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
Genetic Variability and Interrelationships of Grain, Cooking, and Nutritional Quality Traits in Cowpea: Implications for Cowpea Improvement
Agriculture 2024, 14(4), 633; https://doi.org/10.3390/agriculture14040633 (registering DOI) - 19 Apr 2024
Abstract
Grain quality, cooking quality, and nutritional quality traits are some of the major attributes that enhance the uptake and utilization of improved cowpea varieties. Therefore, there is a need for a better understanding of the genetic variation and inter-relationships among these quality traits
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Grain quality, cooking quality, and nutritional quality traits are some of the major attributes that enhance the uptake and utilization of improved cowpea varieties. Therefore, there is a need for a better understanding of the genetic variation and inter-relationships among these quality traits in cowpeas to integrate them into cowpea breeding programs. This study was conducted to determine genetic variability among 306 cowpea genotypes for grain quality, cooking quality, and nutritional quality traits and to understand the interrelationships among these traits for exploitation in breeding programs. The results showed highly significant differences (p < 0.001) among genotypes for grain quality, cooking quality, and nutritional quality traits. The mean performance for these quality traits was also very variable. These results suggest that genetic variability exists in the cowpea genotypes studied, which can be exploited in breeding programs aimed at developing high-performing varieties for the said traits. Significant (p < 0.001) positive correlations were detected for protein content with iron and zinc. On the other hand, nutritional quality traits did not exhibit any association with grain quality or cooking quality traits. Cooking quality traits were also shown to be significantly and positively correlated with grain quality traits. This study has identified several genotypes with desirable quality-related traits that could be used in crossing programs to generate improved varieties with consumer-preferred traits to improve the food, income, and nutritional status of many smallholder farmers that largely depend on cowpeas.
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(This article belongs to the Special Issue Characterization, Evaluation, and Utilization of Crop Germplasm Resources)
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Open AccessReview
Current Flaxseed Dehulling Technology in China
by
Leilei Chang, Ruijie Shi, Fei Dai, Wuyun Zhao, Yiming Zhao and Junzhi Chen
Agriculture 2024, 14(4), 632; https://doi.org/10.3390/agriculture14040632 (registering DOI) - 19 Apr 2024
Abstract
With the improvement in living standards and growing appreciation for flaxseed’s nutritional value, global demand for flaxseed and its economic significance are continuously increasing. As a major flaxseed producer and exporter, China plays a crucial role in the development of its agricultural economy.
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With the improvement in living standards and growing appreciation for flaxseed’s nutritional value, global demand for flaxseed and its economic significance are continuously increasing. As a major flaxseed producer and exporter, China plays a crucial role in the development of its agricultural economy. Flaxseed, one of China’s five key oil crops, is renowned for its rich nutritional content. This study employed a literature review to systematically examine the research status of key flaxseed dehulling technologies in China. It explored the characteristics, efficiencies, and quality differences among various dehulling methods, while also drawing on advanced techniques, such as chemical and ultrasonic dehulling, to provide new perspectives and theoretical support for flaxseed dehulling. Comprehensive analysis revealed that mechanical dehulling (the impact method and rolling and rubbing method) is the primary method used in China. The study also identified the issues in current flaxseed dehulling research in China and offers suggestions to guide future improvements and innovations in flaxseed processing, aiming to enhance the quality and nutritional value of flaxseed to meet diverse market demands.
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(This article belongs to the Section Agricultural Technology)
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Assessing the Sustainability of Urban Agriculture in Shanghai’s Nine Agriculture Districts: A Decadal Analysis (2010–2020)
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Jianyun Nie, Akira Kiminami and Hironori Yagi
Agriculture 2024, 14(4), 631; https://doi.org/10.3390/agriculture14040631 - 19 Apr 2024
Abstract
This research conducts an analysis of the sustainability of urban agriculture in Shanghai over the period 2010 to 2020, employing the Triple Bottom Line (TBL) concept as a framework to evaluate sustainability across economic, environmental, and social dimensions through the formulation and application
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This research conducts an analysis of the sustainability of urban agriculture in Shanghai over the period 2010 to 2020, employing the Triple Bottom Line (TBL) concept as a framework to evaluate sustainability across economic, environmental, and social dimensions through the formulation and application of a comprehensive indicator system. Utilizing the Delphi method alongside the Analytic Hierarchy Process (AHP) for determining indicators and their respective weights, this study adopts a methodologically rigorous approach to analysis. The findings reveal an overall enhancement in agricultural sustainability, albeit accompanied by a decline in economic sustainability. Notably, environmental sustainability emerged as a paramount concern, underscoring the essentiality of incorporating environmental indicators within urban agricultural initiatives. The paper addresses significant challenges such as elevated land prices, demographic shifts, and the imperative for more stringent environmental regulations. It advocates for a multidimensional strategy integrating advanced agricultural technologies and cross-sectoral partnerships to bolster sustainability. Furthermore, the study accentuates the necessity of achieving equilibrium among economic feasibility, environmental stewardship, and social equity to pursue sustainable urban agriculture in Shanghai. Additionally, it highlights the critical role of strategic agricultural policy formulation in fostering sectoral resilience and ensuring enduring sustainability.
