Measurement and analysis of agricultural green total factor productivity based on farmers’ perspectives

CHENG Yongsheng; ZHANG Deyuan; WANG Xia

doi:10.12357/cjea.20220562

Volume 31 Issue 5

May 2023

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Eco-Agriculture > 2023 > 31(5): 820-834

CHENG Y S, ZHANG D Y, WANG X. Measurement and analysis of agricultural green total factor productivity based on farmers’ perspectives[J]. Chinese Journal of Eco-Agriculture, 2023, 31(5): 820−834 doi: 10.12357/cjea.20220562

Citation:

PDF( 4230 KB)

Measurement and analysis of agricultural green total factor productivity based on farmers’ perspectives

doi: 10.12357/cjea.20220562

1.
School of Business, Fuyang Normal University, Fuyang 236032, China
2.
Academy of Strategies for Innovation and Development, Anhui University, Hefei 230601, China

Funds: The study was supported by the Philosophy and Social Science Foundation of Anhui Province (AHSKQ2022D039).

More Information

Corresponding author: CHENG Yongsheng, E-mail: chengyongsheng@aliyun.com
Received Date: 2022-07-21
Accepted Date: 2022-10-20

Available Online: 2022-11-25

Publish Date: 2023-05-10

Abstract

Abstract

Improving agricultural green total factor productivity (AGTFP) and hastening agricultural green transformation are unavoidable choices for comprehensively building a strong socialist, modernized country. Based on a comparative analysis of micro-measurement methods, this study analyzed the status of AGTFP at the farmer household level based on the technically optimized Malmquist-Luenberger index. The kernel density estimation method and the Dagum Gini coefficient method were further used to reveal the dynamic evolution of AGTFP and its regional differences in the micro-sample. The main findings are as follows: 1) From the measurement results, the mean value of AGTFP in the microfield in 2014, 2016 and 2018 was 1.0030, with a good overall development trend. The mean value of AGTFP of farmers in 2016 was 1.0099, and agricultural green development had a good growth trend. The mean values of technical efficiency change and technical progress change were 1.0165 and 0.9928, respectively, indicating that the improvement in farmers’ green agricultural technical efficiency was the main driving factor while the change in technical progress was relatively slow. In 2018, the mean value of AGTFP by farmers was 0.9960, which showed a decreasing trend. The corresponding mean values of technical efficiency change and technical progress change were 0.9765 and 1.0200, respectively, indicating that the technical efficiency improvement of green agriculture did not achieve a sustainable spillover effect and that the innovation function of technical progress change played a role in the improvement. 2) In terms of contributing factors, the use of subjective environmental assessment scores or objective provincial-level environmental pollution data as proxies for non-desired outputs among farmers with higher levels of AGTFP, agricultural green technological progress, and agricultural green technological efficiency was found to be more effective. For farmers with high levels of AGTFP, both green technological advances and green technological efficiency in agriculture were drivers of green growth, and the contribution of the latter was greater than that of the former. 3) From the perspective of a dynamic evolution pattern, in terms of AGTFP, the concentration in 2016 and 2018 was high, showing distinct clustering; however, the divergence phenomenon was not obvious, and the number of farmers with a high level of green development in 2018 was much higher than that in 2016; in terms of the agricultural technical efficiency of farmers, there was no bifurcation in 2016 and 2018. The number of low-level farmers in 2018 was higher than that in 2016, indicating that there was a regression phenomenon, and the difference between the agricultural technical efficiency of high- and low-level farmers was obvious. In terms of agricultural green technical progress of farmers, the overall trend was increasing, the number of low-level farmers in 2016 was lower, and the number of high-level farmers was relatively higher, while in 2018, the number of high- and low-level farmers remained the same, and a spatial clustering effect was evident. In 2018, the number of farmers with low levels of agricultural green technology progress decreased “precipitously.” On the premise that the number of farmers remained unchanged, this part of the low-level farmers moved to the middle- and high-level groups, forming the dynamic transfer effect of “internal push and external pull.” 4) From the perspective of regional disparity, the overall gap in AGTFP in the sample period was decreasing, with a decline of 22.32%. From the source decomposition, the hyper-variance density was the main cause of the overall regional disparity in AGTFP. From the contribution rate, the contribution rate of hyper-variance density was much higher than the contribution rate of intra- and inter-regional disparity, indicating that the cross-over problem between different regions was the main cause of the overall disparity in AGTFP at the farmer level. Further, from the intra-regional disparity, the disparity of AGTFP at the household level decreased within the eastern and western regions; from the inter-regional disparity, the disparity between the eastern and western, eastern and central, and central and western regions decreased continuously during the sample period, and the synergy was the highest, but this gap was susceptible to environmental factors.

