The forecasting of Cattle (Bos taurus) production in the Philippines

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Research Paper 05/07/2023
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The forecasting of Cattle (Bos taurus) production in the Philippines

Elbert M. Galas
Int. J. Biosci. 23(1), 9-17, July 2023.
Copyright Statement: Copyright 2023; The Author(s).
License: CC BY-NC 4.0

Abstract

Cattle (Bos taurus) production forecasting is an important part of the agricultural industry, especially in the Philippines, where the livestock industry is a big part of the income and food security. The goal of this study is to determine possible algorithms that can be used to predict the quantity of cattle production based on the historical data taken from the Philippine Statistical Authority. Advanced statistical methods of predicting the cattle production are used to look at the data and find the most important factors that affect cow production. Machine learning algorithms and predictive analytics are also used to find trends and relationships in the dataset, which makes it easier to make accurate predictions. The forecasting model that was made shows that it is reliable and accurate at predicting future trends. This means that policymakers, livestock farmers, and other stakeholders can make better choices about production planning, resource allocation, and market strategies. The implications of this study are important for the Philippines cattle industry to grow and develop in a way that is sustainable. Reliable production forecasts can help lawmakers make good agricultural policies, like supporting programs to improve breeds, making the best use of feed resources, and making sure there are enough veterinary services. Also, livestock farmers can use these forecasts to make more productive and profitable decisions about how to breed their animals, how to handle their herds, and when to take their animals to market.

Bonfatti V., Albera A., Carnier P. 2013. Genetic associations between daily BW gain and live fleshiness of station-tested young bulls and carcass and meat quality traits of commercial intact males in Piemontese cattle. Journal of Animal Science, 91(5), 2057-2066. DOI: 10.2527/jas.2012-5844

Bonora F., Benni S., Barbaresi A., Tassinari, P., Torreggiani D. 2018. A cluster-graph model for herd characterisation in dairy farms equipped with an automatic milking system. Biosystems Engineering, 167, 1-7. DOI: 10.1016/j.biosystemseng.2017.12.012

Du A., Guo H., Lu J., Su Y., Ma Q., Ruchay A., Marinello F., Pezzuolo A. 2022. Automatic livestock body measurement based on keypoint detection with multiple depth cameras. Computers and Electronics in Agriculture, 198, 107059. DOI: 10.1016/j.compag.2022.107059

Kongsro J. 2014. Estimation of pig weight using a Microsoft Kinect prototype imaging system. Computers and Electronics in Agriculture, 109, 32-35. DOI: 10.1016/j.compag.2014.09.001

Nephawe KA., Cundiff LV., Dikeman ME., Crouse JD., Van Vleck LD. 2004. Genetic relationships between sex-specific traits in beef cattle: Mature weight, weight adjusted for body condition score, height and body condition score of cows, and carcass traits of their steer relatives. Journal of Animal Science, 82(2), 647-653. DOI: 10.2527/2004.822647x

Ouweltjes W., Spoelstra M., Ducro B., De Haas Y., Kamphuis C. 2021. A data-driven prediction of lifetime resilience of dairy cows using commercial sensor data collected during first lactation. Journal of Dairy Science, 104(11), 11759-11769. DOI: 10.3168/jds.2020-20191

Ribeiro AMF., Sanglard LP., Snelling WM., Thallman RM., Kuehn LA., Spangler ML. 2022. Genetic parameters, heterosis, and breed effects for body condition score and mature cow weight in beef cattle. Journal of Animal Science, 100(1), 017. DOI: 10.1093/jas/skab017

Ruchay A., Kober V., Dorofeev K., Kolpakov V., Miroshnikov S. 2020. Accurate body measurement of live cattle using three depth cameras and non-rigid 3D shape recovery. Computers and Electronics in Agriculture, 179, 105821. DOI: 10.1016/j.compag.2020.105821

Ruchay, A., Kober, V., Dorofeev, K., Kolpakov, V., Dzhulamanov, K., Kalschikov, V., & Guo, H. 2022. Comparative analysis of machine learning algorithms for predicting live weight of Hereford cows. Computers and Electronics in Agriculture, 195, 106837. DOI: 10.1016/j.compag.2021.106837

Ruchay A., Kober V., Dorofeev K., Kolpakov V., Gladkov A., Guo H. 2022. Live Weight Prediction of Cattle Based on Deep Regression of RGB-D Images. Agriculture, 12(10), 1794. DOI: 10.3390/agriculture12101794

Ruchay A., Kolpakov V., Kosyan D., Rusakova, E., Dorofeev K., Guo H., Ferrari G., Pezzuolo, A. 2022. Genome-Wide Associative Study of Phenotypic Parameters of the 3D Body Model of Aberdeen Angus Cattle with Multiple Depth Cameras. Animals, 12(10), 2128. DOI: 10.3390/ani12102128

Santana ML. Jr., Eler JP., Bignardi AB., Ferraz, J.B. 2013. Genetic associations among average annual productivity, growth traits, and stayability: A parallel between Nelore and composite beef cattle. Journal of Animal Science, 91(6), 2566-2574. DOI: 10.2527/jas.2012-5841

Weik F., Hickson RE., Morris ST., Garrick, DJ., Archer JA. 2021. Genetic Parameters for Growth, Ultrasound and Carcass Traits in New Zealand Beef Cattle and Their Correlations with Maternal Performance. Animals 12(1), 25. DOI: 10.3390/ani12010025

Witten IH, Frank E, Hall MA, Pal CJ. 2016. The WEKA Workbench. https://www.cs.waikato.ac.nz/ml/weka/Witten_et_al_2016_appendix.pdf

Wolfová M., Wolf J. 2013. Strategies for defining traits when calculating economic values for livestock breeding: A review. Animal, 7(9), 1401-1413. DOI: 10.1017/S1751731113000908

Wongsriworaphon A., Arnonkijpanich B., Pathumnakul S. 2015. An approach based on digital image analysis to estimate the live weights of pigs in farm environments. Computers and Electronics in Agriculture, 115, 26-33. DOI: 10.1016/j.compag.2015.05.005

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