Full article
(This article belongs to the Special Issue Sustainable Agriculture and Food Supply: Scientific, Economic and Policy Aspects)
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Mapping the Soil Salinity Distribution and Analyzing Its Spatial and Temporal Changes in Bachu County, Xinjiang, Based on Google Earth Engine and Machine Learning
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Yue Zhang, Hongqi Wu, Yiliang Kang, Yanmin Fan, Shuaishuai Wang, Zhuo Liu and Feifan He
Agriculture 2024, 14(4), 630; https://doi.org/10.3390/agriculture14040630 - 19 Apr 2024
Abstract
Soil salinization has a significant impact on agricultural production and ecology. There is an urgent demand to establish an effective method that monitors the spatial and temporal distribution of soil salinity. In this study, a multi-indicator soil salinity monitoring model was proposed for
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Soil salinization has a significant impact on agricultural production and ecology. There is an urgent demand to establish an effective method that monitors the spatial and temporal distribution of soil salinity. In this study, a multi-indicator soil salinity monitoring model was proposed for monitoring soil salinity in Bachu County, Kashgar Region, Xinjiang, from 2002 to 2022. The model was established by combining multiple predictors (spectral, salinity, and composite indices and topographic factors) and the accuracy of the four models (Random Forest [RF], Partial Least Squares [PLS], Classification Regression Tree [CART], and Support Vector Machine [SVM]) was compared. The results reveal the high accuracy of the optimized prediction model, and the order of the accuracy is observed as RF > PLS > CART > SVM. The most accurate model, RF, exhibited an R2 of 0.723, a root mean square error (RMSE) of 2.604 g·kg−1, and a mean absolute error (MAE) of 1.95 g·kg−1 at a 0–20 cm depth. At a 20–40 cm depth, RF had an R2 value of 0.64, an RMSE of 3.62 g·kg−1, and an MAE of 2.728 g·kg−1. Spatial changes in soil salinity were observed throughout the study period, particularly increased salinization from 2002 to 2012 in the agricultural and mountainous areas within the central and western regions of the country. However, salinization declined from 2012 to 2022, with a decreasing trend in salinity observed in the top 0–20 cm of soil, followed by an increasing trend in salinity at a 20–40 cm depth. The proposed method can effectively extract large-scale soil salinity and provide a practical basis for simplifying the remote sensing monitoring and management of soil salinity. This study also provides constructive suggestions for the protection of agricultural areas and farmlands.
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(This article belongs to the Section Digital Agriculture)
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Open AccessArticle
Cooperative Fermentation Using Multiple Microorganisms and Enzymes Potentially Enhances the Nutritional Value of Spent Mushroom Substrate
by
Anrong Zhang, Weizhao He, Yunsheng Han, Aijuan Zheng, Zhimin Chen, Kun Meng, Peilong Yang and Guohua Liu
Agriculture 2024, 14(4), 629; https://doi.org/10.3390/agriculture14040629 - 19 Apr 2024
Abstract
Large amounts of spent mushroom substrate (SMS) are produced globally, but their utilization efficiency is low, which leads to negative environmental impacts, such as water, soil, and air pollution. SMS contains nutrients, such as cell proteins, with a potential application in animal feed.
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Large amounts of spent mushroom substrate (SMS) are produced globally, but their utilization efficiency is low, which leads to negative environmental impacts, such as water, soil, and air pollution. SMS contains nutrients, such as cell proteins, with a potential application in animal feed. However, the lignocellulose in SMS restricts animal digestion and absorption, thus hindering its application in animal nutrition. We investigated the potential of cellulase, xylanase, β-galactosidase, and a variety of microorganisms to optimize the conditions for reducing sugars’ (RS) production and the degradation rate of neutral detergent fibers. The results showed that the optimum proportion of multiple enzymes for glucose production of up to 210.89 mg/g were 10% cellulase, 10% xylanase, and 2% β -galactosidase, at 50 °C and 60% moisture for a 20 h hydrolysis duration. To enhance the optimal enzymolysis combination, co-fermentation experiments with multiple microorganisms and enzymes showed that inoculation with 10% Bacillus subtilis, 2% Pediococcus acidilactici, and 2% Saccharomyces cerevisiae, in combination with 10% cellulase, 10% xylanase, 2% β-galactosidase, and 1% urea, at 36.8°C and 59% moisture for 70 h hydrolysis, could lead to a 23.69% degradation rate of the neutral detergent fiber. This process significantly increased the degradation rate of the neutral detergent fiber and the nutrient content of Pleurotus eryngii compared to the initial fermentation conditions. Overall, our study generated optimal co-fermentation conditions for bacteria and enzymes and provides a practical reference for biological feed synthesis using P. eryngii spent mushroom substrate.