FullText(HTML)

References(31)

References

[1]	王建宏, 张文攀. 加快建设乡村全面振兴样板区——访宁夏回族自治区党委副书记陈雍代表[N]. 光明日报, 2022-10-22 WANG J H, ZHANG W P. Accelerating the construction of a model area for the comprehensive revitalization of the countryside: interview with Yong Chen, Deputy Secretary of the Party Committee of Ningxia Hui Autonomous Region[N]. Guangming Daily, 2022-10-22
[2]	冯欣, 姜文来, 刘洋, 等. 绿色发展背景下农业水价综合改革研究[J]. 中国农业资源与区划, 2020, 41(10): 25−31 FENG X, JIANG W L, LIU Y, et al. Research on comprehensive reform of agricultural water price under the background of green development[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2020, 41(10): 25−31
[3]	程永生, 张德元, 赵梦婵. 黄河流域生态保护和高质量发展的时空演变与驱动因素[J]. 经济体制改革, 2021(5): 61−69 CHENG Y S, ZHANG D Y, ZHAO M C. Spatial-temporal evolution and driving factors of ecological protection and high-quality development in the Yellow River Basin[J]. Reform of Economic System, 2021(5): 61−69
[4]	李政大, 赵雅婷, 袁晓玲. 基于公众参与的中国绿色共治实现路径研究[J]. 现代财经(天津财经大学学报), 2021, 41(6): 98−113 LI Z D, ZHAO Y T, YUAN X L. Research on the path of China’s Green Co-governance based on public participation[J]. Modern Finance and Economics-Journal of Tianjin University of Finance and Economics, 2021, 41(6): 98−113
[5]	谭日辉, 刘慧敏. 中国农业绿色全要素生产率空间关联网络特征演化及影响因素[J]. 中国生态农业学报(中英文), 2022, 30(12): 2011−2022 TAN R H, LIU H M. Characteristic evolution and influencing factors of the spatial correlation network of agricultural green total factor productivity in China[J]. Chinese Journal of Eco-Agriculture, 2022, 30(12): 2011−2022
[6]	李翔, 杨柳. 华东地区农业全要素生产率增长的实证分析−基于随机前沿生产函数模型[J]. 华中农业大学学报(社会科学版), 2018(6): 62−68, 154 LI X, YANG L. An empirical analysis of agriculture total factor productivity growth in East China— Based on stochastic frontier production function model[J]. Journal of Huazhong Agricultural University (Social Sciences Edition), 2018(6): 62−68, 154
[7]	黄祖辉, 扶玉枝, 徐旭初. 农民专业合作社的效率及其影响因素分析[J]. 中国农村经济, 2011(7): 4−13, 62 HUANG Z H, FU Y Z, XU X C. Analysis of the efficiency of farmers’ professional cooperatives and its influencing factors[J]. Chinese Rural Economy, 2011(7): 4−13, 62
[8]	EMROUZNEJAD A, YANG G L. A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016[J]. Socio-Economic Planning Sciences, 2018, 61: 4−8 doi: 10.1016/j.seps.2017.01.008
[9]	程永生, 张德元, 赵梦婵, 等. 人力资本视角下雾霾污染对长江经济带绿色高质量发展的影响研究[J]. 重庆大学学报(社会科学版), 2022, 28(5): 46−60 CHENG Y S, ZHANG D Y, ZHAO M C, et al. Research on the impact of haze pollution on the high-quality development of the Yangtze River Economic Belt from the perspective of human capital[J]. Journal of Chongqing University (Social Science Edition), 2022, 28(5): 46−60
[10]	APARICIO J, BARBERO J, KAPELKO M, et al. Testing the consistency and feasibility of the standard Malmquist-Luenberger index: environmental productivity in world air emissions[J]. Journal of Environmental Management, 2017, 196: 148−160
