Classification of small-scale dairy production in the Ecuador-Colombia border area. A comparative study of automatic learning techniques
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Abstract
The socioeconomic factors determining production in dairy farms were researched. The classification of small-scale farmers in the border area between Ecuador and Colombia was involved. A total of 532 farmers participated in the survey and the data collected was analyzed using automatic learning techniques. The data were subjected to an exhaustive preprocessing to remove errors and outliers related to socioeconomic factors in milk production in Carchi, Ecuador. Among the variables examined, economic income, the price per liter of milk and the quantity of liters used for cheese production emerged as the most influential factors. The results showed that automatic learning techniques can effectively classify small-scale dairy production, with accuracy above 96 %. The presence of a child who provides economic support to the house, the allocation of milk for the production and sale of cheese, together with its use for family consumption, significantly influenced 90 % of the surveyed participants.
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References
Abdukarimova, M., Abdukarimov, A. & Abdukarimov, N. 2016. Handbook of Industrial and Innovation Economics, editado por Munisa, 466p. Uzbekistan: Independently. ISBN: 979-8412353852. Available at: https://www.researchgate.net/profile/Munisa-Abdukarimova/publication/344279960_Handbook_of_Industrial_and_innovation_economics/links/62493f3621077329f2ed6414/Handbook-of-Industrial-and-innovation-economics.pdf.
Alwadi, M., Alwadi, A., Chetty, G. & Alnaimi, J. 2024. Smart dairy farming for predicting milk production yield based on deep machine learning. International Journal of Information Technology, 16: 4181-4190, ISSN: 2511-2112. https://doi.org/10.1007/s41870-024-01998-5.
Bai, J., Xue, H., Jiang, X. & Zhou, Y. 2022. Recognition of bovine milk somatic cells based on multi-feature extraction and a GBDT-AdaBoost fusion model. Mathematical Biosciences and Engineering: MBE, 19(6): 5850-5866, ISSN: 1551-0018. https://doi.org/10.3934/mbe.2022274.
Balarezo, L., García-D, J., Hernández, M. & García-L, R. 2016. Metabolic and reproductive state of Holstein cattle in the Carchi region, Ecuador. Cuban Journal of Agricultural Science, 50(3): 381-392, ISSN: 2079-3480. https://cjascience.com/index.php/CJAS/article/view/632/699.
Bentéjac, C., Csörgő, A. & Martínez-Muñoz, G. 2020. A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 54(3): 1937-1967, ISSN: 1573-7462. https://doi.org/10.1007/s10462-020-09896-5.
Bovo, M., Agrusti, M., Benni, S., Torreggiani, D, & Tassinari P. 2021. Random Forest Modelling of Milk Yield of Dairy Cows under Heat Stress Conditions. Animals, 11(5): 1305, ISSN: 2076-2615. https://doi.org/10.3390/ani11051305.
Carvajal, L.A. 2014. La asociatividad en el sector agropecuario del Carchi y su potencial de producir y comercializar semielaborados de papa y leche. SATHIRI, 7(7): 153-163, ISSN: 2631-2905. https://doi.org/10.32645/13906925.348.
CIL Ecuador. 2023. La industria láctea fomenta la economía circular, a través de una producción sostenible, Comprometidos con el Desarrollo de la Cadena Láctea. Available at: https://www.cil-ecuador.org/post/la-industria-láctea-fomenta-la-economía-circular-a-través-de-una-producción-sostenible. [Consulted: March 10, 2024].
Fadillah, A., van den Borne, B.H.P., Poetri, O.N., Hogeveen, H., Umberger, W., Hetherington, J., & Schukken, Y.H. 2023. Smallholder milk-quality awareness in Indonesian dairy farms. Journal of Dairy Science, 106(11): 7965-7973, ISSN: 0022-0302. https://doi.org/10.3168/JDS.2023-23267.
FAO. 2018. Panorama de la pobreza rural en América Latina y el Caribe. Roma, 114p. ISBN: 978-92-5-131085-4 Available at: https://openknowledge.fao.org/handle/20.500.14283/ca2275es. [Consulted: February 03, 2024].
