Agriculture最新文献

{"title":"The Residue Chemistry Transformation Linked to the Fungi Keystone Taxa during Different Residue Tissues Incorporation into Mollisols in Northeast China","authors":"Qi-long Zhang, Xiujun Li, Guoshuang Chen, Nana Luo, Jing Sun, Ezemaduka Anastasia Ngozi, Xinrui Lu","doi":"10.3390/agriculture14060792","DOIUrl":"https://doi.org/10.3390/agriculture14060792","url":null,"abstract":"Managing carbon input from crop straw in cropland ecosystems could increase soil organic carbon (SOC) sequestration to achieve C neutrality and mitigate climate change. The complexity of the chemical structures of crop residue largely affects SOC sequestration. Fungi communities play an important role in the degradation of crop residues. However, the relationship between the fungal community composition and the chemical structures of crop residues remains unclear and requires further investigation. Therefore, a 120-day incubation experiment was conducted in Mollisols in Northeast China to investigate the decomposition processes and dynamics of maize straw stem (ST), leaf (LE) and sheath (SH) residues using 13C-NMR spectroscopy. Additionally, the microbiomes associated with these residues were analyzed through high-throughput sequencing to explore their relationship. Our results showed that the alkyl C contents in all treatments exhibited increases ranging from 15.1% to 49.1%, while the O-alkyl C contents decreased, ranging from 0.02% to 11.2%, with the incubation time. The A/OA ratios of ST, LE and SH treatments were increased by 23.7%, 43.4% and 49.3% with incubation time, respectively. During the early stages of straw decomposition, Ascomycota dominated, and in the later stage, Basidiomycota were predominant. The class of Sordariomycetes played a key role in the chemistry transformation of straw tissues during decomposition. The keystone taxa abundances, Fusarium_kyushuense, and Striatibotrys_eucylindrospora, showed strong negative correlations with di-O-alkyl C and carbonyl-C content and positive correlations with the β-glucosidase and peroxidase enzyme activity, respectively. In conclusion, our study demonstrated that the keystone taxa play a significant role in regulating the chemical structures of straw tissues, providing a better understanding of the influence of residue quality on SOC sequestration.","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"96 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141116359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of Resistance of Barley Varieties to Diseases in Polish Organic Field Trials","authors":"Tomasz Lenartowicz, H. Bujak, Marcin Przystalski, Inna Mashevska, K. Nowosad, Krzysztof Jonczyk, B. Feledyn-Szewczyk","doi":"10.3390/agriculture14050789","DOIUrl":"https://doi.org/10.3390/agriculture14050789","url":null,"abstract":"Leaf rust and net blotch are two important fungal diseases of barley. Leaf rust is the most important rust disease of barley, whereas net blotch can result in significant yield losses and cause the deterioration of crop quality. The best and the most environmentally friendly method to control diseases is to cultivate resistant varieties. The aim of the current study was to identify barley varieties with an improved resistance to leaf rust and net blotch in Polish organic post-registration trials conducted in the years 2020–2022. For this purpose, the cumulative link mixed model with several variance components was applied to model resistance to leaf rust and net blotch. It was found that the reference variety Radek was the most resistant to leaf rust, whereas variety Avatar outperformed the reference variety in terms of resistance to net blotch, although the difference between the two varieties was non-significant. In the present study, the use of the cumulative link mixed model framework made it possible to calculate cumulative probabilities or the probability of a given score for each variety and disease, which might be useful for plant breeders and crop experts. Both, the method of analysis and resistant varieties may be used in the breeding process to derive new resistant varieties suitable for the organic farming system.","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"40 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141118851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online Detection of Dry Matter in Potatoes Based on Visible Near-Infrared Transmission Spectroscopy Combined with 1D-CNN","authors":"Yalin Guo, Lina Zhang, Zhenlong Li, Yakai He, Chengxu Lv, Yongnan Chen, Huangzhen Lv, Zhilong Du","doi":"10.3390/agriculture14050787","DOIUrl":"https://doi.org/10.3390/agriculture14050787","url":null,"abstract":"More efficient resource utilization and increased crop utilization rate are needed to address the growing demand for food. The efficient quality testing of key agricultural products such as potatoes, especially the rapid testing of key nutritional indicators, has become an important strategy for ensuring their quality and safety. In this study, visible and near infrared (Vis/NIR) transmittance spectroscopy (600–900 nm) was used for the online analysis of multiple quality parameters in potatoes. The study concentrated on comparing three one-dimensional convolutional neural network (1D-CNN) models, specifically, the fine-tuned DeepSpectra, the fine-tuned 1D-AlexNet, and classic CNN, with UVE-PLS (uninformative variable elimination–partial least squares) models. These models utilized spectral data for the real-time