Relevant bibliographies by topics / Agricultural machinery

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Agricultural machinery'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Agricultural machinery"

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Indraningsih, K. S., S. H. Susilowati, D. K. S. Swastika, and Syahyuti. "The optimization of agricultural machinery utilization in enhancing the implementation of modern agricultural programs." IOP Conference Series: Earth and Environmental Science 892, no. 1 (November 1, 2021): 012041. http://dx.doi.org/10.1088/1755-1315/892/1/012041.

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Abstract The provision of agricultural machinery in the Modern Agriculture Program is to overcome labor shortages. However, the utilization of agricultural machinery aid is underutilized. This study aims to analyze the optimization of the use of agricultural machinery in enhancing the implementation of modern agricultural programs. West Sumatera, Central Java, Banten, Bali, and South Sulawesi were purposively chosen as the study areas. The study was conducted in 2017. Respondents of this research were policymakers from central to local institutions, key informants, extension agents, agricultural machinery service providers (UPJA), and farmers. The method used was descriptive explanatory and financial analysis. The results showed that the utilization of agricultural machinery in the Modern Agricultural Program was still not optimal. It was still underutilized. There were several obstacles, both from economic and social aspects. The utilization of 4-wheel tractors and rice transplanters has not been economically managed. As a result, they cannot generate funds for equipment maintenance and the development and UPJA’s sustainability. Not all types of agricultural machinery are socially suitable for farmers’ needs. There is a competition between agricultural machinery uses and workers. Moreover, not all local governments fully supports agricultural machinery uses. To optimize and succeed on the modern agriculture program implementation, agricultural extension workers with certification as UPJA Facilitators are required.
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Vitiuk, A. V., and O. A. Smetaniuk. "Economic Interaction of Agricultural Development and Agricultural Machine-Engineering." PROBLEMS OF ECONOMY 4, no. 46 (2020): 134–45. http://dx.doi.org/10.32983/2222-0712-2020-4-134-145.

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The paper deals with the study of the relationship between agricultural development of and agricultural engineering. Production output of certain types of machines and equipment for agriculture are examined. The dynamics of production performance in plant growing and stockbreeding is analyzed. The dynamics of such agricultural development indices as net income, subsidies, taxes, net value added, fixed capital application, etc. are considered. The tripling of output and a steady growth of net profit of agricultural producers are established. It is proven that farm equipment upgrading is less active than net income growth, and thus it was determined that enterprises have the necessary financial resources to generate demand for agricultural machinery. The peculiarities of agricultural machinery production in Ukraine at the present stage are revealed, in particular the causes and consequences of such a situation in production machinery for agricultural enterprises. Two opposite trends in agricultural machinery production are identified, namely, the decline in the production of complex, expensive machinery and the increase in the production of less costly appliances. The condition of the durable equipment in agriculture is analyzed by identifying their value and the degree of wear. Consequences of the increase in the quantity and quality of used machinery in agriculture are revealed. The requirements of agricultural enterprises for the quality of agricultural machinery are identified, and their classification by the requirements for quality, value and service is worked out. The quality requirements include technical and functional characteristics; value requirements consist of equipment prices, discounts, and value-added services. Service requirements comprise quality assurance of information, organizational and maintenance services. In accordance with the established requirements, technical, organizational, economic and social ways to meet these requirements were developed.
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Li, Hongchang, Fang Gao, and GuoCai Zuo. "Research on the Agricultural Machinery Path Tracking Method Based on Deep Reinforcement Learning." Scientific Programming 2022 (March 22, 2022): 1–14. http://dx.doi.org/10.1155/2022/6385972.

