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Optimizing Agricultural Input and Production for Different Types of at-Risk Peasant Households: An Empirical Study of Typical Counties in the Yimeng Mountain Area of Northern China

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  • Yuanhe Yu

    (College of Geography and Environment, Shandong Normal University, Jinan 250358, China
    School of Environment and Natural Resources, Renmin University of China, Beijing 100872, China)

  • Liang Wang

    (Shandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection, Linyi University, Linyi 276000, China)

  • Jinkuo Lin

    (College of Geography and Environment, Shandong Normal University, Jinan 250358, China)

  • Zijun Li

    (College of Geography and Environment, Shandong Normal University, Jinan 250358, China)

Abstract

Using typical counties in the Yimeng Mountain area of northern China as an example, this paper analyzed the household and agricultural input characteristics of different types of peasant households using survey data from 262 farm households. The target minimization of the total absolute deviations (MOTAD) model was applied to determine the optimal combinations in the allocation of agricultural input factors and production for different types of at-risk peasant households to obtain the ideal agricultural income. The relevant results are twofold. (1) The agricultural input behaviors of different types of peasant households vary significantly. The highest levels of agricultural land, labor, and yield-increasing and labor-saving inputs included I part-time peasant households ( I PTPH ), followed by full-time peasant households ( FTPH ), while the input levels of II part-time peasant households ( II PTPH ) and non-agricultural peasant households ( NAPH ) with higher levels of non-agricultural employment gradually decreased. In general, an increase in peasant households’ part-time employment revealed an inverted U-shaped trend in the agricultural input level, with a trajectory of I PTPH > FTPH > II PTPH > NAPH . (2) The current agricultural inputs and production combinations of different types of peasant households have room for improvement. It is necessary to adjust agricultural inputs and optimize production combinations to obtain target incomes. Overall, all types of peasant households must streamline labor inputs and increase capital inputs, except for I PTPH , for which capital inputs should be reduced. Following optimization, economic crops gradually replace grain crops, and the optimal agricultural incomes of peasant households will be improved. The study results provide practical policy insights for reducing agricultural production risks and improving agricultural production incomes.

