Predictive model using multiple linear regression to optimize feed efficiency in automated poultry farms
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Abstract
Introduction: the poultry industry operates in a highly competitive environment, where feed costs represent between 60% and 70% of operating costs. Although automated farms generate large volumes of environmental data through IoT systems, their potential is often underutilized in decision-making. Objectives: to develop a predictive model based on multiple linear regression to estimate the weekly FCR in automated poultry farms under humid tropical conditions of Ecuador. Methodology: a retrospective longitudinal observational study was conducted with 936 weekly records (2022-2025) from seven automated farms. Multiple linear regression was applied with stratified hold-out validation (80/20), assessment of statistical assumptions, and evaluation using R², MAE, and RMSE. Results: the model demonstrated robust predictive capacity with R² = 0.774 (training) and R² = 0.739 (external validation), accompanied by MAE = 0.088 and RMSE = 0.109. Significant variables (p < 0.001) included ambient temperature (β = -0.0445 FCR units/°C), relative humidity (β = +0.00858 FCR units/%), physiological phase (β = +0.198 FCR units for weeks 5-7), and genetic line (β = +0.072 FCR units for ROSS 308). CO₂ and NH₃ did not reach statistical significance. Conclusion: the model demonstrates predictive capacity for preventive FCR management under humid tropical conditions, offering an interpretable tool for immediate implementation that enables reliable weekly projections with direct implications for profitability through optimization of feed efficiency. General study area: Agricultural Engineering. Specific study area: Precision Poultry Farming. Type of study: Original article.
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Riofrio Núñez, E. A., Montaleza Quizhpe, I. P., & Robayo Cabrera, G. F. (2026). Predictive model using multiple linear regression to optimize feed efficiency in automated poultry farms. Ciencia Digital, 10(2), 60-82. https://doi.org/10.33262/cienciadigital.v10i2.3643
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References
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Pereira, W. F., Fonseca, L. S., Putti, F. F., Góes, B. C., & Naves, L. P. (2020). Environmental monitoring in a poultry farm using an instrument developed with the Internet of Things concept. Computers and Electronics in Agriculture, 170, 105257. https://doi.org/10.1016/j.compag.2020.105257
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Tahamtani, F. M., Pedersen, I. J., & Riber, A. B. (2020). Effects of environmental complexity on welfare indicators of fast-growing broiler chickens. Poultry Science, 99(1), 21–29. https://doi.org/10.3382/ps/pez510
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You, J., Lou, E., Afrouziyeh, M., Zukiwsky, N. M., & Zuidhof, M. J. (2021). Using an artificial neural network to predict the probability of oviposition events of precision-fed broiler breeder hens. Poultry Science, 100(8), 101187. https://doi.org/10.1016/j.psj.2021.101187
Ahmed, M. M., Hassanien, E. E., & Hassanien, A. E. (2024). A smart IoT-based monitoring system in poultry farms using chicken behavioural analysis. Internet of Things, 25, 101010. https://doi.org/10.1016/j.iot.2023.101010
Astill, J., Dara, R. A., Fraser, E. D. G., Roberts, B., & Sharif, S. (2020). Smart poultry management: smart sensors, big data, and the internet of things. Computers and Electronics in Agriculture, 170, 105291. https://doi.org/10.1016/j.compag.2020.105291
Batista-Mendoza, G., Cedeño Herrera, E. J., & Cedeño-Batista, G. (2023). Machine learning aplicado al análisis de un conjunto de datos de parámetros ambientales en galpones de pollos de engorde. Visión Antataura, 7(2), 121-146. https://doi.org/10.48204/j.vian.v7n2.a4566
Corporación Nacional de Avicultores del Ecuador [CONAVE]. (2024). CONAVE presenta las estadísticas del sector avícola. https://conave.org/conave-presenta-las-estadisticas-del-sector-avicola/
Elwakeel, A. E. (2025). A smart automatic control and monitoring system for environmental control in poultry houses integrated with earlier warning system. Scientific Reports, 15(1), 31630. https://doi.org/10.1038/s41598-025-17074-2
Fang, C., Huang, J., Cuan, K., Zhuang, X., & Zhang, T. (2020). Comparative study on poultry target tracking algorithms based on a deep regression network. Biosystems Engineering, 190, 176–183. https://doi.org/10.1016/j.biosystemseng.2019.12.002
Freitas, R. C., Calderano, A. A., Oliveira, C. H., Neto, M. G., & Genova, J. L. (2025). Combined analysis of multiple linear regression and principal components for predicting performance indicators in broiler chickens under commercial conditions. Poultry Science, 104(11), 105728. https://doi.org/10.1016/j.psj.2025.105728
Gallard, E., Menichelli, M., Dimasso, R., & Revidatti, F. (2022). Effect of stocking density and shed area on welfare indicators in broiler chickens. Revista Veterinaria, 33(2), 230–234. http://dx.doi.org/10.30972/vet.3326188
Gholami, M., Chamani, M., Seidavi, A., Sadeghi, A. A., & Aminafschar, M. (2020). Effects of stocking density and environmental conditions on performance, immunity, carcass characteristics, blood components and economic parameters of Cobb 500 strain broiler chickens. Italian Journal of Animal Science, 19(1), 524–535. https://doi.org/10.1080/1828051X.2020.1757522
