Design of a neuro-fuzzy model for agricultural employment in Colombia using fuzzy clustering

Juan Sánchez; Juan Rodríguez; Helbert Espitia; Juan Sánchez; Juan Rodríguez; Helbert Espitia

doi:10.3934/environsci.2024038

AIMS Environmental Science

2024, Volume 11, Issue 5: 759-775. doi: 10.3934/environsci.2024038

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Research article

Design of a neuro-fuzzy model for agricultural employment in Colombia using fuzzy clustering

1.
Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá, Colombia
2.
Facultad del Medio Ambiente y Recursos Naturales, Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

Received: 24 June 2024 Revised: 08 August 2024 Accepted: 28 August 2024 Published: 10 September 2024

High levels of poverty in rural areas constitute one of the main challenges for developing countries. Since agricultural employment is the main source of income in these areas, the design of tools that simulate and help public policymakers will be remarkably useful. This work proposes the development of a model for agricultural employment in Colombia, considering input variables such as education, contract, and income, and the output is the amount of agricultural employment. Real data measured in Colombia are used for the design and adjustment of the model. To design the fuzzy system for an agricultural employment model, the methods employed are fuzzy C-means clustering and neuro-fuzzy systems. The systems were tested with different cluster configurations, and a fuzzy system was obtained with an adequate distribution of the fuzzy sets and the respective rules that relate the sets. It was observed that as the clusters increase, the adjustment function decreases. The implementation of neuro-fuzzy systems to model agricultural employment will allow public policymakers to generate guidelines that adjust to their political agendas with a lower degree of uncertainty.
- agricultural employment,
- fuzzy clustering,
- model,
- neuro-fuzzy,
- public policymakers
Citation: Juan Sánchez, Juan Rodríguez, Helbert Espitia. Design of a neuro-fuzzy model for agricultural employment in Colombia using fuzzy clustering[J]. AIMS Environmental Science, 2024, 11(5): 759-775. doi: 10.3934/environsci.2024038

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Abstract

High levels of poverty in rural areas constitute one of the main challenges for developing countries. Since agricultural employment is the main source of income in these areas, the design of tools that simulate and help public policymakers will be remarkably useful. This work proposes the development of a model for agricultural employment in Colombia, considering input variables such as education, contract, and income, and the output is the amount of agricultural employment. Real data measured in Colombia are used for the design and adjustment of the model. To design the fuzzy system for an agricultural employment model, the methods employed are fuzzy C-means clustering and neuro-fuzzy systems. The systems were tested with different cluster configurations, and a fuzzy system was obtained with an adequate distribution of the fuzzy sets and the respective rules that relate the sets. It was observed that as the clusters increase, the adjustment function decreases. The implementation of neuro-fuzzy systems to model agricultural employment will allow public policymakers to generate guidelines that adjust to their political agendas with a lower degree of uncertainty.