Suggested Citation

  • Yuanhe Yu & Liang Wang & Jinkuo Lin & Zijun Li, 2022. "Optimizing Agricultural Input and Production for Different Types of at-Risk Peasant Households: An Empirical Study of Typical Counties in the Yimeng Mountain Area of Northern China," IJERPH, MDPI, vol. 19(21), pages 1-22, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:13938-:d:954307
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    as
    1. Sarah Ann Wheeler & Céline Nauges & Alec Zuo, 2021. "How stable are Australian farmers’ climate change risk perceptions? New evidence of the feedback loop between risk perceptions and behaviour," Post-Print hal-03198062, HAL.
    2. Zimet, David J. & Spreen, Thomas H., 1986. "A Target Motad Analysis of a Crop and Livestock Farm in Jefferson County, Florida," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 18(2), pages 175-186, December.
    3. Ren, Yang & Li, Zehong & Li, Jingnan & Dashtseren, A. & Li, Yu & Altanbagana, M., 2022. "Comparative analysis of driving forces of land use/cover change in the upper, middle and lower reaches of the Selenga River Basin," Land Use Policy, Elsevier, vol. 117(C).
    4. Awais Jabbar & Qun Wu & Jianchao Peng & Ali Sher & Asma Imran & Kunpeng Wang, 2020. "Mitigating Catastrophic Risks and Food Security Threats: Effects of Land Ownership in Southern Punjab, Pakistan," IJERPH, MDPI, vol. 17(24), pages 1-18, December.
    5. Vera Chiodi & Esteban Jaimovich & Gabriel Montes-Rojas, 2012. "Migration, Remittances and Capital Accumulation: Evidence from Rural Mexico," Journal of Development Studies, Taylor & Francis Journals, vol. 48(8), pages 1139-1155, February.
    6. Bauer, Larry L., 1991. "A Target MOTAD Analysis of Sweet Potato Marketing," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 23(2), pages 123-130, December.
    7. Barrett, C. B. & Reardon, T. & Webb, P., 2001. "Nonfarm income diversification and household livelihood strategies in rural Africa: concepts, dynamics, and policy implications," Food Policy, Elsevier, vol. 26(4), pages 315-331, August.
    8. Ji, Yueqing & Yu, Xiaohua & Zhong, Funing, 2012. "Machinery investment decision and off-farm employment in rural China," China Economic Review, Elsevier, vol. 23(1), pages 71-80.
    9. Y. Zhu & Z. Wu & L. Peng & L. Sheng, 2014. "Where did all the remittances go? Understanding the impact of remittances on consumption patterns in rural China," Applied Economics, Taylor & Francis Journals, vol. 46(12), pages 1312-1322, April.
    10. Oliver Musshoff & Norbert Hirschauer, 2009. "Optimizing Production Decisions Using a Hybrid Simulation–Genetic Algorithm Approach," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 57(1), pages 35-54, March.
    11. Fang Su & Udoy SAIKIA & Iain HAY, 2019. "Impact of Perceived Livelihood Risk on Livelihood Strategies: A Case Study in Shiyang River Basin, China," Sustainability, MDPI, vol. 11(12), pages 1-16, June.
    12. Julio Berbel, 1990. "A Comparison of Target MOTAD Efficient Sets and the Choice of Target," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 38(1), pages 149-158, March.
    13. J. Edward Taylor & Alejandro Lopez-Feldman, 2010. "Does Migration Make Rural Households More Productive? Evidence from Mexico," Journal of Development Studies, Taylor & Francis Journals, vol. 46(1), pages 68-90.
    14. Alan De Brauw, 2010. "Seasonal Migration and Agricultural Production in Vietnam," Journal of Development Studies, Taylor & Francis Journals, vol. 46(1), pages 114-139.
    15. Liu, Zhen & Li, Qiuming & Lan, Jing & Abu Hatab, Assem, 2020. "Does participation in the sloping land conversion program reduce the sensitivity of Chinese farmers to climate change?," Land Use Policy, Elsevier, vol. 99(C).
    16. Yu, Peiheng & Fennell, Shailaja & Chen, Yiyun & Liu, Hui & Xu, Lu & Pan, Jiawei & Bai, Shaoyun & Gu, Shixiang, 2022. "Positive impacts of farmland fragmentation on agricultural production efficiency in Qilu Lake watershed: Implications for appropriate scale management," Land Use Policy, Elsevier, vol. 117(C).
    17. repec:hal:pseose:hal-00813389 is not listed on IDEAS
    18. Nong, Yixin & Yin, Changbin & Yi, Xiaoyan & Ren, Jing & Chien, Hsiaoping, 2021. "Smallholder farmer preferences for diversifying farming with cover crops of sustainable farm management: A discrete choice experiment in Northwest China," Ecological Economics, Elsevier, vol. 186(C).
    19. Bauer, Larry L., 1991. "A Target Motad Analysis Of Sweet Potato Marketing," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 23(2), pages 1-7, December.
    20. Jikun Huang & Yunhua Wu & Scott Rozelle, 2009. "Moving off the farm and intensifying agricultural production in Shandong: a case study of rural labor market linkages in China," Agricultural Economics, International Association of Agricultural Economists, vol. 40(2), pages 203-218, March.
    21. Alfandari, Laurent & Plateau, Agnès & Schepler, Xavier, 2015. "A branch-and-price-and-cut approach for sustainable crop rotation planning," European Journal of Operational Research, Elsevier, vol. 241(3), pages 872-879.
    22. Tang, Lin & Luo, Xiaofeng, 2021. "Can agricultural insurance encourage farmers to apply biological pesticides? Evidence from rural China," Food Policy, Elsevier, vol. 105(C).
    23. Abbas Ali Chandio & Yasir A. Nasereldin & Dao Le Trang Anh & Yashuang Tang & Ghulam Raza Sargani & Huaquan Zhang, 2022. "The Impact of Technological Progress and Climate Change on Food Crop Production: Evidence from Sichuan—China," IJERPH, MDPI, vol. 19(16), pages 1-18, August.
    24. Xuesong Zhan & Chaofeng Shao & Rong He & Rongguang Shi, 2021. "Evolution and Efficiency Assessment of Pesticide and Fertiliser Inputs to Cultivated Land in China," IJERPH, MDPI, vol. 18(7), pages 1-21, April.
    25. Salassi, Michael E. & Deliberto, Michael A. & Guidry, Kurt M., 2013. "Economically optimal crop sequences using risk-adjusted network flows: Modeling cotton crop rotations in the southeastern United States," Agricultural Systems, Elsevier, vol. 118(C), pages 33-40.
    26. Chenggang Wang & Nicholas Rada & Lijian Qin & Suwen Pan, 2014. "Impacts of Migration on Household Production Choices: Evidence from China," Journal of Development Studies, Taylor & Francis Journals, vol. 50(3), pages 413-425, March.
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