FAO. 2022a. The State of Food and Agriculture 2022. Roma, 182p. ISBN: 978-92-5-136043-9. https://doi.org/10.4060/cb9479en.
FAO. 2022b. La aplicación de las mejores prácticas de la inteligencia artificial en el contexto de la agricultura, editado por Bishan Dong, 136. Roma: FAO Publications Catalogue 2022. ISBN: 78-92-5-136969-2.
FAO. 2023a. FAO analiza fortalezas y brechas de la producción láctea en América Latina y el Caribe, Más Allá de La Finca Lechera. Available at: https://www.fao.org/americas/noticias/ver/es/c/1617544/. [Consulted: July 18, 2024].
Franco, W. 2016. Propuestas para la innovación en los sistemas agroproductivos y el desarrollo sostenible del Valle Interandino en Carchi, Ecuador. Tierra Infinita, 2(1): 49-87, ISSN: 2631-2921. https://doi.org/10.32645/26028131.104.
Gaudin, Y. & Padilla, R. 2020. Los intermediarios en cadenas de valor agropecuarias: un análisis de la apropiación y generación de valor agregado (N° 186 (LC/TS.2020/77; LC/MEX/TS.2020/15). Serie Estudios y Perspectivas-Sede Subregional de La CEPAL en México. Available at: https://www.cepal.org/es/publicaciones/45796-intermediarios-cadenas-valor-agropecuarias-un-analisis-la-apropiacion-generacion. [Consulted: August 20, 2024].
Gaurav, K.A. & Patel, L. 2020. Machine Learning With R. In S. Khalid (Ed.), Applications of Artificial Intelligence in Electrical Engineering (pp. 291-331), ISBN: 9781799827184. IGI Global. https://doi.org/10.4018/978-1-7998-2718-4.ch015.
Géron, A. 2019. Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems (2nd ed.). O’Reilly Media. ISBN: 978-1-492-03264-9. Available at: https://books.google.com.ec/books?id=HnetDwAAQBAJ&printsec=frontcover&hl=es&source=gbs_book_other_versions#v=onepage&q&f=false. [Consulted: August 10, 2024].
Gil Montelongo, M. & Hernández Villa, X. 2019. Risk management as a tool in the internal control on organizations of the dairy sector. Ekotemas, 5(2): 51-66, ISSN: 2414-4681. https://www.ekotemas.cu/index.php/ekotemas/article/view/63/54.
Hernández-Sampieri, R., & Mendoza, C. 2018. Metodología de la investigación. Las rutas cuantitativa, cualitativa y mixta. In Interamericana (Ed.), McGRAW-HILL Interamericana Editores S.A. de C.V. Mc Graw Hill. ISBN: 978-1-4562-6096-5.
Ionita, E. 2022. La producción de leche en Ecuador, Veterinaria Digital. Available at: https://www.veterinariadigital.com/articulos/la-produccion-de-leche-en-ecuador/. [Consulted: January 20, 2024].
Ji, B., Banhazi, T., Phillips, C.J.C., Wang, C. & Li, B. 2022. A machine learning framework to predict the next month’s daily milk yield, milk composition and milking frequency for cows in a robotic dairy farm. Biosystems Engineering, 216(9): 186-197, ISSN: 1537-5110. https://doi.org/10.1016/j.biosystemseng.2022.02.013.
Kassahun, A., Bloo, R., Catal, C. & Mishra, A. 2022. Dairy Farm Management Information Systems. Electronics, 11(2): 1-18, ISSN: 2079-9292. https://doi.org/10.3390/electronics11020239.
Kliś, P., Piwczyński, D., Sawa, A. & Sitkowska, B. 2021. Prediction of Lactational Milk Yield of Cows Based on Data Recorded by AMS during the Periparturient Period. Animals, 11(383): 1-11, ISSN: 2076-2615. https://doi.org/10.3390/ANI11020383.
