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
Development of a Decision Support System for Animal Health Management Using Geo-Information Technology: A Novel Approach to Precision Livestock Management
Agriculture 2024, 14(5), 696; https://doi.org/10.3390/agriculture14050696 (registering DOI) - 28 Apr 2024
Abstract
Livestock management is challenging for resource-poor (R-P) farmers due to unavailability of quality feed, limited professional advice, and rumor-spreading about animal health condition in a herd. This research seeks to improve animal health in southern Africa by promoting sericea lespedeza (Lespedeza cuneata
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Livestock management is challenging for resource-poor (R-P) farmers due to unavailability of quality feed, limited professional advice, and rumor-spreading about animal health condition in a herd. This research seeks to improve animal health in southern Africa by promoting sericea lespedeza (Lespedeza cuneata), a nutraceutical forage legume. An automated geospatial model for precision agriculture (PA) can identify suitable locations for its cultivation. Additionally, a novel approach of radio-frequency identifier (RFID) supported telemetry technology can track animal movement, and the analyses of data using artificial intelligence can determine sickness of small ruminants. This RFID-based system is being connected to a smartphone app (under construction) to alert farmers of potential livestock health issues in real time so they can take immediate corrective measures. An accompanying Decision Support System (DSS) site is being developed for R-P farmers to obtain all possible support on livestock production, including the designed PA and RFID-based DSS.
Full article
(This article belongs to the Special Issue Advancing Animal Welfare: Precision Livestock Farming Technologies for Monitoring and Preventing Abnormal Behavior)
Open AccessArticle
Estimating Corn Growth Parameters by Integrating Optical and Synthetic Aperture Radar Features into the Water Cloud Model
by
Yanyan Wang, Zhaocong Wu, Shanjun Luo, Xinyan Liu, Shuaibing Liu and Xinxin Huang
Agriculture 2024, 14(5), 695; https://doi.org/10.3390/agriculture14050695 (registering DOI) - 28 Apr 2024
Abstract
Crop growth parameters are the basis for evaluation of crop growth status and crop yield. The aim of this study was to develop a more accurate estimation model for corn growth parameters combined with multispectral vegetation indexes (VIopt) and the differential
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Crop growth parameters are the basis for evaluation of crop growth status and crop yield. The aim of this study was to develop a more accurate estimation model for corn growth parameters combined with multispectral vegetation indexes (VIopt) and the differential radar information (DRI) derived from SAR data. Targeting the estimation of corn plant height (H) and the BBCH (Biologische Bundesanstalt, Bundessortenamt and CHemical industry) phenological parameters, this study compared the estimation accuracies of various multispectral vegetation indexes (VIopt) and the corresponding VIDRI (vegetation index corrected by DRI) indexes in inverting the corn growth parameters. (1) When comparing the estimation accuracies of four multispectral vegetation indexes (NDVI, NDVIre1, NDVIre2, and S2REP), NDVI showed the lowest estimation accuracy, with a normalized root mean square error (nRMSE) of 20.84% for the plant height, while S2REP showed the highest estimation accuracy (nRMSE = 16.05%). In addition, NDVIre2 (nRMSE = 16.18%) and S2REP (16.05%) exhibited a higher accuracy than NDVIre1 (nRMSE = 19.27%). Similarly, for BBCH, the nRMSEs of the four indexes were 24.17%, 22.49%, 17.04% and 16.60%, respectively. This confirmed that the multispectral vegetation indexes based on the red-edge bands were more sensitive to the growth parameters, especially for the Sentinel-2 red-edge 2 band. (2) The constructed VIDRI indexes were more beneficial than the VIopt indexes in enhancing the estimation accuracy of corn growth parameters. Specifically, the nRMSEs of the four VIDRI indexes (NDVIDRI, NDVIre1DRI, NDVIre2DRI, and S2REPDRI) decreased to 19.64%, 18.11%, 15.00%, and 14.64% for plant height, and to 23.24%, 21.58%, 15.79%, and 15.91% for BBCH, indicating that even in cases of high vegetation coverage, the introduction of SAR DRI features can further improve the estimation accuracy of growth parameters. Our findings also demonstrated that the NDVIre2DRI and S2REPDRI indexes constructed using red-edge 2 band information of Sentinel-2 and SAR DRI features had more advantages in improving the estimation accuracy of corn growth parameters.