detection of dry matter (DM) content in potatoes. To address the challenges posed by limited data from Vis/NIR, this study strategically implemented data augmentation techniques. This approach significantly enhanced the robustness and generalization capabilities of the models. The 1D-AlexNet and DeepSpectra models achieved 0.934 and 0.913 R2P and 0.0603 and 0.0695 g/100 g RMSEP for DM, respectively. Compared to UVE-PLS, the R2P value improved by 21.31% (0.770 to 0.934) for the 1D-AlexNet model and 18.64% (0.770 to 0.913) for the DeepSpectra model. The RMSEP value was reduced by 47.31% (0.114 to 0.0603) for 1D-AlexNet, and 39.30% (0.114 to 0.0695) for the DeepSpectra model. As a result, this study would be helpful for researching the online Vis/NIR transmission determination of potato DM using deep learning. These results highlighted the immense potential of employing specific spectral features in deep-learning models for a more precise and efficient online assessment of agricultural quality. This advancement provided some insight and reference for further contributing to the evolution of more targeted and efficient quality assessment methods in agricultural products.","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"78 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141121239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Impact of Agricultural Socialized Service on Grain Production: Evidence from Rural China","authors":"Ruisheng Li, Jiaoyan Chen, Ding-de Xu","doi":"10.3390/agriculture14050785","DOIUrl":"https://doi.org/10.3390/agriculture14050785","url":null,"abstract":"Although China’s grain production has reached nineteen consecutive harvests, the uncertainty of the current domestic and international environment has put more pressure on further increasing grain production in the future. For the past few years, agricultural socialization services have been crucial in boosting grain production and farmers’ revenue by addressing the issue of land cultivation and farming methods. In this regard, the question of whether and how agricultural socialized services may resolve the present grain production conundrum is extremely practical. Therefore, the study employs the China Rural Revitalization Survey data of 3709 households. Based on the 2SLS model, stepwise regression method, and moderated effects model, it creatively takes into account a variety of agricultural production segments, investigates the mechanism of services on grain production from the standpoint of improved production efficiency and plot concentration, and further examines the effects of aging populations and regional variations in grain production areas. The study found the following: (1) The average proportion of grain production area of farmers in the sample is 49%, and 42% of farmers have purchased agricultural socialization services. (2) Agricultural socialization services can significantly promote farmers’ grain cultivation behavior by facilitating connected transfers in and inhibiting connected transfers out to take advantage of plot concentration, and boosting the use of agricultural machines to enhance output efficiency. (3) The aging of the agricultural population will, to a certain extent, strengthen the promoting effect of agricultural socialization services on grain cultivation. Agricultural socialization services affect grain cultivation more in main grain-producing areas. Therefore, emphasizing the role of agricultural socialization services in accelerating the shift to moderate-scale operations, decreasing the non-grain component of the planting structure, and promoting the implementation of policies tailored to actual production needs are important steps to safeguard the production capacity of grain in different regions.","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"28 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Active Disturbance Rejection Control for a Stubble Breaking and Obstacle Avoiding Control System","authors":"Huibin Zhu, Tao Huang, Lizhen Bai, Wenkai Zhang","doi":"10.3390/agriculture14050786","DOIUrl":"https://doi.org/10.3390/agriculture14050786","url":null,"abstract":"In order to improve the obstacle avoidance control performance and anti-interference ability of a stubble breaking device of a no-tillage planter, a back-propagation neural network (BPNN)-optimized fuzzy active disturbance rejection control (ADRC) controller was designed to optimize the control performance of a servo motor. Firstly, a negative feedback mathematical model was established for the obstacle avoidance control system. Then, the nonlinear state error feedback (NLSEF) parameters in the fuzzy ADRC were intelligently optimized by the BPNN algorithm. In this way, a fuzzy ADRC controller based on BPNN optimization was formed to optimize the control process of a servo motor. Matlab/Simulink (R2022b) was used to complete the simulation model design and parameter adjustment. Consequently, the response time was 0.089 s using the BPNN fuzzy ADRC controller, which was shorter than the 0.303 s of the ADRC controller and the 0.100 s of the fuzzy ADRC controller. The overshoot was 0.1% using a BPNN fuzzy ADRC controller, which was less than the 2% of the ADRC controller and the 1% of the fuzzy ADRC controller. After noise signal interference was introduced into the control system, the regression steady state time of the BPNN fuzzy ADRC controller was 0.22 s, which was shorter than the 0.56 s of the ADRC controller and the 0.45 s of the fuzzy