Mannepalli, P.K., Kulurkar, P., Jangade, V., Khan, A., & Singh, P. 2024. An Enhanced Classification Model for Depression Detection Based on Machine Learning with Feature Selection Technique. En P. K. Jha, B. Tripathi, E. Natarajan, & H. Sharma (Eds.), Proceedings of Congress on Control, Robotics, and Mechatronics (Vol. 364, pp. 589-601). Springer Nature Singapore. https://doi.org/10.1007/978-981-99-5180-2_46
Moreno, F. 2018. Caracterización socioeconómica y productiva de la cadena de valor agroalimentaria de la leche en la provincia de Tungurahua. Tesis presentada en opción al Título de carrera de Ingeniería de los alimentos, Universidad Técnica de Ambato, Ecuador.
Morocho, B., Carvajal, H. & Vite, H. 2021. Análisis socioeconómico del agronegocio ganadero: Caso productores de la Aso Ganaderos del Altiplano Orense 5 de noviembre del cantón Atahualpa. Revista Metropolitana de Ciencias Aplicadas, 4(1): 26-32, ISSN: 2631-2662.
Mwanga, G., Lockwood, S., Mujibi, D., Yonah, Z. & Chagunda, M. 2020. Machine learning models for predicting the use of different animal breeding services in smallholder dairy farms in Sub-Saharan Africa.Tropical Animal Health and Production,52(3): 1081-1091, ISSN: 1573-7438. https://doi.org/10.1007/s11250-019-02097-5.
Natekin, A. & Knoll, A. 2013. Gradient boosting machines, a tutorial. Frontiers in Neurorobotics, 7(21): 1-21, ISSN: 1662-5218. https://doi.org/10.3389/fnbot.2013.00021.
Nyambo, D.G., Malamsha, G.C. & Mavura, F. 2023. Leveraging Machine Learning Techniques to Improve Learning and Recommendations Within Dairy Farms: Towards High Milk Yields for Small-Scale Farmers. In F. Mtenzi, G. Oreku, & D. Lupiana (Eds.), Impact of Disruptive Technologies on the Socio-Economic Development of Emerging Countries (pp. 172-188), ISBN: 9781668468739. IGI Global. https://doi.org/10.4018/978-1-6684-6873-9.ch011.
Orús, A. 2022. Leche de vaca: principales productores a nivel mundial en 2022. Estatista. Available at: https://es.statista.com/estadisticas/600241/principales-productores-de-leche-de-vaca-en-el-mundo-en/. [Consulted: April 30, 2024].
Piwczyński, D., Sitkowska, B., Kolenda, M., Brzozowski, M., Aerts, J. & Schork, P.M. 2020. Forecasting the milk yield of cows on farms equipped with automatic milking system with the use of decision trees. Animal Science Journal, 91(1): e13414, ISSN: 1740-0929. https://doi.org/10.1111/asj.13414.
Peña, Y., Benitez, D., Ray, J. & Fernández, Y. 2018. Factores determinantes de la producción ganadera en una comunidad campesina del suroeste de Holguín, Cuba. Cuban Journal of Agricultural Science, 52(2): 155-163, ISSN: 2079-3480. http://scielo.sld.cu/scielo.php?pid=S2079-34802018000200155&script=sci_arttext&tlng=es
Prefectura del Carchi. 2023. Datos informativos de la provincia. Available at: https://carchi.gob.ec/2016f/index.php/informacion-provincial.html. [Consulted: April 25, 2024].
Radwan, H., Qaliouby, H. & Elfadl, E. 2020. Classification and prediction of milk yield level for Holstein Friesian cattle using parametric and non-parametric statistical classification models. Journal of Advanced Veterinary and Animal Research, 7(3): 429-435, ISSN: 2311-7710. https://doi.org/10.5455/javar.2020.g438.
Requelme, N. & Bonifaz, N. 2012. Caracterización de sistemas de producción lechera de Ecuador. La Granja, 15(1): 56-69, ISSN: 1390-3799.