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With the rapid development of information technology, industry and service industries have achieved rapid development in recent years. Then, looking at the development of agriculture, the popularity of informatization lags far behind industry and service industries, directly hindering the digital development of agriculture. Starting from the current agricultural machinery driving operation scene, this paper carried out a simplified research on the traditional agricultural machinery driving operation method through the agricultural machinery kinematics model, and based on the related theory of deep reinforcement learning to study the agricultural machinery path tracking in the agricultural operation scene, it carried out the controller design, built the agricultural machinery autonomous path tracking framework operating mechanism under deep reinforcement learning, and further researched through experimental design and found that the agricultural machinery autonomous path tracking control can achieve better automatic control after empirical learning. I-DQN algorithm enables agricultural robots to adapt to the environment faster when performing path tracking, which improves the performance of path tracking. It has important guiding significance for further promoting the automatic navigation and control of agricultural machinery to realize the efficient operation of agricultural mechanization.
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Sun, Qin, Zuo Li Li, Hui Yu, and Jin Sheng Zhang. "The Analysis on Environmental Protection, Energy Saving and Green Design of Agricultural Machinery." Applied Mechanics and Materials 668-669 (October 2014): 1538–41. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1538.

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The energy saving and environmental protection design of agricultural machinery is a new design concept. Based on the analysis of meaning and significance of agricultural machinery energy saving and environmental protection design, Elaborated the trends of energy saving and environmental protection of agriculture machinery. The paper also pointed out that the green design is the inevitable trend of the development of agricultural machinery.
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Cong, Hong Bin, Ru Xin Li, and Xin Yue Han. "Application of Kinematics Simulation Technology in Agricultural Machinery Design." Advanced Materials Research 97-101 (March 2010): 3447–50. http://dx.doi.org/10.4028/www.scientific.net/amr.97-101.3447.

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In this paper, the research status and the significance of the simulation technology were discussed, and the necessity introducing the simulation technology into the agricultural machinery design was analyzed. Through the material kinematics simulation in processing, both the component kinematics simulation and the system dynamic simulation of the agricultural machinery, a specific application of the simulation technology in the agriculture machinery design was displayed. Application of the simulation technology in the agricultural machinery design has a vital significance for enhancing their design and development ability, improving their product performance, as well as promoting new agricultural machinery and the agricultural mechanization.
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Chen, T. "Current Situation and Development Trend of China’s Agricultural Machinery Distribution." Agricultural Machinery and Technologies 15, no. 4 (December 17, 2021): 19–23. http://dx.doi.org/10.22314/2073-7599-2021-15-4-19-23.

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In the year of 2021, the Chinese government allocated 19 billion RMB for agricultural machinery subsidy. The data that accurately describes the scale of China’s agricultural machinery market could be calculated by analyzing the national agricultural machinery purchase investment, the total value of the parts market and the replacement market could be inferred. Since the implementation of agricultural machinery purchase subsidies, China’s comprehensive mechanization rate of crop cultivation, planting and harvesting has increased rapidly. In 2019, the national comprehensive mechanization rate of crop cultivation, planting and harvesting exceeded 70 percent, and the production of the three major food crops of wheat, rice and corn was basically mechanized. This paper analyzes the scale of the complete machinery market, the market scale of key products in the Chinese agricultural machinery market, the basic situation of Chinese agricultural machinery distribution companies, the basic situation of the Chinese agricultural machinery purchasing groups, and the main gathering platforms of the Chinese agricultural machinery distribution market, explaining the current status of China’s agricultural machinery distribution, putting forward suggestions for the development of China’s agricultural machinery distribution market. There are major contradictions in the agricultural machinery market in China. The most important one is between supply and demand. In terms of crops, the level of comprehensive mechanization of wheat, rice and maize is relatively high, while the level of comprehensive mechanization of cash crops such as cotton oil, sugar and vegetable tea is relatively low; from a regional perspective, the level of mechanization in the northern plains is relatively high, and the level of mechanization in the hilly and mountainous areas of the south is relatively low; from an industrial point of view, the level of mechanization of planting industry is relatively high, while the level of mechanization of animal husbandry, fi shery and processing is low. Meanwhile, the contradiction between manufacturers and distributors, as well as the end-users is further intensifying. This article attempts to explain the effi ciency and benefi t of agriculture and the return on investment of agricultural machinery purchase. Only when agriculture is competitive, has good effi ciency and benefi ts, and the purchase of agricultural machinery has a good rate of return on investment, can the industry develop healthily.
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Fu, Li-Sha, Tao Qin, Gan-Qiong Li, and San-Gui Wang. "Efficiency of Agricultural Insurance in Facilitating Modern Agriculture Development: From the Perspective of Production Factor Allocation." Sustainability 16, no. 14 (July 20, 2024): 6223. http://dx.doi.org/10.3390/su16146223.