Full article
(This article belongs to the Special Issue Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring—2nd Edition)
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Open AccessArticle
Design and Experiment of an Autonomous Navigation System for a Cattle Barn Feed-Pushing Robot Based on UWB Positioning
by
Zejin Chen, Haifeng Wang, Mengchuang Zhou, Jun Zhu, Jiahui Chen and Bin Li
Agriculture 2024, 14(5), 694; https://doi.org/10.3390/agriculture14050694 (registering DOI) - 28 Apr 2024
Abstract
The autonomous navigation system of feed-pushing robots is one of the key technologies for the intelligent breeding of dairy cows, and its accuracy has a significant influence on the quality of feed-pushing operations. Currently, the navigation methods of feed-pushing robots in the complex
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The autonomous navigation system of feed-pushing robots is one of the key technologies for the intelligent breeding of dairy cows, and its accuracy has a significant influence on the quality of feed-pushing operations. Currently, the navigation methods of feed-pushing robots in the complex environment of cattle barns mainly include visual, LiDAR, and geomagnetic navigation, but there are still problems relating to low navigation accuracy. An autonomous navigation system based on ultra-wideband (UWB) positioning utilizing the dynamic forward-looking distance pure pursuit algorithm is proposed in this paper. First, six anchor nodes were arranged in the corners and central feeding aisle of a 30 × 86 m rectangular standard barn to form a rectangular positioning area. Then, utilizing the 9ITL-650 feed-pushing robot as a platform and integrating UWB wireless positioning technology, a global coordinate system for the cattle barn was established, and the expected path was planned. Finally, the pure pursuit model was improved based on the robot’s two-wheel differential kinematics model, and a dynamic forward-looking distance pure pursuit controller based on PID regulation was designed to construct a comprehensive autonomous navigation control system. Subsequently, field experiments were conducted in the cattle barn. The experimental results show that the static positioning accuracy of the UWB system for the feed-pushing robot was less than 16 cm under no-line-of-sight conditions in the cattle barn. At low speeds, the robot was subjected to linear tracking comparative experiments with forward-looking distances of 50, 100, 150, and 200 cm. The minimum upper-line distance of the dynamic forward-looking distance model was 205.43 cm. In the steady-state phase, the average lateral deviation was 3.31 cm, with an average standard deviation of 2.58 cm and the average root mean square error (RMSE) of 4.22 cm. Compared with the fixed forward-looking distance model, the average lateral deviation, the standard deviation, and the RMSE were reduced by 42.83%, 37.07%, and 42.90%, respectively. The autonomous navigation experiments conducted on the feed-pushing robot at travel speeds of 6, 8, and 10 m/min demonstrated that the maximum average lateral deviation was 7.58 cm, the maximum standard deviation was 8.22 cm, and the maximum RMSE was 11.07 cm, meeting the autonomous navigation requirements for feed-pushing operations in complex barn environments. This study provides support for achieving high-precision autonomous navigation control technology in complex environments.
Full article
(This article belongs to the Topic Current Research on Intelligent Equipment for Agriculture)
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Open AccessArticle
Genetic Enhancement of Blast and Bacterial Leaf Blight Resistance in Rice Variety CO 51 through Marker-Assisted Selection
by
Samuthirapandi Subburaj, Thiyagarajan Thulasinathan, Viswabharathy Sakthivel, Bharathi Ayyenar, Rohit Kambale, Veera Ranjani Rajagopalan, Sudha Manickam, Raghu Rajasekaran, Gopalakrishnan Chellappan, Kalaimagal Thiyagarajan, Manonmani Swaminathan and Raveendran Muthurajan
Agriculture 2024, 14(5), 693; https://doi.org/10.3390/agriculture14050693 (registering DOI) - 28 Apr 2024
Abstract
The increased use of chemicals in rice farming poses significant issues regarding the emergence of pesticide/fungicide resistance and environmental sustainability concerns. This study was aimed at the genetic improvement of blast, bacterial leaf blight (BB) and gall midge resistance in a popular rice
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The increased use of chemicals in rice farming poses significant issues regarding the emergence of pesticide/fungicide resistance and environmental sustainability concerns. This study was aimed at the genetic improvement of blast, bacterial leaf blight (BB) and gall midge resistance in a popular rice variety CO 51 which already harbours a blast resistance gene Pi54. Efforts were made to pyramid an additional blast resistance gene Pi9 along with two BB resistance genes (xa13 and Xa21) and two gall midge resistance genes (Gm1 and Gm4) into an elite rice variety CO 51 to enhance the resistance level to biotic stresses. The superior lines were selected using functional markers conferring resistance to blast (NBS4 and Pi54MAS linked to Pi9 and Pi54 genes, respectively) and BB [(xa13Prom (xa13) and pTA248 (Xa21)] and SSR markers linked to Gm1 (RM1328) and Gm4 (RM22550) for phenotypic screening and agronomic evaluation. The genotyping and phenotyping of F6 and BC2F6 progenies of CO 51 X 562-4, for agronomic traits and resistance to BB and blast, identified ten superior progenies in F6 and five superior progenies in BC2F6. The breeding lines harbouring both xa13+Xa21 exhibited high levels of resistance to BB (score ≤ 1 cm) and Pi9+Pi54 exhibited strong resistance to blast (score ≤ 2). Identified lines can be evaluated further for varietal improvement or utilised as genetic stocks in breeding programs.