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(This article belongs to the Section Agricultural Technology)
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Open AccessArticle
Sh-DeepLabv3+: An Improved Semantic Segmentation Lightweight Network for Corn Straw Cover Form Plot Classification
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Yueyong Wang, Xuebing Gao, Yu Sun, Yuanyuan Liu, Libin Wang and Mengqi Liu
Agriculture 2024, 14(4), 628; https://doi.org/10.3390/agriculture14040628 - 18 Apr 2024
Abstract
Straw return is one of the main methods for protecting black soil. Efficient and accurate straw return detection is important for the sustainability of conservation tillage. In this study, a rapid straw return detection method is proposed for large areas. An optimized Sh-DeepLabv3+
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Straw return is one of the main methods for protecting black soil. Efficient and accurate straw return detection is important for the sustainability of conservation tillage. In this study, a rapid straw return detection method is proposed for large areas. An optimized Sh-DeepLabv3+ model based on the aforementioned detection method and the characteristics of straw return in Jilin Province was then used to classify plots into different straw return cover types. The model used Mobilenetv2 as the backbone network to reduce the number of model parameters, and the channel-wise feature pyramid module based on channel attention (CA-CFP) and a low-level feature fusion module (LLFF) were used to enhance the segmentation of the plot details. In addition, a composite loss function was used to solve the problem of class imbalance in the dataset. The results show that the extraction accuracy is optimal when a 2048 × 2048-pixel scale image is used as the model input. The total parameters of the improved model are 3.79 M, and the mean intersection over union (MIoU) is 96.22%, which is better than other comparative models. After conducting a calculation of the form–grade mapping relationship, the error value of the area prediction was found to be less than 8%. The results show that the proposed rapid straw return detection method based on Sh-DeepLabv3+ can provide greater support for straw return detection.
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(This article belongs to the Special Issue Smart Mechanization and Automation in Agriculture)
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An Accurate Approach for Predicting Soil Quality Based on Machine Learning in Drylands
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Radwa A. El Behairy, Hasnaa M. El Arwash, Ahmed A. El Baroudy, Mahmoud M. Ibrahim, Elsayed Said Mohamed, Nazih Y. Rebouh and Mohamed S. Shokr
Agriculture 2024, 14(4), 627; https://doi.org/10.3390/agriculture14040627 - 18 Apr 2024
Abstract
Nowadays, machine learning (ML) is a useful technology due to its high accuracy in constructing non-linear models and algorithms that can adapt to the complexity and diversity of data. Thus, the current work aimed to predict the soil quality index (SQI) from extensive
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Nowadays, machine learning (ML) is a useful technology due to its high accuracy in constructing non-linear models and algorithms that can adapt to the complexity and diversity of data. Thus, the current work aimed to predict the soil quality index (SQI) from extensive soil data, achieving high accuracy with the artificial neural networks (ANN) model. However, the efficiency of ANN depends on the accuracy of the data that is prepared for training. For this purpose, MATLAB programming language was used to enable the calculation, classification, and compilation of the results into databases within a few minutes. The proposed MATLAB program was highly efficient, accurate, and quick in calculating soil big data for training the machine compared with traditional methods. The database contains 306 vector sets, 80% of them are used for training and the remaining 20% are reserved for testing. The optimal model obtained comprises one hidden layer with 250 neurons and one output layer with a sigmoid function. The ANN achieved a high coefficient of determination (R2) values for SQI estimation, with around 0.97 and 0.98 for training and testing, respectively. The results indicate that 36.93% of the total soil samples belonged to the very high quality class (C1). In contrast, the high quality (C2), moderate quality (C3), low quality (C4), and very low quality (C5) classes accounted for 10.46%, 31.37%, 20.92%, and 0.33% of the samples, respectively. The high contents of CaCO3, pH, sodium saturation, salinity, and clay content were identified as limiting factors in certain areas. The results of this study indicated high accuracy of soil quality assessment using physical, chemical, and fertility soil features in regression analysis with ANN. This method, which is suitable for arid zones, enhances agricultural productivity and decision-making by identifying critical soil quality categories and constraints.