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Agricultural insurance is instrumental in consolidating the gains of poverty alleviation and advancing rural revitalization. It significantly aids in the efficient allocation of agricultural production factors, which in turn enhances agricultural output and bolsters the evolution of modern agriculture. Therefore, utilizing data from 583 household surveys and employing endogenous transformation and intermediary effect models, this paper analyzes the production factor allocation effect and specific mechanism of agricultural insurance. It focuses on small-scale farmers and new agricultural operators, exploring how insurance contributes to the advancement of modern agricultural practices. The results show the following: (1) Agriculture insurance can significantly affect the agricultural scale input behavior of farmers such as land input scale and input scale, agricultural machinery application behavior such as the degree of mechanization and water conservancy application, agricultural technology adoption behavior, and planting structure selection behavior, thereby helping to modernize agriculture. (2) There is heterogeneity in the impact of agriculture insurance on the allocation of production factors for small farmers and new agricultural operators. For small farmers, agriculture insurance has a significant promoting effect on their agricultural machinery application behavior, agricultural technology adoption behavior, and planting structure selection behavior. For new agricultural operators, agriculture insurance significantly promotes their agricultural scale input behavior, agricultural machinery application behavior, and agricultural technology adoption behavior. (3) In terms of the mechanism of action, agriculture insurance mainly promotes agricultural scale input behavior through land transfer, facilitates agricultural machinery application behavior by purchasing agricultural machinery equipment and services, encourages agricultural technology adoption behavior by strengthening agricultural technology training, and enhances professional production levels by increasing the scale of insured planting, thereby contributing to the development of modern agriculture. Based on this, several policy suggestions have been proposed. These include enhancing the directionality of agriculture insurance policies, improving the collaborative interaction mechanism between agriculture insurance and agricultural credit financing, and adopting certain reward and punishment measures to curb moral hazard.
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Yang, Siyu, and Wei Li. "The Impact of Socialized Agricultural Machinery Services on the Labor Transfer of Maize Growers." Agriculture 13, no. 6 (June 14, 2023): 1249. http://dx.doi.org/10.3390/agriculture13061249.

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Socialized agricultural machinery services, effectively cracking the “who to plant, how to plant” dilemma, are an important grasp of the development of modern agriculture. Based on the specialization division of labor theory, using the 2019 national survey data of maize growers in 13 provinces, the instrumental variable method and systematic generalized moment estimation (GMM) were used to overcome the endogeneity problem of mutual causality between socialized agricultural machinery services and labor transfer, analyze the impact of socialized agricultural machinery services on the labor transfer of maize growers and its link to heterogeneity, and explore the impact effect in different terrain conditions, part-time. We also explored the cohort differences in the effect in different terrain conditions and degree of part-time work. The endogenous switching regression model (ESR) was also applied to construct a counterfactual framework to further analyze the impact effect of socialized agricultural machinery services on labor transfer. The results showed that socialized agricultural machinery services could effectively promote labor transfer among maize farmers. Compared with maize farmers in other terrain conditions and part-time degree, the impact effect of agricultural machinery socialization services on labor transfer of flatland and pure farming households was more significant. Socialized agricultural machinery services play an important role in driving traditional farming households to labor transfer and realizing their organic connection with modern agriculture.
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Liu, Hui, Yujie Qiao, Guofa Zhao, Jingping Cheng, and Zhijun Meng. "Agricultural Machinery Abnormal Trajectory Recognition." International Journal of Machine Learning and Computing 11, no. 4 (August 2021): 291–97. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1050.

