The Global Goose Meat Production Quantity Forecast for the 2023–2027 Years

Year 2024, Volume: 38 Issue: 2, 326 - 341, 22.08.2024

Büşra Dumlu

Abstract

This study examines the growing acceptance of goose meat production, its nutritional value, and its varied cultural and gastronomic significance. The purpose of this study was to evaluate the global output of goose meat between 2023 and 2027. The investigation was carried out using statistical data websites, such as FAOSTAT. Forecasts for upcoming years were created by combining data on the production of goose meat from 1961 to 2022. The ARIMA model was used to create forecasts, and the most appropriate model was found using the SAS statistical program. Because it outperformed the other models on several metrics, including AIC, BIC, SSE, MSE, SBC, MAE, MAPE, DW, RMSE, HQC, and R2, the ARIMA (3,1,1) model was determined to be the most suitable model. It is projected that the amount of goose meat produced worldwide will rise from 150 thousand tons in 1961 to 4 million 751 thousand tons in 2027. A change of -0.019% was computed based on the differences between the average of the 61 years that followed this period and the actual production figures for the 62 years between 1961 and 2022. The current study predicts that global goose meat production will increase by 246.32% in the five years between 2023 and 2027, compared to the average of the previous 62 years. The results of this study, which used advanced statistical methods and market analysis, suggest that goose meat production will increase over the next five years.