Full article
(This article belongs to the Special Issue Feature Papers in Genotype Evaluation and Breeding)
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Open AccessArticle
Impact of Rural E-Commerce Participation on Farmers’ Household Development Resilience: Evidence from 1229 Farmers in China
by
Xiaojing Li, Yanhua Li and Zhe Chen
Agriculture 2024, 14(5), 692; https://doi.org/10.3390/agriculture14050692 (registering DOI) - 28 Apr 2024
Abstract
This paper investigates the impact of e-commerce participation on household development resilience using a sample of 1229 households in the Shandong and Shaanxi provinces of China in 2022. It constructs the developmental resilience index of farm households from three dimensions of economy, society
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This paper investigates the impact of e-commerce participation on household development resilience using a sample of 1229 households in the Shandong and Shaanxi provinces of China in 2022. It constructs the developmental resilience index of farm households from three dimensions of economy, society and culture using the entropy method, and establishes a counterfactual framework using the Propensity Score Matching (PSM) method. The results suggest that participation in e-commerce has a significant and positive impact on farming household development resilience. The PSM method estimates that participation in e-commerce increases the developmental resilience of farming households by 9.63% compared to non-participation, with economic, social, and cultural resilience increasing by 9.29%, 9.84%, and 9.92%, respectively. The robustness test results confirm the findings. Further analysis reveals that participation in e-commerce enhances farm household development resilience through three mechanisms: improving economic efficiency, network relationship linkage, and risk appetite. Heterogeneity analysis shows that the impact of e-commerce participation on household development resilience varies among farmers with different endowment constraints. In particular, farmers with more years of education and cooperative members benefit more from e-commerce participation, especially live and platform e-commerce.
Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Open AccessArticle
PL-DINO: An Improved Transformer-Based Method for Plant Leaf Disease Detection
by
Wei Li, Lizhou Zhu and Jun Liu
Agriculture 2024, 14(5), 691; https://doi.org/10.3390/agriculture14050691 (registering DOI) - 28 Apr 2024
Abstract
Agriculture is important for ecology. The early detection and treatment of agricultural crop diseases are meaningful and challenging tasks in agriculture. Currently, the identification of plant diseases relies on manual detection, which has the disadvantages of long operation time and low efficiency, ultimately
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Agriculture is important for ecology. The early detection and treatment of agricultural crop diseases are meaningful and challenging tasks in agriculture. Currently, the identification of plant diseases relies on manual detection, which has the disadvantages of long operation time and low efficiency, ultimately impacting the crop yield and quality. To overcome these disadvantages, we propose a new object detection method named “Plant Leaf Detection transformer with Improved deNoising anchOr boxes (PL-DINO)”. This method incorporates a Convolutional Block Attention Module (CBAM) into the ResNet50 backbone network. With the assistance of the CBAM block, the representative features can be effectively extracted from leaf images. Next, an EQualization Loss (EQL) is employed to address the problem of class imbalance in the relevant datasets. The proposed PL-DINO is evaluated using the publicly available PlantDoc dataset. Experimental results demonstrate the superiority of PL-DINO over the related advanced approaches. Specifically, PL-DINO achieves a mean average precision of 70.3%, surpassing conventional object detection algorithms such as Faster R-CNN and YOLOv7 for leaf disease detection in natural environments. In brief, PL-DINO offers a practical technology for smart agriculture and ecological monitoring.
Full article
(This article belongs to the Section Digital Agriculture)
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Open AccessArticle
The Impact of Income Disparity on Food Consumption—Microdata from Rural China
by
Jing Li, Kelin Chen, Chao Yan and Zhong Tang
Agriculture 2024, 14(5), 689; https://doi.org/10.3390/agriculture14050689 (registering DOI) - 28 Apr 2024
Abstract
This paper examines the relationship between income inequality and consumption, utilizing panel data from rural China over a span of four years to validate the application of relative income theory in the domain of food consumption. Food consumption represents a significant portion of
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This paper examines the relationship between income inequality and consumption, utilizing panel data from rural China over a span of four years to validate the application of relative income theory in the domain of food consumption. Food consumption represents a significant portion of expenditures for the low-income demographic and is of vital importance to China’s food security and agricultural development. To ascertain the impact of income inequality on food consumption, this paper employs a bi-directional fixed-effects model, a mediation effect model, and machine learning causal analysis methods. Utilizing four years of rural resident survey data from the China Health and Nutrition Survey database, the study empirically tests the effect of income inequality on various types of food consumption, the channels through which it operates, and the heterogeneity among different income groups and educational backgrounds. The findings indicate that (1) income inequality within rural communities positively influences food consumption, and this conclusion remains robust under endogeneity treatment and robustness checks, positively affecting the transformation of food consumption and healthy intake; (2) income inequality among rural residents promotes food consumption through two mediating channels: the “demonstration effect” and the “ratchet effect;” (3) the impact of income inequality on food consumption exhibits heterogeneity among rural residents of different income levels and educational backgrounds.
Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Open AccessArticle
E-commerce Participation, Subjective Norms and Grassland Utilization Pressure: An Empirical Evidence of Herdsmen in Inner Mongolia, China
by
Mingjun Tian and Yunhua Wu
Agriculture 2024, 14(5), 690; https://doi.org/10.3390/agriculture14050690 (registering DOI) - 27 Apr 2024
Abstract
The general requirements of China’s rural revitalization strategy are industrial prosperity, ecological livability and rich life. However, the traditional livestock breeding model has struggled to balance the dual requirements of production development and ecological protection, and it is urgent to inject new impetus
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The general requirements of China’s rural revitalization strategy are industrial prosperity, ecological livability and rich life. However, the traditional livestock breeding model has struggled to balance the dual requirements of production development and ecological protection, and it is urgent to inject new impetus and explore new development paths. At present, e-commerce has become a bridge between pastoral areas and cities, herdsmen and consumers. E-commerce participation is not only an important starting point for herdsmen to increase the added value and profit space of livestock products, but also an effective way to change the original breeding behavior based on the premise of destroying grassland. Therefore, this paper presents an in-depth study on the issue of e-commerce enabling grassland ecological restoration, aiming to provide more scientific and effective guidance for e-commerce to be used to achieve a win–win situation in economy and ecology. Therefore, based on the data of 271 herdsmen in pastoral areas of Inner Mongolia, we used the OLS model and the two-stage least squares (2SLS) method to identify the direct impact of herdsmen’s e-commerce participation on grassland utilization pressure. The empirical results show that e-commerce participation can significantly decrease the grassland utilization pressure. The conclusion was still valid after alleviating endogeneity and conducting a robustness test. The results of a mechanism analysis suggest that the reduction effect of e-commerce participation on grassland utilization pressure is mainly due to price incentive, reputation incentive and place identity. Subjective norms can strengthen the inhibitory effect of e-commerce participation on grassland utilization pressure. Furthermore, a heterogeneity analysis demonstrates that e-commerce participation has a better, decreased impact on the grassland utilization pressure on the banners of China’s rural e-commerce demonstration county program. Under a counterfactual assumption, if herdsmen who can participate in e-commerce choose not to do this, their grassland utilization pressure will increase.
Full article
(This article belongs to the Topic Novel Studies in Agricultural Economics and Sustainable Farm Management)
Open AccessArticle
A New Dissimilarity Metric for Anomaly Detection in Management Zones Delineation Constructed from Time-Varying Satellite Images
by
Roghayeh Heidari and Faramarz F. Samavati
Agriculture 2024, 14(5), 688; https://doi.org/10.3390/agriculture14050688 (registering DOI) - 27 Apr 2024
Abstract
A field’s historical performance data are used for management zone delineation in precision agriculture, but including abnormal data leads to inappropriate zones. This paper introduces a framework incorporating historical performance data and a new Zoning Dissimilarity Metric ( ) to
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A field’s historical performance data are used for management zone delineation in precision agriculture, but including abnormal data leads to inappropriate zones. This paper introduces a framework incorporating historical performance data and a new Zoning Dissimilarity Metric ( ) to detect abnormal zoning data automatically. The methodology identifies abnormal zoning data among the field’s performance indicators extracted from satellite images to enhance the accuracy of the delineated zones. We experimented with our framework using Sentinel-2 images on 39 fields across Canada. Our experimental results, which involve both real and synthetic data, clearly demonstrate the importance of in effectively excluding abnormal data during the zone delineation process.
Full article
(This article belongs to the Topic Geospatial Digital Innovations for Smart Agriculture and Forestry)
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Open AccessArticle
Has the Household Old-Age Burden Affected Farm Household Incomes? Evidence from a Survey of Chinese Farm Households
by
Hongwei Lu, Mingjie Gao, Guojing Li, Tingting Li and Qiyou Luo
Agriculture 2024, 14(5), 687; https://doi.org/10.3390/agriculture14050687 (registering DOI) - 27 Apr 2024
Abstract
Income increase is an important way to achieve comprehensive human development and to escape from poverty, and the growing aging problem in rural China poses a challenge to farm household income increase. In order to gain a deeper understanding of the impact of
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Income increase is an important way to achieve comprehensive human development and to escape from poverty, and the growing aging problem in rural China poses a challenge to farm household income increase. In order to gain a deeper understanding of the impact of China’s rural old-age burden on farm household income, this paper empirically examines the impact and mechanism of household old-age burden on farm household income based on the data from the 2023 micro-farm field survey of China’s Henan Province, utilizing linear regression modeling and mediation effect modeling, filling the research gaps in the related fields. The results of the study found that, firstly, family old-age burden has a significant impact on the income of farm households, and that the heavier the family old-age burden, the lower the total income of farm households. Secondly, from the results of the heterogeneity of the impact, the poorer the health condition, the greater the negative impact of family old-age burden on farm household income. Old-age burden has a greater impact on high-income farm households than on low-income farm households, and old-age burden has a significant impact on the income of part-time farm households, while the impact is not significant on purely farm and non-farm households. Thirdly, the heavier the household old-age burden, the more unfavorable it is to the non-farm employment of farm households, thus affecting the income capacity of farm households. Finally, corresponding countermeasures and recommendations are put forward in three areas, namely, the continuous improvement of the social old-age security system, the realization of the function of the social old-age mechanism as an old-age pocket for key special groups, and the improvement of the social flexible employment mechanism.
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(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Spatial Distribution and the Key Impact Factors of Soil Selenium of Cultivated Land in Lianyuan City, China
by
Siyu Guo, Xinyue Chen, Zhijia Lin, Feng Yin, Pengyuan Jia and Keyun Liao
Agriculture 2024, 14(5), 686; https://doi.org/10.3390/agriculture14050686 (registering DOI) - 27 Apr 2024
Abstract
Selenium (Se) is a micronutrient that has attracted significant attention, because the threshold for human health is low. During soil surveys in China, large areas of low-Se soil were found, and this condition may increase the probability of people suffering from Se deficiency.