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The service system of supervision of agricultural machinery subsoiling operation enables acquisition of a large amount of agricultural machinery movement track data. These trajectories include not only farmland operation track data, but also road driving track data. Their spatial distribution characteristics and attribute data are different. In this paper, we make a study of the abnormal trajectory data in data set, and propose an abnormal trajectory recognition algorithm based on DBSCAN clustering. According to the attribute data of agricultural machinery trajectory, the trajectory is divided to form different types of motion trajectory, then to judge the spatial distribution of different types of agricultural machinery tracks. If the attribute data of the tracks are inconsistent with their spatial distribution, it will be judged as abnormal tracks. The experimental results show that both the accuracy of the algorithm and the recall rate is 98.61%, which can identify the abnormal tracks of agricultural machinery.
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Syahyuti, K. S. Indraningsih, D. K. S. Swastika, S. H. Susilowati, and S. Suharyono. "The role of stakeholders to support implementation of modern agricultural programs." IOP Conference Series: Earth and Environmental Science 892, no. 1 (November 1, 2021): 012024. http://dx.doi.org/10.1088/1755-1315/892/1/012024.

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Abstract The Ministry of Agriculture has implemented the Modern Agriculture Program since 2016 by distributing agricultural machinery to farmers, in order to improve Indonesian agriculture which is characterized by small scale, low productivity, low labor productivity, and national production that is not self-sufficient. The objective of this research is to analyze the role of stakeholders to support the implementation of the programs. South Sulawesi was chosen purposively as the research location. The interviewees are service institutions from central to regional, key informants, farmer groups, extension agents, agricultural machinery service providers, and individual farmers. The analysis used was stakeholder analysis. The results showed that the main actors in the Modern Agricultural Program were machinery operators and UPJA administrators. The beneficiaries of Modern Agricultural services are mainly the share and tenant farmers. The machinery operators have the strongest position. In terms of the external network, the machinery operators and “area brokers” are the most important parties and determine the operation of the machinery. In order for the Modern Agriculture Program to provide positive benefits to all parties, it is necessary to strive for a system of work with fair profit sharing and to provide wider opportunities for businessmen to take part in the program.
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Dissertations / Theses on the topic "Agricultural machinery"

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Glascock, Leslie A. "Increasing the conspicuity of agricultural machinery extremities." Connect to resource, 1995. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1202415272.

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Ridgway, Ian Gilbert. "Prototype combine for grain harvesting based on internal pneumatic transfer /." Title page, table of contents and abstract only, 2002. http://web4.library.adelaide.edu.au/theses/09AFM/09afmr5447.pdf.

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Foster, Christopher A. "Intelligent control study of drive-by-wire agricultural vehicles." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file 3.19 Mb., 221 p, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:3221073.

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Schäffer, Beat. "Compaction of restored soil by heavy agricultural machinery /." Zürich : ETH, 2007. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=17273.

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Lee, Tien-Li. "A heuristic algorithm for agricultural machinery production scheduling." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0003/MQ45334.pdf.

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Elbanna, E. B. E. "Agricultural machinery selection : Soil strength and operational timeliness." Thesis, University of Edinburgh, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.371883.

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Saadoun, Tahar. "Agricultural machinery selection and scheduling of farm operations." Thesis, University of Edinburgh, 1989. http://hdl.handle.net/1842/27322.