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(This article belongs to the Special Issue Applications of Data Analysis in Agriculture—2nd Edition)
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Multi-Trait Bayesian Models Enhance the Accuracy of Genomic Prediction in Multi-Breed Reference Populations
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Weining Li, Meilin Zhang, Heng Du, Jianliang Wu, Lei Zhou and Jianfeng Liu
Agriculture 2024, 14(4), 626; https://doi.org/10.3390/agriculture14040626 - 18 Apr 2024
Abstract
Performing joint genomic predictions for multiple breeds (MBGP) to expand the reference size is a promising strategy for improving the prediction for limited population sizes or phenotypic records for a single breed. This study proposes an MBGP model—mbBayesAB, which treats the same traits
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Performing joint genomic predictions for multiple breeds (MBGP) to expand the reference size is a promising strategy for improving the prediction for limited population sizes or phenotypic records for a single breed. This study proposes an MBGP model—mbBayesAB, which treats the same traits of different breeds as potentially genetically related but different, and divides chromosomes into independent blocks to fit heterogeneous genetic (co)variances. Best practices of random effect (co)variance matrix priors in mbBayesAB were analyzed, and the prediction accuracies of mbBayesAB were compared with within-breed (WBGP) and other commonly used MBGP models. The results showed that assigning an inverse Wishart prior to the random effect and obtaining information on the scale of the inverse Wishart prior from the phenotype enabled mbBayesAB to achieve the highest accuracy. When combining two cattle breeds (Limousin and Angus) in reference, mbBayesAB achieved higher accuracy than the WBGP model for two weight traits. For the marbling score trait in pigs, MBGP of the Yorkshire and Landrace breeds led to a 6.27% increase in accuracy for Yorkshire validation using mbBayesAB compared to that using the WBGP model. Therefore, considering heterogeneous genetic (co)variance in MBGP is advantageous. However, determining appropriate priors for (co)variance and hyperparameters is crucial for MBGP.
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(This article belongs to the Section Farm Animal Production)
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The Effect of Crop Production Systems and Cultivars on Spring Wheat (Triticum aestivum L.) Yield in a Long-Term Experiment
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Beata Feledyn-Szewczyk, Krzysztof Jończyk and Jarosław Stalenga
Agriculture 2024, 14(4), 625; https://doi.org/10.3390/agriculture14040625 - 17 Apr 2024
Abstract
The aim of this study was to determine the impact of different crop production systems (organic, integrated, and conventional) on the yields of several spring wheat (Triticum aestivum L.) cultivars. A field experiment was carried out at the Agricultural Experimental Station of
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The aim of this study was to determine the impact of different crop production systems (organic, integrated, and conventional) on the yields of several spring wheat (Triticum aestivum L.) cultivars. A field experiment was carried out at the Agricultural Experimental Station of the Institute of Soil Science and Plant Cultivation in Osiny (Poland) in three consecutive growing seasons (2014, 2015, and 2016). Two factors were included in the experiment: the crop production system (organic, integrated, and conventional) and spring wheat cultivars (Kandela, Izera, Ostka Smolicka, and Waluta). The crop production system significantly differentiated the yield, health, and weed infestation of the spring wheat. Wheat yield in the conventional system (6.12 t·ha−1) was higher than in the organic system (3.68 t·ha−1) by 67%, whereas, in the integrated system (7.61 t·ha−1), it was greater than in the organic system by 109%. The lower yields in the organic system were mainly due to fewer ears per m2 and a smaller 1000-grain weight. In the organic system, we also observed a higher infestation of wheat by foliar fungal pathogens and weeds compared with the conventional and integrated systems. The spring wheat cultivars differed in yield structure and resistance to infestation by fungal pathogens. The Waluta and Izera cultivars performed well in all systems but yielded the best in the integrated and conventional ones. The Kandela cultivar was the most suitable for the organic system, as it achieved the highest yield (4.16 t·ha−1). This was mainly due to its ability to form a compact canopy with relatively high ear density, a large 1000-grain weight, and the highest resistance to fungal pathogens. The results for cultivars’ performance in the organic system may be useful for farmers in decreasing yield gaps in relation to integrated and conventional systems.
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(This article belongs to the Special Issue Advances in Organic Agriculture—Decreasing Yield Gap via Optimising Cultivation Methods and Agrarian Policy)
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Recognition and Positioning of Strawberries Based on Improved YOLOv7 and RGB-D Sensing
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Yuwen Li, Wei Wang, Xiaohuan Guo, Xiaorong Wang, Yizhe Liu and Daren Wang
Agriculture 2024, 14(4), 624; https://doi.org/10.3390/agriculture14040624 - 17 Apr 2024
Abstract
To improve the speed and accuracy of the methods used for the recognition and positioning of strawberry plants, this paper is concerned with the detection of elevated-substrate strawberries and their picking points, using a strawberry picking robot, based on the You Only Look
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To improve the speed and accuracy of the methods used for the recognition and positioning of strawberry plants, this paper is concerned with the detection of elevated-substrate strawberries and their picking points, using a strawberry picking robot, based on the You Only Look Once version 7 (YOLOv7) object detection algorithm and Red Green Blue-Depth (RGB-D) sensing. Modifications to the YOLOv7 model include the integration of more efficient modules, incorporation of attention mechanisms, elimination of superfluous feature layers, and the addition of layers dedicated to the detection of smaller targets. These modifications have culminated in a lightweight and improved YOLOv7 network model. The number of parameters is only 40.3% of that of the original model. The calculation amount is reduced by 41.8% and the model size by 59.2%. The recognition speed and accuracy are also both improved. The frame rate of model recognition is increased by 19.3%, the accuracy of model recognition reaches 98.8%, and [email protected] reaches 96.8%. In addition, we have developed a method for locating strawberry picking points based on strawberry geometry. The test results demonstrated that the average positioning success rate and average positioning time were 90.8% and 76 ms, respectively. The picking robot in the laboratory utilized the recognition and positioning method proposed in this paper. The error of hand–eye calibration is less than 5.5 mm on the X-axis, less than 1.6 mm on the Y-axis, and less than 2.7 mm on the Z-axis, which meets the requirements of picking accuracy. The success rate of the picking experiment was about 90.8%, and the average execution time for picking each strawberry was 7.5 s. In summary, the recognition and positioning method proposed in this paper provides a more effective method for automatically picking elevated-substrate strawberries.
