Abstract
With the deep implementation of the rural revitalization strategy, increasing farmers’ income has also become a key measure to solve the three rural issues. The transformation of rural production environment and ecological environment has brought opportunities and challenges for farmers to increase their income. Analysis of factors affecting agricultural income is of great significance for improving farmers’ lives and increasing farmers’ income. In this paper, the grey relational theory is used to analyze the correlation between indicators and farmers’ income. The internal relationship between natural disasters and agricultural income is studied, and the effective way to increase farmers’ income is proposed. The assessment results show that, first of all, first of all, the income of farmers’ family business in the study area is affected by the level of agricultural science and technology, land productivity and disaster mechanism. Secondly, the application of agricultural science and technology with machinery and fertilizer as the mains has the greatest impact on farmers’ income. Third, in the middle and lower reaches of the Yangtze River, the income of farmers’ family business is threatened by droughts and floods. Through the analysis of the grey correlation model, the intrinsic link between farmers’ income and indicators is more intuitive, and the difference in the importance of indicators is more obvious. According to the analysis, the government and farmers can better allocate limited resources, be alert to the threat of natural disasters, and increase farmers’ income.
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References
Alemu, A.E., Maertens, M., et al.: Impact of supply chain coordination on honey farmers’ income in Tigray, Northern Ethiopia. Agric. Food Econ. 4(1), 9 (2016)
Chen, S., Pu, C., et al.: Empirical analysis of factors affecting farmers’ income increase in Awat county of Xinjiang. Acta Agriculturae Jiangxi 25(6), 140–146 (2013)
Constant, L.A, Winkler, H.: Linkage of agricultural productivity improvement and climate mitigation action in Africa (2015)
Dawa, D.H.: Gm (1,1) model and factor analysis of the development trend of per capita net income of rural residents in Tibet autonomous region (2010)
Deng, J.L.: Control problems of grey systems. Syst. Control Lett. 1(5), 288–294 (1982)
De Silva, M., Kawasaki, A.: Socioeconomic vulnerability to disaster risk: a case study of flood and drought impact in a rural Sri Lankan community. Ecol. Econ. 152, 131–140 (2018)
Doppler, W., Bahadur, K.K.: Impact of natural resources and infrastructure on future livelihood in mountain farming in the Himalaya region. In: The Future of Mountain Agriculture, pp. 145–170. Springer (2013)
Ishizawa, O.A., Miranda, J.J.: Weathering storms: understanding the impact of natural disasters in Central America. Environ. Resour. Econ. 73(1), 181–211 (2019)
Karaaslan, A., Özden, K.Ö.: Forecasting Turkey’s credit ratings with multivariate grey model and grey relational analysis. J. Quan. Econ. 15(3), 583–610 (2017)
Leipnik, M., Su, Y., et al.: Agriculture and food production in China and the US. In: A Comparative Geography of China and the US, pp. 117–158. Springer (2014)
Liu, Y., You, M., et al.: Integrated risk assessment for agricultural drought and flood disasters based on entropy information diffusion theory in the middle and lower reaches of the Yangtze river, China. Int. J. Disaster Risk Reduct. 38(101), 194 (2019)
Lu, W.: Agricultural technological progress and farmers’ income growth in China. J. Zhejiang Univ. (Hum. Soci. Sci.) 4 (2013). (in Chinese)
Mendelsohn, R., Basist, A., et al.: Climate and rural income. Clim. Change 81(1), 101–118 (2007)
Meng, Z., Xu, C., Lan, H.: Fuzzy gm (1, 1) model based per capital income predicted of farmers in the world natural and cultural heritage area: take Leshan city for an example. In: International Conference on Management Science and Engineering Management, pp. 1007–1018. Springer (2017)
Mottaleb, K.A., Mohanty, S., et al.: The effects of natural disasters on farm household income and expenditures: a study on rice farmers in Bangladesh. Agric. Syst. 121, 43–52 (2013)
Pan, H., Liu, Y., Gao, H.: Impact of agricultural industrial structure adjustment on energy conservation and income growth in Western China: a statistical study. Ann. Oper. Res. 228(1), 23–33 (2015)
Paudel, G.P., KC, D.B., et al.: Scale-appropriatemechanization impacts on productivity among smallholders: evidence from rice systems in the mid-hills of Nepal. Land Use Pol. 85, 104–113 (2019)
Pishbahar, E., Abedi, S., et al.: Agricultural risk management through weather-based insurance in Iran. In: Sustainable Agriculture and Agribusiness in Iran, pp. 11–28. Springer (2019)
Prokopy, L.S., Arbuckle, J.G., et al.: Farmers and climate change: a cross-national comparison of beliefs and risk perceptions in high-income countries. Environ. Manag. 56(2), 492–504 (2015)
Safa, M.S.: Socio-economic factors affecting the income of small-scale agroforestry farms in hill country areas in Yemen: a comparison of OLS and WLS determinants. Small-scale For. Econ. Manag. Pol. 4(1), 117–134 (2005)
Shan, L., Zhang, L., et al.: Characteristics of dry-wet abrupt alternation events in the middle and lower reaches of the Yangtze river basin and the relationship with Enso. J. Geogr. Sci. 28(8), 1039–1058 (2018)
Singh, A.: Optimal allocation of water and land resources for maximizing the farm income and minimizing the irrigation-induced environmental problems. Stoch. Environ. Res. Risk Assess. 31(5), 1147–1154 (2017)
Tabo, R., Bationo, A., et al.: Improving cereal productivity and farmers’ income using a strategic application of fertilizers in West Africa. In: Advances in Integrated Soil Fertility Management in Sub-Saharan Africa: Challenges and opportunities, pp. 201–208. Springer (2007)
Verma, P., Singh, D., et al.: Strategies to improve agriculture sustainability, soil fertility and enhancement of farmers income for the economic development. In: Soil Fertility Management for Sustainable Development, pp. 43–70. Springer (2019)
Wang, X., Shao, S., Li, L.: Agricultural inputs, urbanization, and urban-rural income disparity: evidence from china. China Econ. Rev. 55, 67–84 (2019). (in Chinese)
Ye, M., Qian, Z.H., Wu, Y.P.: Spatiotemporal evolution of the droughts and floods over China (2013)
Zhang, Y., Bo, T., Li, Y.: Assessment on the vulnerability of different ecosystems to extreme rainfalls in the middle and lower reaches of Yangtze river. Theoret. Appl. Climatol. 121(1–2), 157–166 (2015)
Zheng, W., Li, X., et al.: Impact of human activities on haze in Beijing based on grey relational analysis. Rendiconti Lincei 26(2), 187–192 (2015)
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This research work is supported by the Ministry of education “humanities and social sciences youth project” of China [Grant No. 15YJC630081].
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You, M., Shao, X., Liu, Y. (2020). An Empirical Analysis of the Influencing Factors of Farmers’ Income Growth in the Middle and Lower Reaches of the Yangtze River Based on the Grey Correlation Model. In: Xu, J., Duca, G., Ahmed, S., García Márquez, F., Hajiyev, A. (eds) Proceedings of the Fourteenth International Conference on Management Science and Engineering Management. ICMSEM 2020. Advances in Intelligent Systems and Computing, vol 1190. Springer, Cham. https://doi.org/10.1007/978-3-030-49829-0_23
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