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The procedure of calculating annual machinery ownership costs from the discounted cash flows of the mortgaged capital cost, the repair and insurance charges and the resale income is extended to include the effect of loan rate and loan period on interest charges, the effect of capital allowances taking account of the actual balancing charges at the end of the period of ownership, and the effect of tax relief on the interest charges, repair costs and insurance premiums. The concept of marginal holding cost is applied to determine the optimum ownership period. The selection of tractor- plough combinations is based on the prediction of soil characteristics such as moisture content, strength, and workability, all of which influencing the assessment of plough draught and tractor power. A number of filters are used to select the appropriate and realistic tractor /implement combinations with different sizes of fully mounted plough depending on the draught, and the speed of each selected gear of the tractor. For each acceptable combina- tion of tractor and fully mounted plough determined, the costing routine is used to calculate the annual costs. The branch and bound algorithm is suitable for mixed integer solutions to the farm machinery selection problem. Machinery sets are selected simultaneously with the chosen cropping pattern on a given land area. Machinery sets are matched correctly to the tractor sizes. Four sizes of tractor are available (45 kW, 61 kW, 74 kW and 94 kW,. Field operations take place in discrete time periods during which available work days are predicted from soil type and weather records for the specific site. Cereal and root crops are distinguished by optimum sowing and harvesting date. Discrete time periods are defined in relation to these optimal dates and give rise to overlapping operations for different crops. The calculation of probability levels for available work days when operations are subject to different criteria is discussed. A single arbitrary value of 75% probability for available work days is adopted in the linear programming model for the main part of the study. Two stage processes are used to simulate available work days in each time period. The patterns generated converge on the relative frequency pattern laid down by the generating process. The range of experience is wider than that contained in the short series of 24 years historical data. The simulation model generates results suitable for stochastic dominance ranking. In a simulation experiment on a 250 ha arable farm cropping cereals and potatoes, alternative solutions are obtained by integer linear programming, the solutions being ranked according to gross revenue. Annual costs of operating farm machinery are derived from a separate costing algorithm based on the annual hours of use which are determined by the size of the task and not by the sequence of work days. After deducting the annual costs of machinery operation, the cumulative net revenue curves cross and second order stochastic dominance ranking is used to identify the optimum (maximum profit) solution. The current study demonstrates the viability of the analytical procedures but further work is now required to reduce the computing time involved for the complete machinery selection procedure. Meanwhile, a commercial software package is prepared on the calculation of annual machinery ownership costs.
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Jepsen, Shelly Dee. "Assessment of the U.S. Department of Labor's Tractor and Machinery Certification Program." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1149104900.

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Berli, Markus. "Compaction of agricultural subsoils by tracked heavy construction machinery /." Zürich, 2001. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=14132.

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Xiong, Huanbao. "Machinery selection and scheduling model for Virginia." Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-07102009-040441/.

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Books on the topic "Agricultural machinery"

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Canada, Industry Science and Technology Canada. Agricultural machinery. Ottawa: Industry, Science and Technology Canada, 1988.

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Samantha, Miller, and Key Note Publications, eds. Agricultural machinery. Hampton: Key Note Publications, 1994.

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Publications, Key Note, ed. Agricultural machinery. 9th ed. Hampton: Key Note Publications, 1991.

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Publications, Key Note, ed. Agricultural machinery. London: Key Note Publications., 1989.

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Canada. Industry, Science and Technology Canada. Agricultural machinery. Ottawa: Industry, Science and Technology Canada, 1991.

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Marketing Strategies for Industry (UK) Ltd., ed. Agricultural machinery: UK. Mitcham: MSI, 1985.

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Louis, Barfe, and KeyNote, eds. Agricultural machinery - Keynote. Hampton: Key Note Ltd, 1997.

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Marketing Strategies for Industry (UK) Ltd. Agricultural machinery, UK. Mitcham: MSI, 1985.

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Sharon, Rudnitski, Birenbaum Rhonda, Heslop L, UMA Engineering Ltd, and Canada. Agriculture Canada. Research Branch., eds. Handling agricultural materials. [Ottawa]: Research Branch, Agriculture Canada, 1990.