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(This article belongs to the Special Issue Sensing and Imaging for Quality and Safety of Agricultural Products)
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Open AccessEditorial
Rural Areas Facing the Challenge of Economic Diversification: Threats and Opportunities
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Francisco Javier Castellano-Álvarez, Rafael Robina-Ramírez and Francisco Silva
Agriculture 2024, 14(4), 623; https://doi.org/10.3390/agriculture14040623 - 17 Apr 2024
Abstract
This Special Issue delves into the challenges and threats associated with rural economic diversification [...]
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(This article belongs to the Special Issue Rural Areas Facing the Challenge of Economic Diversification: Threats and Opportunities)
Open AccessArticle
Safflower Picking Trajectory Planning Strategy Based on an Ant Colony Genetic Fusion Algorithm
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Hui Guo, Zhaoxin Qiu, Guomin Gao, Tianlun Wu, Haiyang Chen and Xiang Wang
Agriculture 2024, 14(4), 622; https://doi.org/10.3390/agriculture14040622 - 17 Apr 2024
Abstract
In order to solve the problem of the low pickup efficiency of the robotic arm when harvesting safflower filaments, we established a pickup trajectory cycle and an improved velocity profile model for the harvest of safflower filaments according to the growth characteristics of
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In order to solve the problem of the low pickup efficiency of the robotic arm when harvesting safflower filaments, we established a pickup trajectory cycle and an improved velocity profile model for the harvest of safflower filaments according to the growth characteristics of safflower. Bezier curves were utilized to optimize the picking trajectory, mitigating the abrupt changes produced by the delta mechanism during operation. Furthermore, to overcome the slow convergence speed and the tendency of the ant colony algorithm to fall into local optima, a safflower harvesting trajectory planning method based on an ant colony genetic algorithm is proposed. This method includes enhancements through an adaptive adjustment mechanism, pheromone limitation, and the integration of optimized parameters from genetic algorithms. An optimization model with working time as the objective function was established in the MATLAB environment, and simulation experiments were conducted to optimize the trajectory using the designed ant colony genetic algorithm. The simulation results show that, compared to the basic ant colony algorithm, the path length with the ant colony genetic algorithm is reduced by 1.33% to 7.85%, and its convergence stability significantly surpasses that of the basic ant colony algorithm. Field tests demonstrate that, while maintaining an S-curve velocity, the ant colony genetic algorithm reduces the harvesting time by 28.25% to 35.18% compared to random harvesting and by 6.34% to 6.81% compared to the basic ant colony algorithm, significantly enhancing the picking efficiency of the safflower-harvesting robotic arm.
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(This article belongs to the Topic Current Research on Intelligent Equipment for Agriculture)
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Beyond the Traditional Mountain Emmental Cheese in “Ţara Dornelor”, Romania: Consumer and Producer Profiles, and Product Sensory Characteristics
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Doru Necula, Mădălina Ungureanu-Iuga and Laurenț Ognean
Agriculture 2024, 14(4), 621; https://doi.org/10.3390/agriculture14040621 - 16 Apr 2024
Abstract
Emmental or Swiss cheese is a hard, ripened cheese appreciated by consumers for its appearance and taste. This study aimed to investigate the profile of Swiss cheese consumers and producers from Ţara Dornelor area, Romania, along with the sensory analysis of the Dorna
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Emmental or Swiss cheese is a hard, ripened cheese appreciated by consumers for its appearance and taste. This study aimed to investigate the profile of Swiss cheese consumers and producers from Ţara Dornelor area, Romania, along with the sensory analysis of the Dorna Swiss cheese produced there. For this purpose, a questionnaire was applied to 268 participants to evaluate consumer behavior. Consumers were grouped depending on consumption frequency (low—once or a few times a year, medium—once a month, and high—once a week or more), and the behavior of groups was evaluated. Producer opinion was assessed by interview and Swiss cheese sensory characteristics in two seasons were determined by sensory analysis using a semi-trained panel. The results showed that the main factors affecting consumer purchase decision are the ingredients (4.43), taste and flavor (4.41), appearance and texture (4.23), producer (3.98), nutritional value (3.88), and product history (3.67). Clustering of consumers depending on consumption frequency revealed significant differences (p < 0.05) regarding the purchase place and some factors influencing the purchase decision such as price, health benefits, and nutritional value. Producers asserted that the quality of milk is the main problem in Swiss cheese production. They consider that the raw material quality and origin, hygiene, utilities, and legislation have the greatest impact on the production process, while the trading is mainly affected by the product taste and flavor, appearance and texture, quality label, price, and product history. The sensory characteristics differed significantly (p < 0.05) between producers and seasons, with the sample produced in a stainless-steel tank and without exogenous microflora being the most appreciated in summer. These results could help producers adapt their product quality and marketing policy to consumer preferences.