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Selenium (Se) is a micronutrient that has attracted significant attention, because the threshold for human health is low. During soil surveys in China, large areas of low-Se soil were found, and this condition may increase the probability of people suffering from Se deficiency. A multi-purpose regional geochemical survey conducted in the Lou Shao basin of Hunan Province found abundant Se-rich soils in Lianyuan City. However, as the primary grain-producing area in Hunan Province, the key factors affecting the spatial distribution of soil Se in the cultivated land of Lianyuan City remain to be elucidated. Therefore, based on the data of 5516 topsoil samples (0–20 cm) of cultivated land in Lianyuan City, we used geostatistics, correlation analysis, and a Geodetector to explore the effects of geological conditions (strata), soil types, soil properties, and topography on the distribution of Se in soil. The results showed that (1) in comparison to cultivated land in the Chinese mainland, Japan, Belgium, and Sweden, the cultivated land in Lianyuan City exhibits higher Se contents, with Se-sufficient and Se-rich areas accounting for 9.74% and 88.96% of the total area, respectively; (2) the distribution of high-Se soil was consistent with that in the Longtan Formation, Dalong Formation, and Daye Formation; (3) organic matter (OM) showed a positive correlation with Se, while both the elevation and slope were negatively correlated with Se; (4) stratum had the most significant effect on the spatial variation in soil Se, followed by OM. Lianyuan City is a typical Se-rich area, and the high level of Se in soil reduces the risk of local residents suffering with diseases caused by Se deficiency. The synergistic effect of stratum and OM is the key factor influencing Se enrichment in soils. Moreover, low-lying flat areas are more conducive to the accumulation of Se. This study will help farmers to identify suitable Se-rich cultivation areas in order to increase the Se content in crops, thereby providing a valuable basis for improvements in human health and the optimization of agricultural strategies.
Full article
(This article belongs to the Section Agricultural Soils)
Open AccessArticle
Effect of Algae Supplementation on the Gene Expression of Liver Lipid Metabolism in Dairy Goat Bucks
by
Mengke Ni, Zhen Zhang, Xinran Luo, Min Tian, Yifan Zhu, Meiwen Song, Huan Lei, Zhi Chen and Cong Li
Agriculture 2024, 14(5), 685; https://doi.org/10.3390/agriculture14050685 (registering DOI) - 27 Apr 2024
Abstract
This study aimed to investigate how diets supplemented with DHA-rich algae affect the expression of liver lipid synthesis genes in dairy goat bucks. The results revealed that when supplemented with DHA-rich algae, liver weight and serum HDL-C were significantly increased (p <
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This study aimed to investigate how diets supplemented with DHA-rich algae affect the expression of liver lipid synthesis genes in dairy goat bucks. The results revealed that when supplemented with DHA-rich algae, liver weight and serum HDL-C were significantly increased (p < 0.05), as well as serum LDL-C was significantly decreased (p < 0.05). Transcriptome sequencing indicated that algae supplementation alters liver gene expression. The differentially expressed genes were predominantly enriched in fatty acid metabolism and the biosynthesis of unsaturated fatty acids. The expression of fatty acid desaturation and transcription factors (SCD, FADS1, INSIG1), de novo synthesis fatty acids (FASN), fatty acid transport (LDLR), and cholesterol and steroid synthesis (HMGCR, HMGCS1, SQLE) genes were significantly increased (p < 0.05), and fatty acid oxidation (ALDH3B1) genes were significantly decreased (p < 0.05). In conclusion, this research provided preliminary evidence that supplementation with algae in dietary supplements altered the expression of the liver lipid synthesis genes in the Saanen dairy goat bucks.
Full article
(This article belongs to the Special Issue Productivity, Performance and Health of Dairy Ruminants)
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Open AccessArticle
Combination Mechanism of Soil Dissolved Organic Matter and Cu2+ in Vegetable Fields, Forests and Dry Farmland in Lujiang County
by
Youru Yao, Jingyi Zhang, Kang Ma, Jing Li, Xin Hu, Yusi Wang, Yuesheng Lin, Fengman Fang and Shiyin Li
Agriculture 2024, 14(5), 684; https://doi.org/10.3390/agriculture14050684 (registering DOI) - 27 Apr 2024
Abstract
Dissolved organic matter (DOM) serves as a critical link in the migration and transformation of heavy metals at the soil–solid interface, influencing the migration behaviour and transformation processes of Cu2+ in soil. There have been studies on the combination mechanisms between DOM
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Dissolved organic matter (DOM) serves as a critical link in the migration and transformation of heavy metals at the soil–solid interface, influencing the migration behaviour and transformation processes of Cu2+ in soil. There have been studies on the combination mechanisms between DOM and Cu2+ in paddy soils. However, the adsorption/complexation and redox processes between DOM and Cu2+ in other agricultural soil types (such as dry farmland and vegetable fields) are unclear. In order to reveal the combination process of DOM with Cu in different agricultural soil types and the dynamic changes in chemical behaviour that occur, this study analysed the variability of DOM components and structure in three soils using three-dimensional fluorescence spectroscopy and X-ray photoelectron spectroscopy. In addition, the priority order of different DOM compounds in combination with Cu and the change process in relation to the Cu valence state in the soil of Lujiang County, Anhui Province, was revealed based on laboratory experiments. The results showed that the composition of soil DOM was mainly composed of humic-like and fulvic-like substances with a clear terrestrial origin and that the organic matter showed a high degree of decomposition characteristics. The results indicated that the composition of soil DOM is mainly composed of humic and fulvic acid-like substances, and they have obvious characteristics of terrestrial origin. In addition, the soil organic matter showed high decomposition characteristics. The complex stability constants (lgKM) of humic acid-like substances with Cu2+ follow the order of forest land (lgKM = 5.21), vegetable land (lgKM = 4.90), and dry farmland (lgKM = 4.88). The lgKM of fulvic acid-like substances with Cu2+ is in the order of dry farmland (lgKM = 4.51) and vegetable land (lgKM = 4.39). Humic acid-like substances in soil DOM combine preferentially with Cu2+, showing a stronger chelating affinity than fulvic acid-like substances. Cu2+ complexes mainly include hydroxyl, phenolic hydroxyl and amino functional groups are included in soil DOM, accompanied by redox reactions. In comparison to dry farmland, the soil DOM in forest and vegetable fields undergoes more intense redox reactions simultaneously with the chelation of Cu2+. Therefore, the application of organic fertilisers to vegetable and forest soils may lead to uncertainties concerning the fate of heavy metals with variable chemical valence. These results contribute to a deeper understanding of the interaction mechanisms between DOM and Cu2+ in agricultural soils.