González Martínez, A. A., de Aleancar Nääs, I., Ridolfi de Carvalho-Curi, T. M., Minoro Abe, J., & Duarte da Silva Lima, N. (2021). A heuristic and data mining model to predict the environmental suitability of broiler houses. Animals, 11(10), 2780. https://doi.org/10.3390/ani11102780
Granda Tola, C. F., Cobos Mora, S. L., & Vásquez Quiroz, P. T. (2023). Rendimiento de mano de obra en excavaciones a mano mediante regresión lineal. Caso de estudio: ciudad de Cuenca. Ciencia Digital, 7(3), 124–146. https://doi.org/10.33262/cienciadigital.v7i3.2629
Hidalgo López, G. Y., Zambrano Villacís, J. J., & Marini, P. R. (2024). Indicadores de eficiencia productiva en granjas avícolas convencionales vs. técnicas ubicadas en la provincia de Manabí - Ecuador. Ciencia Digital, 8(3), 122–136. https://doi.org/10.33262/cienciadigital.v8i3.2963
Jackman, P., Penya, H., & Ross, R. (2020). The role of information and communication technologies in broiler production process control: A review. International Agricultural Engineering: CIGR Journal, 22(3), 284–299. https://researchprofiles.tudublin.ie/en/publications/the-role-of-information-and-communication-technology-in-poultry-b-2/
Li, M., Zhou, Z., Zhang, Q., Zhang, J., Suo, Y., Liu, J., … Li, C. (2024). Multivariate analysis for data mining to characterize poultry house environment in winter. Poultry Science, 103(5), 103633. https://doi.org/10.1016/j.psj.2024.103633
Liu, G., Guo, H., Ruchay, A., & Pezzuolo, A. (2023). Recent advances in precision livestock farming. Agriculture, 13(9), 1652. https://doi.org/10.3390/agriculture13091652
Liu, M., Chen, H., Zhou, Z., Du, X., Zhao, Y., Ji, H., & Teng, G. (2024). Development of an intelligent service platform for a chicken house facility environment based on the internet of things. Agriculture, 14(8), 1277. https://doi.org/10.3390/agriculture14081277
Muyulema Allaica, C. A., Muyulema Allaica, J. C., Pucha Medina, P. M., & Ocaña Parra, S. V. (2020). Los costos de producción y su incidencia en la rentabilidad de una empresa avícola integrada del Ecuador: caso de estudio. Visionario Digital, 4(1), 43–66. https://doi.org/10.33262/visionariodigital.v4i1.1089
Nasiri, A., Yoder, J., Zhao, Y., Hawkins, S., Prado, M., & Gan, H. (2022). Pose estimation-based lameness recognition in broiler using CNN-LSTM network. Computers and Electronics in Agriculture, 197, 106931. https://doi.org/10.1016/j.compag.2022.106931
Natho, P., Boonying, S., Bonguleaum, P., Tantidontanet, N., & Chamuthai, L. (2025). An enhanced machine vision system for smart poultry farms using deep learning. Smart Agricultural Technology, 12, 101083. https://doi.org/10.1016/j.atech.2025.101083
Nyalala, I., Okinda, C., Kunjie, C., Korohou, T., Nyalala, L., & Chao, Q. (2021). Weight and volume estimation of poultry and products based on computer vision systems: a review. Poultry Science, 100(5), 101072. https://doi.org/10.1016/j.psj.2021.101072
Ojo, R. O., Ajayi, A. O., Owolabi, H. A., Oyedele, L. O., & Akanbi, L. A. (2022). Internet of Things and machine learning techniques in poultry health and welfare management: A systematic literature review. Computers and Electronics in Agriculture, 200, 107266. https://doi.org/10.1016/j.compag.2022.107266
Oke, O. E., Akosile, O. A., Uyanga, V. A., Oke, F. O., Oni, A. I., Tona, K., & Onagbesan, O. M. (2024). Climate change and broiler production. Veterinary Medicine and Science, 10(3), e1416. https://doi.org/10.1002/vms3.1416
Pereira, W. F., Fonseca, L. S., Putti, F. F., Góes, B. C., & Naves, L. P. (2020). Environmental monitoring in a poultry farm using an instrument developed with the Internet of Things concept. Computers and Electronics in Agriculture, 170, 105257. https://doi.org/10.1016/j.compag.2020.105257
Qaid, M., Albatshan, H., Hussein, E., & Al-Garadi, M. (2023). Effect of housing system and housing density on performance, viability, and gastrointestinal tract growth of broiler chicks during the first 2 weeks of age. Poultry Science, 102(7), 102752. https://doi.org/10.1016/j.psj.2023.102752
Quintana-Ospina, G. A., Alfaro-Wisaquillo, M. C., Oviedo-Rondón, E. O., Ruiz-Ramírez, J. R., Bernal-Arango, L. C., & Martínez-Bernal, G. D. (2023). Data analysis of growth dynamics and feed conversion rate of broiler chickens raised up to 35 days under tropical commercial conditions. Animals, 13(15), 2447. https://doi.org/10.3390/ani13152447
Shynkaruk, T., Long, K., LeBlanc, M., & Schwean-Lardner, K. (2023). Impact of stocking density on the welfare and productivity of broiler chickens reared to 34 d of age. Journal of Applied Poultry Research, 32(2), 100344. https://doi.org/10.1016/j.japr.2023.100344
Tahamtani, F. M., Pedersen, I. J., & Riber, A. B. (2020). Effects of environmental complexity on welfare indicators of fast-growing broiler chickens. Poultry Science, 99(1), 21–29. https://doi.org/10.3382/ps/pez510
Vilema Lara, P. H., García Mora, F. A., & Gallegos Londoño, C. M. (2022). Aprendizaje de máquina para mantenimiento predictivo: un problema de clasificación binaria. Conciencia Digital, 5(2.1), 45–68. https://doi.org/10.33262/concienciadigital.v5i2.1.2150
You, J., Lou, E., Afrouziyeh, M., Zukiwsky, N. M., & Zuidhof, M. J. (2021). Using an artificial neural network to predict the probability of oviposition events of precision-fed broiler breeder hens. Poultry Science, 100(8), 101187. https://doi.org/10.1016/j.psj.2021.101187