liu, Chenyu Lei, Jie Li, Yangjuan Long and Chen Lu
Agriculture 2024, 14(5), 788; https://doi.org/10.3390/agriculture14050788 - 20 May 2024
Abstract
A head loss model for pressureless mesh filters used in farmland irrigation was developed by integrating the four basic test factors: irrigation flow, filter cartridge speed, self-cleaning flow, and initial sand content. The model’s coefficient of determination was found to be 98.61%. Among
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A head loss model for pressureless mesh filters used in farmland irrigation was developed by integrating the four basic test factors: irrigation flow, filter cartridge speed, self-cleaning flow, and initial sand content. The model’s coefficient of determination was found to be 98.61%. Among the basic factors, the total irrigation flow accounted for only 17.20% of the relatively small self-cleaning flow. The contribution of initial sand content was found to be the smallest, with a coefficient of only 0.0166. Furthermore, the contribution rate of the flow term was significantly higher than that of the initial sand content, with a value of 159.73%. In terms of quadratic interaction, the difference between the interaction term of flushing flow and filter cartridge speed, and the interaction term of filter cartridge speed and self-cleaning flow was 38.42%. On the other hand, the difference within this level for the interaction term between initial sand content and filter cartridge speed, as well as the interaction term between irrigation flow and self-cleaning flow, was 2.82%. Finally, through joint optimization of the response surface and model, the optimal values for the irrigation flow rate, filter cartridge speed, self-cleaning flow rate, and initial sand content were determined to be 121.687 m3·h−1, 1.331 r·min−1, 19.980 m3·h−1, and 0.261 g·L−1; the measured minimum head loss was found to be 21.671 kPa. These research findings can serve as a reference for enhancing the design of farmland filters and optimizing irrigation systems.
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(This article belongs to the Special Issue Sustainable Water-Resource Strategies in Agriculture for Climate Change Adaptation)
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Open AccessArticle
Online Detection of Dry Matter in Potatoes Based on Visible Near-Infrared Transmission Spectroscopy Combined with 1D-CNN
by
Yalin Guo, Lina Zhang, Zhenlong Li, Yakai He, Chengxu Lv, Yongnan Chen, Huangzhen Lv and Zhilong Du
Agriculture 2024, 14(5), 787; https://doi.org/10.3390/agriculture14050787 - 20 May 2024
Abstract
More efficient resource utilization and increased crop utilization rate are needed to address the growing demand for food. The efficient quality testing of key agricultural products such as potatoes, especially the rapid testing of key nutritional indicators, has become an important strategy for
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More efficient resource utilization and increased crop utilization rate are needed to address the growing demand for food. The efficient quality testing of key agricultural products such as potatoes, especially the rapid testing of key nutritional indicators, has become an important strategy for ensuring their quality and safety. In this study, visible and near infrared (Vis/NIR) transmittance spectroscopy (600–900 nm) was used for the online analysis of multiple quality parameters in potatoes. The study concentrated on comparing three one-dimensional convolutional neural network (1D-CNN) models, specifically, the fine-tuned DeepSpectra, the fine-tuned 1D-AlexNet, and classic CNN, with UVE-PLS (uninformative variable elimination–partial least squares) models. These models utilized spectral data for the real-time detection of dry matter (DM) content in potatoes. To address the challenges posed by limited data from Vis/NIR, this study strategically implemented data augmentation techniques. This approach significantly enhanced the robustness and generalization capabilities of the models. The 1D-AlexNet and DeepSpectra models achieved 0.934 and 0.913 R2P and 0.0603 and 0.0695 g/100 g RMSEP for DM, respectively. Compared to UVE-PLS, the R2P value improved by 21.31% (0.770 to 0.934) for the 1D-AlexNet model and 18.64% (0.770 to 0.913) for the DeepSpectra model. The RMSEP value was reduced by 47.31% (0.114 to 0.0603) for 1D-AlexNet, and 39.30% (0.114 to 0.0695) for the DeepSpectra model. As a result, this study would be helpful for researching the online Vis/NIR transmission determination of potato DM using deep learning. These results highlighted the immense potential of employing specific spectral features in deep-learning models for a more precise and efficient online assessment of agricultural quality. This advancement provided some insight and reference for further contributing to the evolution of more targeted and efficient quality assessment methods in agricultural products.