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Sharon, Rudnitski, Birenbaum Rhonda, Canada. Agriculture Canada. Research Branch., and UMA Engineering Ltd, eds. Handling agricultural materials: Air and pneumatic conveyors. Ottawa, Ont: Research Branch, Agriculture Canada, 1989.

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Book chapters on the topic "Agricultural machinery"

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Roth, Lawrence O., and Harry L. Field. "Machinery Calibration." In Introduction to Agricultural Engineering, 91–110. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3594-2_9.

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Field, Harry L., and John M. Long. "Machinery Calibration." In Introduction to Agricultural Engineering Technology, 107–42. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-69679-9_8.

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Roth, Lawrence O., and Harry L. Field. "Economics of Agricultural Machinery." In An Introduction to Agricultural Engineering: A Problem-Solving Approach, 111–34. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4684-1425-7_10.

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Roth, Lawrence O., and Harry L. Field. "Economics of Agricultural Machinery." In Introduction to Agricultural Engineering, 111–34. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3594-2_10.

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Field, Harry L., and John M. Long. "Economics of Agricultural Machinery." In Introduction to Agricultural Engineering Technology, 155–79. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-69679-9_10.

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Chen, Du, and Xu Mao. "Mechatronics in Agricultural Machinery." In Encyclopedia of Smart Agriculture Technologies, 1–12. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89123-7_178-2.

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Chen, Du, and Xu Mao. "Mechatronics in Agricultural Machinery." In Encyclopedia of Smart Agriculture Technologies, 1–12. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89123-7_178-1.

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de Melo, Rodnei Regis. "Electrical-Powered Agricultural Machinery." In Encyclopedia of Smart Agriculture Technologies, 1–13. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89123-7_230-1.

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de Melo, Rodnei Regis. "Electrical-Powered Agricultural Machinery." In Encyclopedia of Digital Agricultural Technologies, 447–60. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-24861-0_230.

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Chen, Du, and Xu Mao. "Mechatronics in Agricultural Machinery." In Encyclopedia of Digital Agricultural Technologies, 817–28. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-24861-0_178.

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Conference papers on the topic "Agricultural machinery"

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Pickett, T. D., and D. H. Ziegler. "9000 SERIES COMBINE ELECTRONICS." In Agricultural Machinery Conference. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1989. http://dx.doi.org/10.4271/891413.

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Cornish, G. K. "JOHN DEERE 9000 SERIES COMBINES." In Agricultural Machinery Conference. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1989. http://dx.doi.org/10.4271/891389.

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Grapentine, Terry. "IDENTIFYING CUSTOMER WANTS THROUGH QUANTITATIVE RESEARCH." In Agricultural Machinery Conference. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1989. http://dx.doi.org/10.4271/891400.

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Barquist, L. R., and R. L. Malcolm. "SERVICEABILITY A KEY FACTOR IN DESIGN." In Agricultural Machinery Conference. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1989. http://dx.doi.org/10.4271/891402.

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Mason, R. N., and M. K. Wyffels. "SIMULTANEOUS ENGINEERING 9000 SERIES COMBINES BEST IN CLASS." In Agricultural Machinery Conference. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1989. http://dx.doi.org/10.4271/891407.

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Battles, C. Sean. "WORLD-CLASS MANUFACTURING A FACTORY PROVEN, STRUCTURED APPROACH." In Agricultural Machinery Conference. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1989. http://dx.doi.org/10.4271/891408.

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Lang, Stephen. "An Intelligent Monitoring System for Agricultural Tractors." In Agricultural Machinery Conference. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1989. http://dx.doi.org/10.4271/891412.

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Marhefka, Russell E., and Alan L. Dorris. "An Historical Review of Signal Words: Their Purpose, Meaning and Effect on Risk Perception." In Agricultural Machinery Conference. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1990. http://dx.doi.org/10.4271/901095.

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Vogelaar, Bernard F. "Thirty Years of Combine Usage and Development in North America." In Agricultural Machinery Conference. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1985. http://dx.doi.org/10.4271/851106.