Full article
(This article belongs to the Special Issue Migration and Diffusion of Heavy Metals and Metalloids in Agri-Soil Systems)
Open AccessArticle
Research on the Evolution of the Spatial Association Network Structure and Driving Factors of China’s Agricultural Green Development
by
Feng Zhou and Chunhui Wen
Agriculture 2024, 14(5), 683; https://doi.org/10.3390/agriculture14050683 (registering DOI) - 26 Apr 2024
Abstract
Against the backdrop of global environmental challenges and sustainable development goals, this paper pioneers the application of social network analysis to the study of spatial associations in China’s agricultural green development. It not only enhances the understanding of the spatial interconnectivity and network
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Against the backdrop of global environmental challenges and sustainable development goals, this paper pioneers the application of social network analysis to the study of spatial associations in China’s agricultural green development. It not only enhances the understanding of the spatial interconnectivity and network structural characteristics of agricultural green developments, but also captures the complex dependencies and interactions among provinces through a network lens, offering a fresh perspective on regional agricultural cooperation and competition. The study reveals: (1) The spatial network of China’s agricultural green development displays strong overall connectivity and enhanced stability, with regional green development trends becoming increasingly interlinked and interdependent. (2) The network exhibits a clear hierarchical and core-periphery structure which, over time, shows signs of diminishing, indicating a narrowing of developmental disparities among regions. (3) Significant shifts in the roles and positions of provinces within the network occur due to the relocation of industrial focal points and adjustments in development strategies, highlighting the complexity of dynamic changes among regions. (4) The spatial association network can be divided into four main clusters: Net spillover block, Bidirectional spillover block, Net beneficial block, and Broker block, with significant gradient characteristics in the relationships between these clusters, suggesting directional and differential flows and exchanges of resources and information among regions. (5) Geographic proximity, economic development level, informatization, and agricultural technological advancement significantly influenced the development and structural evolution of the network.
Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
Open AccessArticle
Parameter Optimization of a Conveying and Separating Device Based on a Five-Stage Screw and Vibrating Screen for Tiger Nut Harvesters
by
Jiangtao Qi, Jianping Gao, Shan Chen, Wenhui Chen, Luoyi Yang, Hewei Meng and Za Kan
Agriculture 2024, 14(5), 682; https://doi.org/10.3390/agriculture14050682 - 26 Apr 2024
Abstract
To tackle problems such as the difficult separation from sand and the high power consumption of tiger nut harvesting in the sandy areas of Xinjiang, a conveying and separating device for tiger nut harvesters was designed. The axial and radial migrations of materials
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To tackle problems such as the difficult separation from sand and the high power consumption of tiger nut harvesting in the sandy areas of Xinjiang, a conveying and separating device for tiger nut harvesters was designed. The axial and radial migrations of materials under screw action and the separation process of materials under vibratory action were analyzed dynamically. A simulation analysis was carried out on the conveying and separating process based on EDEM software. The migration trajectories of tiger nuts and sand particles were extracted, the displacement variations of sand particles on the X-axis, Y-axis, and Z-axis were analyzed in the action area of the screen-cleaning spike teeth and the screw action area, respectively, and the conveying and separation law of the tiger nut harvest mixture was clarified. With key parameters such as the screw velocity ratio, amplitude, vibration frequency, and machine operation velocity as test factors, and with the sand removal rate, crushing rate, and power consumption as test evaluation indicators, a four-factor, five-level orthogonal central composite test design was implemented. The test results were analyzed via the regression variance analysis method, and relation models between variable factors and evaluation indicators were constructed. The test results show that under the combined conditions of a screw velocity ratio of 0.88, an amplitude of 4.7 mm, a vibration frequency of 7.5 Hz, and a machine operation velocity of 0.92 km/h, the sand removal rate is 90.40%, the crushing rate is 1.66%, and the power consumption is 2.24 kW in theory. The optimized results were verified by tests. The sand removal rate was 88.92%, the crushing rate was 1.71%, the total power consumption was 2.29 kW, and the errors from the predicted values were 1.6%, 3.0%, and 2.2%, respectively, meeting the requirements for tiger nut harvesting conveyance and separation. This research can provide support for the development of technology and equipment for mechanized harvesting of tiger nuts in the sandy areas of Xinjiang.