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(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Open AccessReview
Precision Livestock Farming Technology: Applications and Challenges of Animal Welfare and Climate Change
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Georgios I. Papakonstantinou, Nikolaos Voulgarakis, Georgia Terzidou, Lampros Fotos, Elisavet Giamouri and Vasileios G. Papatsiros
Agriculture 2024, 14(4), 620; https://doi.org/10.3390/agriculture14040620 - 16 Apr 2024
Abstract
This study aimed to review recent developments in the agri-food industry, focusing on the integration of innovative digital systems into the livestock industry. Over the last 50 years, the production of animal-based foods has increased significantly due to the rising demand for meat.
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This study aimed to review recent developments in the agri-food industry, focusing on the integration of innovative digital systems into the livestock industry. Over the last 50 years, the production of animal-based foods has increased significantly due to the rising demand for meat. As a result, farms have increased their livestock numbers to meet consumer demand, which has exacerbated challenges related to environmental sustainability, human health, and animal welfare. In response to these challenges, precision livestock farming (PLF) technologies have emerged as a promising solution for sustainable livestock production. PLF technologies offer farmers the opportunity to increase efficiency while mitigating environmental impact, securing livelihoods, and promoting animal health and welfare. However, the adoption of PLF technologies poses several challenges for farmers and raises animal welfare concerns. Additionally, the existing legal framework for the use of PLF technologies is discussed. In summary, further research is needed to advance the scientific understanding of PLF technologies, and stakeholders, including researchers, policymakers, and funders, need to prioritize ethical considerations related to their implementation.
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(This article belongs to the Section Digital Agriculture)
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Open AccessArticle
Meteorological Impacts on Rubber Tree Powdery Mildew and Projections of Its Future Spatiotemporal Pattern
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Jiayan Kong, Lan Wu, Jiaxin Cao, Wei Cui, Tangzhe Nie, Yinghe An and Zhongyi Sun
Agriculture 2024, 14(4), 619; https://doi.org/10.3390/agriculture14040619 - 16 Apr 2024
Abstract
Meteorological conditions play a crucial role in driving outbreaks of rubber tree powdery mildew (RTPM). As the climate warms and techniques improve, rubber cultivation is expanding to higher latitudes, and the changing climate increases the RTPM risk. Rubber plantations on Hainan Island, situated
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Meteorological conditions play a crucial role in driving outbreaks of rubber tree powdery mildew (RTPM). As the climate warms and techniques improve, rubber cultivation is expanding to higher latitudes, and the changing climate increases the RTPM risk. Rubber plantations on Hainan Island, situated on the northern margin of the tropics, have been selected as a case study to explore the meteorological mechanisms behind RTPM outbreaks quantitatively using a structural equation model, and project current and future RTPM outbreak patterns under different climate change scenarios by building predictive models based on data-driven algorithms. The following results were obtained: (1) days with an average temperature above 20 °C and days with light rain were identified as key meteorological drivers of RTPM using structural equation modeling (R2 = 0.63); (2) the Bayesian-optimized least-squares boosted trees ensemble model accurately predicted the interannual variability in the historical RTPM disease index (R2 = 0.79); (3) currently, due to the increased area of rubber plantations in the central region of Hainan, there is a higher risk of RTPM; and (4) under future climate scenarios, RTPM shows a decreasing trend (at a moderate level), with oscillating and sporadic outbreaks primarily observed in the central and northwest regions. We attribute this to the projected warming and drying trends that are unfavorable for RTPM. Our study is expected to enhance the understanding of the impact of climate change on RTPM, provide a prediction tool, and underscore the significance of the climate-aware production and management of rubber.