ADRC controller. A hardware-in-the-loop simulation experimental platform of the obstacle avoidance control system was constructed. The experiment results show that the servo motor control system has a fast dynamic response, small steady-state error and strong anti-interference ability for obstacle avoidance at the target height. Then, the control system error was within the allowable range. The servo motor control effect of the BPNN fuzzy ADRC was better than the ADRC and fuzzy ADRC. This optimized servo motor control method can provide a reference for improving the obstacle avoidance control effect problem of no-tillage seeders in stubble breaking operations on rocky desertification areas.","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"17 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141119467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Standardized Treatment Model for Head Loss of Farmland Filters Based on Interaction Factors","authors":"Zhenji Liu, Chenyu Lei, Jie Li, Yangjuan Long, Chen Lu","doi":"10.3390/agriculture14050788","DOIUrl":"https://doi.org/10.3390/agriculture14050788","url":null,"abstract":"A head loss model for pressureless mesh filters used in farmland irrigation was developed by integrating the four basic test factors: irrigation flow, filter cartridge speed, self-cleaning flow, and initial sand content. The model’s coefficient of determination was found to be 98.61%. Among the basic factors, the total irrigation flow accounted for only 17.20% of the relatively small self-cleaning flow. The contribution of initial sand content was found to be the smallest, with a coefficient of only 0.0166. Furthermore, the contribution rate of the flow term was significantly higher than that of the initial sand content, with a value of 159.73%. In terms of quadratic interaction, the difference between the interaction term of flushing flow and filter cartridge speed, and the interaction term of filter cartridge speed and self-cleaning flow was 38.42%. On the other hand, the difference within this level for the interaction term between initial sand content and filter cartridge speed, as well as the interaction term between irrigation flow and self-cleaning flow, was 2.82%. Finally, through joint optimization of the response surface and model, the optimal values for the irrigation flow rate, filter cartridge speed, self-cleaning flow rate, and initial sand content were determined to be 121.687 m3·h−1, 1.331 r·min−1, 19.980 m3·h−1, and 0.261 g·L−1; the measured minimum head loss was found to be 21.671 kPa. These research findings can serve as a reference for enhancing the design of farmland filters and optimizing irrigation systems.","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"36 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141119195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simultaneous Localization and Mapping System for Agricultural Yield Estimation Based on Improved VINS-RGBD: A Case Study of a Strawberry Field","authors":"Quanbo Yuan, Penggang Wang, Wei Luo, Yongxu Zhou, Hongce Chen, Zhaopeng Meng","doi":"10.3390/agriculture14050784","DOIUrl":"https://doi.org/10.3390/agriculture14050784","url":null,"abstract":"Crop yield estimation plays a crucial role in agricultural production planning and risk management. Utilizing simultaneous localization and mapping (SLAM) technology for the three-dimensional reconstruction of crops allows for an intuitive understanding of their growth status and facilitates yield estimation. Therefore, this paper proposes a VINS-RGBD system incorporating a semantic segmentation module to enrich the information representation of a 3D reconstruction map. Additionally, image matching using L_SuperPoint feature points is employed to achieve higher localization accuracy and obtain better map quality. Moreover, Voxblox is proposed for storing and representing the maps, which facilitates the storage of large-scale maps. Furthermore, yield estimation is conducted using conditional filtering and RANSAC spherical fitting. The results show that the proposed system achieves an average relative error of 10.87% in yield estimation. The semantic segmentation accuracy of the system reaches 73.2% mIoU, and it can save an average of 96.91% memory for point cloud map storage. Localization accuracy tests on public datasets demonstrate that, compared to Shi–Tomasi corner points, using L_SuperPoint feature points reduces the average ATE by 1.933 and the average RPE by 0.042. Through field experiments and evaluations in a strawberry field, the proposed system demonstrates reliability in yield estimation, providing guidance and support for agricultural production planning and risk management.","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141123228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on Rapeseed Seedling Counting Based on an Improved Density Estimation Method","authors":"Qi Wang, Chunpeng Li, Lili Huang, Liqing Chen, Quan Zheng, Lichao Liu","doi":"10.3390/agriculture14050783","DOIUrl":"https://doi.org/10.3390/agriculture14050783","url":null,"abstract":"The identification of seedling numbers is directly related to the acquisition of seedling information, such as survival rate and emergence rate. It indirectly affects detection efficiency and yield evaluation. Manual counting methods are time-consuming and laborious, and the accuracy is not high in complex backgrounds or high-density environments. It is challenging to achieve improved results using