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Pickett, Leroy K. "Rotary Threshing and Separation - From a Century of Promise to a Decade of Acceptance." In Agricultural Machinery Conference. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1985. http://dx.doi.org/10.4271/851107.

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Reports on the topic "Agricultural machinery"

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Takeshima, Hiroyuki, and Yanyan Liu. Determinants of agricultural machinery adoption intensities in Ghana. Washington, DC: International Food Policy Research Institute, 2019. http://dx.doi.org/10.2499/p15738coll2.133387.

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Bhattarai, Madhusudan, Gajendra Singh, Hiroyuki Takeshima, and Ravindra S. Shekhawat. Farm machinery use and the agricultural machinery industries in India: Status, evolution, implications, and lessons learned. Washington, DC: International Food Policy Research Institute, 2020. http://dx.doi.org/10.2499/9780896293809_03.

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Moorehead, Stewart. Unsettled Issues Regarding the Commercialization of Autonomous Agricultural Vehicles. SAE International, February 2022. http://dx.doi.org/10.4271/epr2022003.

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Autonomous agricultural vehicles are entering the marketplace, performing jobs that current equipment cannot do or are too dangerous for humans to perform. They offer the prospect of greater farm productivity, and they will help to feed the world’s growing population. This report looks at several topics that impact the commercial success of autonomous agricultural vehicles. The economic benefit that an autonomous system brings to a farm will be discussed alongside machine utilization rates, job quality, and labor savings. The need for standards and regulations to help promote the development of safe systems—as well as to define the language around autonomous agriculture—is also considered. Additionally, this report will highlight the importance of reliability in agricultural machinery and how successful commercialization of autonomy will depend on the ability to do the job correctly and consistently. A critical part of commercial success is how the autonomous agricultural vehicle fits into existing farm processes to provide a complete solution for the farmer. It is hoped that this report will help developers interested in commercializing autonomous agricultural vehicles consider more than just the technical problems to solve and make choices beneficial to market adoption.
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Sergeevich, Shpinev Iurii. Legal regulation of investments in agriculture. DOI CODE, 2020. http://dx.doi.org/10.18411/1311-1972-2020-00020.

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The article deals with current problems of legal regulation of investment relations in the agricultural sector of the national economy, including leasing of agricultural machinery, agricultural territories with a special regime of business activity, as well as issues of regulation of digitalization of the agroindustrial complex.
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Research Institute (IFPRI), International Food Policy. Farm machinery use and agricultural industries in India: Status, evolution, implications and lessons learned. Washington, DC: International Food Policy Research Institute, 2018. http://dx.doi.org/10.2499/1032568654.

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Chami Batista, Jorge. The Transport Costs of Brazil's Exports: A Case Study of Agricultural Machinery and Soybeans. Inter-American Development Bank, December 2008. http://dx.doi.org/10.18235/0011062.

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This report is a case study aimed at shedding some light on the magnitude and determinants of Brazil's exporting costs. Two exporting firms were selected to provide information on these costs: one exports primary goods (soybeans), while the other exports manufactured goods (agricultural equipment). It is shown that non-policy trade costs tend to be much higher than policy trade costs, especially in the case of Brazil's exports of soybeans. International freight costs are also significant in the case of agricultural equipment. Therefore, much larger cost reductions might be achieved through cuts in non-policy trade costs than in policy trade costs.
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Yedidia, I., H. Senderowitz, and A. O. Charkowski. Small molecule cocktails designed to impair virulence targets in soft rot Erwinias. Israel: United States-Israel Binational Agricultural Research and Development Fund, 2020. http://dx.doi.org/10.32747/2020.8134165.bard.