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(This article belongs to the Section Agricultural Technology)
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Open AccessArticle
The Two Mycological Sides of Ultraviolet-B Radiation: Harmless for Mushroom Mycelia, Harmful for Mycopathogenic Mould Spores
by
Raquel Hidalgo-Sanz, María-Ángeles Del-Castillo-Alonso, Susana Sanz, Carmen Olarte, Javier Martínez-Abaigar and Encarnación Núñez-Olivera
Agriculture 2024, 14(5), 681; https://doi.org/10.3390/agriculture14050681 - 26 Apr 2024
Abstract
Mycopathogenic moulds are responsible for the greatest crop losses of cultivated mushrooms, thus having a significant negative economic impact on industry. Pesticides are the most common treatment against mycopathogenic moulds, but ultraviolet-B (UV-B, 280–315 nm) radiation could be a more ecological alternative. Thus,
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Mycopathogenic moulds are responsible for the greatest crop losses of cultivated mushrooms, thus having a significant negative economic impact on industry. Pesticides are the most common treatment against mycopathogenic moulds, but ultraviolet-B (UV-B, 280–315 nm) radiation could be a more ecological alternative. Thus, we studied the effect of UV-B (at doses from 8 to 192 kJ m−2) on four common mycopathogenic moulds (Cladobotryum mycophilum, Lecanicillium fungicola, Trichoderma aggressivum, and Mycogone perniciosa) under in vitro conditions, using four different culture media. UV-B was tremendously effective in inactivating mould spores even at the lowest dose, with the exception of those of T. aggresivum. Contrarily, UV-B did not present any effect on the development of the host mycelium (Agaricus bisporus), even at the highest dose, when cultivated on Compost Tea medium (CT). This is the most similar medium to the substrate used for commercial mushroom cultivation. UV-B reduced the mould mycelia development in a dose-response manner, but this reduction depended on the species, with the strongly pigmented T. aggressivum as the most tolerant species. Regarding the culture media, all of them (especially CT) absorbed UV-B intensely, contributing to the protection of the mycelia. Overall, UV-B radiation could constitute an ecologically friendly alternative to chemical treatments against mycopathogenic moulds, due to its capacity to inactivate their spores and (in some cases) their mycelia without affecting their hosts.
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(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)
Open AccessArticle
How Does Information Acquisition Ability Affect Farmers’ Green Production Behaviors: Evidence from Chinese Apple Growers
by
Zheng Li, Disheng Zhang and Xiaohuan Yan
Agriculture 2024, 14(5), 680; https://doi.org/10.3390/agriculture14050680 - 26 Apr 2024
Abstract
Green production is crucial in promoting sustainable agricultural practices, ensuring food safety, and protecting the rural ecological environment. Farmers, as the main decision makers of agricultural production, and their green production behaviors (GPBs), directly determine the process of agricultural green development. Based on
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Green production is crucial in promoting sustainable agricultural practices, ensuring food safety, and protecting the rural ecological environment. Farmers, as the main decision makers of agricultural production, and their green production behaviors (GPBs), directly determine the process of agricultural green development. Based on the survey data of 656 apple growers in Shaanxi and Gansu provinces in 2022, this paper uses a graded response model to measure the information acquisition ability (IAA) of farmers and constructs an ordered Logit model to empirically explore the influence mechanisms of IAA, green benefit cognition (GBC), and new technology learning attitude (NTLA) on farmers’ GPBs. The results show the following: (1) IAA has a significantly positive impact on the adoption of GPBs by farmers, and farmers with a high IAA are more conscious to adopt green production technologies; (2) in the process of IAA affecting farmers’ adoption of GPBs, GBC plays a positive mediating role; (3) NTLAs have a positive moderating effect on the process of GBC affecting farmers’ GPB adoption; (4) there are generational, educational and regional differences in the impact of IAA on farmers’ GPBs. Policy makers should improve rural information facilities, strengthen agricultural technology promotion and training, improve farmers’ IAA and benefit awareness level, and formulate relevant policies to mobilize farmers’ enthusiasm for learning new technologies.