[11]	潘丹. 考虑资源环境因素的中国农业生产率研究[D]. 南京: 南京农业大学, 2012 PAN D. Study on China agricultural productivity incorporating water resource and agricultural non-point source pollution factors[D]. Nanjing: Nanjing Agricultural University, 2012
[12]	程永生. 农业社会化服务对于绿色全要素生产率的影响研究——基于农户要素禀赋的视角[D]. 合肥: 安徽大学, 2022 CHENG Y S. Research on the influence of agricultural socialization service on green total factor productivity: based on the perspective of farmer household’s factor endowments[D]. Hefei: Anhui University, 2022
[13]	康亚文, 彭博, 赵浚夷, 等. 基于Meta分析的中国农业全要素生产率研究[J]. 中国农业资源与区划, 2022, 43(2): 67−80 KANG Y W, PENG B, ZHAO J Y, et al. Study on the China’s agricultural total factor productivity estimations based on Meta-analysis[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2022, 43(2): 67−80
[14]	王璐, 杨汝岱, 吴比. 中国农户农业生产全要素生产率研究[J]. 管理世界, 2020, 36(12): 77−93 doi: 10.3969/j.issn.1002-5502.2020.12.008 WANG L, YANG R D, WU B. A study on total factor productivity of agricultural production of rural households in China[J]. Management World, 2020, 36(12): 77−93 doi: 10.3969/j.issn.1002-5502.2020.12.008
[15]	李谷成, 冯中朝, 范丽霞. 小农户真的更加具有效率吗? 来自湖北省的经验证据[J]. 经济学(季刊), 2010, 9(1): 95−124 LI G C, FENG Z C, FAN L X. Is the small-sized rural household more efficient? the empirical evidence from Hubei Province[J]. China Economic Quarterly, 2010, 9(1): 95−124
[16]	黄书苑, 马丁丑. 西北地区家庭林业全要素生产率测算及收敛性分析−基于陕甘两省7年的1000个固定样本[J]. 干旱区资源与环境, 2021, 35(2): 21−27 doi: 10.13448/j.cnki.jalre.2021.034 HUANG S Y, MA D C. Measurement and convergence analysis of total factor productivity of family forestry in Northwest China[J]. Journal of Arid Land Resources and Environment, 2021, 35(2): 21−27 doi: 10.13448/j.cnki.jalre.2021.034
[17]	XIE Y. The User’s Guide of the China Family Panal Studied[M]. Beijing: Institute of Scocial Science Survey, Peking University, 2012
[18]	周应恒, 杨宗之. 生态价值视角下中国省域粮食绿色全要素生产率时空特征分析[J]. 中国生态农业学报(中英文), 2021, 29(10): 1786−1799 ZHOU Y H, YANG Z Z. Temporal and spatial characteristics of China’s provincial green total factor productivity of grains from the ecological value perspective[J]. Chinese Journal of Eco-Agriculture, 2021, 29(10): 1786−1799
[19]	程永生, 张德元, 汪侠. 农业社会化服务的绿色发展效应: 基于农户视角[J]. 资源科学, 2022, 44(9): 1848−1864 doi: 10.18402/resci.2022.09.09 CHENG Y S, ZHANG D Y, WANG X. Green development effect of agricultural socialized services: an analysis based on farming households’ perspective[J]. Resources Science, 2022, 44(9): 1848−1864 doi: 10.18402/resci.2022.09.09
[20]	MATHENGE M, SMALE M, TSCHIRLEY D. Off farm employment and input intensification among smallholder maize farmers in Kenya[J]. Journal of Agricultural Economics, 2015, 66: 519−536 doi: 10.1111/1477-9552.12093