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(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)
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Open AccessArticle
Maize Kernel Quality Detection Based on Improved Lightweight YOLOv7
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Lili Yang, Chengman Liu, Changlong Wang and Dongwei Wang
Agriculture 2024, 14(4), 618; https://doi.org/10.3390/agriculture14040618 - 16 Apr 2024
Abstract
As an important cereal crop, maize is a versatile and multi-purpose crop, primarily used as a feed globally, but also is important as a food crop, and has other uses such as oil and industrial raw materials. Quality detection is an indispensable part
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As an important cereal crop, maize is a versatile and multi-purpose crop, primarily used as a feed globally, but also is important as a food crop, and has other uses such as oil and industrial raw materials. Quality detection is an indispensable part of functional and usage classification, avoiding significant waste as well as increasing the added value of the product. The research on algorithms for real-time, accurate, and non-destructive identification and localization of corn kernels based on quality classification and equipped with non-destructive algorithms suitable for embedding in intelligent agricultural machinery systems is a key step in improving the effective utilization rate of maize kernels. The difference in maize kernel quality leads to significant differences in price and economic benefits. This algorithm reduced unnecessary waste caused by the low efficiency and accuracy of manual and mechanical detection. Image datasets of four kinds of maize kernel quality were established and each image contains a total of about 20 kernels of different quality randomly distributed. Based on the self-built dataset, the YOLOv7-tiny, as the backbone network, was used to design a maize kernel detection and recognition model named “YOLOv7-MEF”. Firstly, the backbone feature layer of the algorithm was replaced by MobileNetV3 as the feature extraction backbone network. Secondly, ESE-Net was used to enhance feature extraction and obtain better generalization performance. Finally, the loss function was optimized and replaced with the Focal-EOIU loss function. The experiment showed that the improved algorithm achieved an accuracy of 98.94%, a recall of 96.42%, and a Frame Per Second (FPS) of 76.92 with a model size of 9.1 M. This algorithm greatly reduced the size of the model while ensuring high detection accuracy and has good real-time performance. It was suitable for deploying embedded track detection systems in agricultural machinery equipment, providing a powerful theoretical research method for efficient detection of corn kernel quality.
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(This article belongs to the Section Digital Agriculture)
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Assessing the Contribution of Smallholder Irrigation to Household Food Security in Zimbabwe
by
Norman Mupaso, Godswill Makombe, Raymond Mugandani and Paramu L. Mafongoya
Agriculture 2024, 14(4), 617; https://doi.org/10.3390/agriculture14040617 - 16 Apr 2024
Abstract
Sustainable Development Goal (SDG) 2 seeks to end hunger and guarantee food and nutrition security worldwide by 2030. Smallholder irrigation development remains a key strategy to achieve SDG 2. This study assesses how smallholder irrigation contributes to household food security in Mberengwa district,
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Sustainable Development Goal (SDG) 2 seeks to end hunger and guarantee food and nutrition security worldwide by 2030. Smallholder irrigation development remains a key strategy to achieve SDG 2. This study assesses how smallholder irrigation contributes to household food security in Mberengwa district, Zimbabwe. Primary data were gathered from a randomly chosen sample of 444 farmers (344 irrigators and 100 non-irrigators) using a structured questionnaire. Microsoft Excel and Statistical Package for Social Sciences version 27 software packages were used to analyse the data. Descriptive statistics, chi-square test, t-test, and binary logistic regression were performed. The t-test results show significant differences in mean between irrigators and non-irrigators for household size, the dependency ratio, farming experience, farm income, food expenditure share, and livestock owned (p < 0.05). Irrigators had significantly higher area planted, yield, and quantity sold for maize during the summer than non-irrigators (p < 0.05). Food Consumption Score results show that 97% of irrigators and 45% of non-irrigators were food secure. Binary logistic regression results reveal a significant association between food security and household size, irrigation access, and farm income (p < 0.05). In conclusion, access to smallholder irrigation increases household food security. The government and its development partners should prioritise investments in smallholder irrigation development, expansion, and rehabilitation.
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(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Viability, Government Support and the Service Function of Farmer Professional Cooperatives—Evidence from 487 Cooperatives in 13 Cities in Heilongjiang, China
by
Yuxin Liu, Lihan Cao, Yijia Wang and Eryang Liu
Agriculture 2024, 14(4), 616; https://doi.org/10.3390/agriculture14040616 - 15 Apr 2024
Abstract
As the main body that unites farmers internally and connects with markets externally, professional farmer cooperatives are playing an increasingly important role around the world. In order to investigate the significant factors influencing this subject in agricultural socialization services, 487 cooperatives in Heilongjiang
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As the main body that unites farmers internally and connects with markets externally, professional farmer cooperatives are playing an increasingly important role around the world. In order to investigate the significant factors influencing this subject in agricultural socialization services, 487 cooperatives in Heilongjiang Province were selected for investigation. The field survey found that many of the better-developed cooperatives have a certain degree of inadequacy in the performance of their service functions. This paper proposes that viability and government support are important factors influencing the realization of the service function of farmer professional cooperatives. Based on the empirical analysis of sample survey data and econometric models, it was demonstrated that the experience of the chairman, the number of board members, the distribution of members, the scale of land operation, profitability, and the institutional arrangement of the cooperatives are an important embodiment of the viability of the cooperatives. These factors significantly influence the service function of cooperatives in different sections, including pre-production, mid-production, and post-production. Moreover, obtaining external support from the government is necessary for cooperatives at their primary stage of development, especially regarding relevant training, which can facilitate the realization of the service functions of cooperatives in all aspects.