traditional target detection methods and improved methods. Therefore, this paper adopted the density estimation method and improved the population density counting network to obtain the rapeseed seedling counting network named BCNet. BCNet uses spatial attention and channel attention modules and enhances feature information and concatenation to improve the expressiveness of the entire feature map. In addition, BCNet uses a 1 × 1 convolutional layer for additional feature extraction and introduces the torch.abs function at the network output port. In this study, distribution experiments and seedling prediction were conducted. The results indicate that BCNet exhibits the smallest counting error compared to the CSRNet and the Bayesian algorithm. The MAE and MSE reach 3.40 and 4.99, respectively, with the highest counting accuracy. The distribution experiment and seedling prediction showed that, compared with the other density maps, the density response points corresponding to the characteristics of the seedling region were more prominent. The predicted number of the BCNet algorithm was closer to the actual number, verifying the feasibility of the improved method. This could provide a reference for the identification and counting of rapeseed seedlings.","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"111 31","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141124932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unraveling the Major Determinants behind Price Changes in Four Selected Representative Agricultural Products","authors":"Nisa Şansel Tandoğan Aktepe, Ihsan Erdem Kayral","doi":"10.3390/agriculture14050782","DOIUrl":"https://doi.org/10.3390/agriculture14050782","url":null,"abstract":"This study aims to analyze the drivers behind price changes in agricultural products in Türkiye from 2002 to 2021, considering the impacts of three crises of different causes which are the global food crisis, the Russia–Türkiye aircraft crisis, and the COVID-19 pandemic. The potential factors are categorized into four subgroups: governmental effects, agricultural inputs, macroeconomic indicators, and climatic conditions. The selected agricultural goods for price change measurement include wheat and maize representing subsistence goods, and olive oil and cotton as marketing goods. The autoregressive distributed lag (ARDL) model is applied to observe both the short- and long-term impacts of the variables on price developments. The results suggest that government effectiveness, regulatory quality, nitrogen use, water price, money supply, exchange rate, and GDP under the related categories are the most effective factors in price changes. Among the variables under the category of climatic conditions, significant values are obtained only in the analysis of the temperature impact on olive oil. The analysis also reveals the variable impact of crises on the prices of the chosen products, depending on the goods involved. The maize and wheat analyses yield particularly noteworthy results. In the long run, nitrogen use demonstrates a substantial positive impact, registering at 29% for wheat and 19.47% for maize, respectively. Conversely, GDP exhibits a significant negative impact, with 26.15% and 20.08%. Short-term observations reveal that a unit increase in the governmental effect leads to a reduction in inflation for these products by 17.01% and 21.42%. However, changes in regulatory quality result in an increase in inflation by 25.45% and 20.77% for these products, respectively.","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"113 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141124432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dust and Bacterial Air Contamination in a Broiler House in Summer and Winter","authors":"Ivica Ravić, M. Ostović, A. Ekert Kabalin, Matija Kovačić, Kristina Matković, Ž. Gottstein, D. Horvatek Tomić","doi":"10.3390/agriculture14050778","DOIUrl":"https://doi.org/10.3390/agriculture14050778","url":null,"abstract":"This study aimed to investigate dust and bacterial air contamination in a broiler house during different seasons. The study was carried out in commercial housing conditions during five weeks of the rearing cycle in summer and winter. The total dust concentration ranged from 1.90 to 4.50 mg/m3 in summer and from 2.80 to 5.10 mg/m3 in winter. The total bacterial count ranged from 2.85 × 104 to 1.03 × 105 CFU/m3 in summer and from 2.12 × 104 to 2.28 × 105 CFU/m3 in winter. The study results showed the dust concentration to be increased in winter as compared to summer, yielding a significant correlation (r = 0.602, p < 0.05) with a significantly higher airborne bacterial count in winter (p < 0.001). Furthermore, dust concentration showed significant correlations (p < 0.05) with air temperature (r = −0.418), relative humidity (r = 0.673), and broiler activity (r = 0.709), while bacterial count yielded significant correlations (p < 0.05) with air temperature (r = −0.756), relative humidity (r = 0.831), and airflow rate (r = 0.511). The results obtained in the study can prove useful in the field. Seasonal variability in dust and bacterial air contamination should be considered in the development of guidelines or standards of air quality in broiler housing and evaluation of the effectiveness of remedial strategies.","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"105 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141124848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}