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(This article belongs to the Special Issue Application of Spectroscopy and Sensor Technology in Agricultural Products—Series II)
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Open AccessArticle
Optimizing Active Disturbance Rejection Control for a Stubble Breaking and Obstacle Avoiding Control System
by
Huibin Zhu, Tao Huang, Lizhen Bai and Wenkai Zhang
Agriculture 2024, 14(5), 786; https://doi.org/10.3390/agriculture14050786 - 20 May 2024
Abstract
In order to improve the obstacle avoidance control performance and anti-interference ability of a stubble breaking device of a no-tillage planter, a back-propagation neural network (BPNN)-optimized fuzzy active disturbance rejection control (ADRC) controller was designed to optimize the control performance of a servo
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In order to improve the obstacle avoidance control performance and anti-interference ability of a stubble breaking device of a no-tillage planter, a back-propagation neural network (BPNN)-optimized fuzzy active disturbance rejection control (ADRC) controller was designed to optimize the control performance of a servo motor. Firstly, a negative feedback mathematical model was established for the obstacle avoidance control system. Then, the nonlinear state error feedback (NLSEF) parameters in the fuzzy ADRC were intelligently optimized by the BPNN algorithm. In this way, a fuzzy ADRC controller based on BPNN optimization was formed to optimize the control process of a servo motor. Matlab/Simulink (R2022b) was used to complete the simulation model design and parameter adjustment. Consequently, the response time was 0.089 s using the BPNN fuzzy ADRC controller, which was shorter than the 0.303 s of the ADRC controller and the 0.100 s of the fuzzy ADRC controller. The overshoot was 0.1% using a BPNN fuzzy ADRC controller, which was less than the 2% of the ADRC controller and the 1% of the fuzzy ADRC controller. After noise signal interference was introduced into the control system, the regression steady state time of the BPNN fuzzy ADRC controller was 0.22 s, which was shorter than the 0.56 s of the ADRC controller and the 0.45 s of the fuzzy ADRC controller. A hardware-in-the-loop simulation experimental platform of the obstacle avoidance control system was constructed. The experiment results show that the servo motor control system has a fast dynamic response, small steady-state error and strong anti-interference ability for obstacle avoidance at the target height. Then, the control system error was within the allowable range. The servo motor control effect of the BPNN fuzzy ADRC was better than the ADRC and fuzzy ADRC. This optimized servo motor control method can provide a reference for improving the obstacle avoidance control effect problem of no-tillage seeders in stubble breaking operations on rocky desertification areas.
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(This article belongs to the Section Agricultural Technology)
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The Impact of Agricultural Socialized Service on Grain Production: Evidence from Rural China
by
Ruisheng Li, Jiaoyan Chen and Dingde Xu
Agriculture 2024, 14(5), 785; https://doi.org/10.3390/agriculture14050785 - 20 May 2024
Abstract
Although China’s grain production has reached nineteen consecutive harvests, the uncertainty of the current domestic and international environment has put more pressure on further increasing grain production in the future. For the past few years, agricultural socialization services have been crucial in boosting
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Although China’s grain production has reached nineteen consecutive harvests, the uncertainty of the current domestic and international environment has put more pressure on further increasing grain production in the future. For the past few years, agricultural socialization services have been crucial in boosting grain production and farmers’ revenue by addressing the issue of land cultivation and farming methods. In this regard, the question of whether and how agricultural socialized services may resolve the present grain production conundrum is extremely practical. Therefore, the study employs the China Rural Revitalization Survey data of 3709 households. Based on the 2SLS model, stepwise regression method, and moderated effects model, it creatively takes into account a variety of agricultural production segments, investigates the mechanism of services on grain production from the standpoint of improved production efficiency and plot concentration, and further examines the effects of aging populations and regional variations in grain production areas. The study found the following: (1) The average proportion of grain production area of farmers in the sample is 49%, and 42% of farmers have purchased agricultural socialization services. (2) Agricultural socialization services can significantly promote farmers’ grain cultivation behavior by facilitating connected transfers in and inhibiting connected transfers out to take advantage of plot concentration, and boosting the use of agricultural machines to enhance output efficiency. (3) The aging of the agricultural population will, to a certain extent, strengthen the promoting effect of agricultural socialization services on grain cultivation. Agricultural socialization services affect grain cultivation more in main grain-producing areas. Therefore, emphasizing the role of agricultural socialization services in accelerating the shift to moderate-scale operations, decreasing the non-grain component of the planting structure, and promoting the implementation of policies tailored to actual production needs are important steps to safeguard the production capacity of grain in different regions.
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(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Simultaneous Localization and Mapping System for Agricultural Yield Estimation Based on Improved VINS-RGBD: A Case Study of a Strawberry Field
by
Quanbo Yuan, Penggang Wang, Wei Luo, Yongxu Zhou, Hongce Chen and Zhaopeng Meng
Agriculture 2024, 14(5), 784; https://doi.org/10.3390/agriculture14050784 - 19 May 2024
Abstract
Crop yield estimation plays a crucial role in agricultural production planning and risk management. Utilizing simultaneous localization and mapping (SLAM) technology for the three-dimensional reconstruction of crops allows for an intuitive understanding of their growth status and facilitates yield estimation. Therefore, this paper
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Crop yield estimation plays a crucial role in agricultural production planning and risk management. Utilizing simultaneous localization and mapping (SLAM) technology for the three-dimensional reconstruction of crops allows for an intuitive understanding of their growth status and facilitates yield estimation. Therefore, this paper proposes a VINS-RGBD system incorporating a semantic segmentation module to enrich the information representation of a 3D reconstruction map. Additionally, image matching using L_SuperPoint feature points is employed to achieve higher localization accuracy and obtain better map quality. Moreover, Voxblox is proposed for storing and representing the maps, which facilitates the storage of large-scale maps. Furthermore, yield estimation is conducted using conditional filtering and RANSAC spherical fitting. The results show that the proposed system achieves an average relative error of 10.87% in yield estimation. The semantic segmentation accuracy of the system reaches 73.2% mIoU, and it can save an average of 96.91% memory for point cloud map storage. Localization accuracy tests on public datasets demonstrate that, compared to Shi–Tomasi corner points, using L_SuperPoint feature points reduces the average ATE by 1.933 and the average RPE by 0.042. Through field experiments and evaluations in a strawberry field, the proposed system demonstrates reliability in yield estimation, providing guidance and support for agricultural production planning and risk management.
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(This article belongs to the Topic Current Research on Intelligent Equipment for Agriculture)
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