References

[1]	ONU. Portada-desarrollo sostenible, 2015. Available from: https://www.un.org/sustainabledevelopment/es/.
[2]	DANE. Pobreza multidimensional, 2023. Available from: https://www.dane.gov.co/index.php/estadisticas-por-tema/pobreza-y-condiciones-de-vida/pobreza-multidimensional.
[3]	DANE. Boletín técnico (pobreza multidimensional en colombia, 2022. Available from: https://img.lalr.co/cms/2021/09/03041930/boletin-tec-pobreza-multidimensional-20.pdf.
[4]	DANE. Empleo y desempleo, 2024. Available from: https://www.dane.gov.co/index.php/estadisticas-por-tema/mercado-laboral/empleo-y-desempleo.
[5]	Jiménez WS, Gómez LEN, Díaz RG (2018) Cambio estructural de la vocación agrícola y pecuaria en el municipio de purificación, tolima, Colombia. Libre Empresa 15: 137–148. https://doi.org/10.18041/1657-2815/libreempresa.2018v15n2.5361 doi: 10.18041/1657-2815/libreempresa.2018v15n2.5361
[6]	Rios LAM, Villegas JV, Suarez A (2020) Local perceptions about rural abandonment drivers in the colombian coffee region: Insights from the city of manizales. Land Use Policy 91: 104361. https://doi.org/10.1016/j.landusepol.2019.104361 doi: 10.1016/j.landusepol.2019.104361
[7]	Gottlieb C, Grobovšek J (2019) Communal land and agricultural productivity. J Dev Econ 138: 135–152. https://doi.org/10.1016/j.jdeveco.2018.11.001 doi: 10.1016/j.jdeveco.2018.11.001
[8]	Zhang YM, Diao XS (2020) The changing role of agriculture with economic structural change–- the case of China. China Econ Rev 62: 101504. https://doi.org/10.1016/j.chieco.2020.101504 doi: 10.1016/j.chieco.2020.101504
[9]	Rijnks RH, Crowley F, Doran J (2022) Regional variations in automation job risk and labour market thickness to agricultural employment. J Rural Stud 91: 10–23. https://doi.org/10.1016/j.jrurstud.2021.12.012 doi: 10.1016/j.jrurstud.2021.12.012
[10]	Edeme RK, Nkalu NC, Idenyi JC, et al. (2020) Infrastructural development, sustainable agricultural output and employment in ecowas countries. Sustain Futures 2: 100010. https://doi.org/10.1016/j.sftr.2020.100010 doi: 10.1016/j.sftr.2020.100010
[11]	Diaz RT, Osorio DP, Hernández EM, et al. (2022) Socioeconomic determinants that influence the agricultural practices of small farm families in northern colombia. J Saudi Soc Agric Sci 21: 440–451. https://doi.org/10.1016/j.jssas.2021.12.001 doi: 10.1016/j.jssas.2021.12.001
[12]	Sofer M (2001) Pluriactivity in the moshav: Family farming in israel. J Rural Stud 17: 363–375. https://doi.org/10.1016/S0743-0167(01)00012-2 doi: 10.1016/S0743-0167(01)00012-2
[13]	Castaneda A, Doan D, Newhouse D, et al. (2018) A new profile of the global poor. World Dev 101: 250–267. https://doi.org/10.1016/j.worlddev.2017.08.002 doi: 10.1016/j.worlddev.2017.08.002
[14]	Xie Y, Jiang QB (2016) Land arrangements for rural-urban migrant workers in china: Findings from jiangsu province. Land Use Policy 50: 262–267. https://doi.org/10.1016/j.landusepol.2015.10.010 doi: 10.1016/j.landusepol.2015.10.010
[15]	Silva RP (2023) Current state and transformations of rural employment in latin america. an analysis of the case of chile. Chil J Agric Anim Sc 39: 121–132. https://doi.org/10.29393/CHJAA39-10EARP10010 doi: 10.29393/CHJAA39-10EARP10010
[16]	Perazzi JR, Merli GO (2019) Labor elasticity of growth by sector and department in colombia: The importance of the agricultural employment elasticity. Agroalimentaria 25: 19–34.
[17]	Sen B, Dorosh P, Ahmed M (2021) Moving out of agriculture in bangladesh: The role of farm, non-farm and mixed households. World Dev 144: 105479. https://doi.org/10.1016/j.worlddev.2021.105479 doi: 10.1016/j.worlddev.2021.105479
[18]	Akopov AS, Beklaryan LA, Beklaryan AL (2020) Cluster-based optimization of an evacuation process using a parallel bi-objective real-coded genetic algorithm. Cybern Inf Technol 20: 45–63. https://doi.org/10.2478/cait-2020-0027 doi: 10.2478/cait-2020-0027
[19]	Zhang XW, Zhang YY (2022) Optimization of regional economic industrial structure based on edge computing and fuzzy k-means clustering. Wirel Commun Mob Com 2022: 8775138. https://doi.org/10.1155/2022/8775138 doi: 10.1155/2022/8775138
[20]	Heil J, Häring V, Marschner B, et al. (2019) Advantages of fuzzy k-means over k-means clustering in the classification of diffuse reflectance soil spectra: A case study with west african soils. Geoderma 337: 11–21. https://doi.org/10.1016/j.geoderma.2018.09.004 doi: 10.1016/j.geoderma.2018.09.004
[21]	Metwally MS, Shaddad SM, Liu MG, et al. (2019) Soil properties spatial variability and delineation of site-specific management zones based on soil fertility using fuzzy clustering in a hilly field in Jianyang, Sichuan, China. Sustainability 11: 7084. https://doi.org/10.3390/su11247084 doi: 10.3390/su11247084
[22]	Zeraatpisheh M, Bakhshandeh E, Emadi M, et al. (2020) Integration of pca and fuzzy clustering for delineation of soil management zones and cost-efficiency analysis in a citrus plantation. Sustainability 12: 1–17. https://doi.org/10.3390/su12145809 doi: 10.3390/su12145809
[23]	Novák V, Perfilieva I, Dvořák A (2016) Fuzzy cluster analysis, John Wiley & Sons, 6: 137–148. https://doi.org/10.1002/9781119193210.ch6
[24]	Ramamoorthy V (2019) Fuzzy C-mean clustering using data mining. BookRix.
[25]	Bejarano LA, Espitia HE, Montenegro CE (2022) Clustering analysis for the pareto optimal front in multi-objective optimization. Computation 10. https://doi.org/10.3390/computation10030037 doi: 10.3390/computation10030037
[26]	Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [book review]. IEEE T Automat Contr 42: 1482–1484. https://doi.org/10.1109/TAC.1997.633847 doi: 10.1109/TAC.1997.633847
[27]	Cohen MD, March JG, Olsen JP (1972) A garbage can model of organizational choice. Admin Sci Quart 17: 1–25. https://doi.org/10.2307/2392088 doi: 10.2307/2392088
[28]	Simon HA (1979) Rational decision making in business organizations. Am Econ Rev 69: 493–513.

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