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(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Challenges of Including Wet Grasslands with Variable Groundwater Tables in Large-Area Crop Production Simulations
by
Valeh Khaledi, Bahareh Kamali, Gunnar Lischeid, Ottfried Dietrich, Mariel F. Davies and Claas Nendel
Agriculture 2024, 14(5), 679; https://doi.org/10.3390/agriculture14050679 - 26 Apr 2024
Abstract
Large-scale assessments of agricultural productivity necessitate integrated simulations of cropland and grassland ecosystems within their spatiotemporal context. However, simultaneous simulations face limitations due to assumptions of uniform species distribution. Grasslands, particularly those with shallow groundwater tables, are highly sensitive to water availability, undergoing
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Large-scale assessments of agricultural productivity necessitate integrated simulations of cropland and grassland ecosystems within their spatiotemporal context. However, simultaneous simulations face limitations due to assumptions of uniform species distribution. Grasslands, particularly those with shallow groundwater tables, are highly sensitive to water availability, undergoing rapid species composition changes. We hypothesised that predicting above-ground biomass (AGB) remains challenging due to these dynamic responses. Ten years of data from four lysimeters at a German wet grassland site, with varying water table treatments, was utilised to test this hypothesis. Correlation analysis revealed a strong positive indirect effect of the water regime on AGB, with a one-year time lag (r = 0.97). The MONICA model initially exhibited fair agreement (d = 0.69) in simulating Leaf-Area-Index (LAI) but performed poorly in replicating AGB (d = 0.3). After removing the species composition change effect from the LAI and AGB datasets, the simulation notably improved, with the overall relative root mean square error (rRMSE) of AGB decreasing from 1.55 to 0.90 between the first and second simulations. This demonstrates MONICA’s ability to predict grass growth patterns amidst changing water supply levels for constant species composition. However, it needs a competition model to capture biomass growth changes with varying water supply.
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(This article belongs to the Section Agricultural Water Management)
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Delayed Sowing Can Improve Potassium Utilization Efficiency and Grain Potassium Concentration in Winter Wheat
by
Lijun Yin, Yaxin Liao and Xiao Mou
Agriculture 2024, 14(5), 678; https://doi.org/10.3390/agriculture14050678 - 26 Apr 2024
Abstract
Economic consumption and environmental impacts due to potassium (K) inputs in agriculture are gaining increasing attention. It is urgent to improve K use efficiency (KUE) for agricultural development. Delayed sowing has been shown to maintain grain yield in winter wheat. Still, there needs
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Economic consumption and environmental impacts due to potassium (K) inputs in agriculture are gaining increasing attention. It is urgent to improve K use efficiency (KUE) for agricultural development. Delayed sowing has been shown to maintain grain yield in winter wheat. Still, there needs to be more information regarding the effect of sowing date on crop K status evaluated by the K nutrition index (KNI), KUE, K uptake efficiency (UPE), K utilization efficiency (UTE), and grain K concentration (GKC). Here, we assessed Shannong23 and Tainong18 winter wheat cultivars with three sowing date treatments composed of 26 September (early sowing), 8 October (normal sowing), and 22 October (late sowing) in the 2021–2022 and 2022–2023 growing seasons. The influences of sowing date on the KNI, tillering, grain yield formation, KUE, UPE, UTE, K transport, and GKC were examined. Our study indicated that late sowing in winter wheat was an almost optimal K nutritional situation, whereas early and normal sowing were under situations of excess K. As sowing was delayed, aboveground K uptake (AGK), UPE, and spike number per unit area decreased; UTE and grain number per spike increased; and grain yield and KUE were unchanged. A positive correlation between KNI and UPE and spike number per unit area and a negative correlation between KNI and UTE and grain number per spike were found, whereas no significant correlation between KNI and KUE was observed. Late sowing promoted K transport from pre-anthesis accumulation in vegetative organs to grain, resulting in a higher GKC, which could lead to high grain quality and K recovery. Therefore, late sowing winter wheat can use K more efficiently and increase GKC, implying that delayed sowing can reduce K input, favoring sustainable agriculture development.
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(This article belongs to the Special Issue Research on Technologies for Achieving High-Yield Wheat)
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Impacts of the National Nutrition Plan 2017–2030 on Listed Agrifood Enterprises: A Financial Statement Perspective
by
Jianxiong Chen, Chung-Cheng Yang and Yu Lin
Agriculture 2024, 14(5), 677; https://doi.org/10.3390/agriculture14050677 - 26 Apr 2024
Abstract
The Chinese government promulgated the National Nutrition Plan 2017–2030 to provide scientific guidance for agrifood consumption and enhance nutrition intake. We categorized the sample into pre-2018 and post-2018 periods. By evaluating the effects of the National Nutrition Plan 2017–2030 through economic theory and
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The Chinese government promulgated the National Nutrition Plan 2017–2030 to provide scientific guidance for agrifood consumption and enhance nutrition intake. We categorized the sample into pre-2018 and post-2018 periods. By evaluating the effects of the National Nutrition Plan 2017–2030 through economic theory and a translog revenue function model based on financial statement data from 2015 to 2022, our findings indicate that the National Nutrition Plan 2017–2030 has increased the overall agrifood sales of listed agrifood enterprises, but the increase in agrifood sales produced by large listed agrifood enterprises has been slight. Finally, we offer policy recommendations for regulatory authorities and develop strategies for agrifood firms to encourage local food procurement. This study also contributes to our understanding of China’s agrifood industry dynamics and underscores the significance of the National Nutrition Plan 2017–2030 in enhancing nutritional intake and fostering sustainable growth in China’s agriculture industry.
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(This article belongs to the Special Issue Agricultural Markets and Agrifood Supply Chains)
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