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(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Combined Transcriptome and Proteome Analysis Reveals the Molecular Mechanism by Which ZmPDI Improves Salt Resistance in Rice (Oryza sativa)
by
Jingjing Wang, Kai Wang, Ling Li, Qixue Sun, Dandan Li, Dongli Hao, Jingbo Chen, Junqin Zong, Jianxiu Liu, Hailin Guo and Rongrong Chen
Agriculture 2024, 14(4), 615; https://doi.org/10.3390/agriculture14040615 - 15 Apr 2024
Abstract
As one of the most salt-tolerant grasses, characterizing salt-tolerance genes of Zoysia matrella [L.] Merr. not only broaden the theoretical information of salt tolerance, but also provide new salt-resistant genetic resources for crop breeding. The salt-inducible protein disulfide isomerase (ZmPDI) of
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As one of the most salt-tolerant grasses, characterizing salt-tolerance genes of Zoysia matrella [L.] Merr. not only broaden the theoretical information of salt tolerance, but also provide new salt-resistant genetic resources for crop breeding. The salt-inducible protein disulfide isomerase (ZmPDI) of Zoysia matrella [L.] Merr. was proved to enhance salt tolerance in homologous overexpression transgenic plants. In order to evaluate its potential application in crops, we conducted the salt tolerance evaluation in heterologous overexpression transgenic rice (OX-ZmPDI), Wild-type (WT) rice, and LOC_Os11g09280 (OsPDI, homologous gene of ZmPDI in rice) knock-out rice generated by CRISPR-Cas9 system (CR-OsPDI). Our findings revealed that OX-ZmPDI rice was higher and exhibited longer main root length, more proline (Pro) and malondialdehyde (MDA), and higher peroxidase (POD) activity than WT control after salt treatment, while CR-OsPDI resulted in contrary phenotypes. These results indicated that ZmPDI can significantly enhance the salt tolerance in rice, whereas loss-of-function of OsPDI reduces the salt tolerance. To further investigate these differences at the molecular level, we collected roots from OX-ZmPDI transgenic, CR-OsPDI transgenic, and wild-type (WT) plants at 0 and 24 h after salt treatment for RNA-seq and data-independent acquisition (DIA) proteome sequencing. Combined analysis of the transcriptome and proteome revealed that ZmPDI has the potential to enhance the salt tolerance of rice by modulating the expression of laccase-6, zingipain-2, WIP3, FKBP65, AKR4C10, GBSSII, Pho1, and TRXf1. Those results provided new information for the molecular regulation mechanism by which ZmPDI improves salt tolerance, and prove the potential of ZmPDI for application in crop breeding.
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(This article belongs to the Special Issue Gene Expression and Molecular Effects in Plants under Abiotic Stress)
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Investigating the Adoption of Blockchain Technology in Agri-Food Supply Chains: Analysis of an Extended UTAUT Model
by
Diana-Cezara Toader, Corina Michaela Rădulescu and Cezar Toader
Agriculture 2024, 14(4), 614; https://doi.org/10.3390/agriculture14040614 - 15 Apr 2024
Abstract
Against a backdrop of globalization, dynamic shifts in consumer demand, and climate change impact, the intricacies of agri-food supply chains have become increasingly convoluted, necessitating innovative measures to guarantee agri-food security and authenticity. Blockchain technology emerges as a promising solution, offering transparency, immutability,
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Against a backdrop of globalization, dynamic shifts in consumer demand, and climate change impact, the intricacies of agri-food supply chains have become increasingly convoluted, necessitating innovative measures to guarantee agri-food security and authenticity. Blockchain technology emerges as a promising solution, offering transparency, immutability, traceability, and efficiency in the overall supply chain. This study aims to investigate determinants impacting both the intention to use and the actual usage of blockchain-driven agri-food supply chain platforms. To achieve this, an expanded and adapted conceptual model rooted in the Unified Theory of Acceptance and Use of Technology (UTAUT) was formulated and empirically examined through Partial Least Squares Structural Equation Modeling using data from 175 respondents from agri-food companies across eight European countries. Agri-Food Supply Chain Partner Preparedness (FSCPP) emerged as the pivotal factor with the highest degree of influence on the intention to use blockchain-driven supply chain platforms. Additionally, the results from this study offer support for the significant influence of Performance Expectancy (PE), Effort Expectancy (EE), and Perceived Trust (PT) on usage intention, while also revealing the positive impact of Organizational Blockchain Readiness (OBR) on expected Usage Behavior (UB). This study provides significant insights into blockchain adoption within agri-food supply chains, contributing to the existing literature through an extended UTAUT framework.
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(This article belongs to the Special Issue Agricultural Markets and Agrifood Supply Chains)
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