Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning
When analyzing agriculture’s total factor productivity, traditional measurement approaches do not take into account the inefficiency value. The production functions are assumed to be analyzed on basis of the random boundaries, which makes the analysis results inaccurate and unreliable. As a result,...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Food Quality |
| Online Access: | http://dx.doi.org/10.1155/2022/9297205 |
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| _version_ | 1850216027681456128 |
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| author | Yanzi Li Cai Chen Fuqiang Liu Jian Wang |
| author_facet | Yanzi Li Cai Chen Fuqiang Liu Jian Wang |
| author_sort | Yanzi Li |
| collection | DOAJ |
| description | When analyzing agriculture’s total factor productivity, traditional measurement approaches do not take into account the inefficiency value. The production functions are assumed to be analyzed on basis of the random boundaries, which makes the analysis results inaccurate and unreliable. As a result, this paper proposes an analytical approach for agricultural total factor productivity based on the stochastic block model (SBM), which combines the benefits of statistics and machine learning. It uses the SBM direction distance function and the Luenberger productivity index to measure the agricultural efficiency, total factor productivity, and their components. The research study considers the data from 31 provinces from 2006 to 2018 years. First, one output indicator and six input indicators are established. The time-varying variations of the national agricultural inefficiency value and its source decomposition under variable scale returns are then determined using the SBM-based algorithm of agricultural total factor productivity and the obtained sample data. The changes of the agricultural total factor productivity and its components are comprehensively analyzed. Following an examination of the elements impacting agricultural efficiency and productivity, the socioeconomic development of the agricultural total factor productivity is examined in order to achieve efficient growth. |
| format | Article |
| id | doaj-art-7e1329b928084bb789ecc71fadb7abdc |
| institution | OA Journals |
| issn | 1745-4557 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Food Quality |
| spelling | doaj-art-7e1329b928084bb789ecc71fadb7abdc2025-08-20T02:08:26ZengWileyJournal of Food Quality1745-45572022-01-01202210.1155/2022/9297205Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine LearningYanzi Li0Cai Chen1Fuqiang Liu2Jian Wang3College of Economics and ManagementInstitute Education CollegeBaoding Academy of Agricultural SciencesCollege of Economics and ManagementWhen analyzing agriculture’s total factor productivity, traditional measurement approaches do not take into account the inefficiency value. The production functions are assumed to be analyzed on basis of the random boundaries, which makes the analysis results inaccurate and unreliable. As a result, this paper proposes an analytical approach for agricultural total factor productivity based on the stochastic block model (SBM), which combines the benefits of statistics and machine learning. It uses the SBM direction distance function and the Luenberger productivity index to measure the agricultural efficiency, total factor productivity, and their components. The research study considers the data from 31 provinces from 2006 to 2018 years. First, one output indicator and six input indicators are established. The time-varying variations of the national agricultural inefficiency value and its source decomposition under variable scale returns are then determined using the SBM-based algorithm of agricultural total factor productivity and the obtained sample data. The changes of the agricultural total factor productivity and its components are comprehensively analyzed. Following an examination of the elements impacting agricultural efficiency and productivity, the socioeconomic development of the agricultural total factor productivity is examined in order to achieve efficient growth.http://dx.doi.org/10.1155/2022/9297205 |
| spellingShingle | Yanzi Li Cai Chen Fuqiang Liu Jian Wang Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning Journal of Food Quality |
| title | Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning |
| title_full | Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning |
| title_fullStr | Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning |
| title_full_unstemmed | Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning |
| title_short | Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning |
| title_sort | analysis method of agricultural total factor productivity based on stochastic block model sbm and machine learning |
| url | http://dx.doi.org/10.1155/2022/9297205 |
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