Efficiency Analysis of the Crop Production in China in 2019 and 2020: Role of Uncertainty Perceptions in COVID-19
This paper employs data envelopment analysis (DEA) to determine crop production efficiency in 15 major provinces of China during 2019-2020. The total power of agricultural machinery, the application amount of chemical fertilizer, the irrigation area of cultivated land, the area of grain sowing, and...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/7044474 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849308770624077824 |
|---|---|
| author | Yue-Si Yuan Zi-Yi Cao Yu-Ting Chen Pei-Lin Gong Guo-Hui Huang Lu He |
| author_facet | Yue-Si Yuan Zi-Yi Cao Yu-Ting Chen Pei-Lin Gong Guo-Hui Huang Lu He |
| author_sort | Yue-Si Yuan |
| collection | DOAJ |
| description | This paper employs data envelopment analysis (DEA) to determine crop production efficiency in 15 major provinces of China during 2019-2020. The total power of agricultural machinery, the application amount of chemical fertilizer, the irrigation area of cultivated land, the area of grain sowing, and the total capacity of reservoirs in each province are defined as the input items. The production of food, production of oil plants, and production of fruits are considered output items. According to the findings from the DEA, the most efficient crop production is observed in Shandong and Xinjiang provinces. We also discuss the role of farmers’ uncertainty perceptions in COVID-19. By cluster analysis, the provinces with large grain sown area and high grain yield are Henan and Heilongjiang, the provinces with moderate grain production in the grain sown area are Hunan, Hubei, Jiangxi, Guizhou, and Yunnan, and Xinjiang, Shandong, Hebei, Anhui, Sichuan, Jiangsu, Inner Mongolia, and Jilin are the provinces with low grain production. |
| format | Article |
| id | doaj-art-1b6ea88cb9bb4aa6816dc5d7c252a0b7 |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-1b6ea88cb9bb4aa6816dc5d7c252a0b72025-08-20T03:54:23ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/7044474Efficiency Analysis of the Crop Production in China in 2019 and 2020: Role of Uncertainty Perceptions in COVID-19Yue-Si Yuan0Zi-Yi Cao1Yu-Ting Chen2Pei-Lin Gong3Guo-Hui Huang4Lu He5School of Economics and ManagementSchool of Economics and ManagementSchool of Economics and ManagementSchool of Economics and ManagementSchool of Economics and ManagementSchool of Economics and ManagementThis paper employs data envelopment analysis (DEA) to determine crop production efficiency in 15 major provinces of China during 2019-2020. The total power of agricultural machinery, the application amount of chemical fertilizer, the irrigation area of cultivated land, the area of grain sowing, and the total capacity of reservoirs in each province are defined as the input items. The production of food, production of oil plants, and production of fruits are considered output items. According to the findings from the DEA, the most efficient crop production is observed in Shandong and Xinjiang provinces. We also discuss the role of farmers’ uncertainty perceptions in COVID-19. By cluster analysis, the provinces with large grain sown area and high grain yield are Henan and Heilongjiang, the provinces with moderate grain production in the grain sown area are Hunan, Hubei, Jiangxi, Guizhou, and Yunnan, and Xinjiang, Shandong, Hebei, Anhui, Sichuan, Jiangsu, Inner Mongolia, and Jilin are the provinces with low grain production.http://dx.doi.org/10.1155/2022/7044474 |
| spellingShingle | Yue-Si Yuan Zi-Yi Cao Yu-Ting Chen Pei-Lin Gong Guo-Hui Huang Lu He Efficiency Analysis of the Crop Production in China in 2019 and 2020: Role of Uncertainty Perceptions in COVID-19 Discrete Dynamics in Nature and Society |
| title | Efficiency Analysis of the Crop Production in China in 2019 and 2020: Role of Uncertainty Perceptions in COVID-19 |
| title_full | Efficiency Analysis of the Crop Production in China in 2019 and 2020: Role of Uncertainty Perceptions in COVID-19 |
| title_fullStr | Efficiency Analysis of the Crop Production in China in 2019 and 2020: Role of Uncertainty Perceptions in COVID-19 |
| title_full_unstemmed | Efficiency Analysis of the Crop Production in China in 2019 and 2020: Role of Uncertainty Perceptions in COVID-19 |
| title_short | Efficiency Analysis of the Crop Production in China in 2019 and 2020: Role of Uncertainty Perceptions in COVID-19 |
| title_sort | efficiency analysis of the crop production in china in 2019 and 2020 role of uncertainty perceptions in covid 19 |
| url | http://dx.doi.org/10.1155/2022/7044474 |
| work_keys_str_mv | AT yuesiyuan efficiencyanalysisofthecropproductioninchinain2019and2020roleofuncertaintyperceptionsincovid19 AT ziyicao efficiencyanalysisofthecropproductioninchinain2019and2020roleofuncertaintyperceptionsincovid19 AT yutingchen efficiencyanalysisofthecropproductioninchinain2019and2020roleofuncertaintyperceptionsincovid19 AT peilingong efficiencyanalysisofthecropproductioninchinain2019and2020roleofuncertaintyperceptionsincovid19 AT guohuihuang efficiencyanalysisofthecropproductioninchinain2019and2020roleofuncertaintyperceptionsincovid19 AT luhe efficiencyanalysisofthecropproductioninchinain2019and2020roleofuncertaintyperceptionsincovid19 |