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Chemical signaling between beneficial or pathogenic bacteria and plants is a central factor in determining the outcome of plant-microbe interactions. Pectobacterium and Dickeya (soft rot Erwinias) are the major cause of soft rot, stem rot, and blackleg formed on potato and ornamentals, currently with no effective control. Our major aim was to establish and study specific bacterial genes/proteins as targets for anti-virulence compounds, by combining drug design tools and bioinformatics with experimental work. The approach allowed us to identify and test compounds (small molecules) that specifically interfere with the activities of these targets, by this impairing bacterial virulence. Two main targets were selected within the frame of the BARD project. The first is the ATP-binding cassette (ABC) transporters and methyl-accepting chemotaxis proteins (MCP) that have been characterized here for the first time in Pectobacteriaceae, and the second is the quorum sensing (QS) machinery of Pectobacterium with its major proteins and in particular, the AHL synthase ExpI that was identified as the preferred target for inhibition. Both systems are strongly associated with bacterial virulence and survival in planta. We found that Pectobacteriaceae, namely Dickeya and Pectobacterium, encode more ABC transporters and MCP in their genomes, compared to other bacteria in the order. For MCP, soft rot Pectobacteriaceae not only contain more than 30 MCP genes per strain, but also have more diverse ligand binding domains than other species in the Enterobacteriales. These findings suggest that both ABC transporters and MCP are important for soft rot Pectobacteriaceae pathogenicity. We now have a selection of mutants in these proteins that may be further explored to understand their direct involvement in virulence. In parallel, we studied the QS central proteins in pectobacteria, the signaling molecule N-acyl-homoserine lactone synthase, ExpI, and the response regulator ExpR, and established their phylogenetic relations within plant pathogenic Gram negative bacteria. Next, these proteins were used for virtual screening of millions of compounds in order to discover new compounds with potential to interfere with the QS machinery. Several natural compounds were tested for their interference with virulence related traits in Pectobacterium and their capability to minimize soft rot infections. Our findings using microcalorimetric binding studies have established for the first time direct interaction between the protein ExpI and two natural ligands, the plant hormone salicylic acid and the volatile compound carvacrol. These results supported a model by which plants interfere with bacterial communication through interkingdom signaling. The collaborative project yielded two research papers and a comprehensive review, which included new computational and bioinformatics data, in Annu. Rev. Phytopathol., the highest ranked journal in phytopathology. Additional two papers are in preparation. In order to transform the fundamental knowledge that have been gained during this collaborative BARD project into agricultural practice, to control soft rot bacteria, we have submitted a continual project.
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Liu, Yanyan, and Yuan Zhou. Land plot size, machine use and agricultural intensification in China. Washington, DC: International Food Policy Research Institute, 2019. http://dx.doi.org/10.2499/p15738coll2.133385.

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Gopinath, Munisamy, Feras Batarseh, and Jayson Beckman. Machine Learning in Gravity Models: An Application to Agricultural Trade. Cambridge, MA: National Bureau of Economic Research, May 2020. http://dx.doi.org/10.3386/w27151.

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Marra de Artiñano, Ignacio, Franco Riottini Depetris, and Christian Volpe Martincus. Automatic Product Classification in International Trade: Machine Learning and Large Language Models. Inter-American Development Bank, July 2023. http://dx.doi.org/10.18235/0005012.

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Accurately classifying products is essential in international trade. Virtually all countries categorize products into tariff lines using the Harmonized System (HS) nomenclature for both statistical and duty collection purposes. In this paper, we apply and assess several different algorithms to automatically classify products based on text descriptions. To do so, we use agricultural product descriptions from several public agencies, including customs authorities and the United States Department of Agriculture (USDA). We find that while traditional machine learning (ML) models tend to perform well within the dataset in which they were trained, their precision drops dramatically when implemented outside of it. In contrast, large language models (LLMs) such as GPT 3.5 show a consistently good performance across all datasets, with accuracy rates ranging between 60% and 90% depending on HS aggregation levels. Our analysis highlights the valuable role that artificial intelligence (AI) can play in facilitating product classification at scale and, more generally, in enhancing the categorization of unstructured data.
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