Saini, A. 2021. Gradient Boosting Algorithm: A Complete Guide for Beginners. Analytics Vidhya. Available at: https://www.analyticsvidhya.com/blog/2021/09/gradient-boosting-algorithm-a-complete-guide-for-beginners/. [Consulted: March 21, 2024].
Siddiqui, T. & Amer, A.Y.A. 2024. A comprehensive review on text classification and text mining techniques using spam dataset detection. In Mathematics and Computer Science, vol. 2, editado por Ghosh, S., Niranjanamurthy, M., Deyasi, K., Mallik, B. & Das, S., 1-17. Editorial Wiley, ISBN: 978-111989671-5. https://doi.org/10.1002/9781119896715.ch1.
Slob, N., Catal, C. & Kassahun, A. 2021. Application of machine learning to improve dairy farm management: A systematic literature review. Preventive Veterinary Medicine, 187: 105237, ISSN: 1873-1716. https://doi.org/10.1016/j.prevetmed.2020.105237.
Suthaharan, S. 2016. Decision Tree Learning, In Machine Learning Models and Algorithms for Big Data Classification, Integrated Series in Information Systems, vol 36. Springer, Boston, MA., 237-269, ISBN: 9781489976413. https://doi.org/10.1007/978-1-4899-7641-3_10.
Suseendran, G. & Duraisamy, B. 2021. Predication of Dairy Milk Production Using Machine Learning Techniques. In: Peng, SL., Hsieh, SY., Gopalakrishnan, S., Duraisamy, B. (eds) Intelligent Computing and Innovation on Data Science. Lecture Notes in Networks and Systems, 248: Springer, Singapore, ISBN: 978-981-16-3153-5. https://doi.org/10.1007/978-981-16-3153-5_60.
Tangorra, F. M., Calcante, A., Vigone, G., Assirelli, A. & Bisaglia, C. 2022. Assessment of technical-productive aspects in Italian dairy farms equipped with automatic milking systems: A multivariate statistical analysis approach. Journal of Dairy Science, 105(9): 7539-7549, ISSN: 0022-0302. https://doi.org/10.3168/jds.2021-20859.
Terán, G. & Cobo, R. 2017. Determining management factors in dairy farms in Carchi, Ecuador. Cuban Journal of Agricultural Science, 51(2): 175-182, ISSN: 2079-3480. http://cjascience.com/index.php/CJAS/article/view/724.
Treviño Cantú, J.A. 2022. Alternativas de estandarización para índices compuestos espacio-temporales. El caso del rezago educativo en los estados de México, 2000 a 2020. Investigaciones Geográficas, 109: 1-14, ISSN: 2448-7279. https://doi.org/10.14350/rig.60615.
Valdez, A. 2019. Machine Learning para todos. En IV Congreso Nacional de Profesionales de Computación, Informática y Tecnologías. pp. 60. Perú: Ministerio de Educación. https://doi.org/10.13140/RG.2.2.13786.70086.
Vásquez, H., Barrantes, C., Vigo, C. & Maicelo, J. 2022. Factores socioeconómicos que influyen en la adopción de tecnologías para mejoramiento genético de ganado vacuno en Perú. Agricultura, Sociedad y Desarrollo, 19(3): 312-330, ISSN: 2594-0244. https://doi.org/10.22231/asyd.v19i3.1358.
Velasteguí, N. 2019. Cadena productiva del sector lechero en la provincia de Tungurahua, cantón Píllaro: Un estudio socio-económico de la producción de la leche cruda. Tesis presentada en opción al Título de carrera de Economía, Universidad Técnica de Ambato, Ecuador.
Zemarku, Z., Senapathy, M. & Bojago, E. 2022. Determinants of Adoption of Improved Dairy Technologies: The Case of Offa Woreda, Wolaita Zone, Southern Ethiopia. Advances in Agriculture, 2022: 1-19, ISSN: 2314-7539. https://doi.org/10.1155/2022/3947794.