[21]	白南生, 李靖, 陈晨. 子女外出务工、转移收入与农村老人农业劳动供给−基于安徽省劳动力输出集中地三个村的研究[J]. 中国农村经济, 2007(10): 48−54 BAI N S, LI J, CHEN C. Children’s work outside the home, transfer income and agricultural labor supply of rural elders: a study based on three villages in Anhui Province where labor export is concentrated[J]. Chinese Rural Economy, 2007(10): 48−54
[22]	刘敏. 农机投入对农业绿色全要素生产率的影响及门槛效应研究[D]. 长春: 吉林农业大学, 2020 LIU M. Study on the influence and threshold effect of farm machinery input on green total factor productivity of agriculture[D]. Changchun: Jilin Agricultural University, 2020
[23]	王云凤. 技术异质性视角下中国农业效率综合评价研究[D]. 天津: 天津商业大学, 2022 WANG Y F. Research on comprehensive evaluation of agricultural efficiency in China under the view of technological heterogeneity[D]. Tianjin: Tianjin University of Commerce, 2022
[24]	钱龙. 非农就业、农地流转与农户农业生产变化[D]. 杭州: 浙江大学, 2017 QIAN L. Off-farm employment, land transfer and changes in agricultural production[D]. Hangzhou: Zhejiang University, 2017
[25]	张景娜, 张雪凯. 互联网使用对农地转出决策的影响及机制研究−来自CFPS的微观证据[J]. 中国农村经济, 2020(3): 57−77 ZHANG J N, ZHANG X K. The impact of Internet use on the decision-making of farmland transfer and its mechanism: evidence from the CFPS data[J]. Chinese Rural Economy, 2020(3): 57−77
[26]	杨芳. 社会网络对农户生产决策的影响研究[D]. 重庆: 西南大学, 2019 YANG F. Research on the impact of social network on the production decision of rural household[D]. Chongqing: Southwest University, 2019
[27]	王越晗, 黄雨露, 夏煜, 等. 基于文献计量和可视化分析的中国水生态环境治理研究热点与趋势[J]. 长江科学院院报, 2022, 39(9): 137−143 doi: 10.11988/ckyyb.20220218 WANG Y H, HUANG Y L, XIA Y, et al. Research hotspots and trends of water eco-environmental governance in China based on bibliometric and visual analysis[J]. Journal of Yangtze River Scientific Research Institute, 2022, 39(9): 137−143 doi: 10.11988/ckyyb.20220218
[28]	常明. 农户兼业行为影响灌溉效率吗?−基于CFPS的微观证据[J]. 农林经济管理学报, 2020, 19(6): 681−689 CHANG M. Can farmers’ concurrent business behavior affect irrigation efficiency? A study based on microscopic evidence from CFPS[J]. Journal of Agro-Forestry Economics and Management, 2020, 19(6): 681−689
[29]	吴国松, 姚升. 要素市场扭曲下农业绿色全要素生产率测度及效应研究[J]. 生态经济, 2021, 37(1): 96−102, 115 WU G S, YAO S. Measurement and effect of agricultural green TFP under factor market distortion[J]. Ecological Economy, 2021, 37(1): 96−102, 115
[30]	赖斯芸, 杜鹏飞, 陈吉宁. 基于单元分析的非点源污染调查评估方法[J]. 清华大学学报(自然科学版), 2004, 9(9): 1184−1187 doi: 10.3321/j.issn:1000-0054.2004.09.009 LAI S Y, DU P F, CHEN J N. Evaluation of non-point source pollution based on unit analysis[J]. Journal of Tsinghua University (Science and Technology), 2004, 9(9): 1184−1187 doi: 10.3321/j.issn:1000-0054.2004.09.009
[31]	CHENG Y S, ZHANG D Y. Spatial and temporal differentiation trends and attributions of high-quality development in the Huaihe Eco-economic Belt[J]. Journal of Resources and Ecology, 2023, 14(3): 517−532