Keywords

Goose meat, Production, Forecast, ARIMA

References

• Alabdulrazzaq H, Alenezi MN, Rawajfih Y, Alghannam BA, Al-Hassan AA, AlAnzi FS (2021). On the accuracy of ARIMA based prediction of COVID-19 spread. Results in Physics 27: 104509.
• Ambikapathi R, Schneider KR, Davis B, Herrero M, Winters P, Fanzo JC (2022). Global food systems transitions have enabled affordable diets but had less favourable outcomes for nutrition. environmental health. inclusion and equity. Nature Food 3: 764-779.
• Andipara R (2022). Applying ARIMA-GARCH models for time series analysis on Seasonal and Nonseasonal datasets. Stevens Institute of Technology.
• Anonim (2024a). Global Duck and Goose Meat Market to Keep Growing. Driven by Strong Demand in Asia. URL: https://www.globaltrademag.com/global-duck-and-goose-meat-market-to-keep-growing-driven-by-strong-demand-in-asia/ (accessed date: 20.01.2024).
• Anonim (2024b). Value Added Goose Meat-Tridge. URL: https://www.tridge.com/intelligences/processed-goose-meat-products/price (accessed date: 20.01.2024).
• Anonim (2024c). Global Duck and Goose Meat Market Report 2024. URL: https://www.indexbox.io/store/world-duck-and-goose-meat-market-analysis-forecast-size-trends-and-insights/ (accessed date: 20.01.2024).
• Anonim (2024d). The global duck and goose meat consumption will grow to 8 million tons. URL: https://www.euromeatnews.com/Article-The-global-duck-and-goose-meat_consumption-will-grow-to-8-million-tons/3267 (accessed date: 20.01.2024).
• ArunKumar KE, Kalaga DV, Kumar CMS, Chilkoor G, Kawaji M, Brenza TM (2021). Forecasting the Dynamics of Cumulative Covıd-19 Cases (Confirmed. Recovered and Deaths) for Top-16 Countries Using Statistical Machine Learning Models: AutoRegressive Integrated Moving Average (ARIMA) And Seasonal Auto-Regressive Integrated Moving Average (SARIMA). Applied Soft Computing 103: 107161.
• Bai L, Lu K, Dong Y, Wang X, Gong Y, Xia Y, Wang X, Chen L, Yan S, Tang Z, Li C (2023). Predicting monthly hospital outpatient visits based on meteorological environmental factors using the ARIMA model. Scientific Reports 13: 2691. https://doi.org/10.1038/s41598-023-29897-y.
• Box GEP, Jenkins GM, Reinsel GC, Ljung GM (2016). Time Series Analysis: Forecasting and Control. Fifth Edition. John Wiley and Sons Inc. Hoboken, New Jersey, USA.
• Brownlee J (2020). A Gentle Introduction to the Box-Jenkins Method for Time Series Forecasting. Machine learning mastery. Retrieved from https://machinelearningmastery.com/gentle-introductionbox-jenkins-method-time-series-forecasting.
• Buzała M, Adamski M, Janicki B (2014). Characteristics of performance traits and the quality of meat and fat in Polish oat geese. World’s Poultry Science Journal 70(3): 531-542. doi: https://doi.org/10.1017/S0043933914000580.
• Cowpertwait PSP, Metcalfe AV (2009). Introductory Time Series With R. Springer Science+Business Media. LLC. doi: 10.1007/978-0-387-88698-5.
• Ederer P, Baltenweck I, Blignaut JN, Moretti C, Tarawali S (2023). Affordability of meat for global consumers and the need to sustain investment capacity for livestock farmers. Animal Frontiers 13(2): 45-60. https://doi.org/10.1093/af/vfad004.
• Elmadfa I, Meyer AL (2017). Animal Proteins as Important Contributors to a Healthy Human Diet. Annual Review of Animal Biosciences 8(5): 111-131. doi: 10.1146/annurev-animal-022516-022943.
• FAOSTAT (2024). Meat Production in the World. http://www.fao.org/faostat/en/#data/QC. (access date: 26.01.2024).
• Fatta J, Ezzine L, Aman Z, Moussami HE, Lachhab A (2018). Forecasting of demand using ARIMA model. International Journal of Engineering Business Management 10(2): 184797901880867. DOI:10.1177/1847979018808673.
• Goluch Z, Haraf G (2023). Goose Meat as a Source of Dietary Manganese-A Systematic Review. Animals 13(5): 840. doi: 10.3390/ani13050840.
• Gürer B (2021). Evaluation of the supply and demand for animal products in terms of sufficient and balanced nutrition in Turkey. GIDA 46(6): 1450-1466 doi: 10.15237/gida.GD21083.
• Hamel AA, Ismael B (2022). Time Series Forecasting Using ARIMA Model. https://www.researchgate.net/publication/358891394_Time_series_Forecasting_Using_ARIMA_model.
• Kılıç B (2021). The enjoyment of goose meat as gastronomic and economic value: a study on sensory criteria. Journal of Yasar University 16(62): 560-586.
• Kohvakka S (2017). Forecasting Univariate Time Series-Comparison of Statistical Methods and Software Resources Available to Undergraduate Students. MS Thesis; USA.
• Kozák J (2021). Goose production and goose products. World's Poultry Science Journal. doi: 10.1080/00439339.2021.1885002.
• Kurtoğlu S, Uzundumlu AS, Gövez E (2024). Olive oil production forecasts for a macro perspective during 2024–2027. Applied Fruit Science 66(3): 1089-1100 https://doi.org/10.1007/s10341-024-01064-1.
• Linardatos P, Papastefanopoulos V, Panagiotakopoulos T, Kotsiantis S (2023). CO2 concentration forecasting in smart cities using a hybrid ARIMA–TFT model on multivariate time series IoT data. Scientific Reports 13: 17266. https://doi.org/10.1038/s41598-023-42346-0.
• Makridakis S, Wheelwright SC (1978). Forecasting Methods and Application. JhonWilney. Nason GP (2006). Stationary and non-stationary time series. Book Chapter. https://doi.org/10.1144/IAVCEI001.11.
• Orkusz A, Wolanska W, Krajinska U (2021). The Assessment of Changes in the Fatty Acid Profile and Dietary Indicators Depending on the Storage Conditions of Goose Meat. Molecules 26(17): 5122. https://doi.org/10.3390/molecules26175122.
• Öz F, Çelik T (2015). Proximate composition. color and nutritional profile of raw and cooked goose meat with different methods. Journal of Food Processing and Preservation 39(6): 2442-2454. https://doi.org/10.1111/jfpp.12494.
• Palabıçak MA (2019). Red meat sector and equilibrium of production and consumption analysis for the future in Turkey. Master’s Thesis, Harran University (Unpublished).
• Pereira PM, Vicente AF (2013). Meat nutritional composition and nutritive role in the human diet. Meat Science 93(3): 586-592. doi: 10.1016/j.meatsci.2012.09.018.
• Prabhakaran S (2019). ARIMA Model-Complete Guide to Time Series Forecasting in Python. Machine Learning. https://www.machinelearningplus.com/time-series/arima-model-time-series-forecasting-python.
• Reid-McCann RJ, Brennan SF, McKinley MC, McEvoy CT (2022). The effect of animal versus plant protein on muscle mass. muscle strength. physical performance and sarcopenia in adults: protocol for a systematic review. Systematic Reviews 11(1): 64. doi: 10.1186/s13643-022-01951-2.
• Reinsel GC (1994). Time Series Analysis: Forecasting and Control. Journal of Marketing Research 14(2): 5561569.
• SAS (2014). SAS 13.2 User’s Guide The ARIMA Procedure. SAS Institute Inc.. Cary. NC. USA. https://support.sas.com/documentation/onlinedoc/ets/132/arima.pdf. (access date: 24.06.2021).
• Schneider UA, Havlík P, Schmid E, Valin H, Mosnier A, Obersteiner M, Böttcher H, Skalský R, Balkovič J, Sauer T, Fritz S (2011). Impacts of population growth. economic development. and technical change on food production and consumption. Agricultural Systems 104(2): 204-215. https://doi.org/10.1016/j.agsy.2010.11.003.
• Silva ABDS, Frias PG, Bonfim C (2021). Auto-Regressive Integrated Moving Average Model (ARIMA): conceptual and methodological aspects and applicability in infant mortality. Article in Revista Brasileira de Saúde Materno Infantil 21(2): 647-656. DOI: 10.1590/1806-93042021000200016.
• Agricultural Economics and Policy Development Institute (TEPGE). 2024. https://arastirma.tarimorman.gov.tr/tepge. (access date:31.01.2024).
• Uhlířová L, Tůmová E, Chodová D, Vlčková J, Ketta M, Volek Z, Skřivanová V (2018). The effect of age. genotype and sex on carcass traits. meat quality and sensory attributes of geese. Asian-Australasian Journal of Animal Science 31(3): 421-428. doi: 10.5713/ajas.17.0197.
• Uzundumlu AS, Dilli M (2023). Estimating chicken meat productions of leader countries for 2019-2025 years. Ciência Rural 53(2): 1-12. https: //doi.org/10.1590/0103-8478cr20210477
• Uzundumlu AS, Zeynalova A, Engindeniz S (2023). Cotton production forecasts of Azerbaijan in the 2023-2027 periods. Journal of Agriculture Faculty of Ege University 60(2): 235-245. https://doi.org/10.20289/zfdergi.1296642.
• Wagner B, Cleland K (2023). Using autoregressive integrated moving average models for time series analysis of observational data. The BMJ 383.2739. doi: https://doi.org/10.1136/bmj.p2739.
• Wickramasinghe K, Mathers JC, Wopereis S, Marsman DS, Griffiths JC (2020). From lifespan to healthspan: the role of nutrition in healthy ageing. Journal of Nutritional Science 24(9): e33. doi: 10.1017/jns.2020.26.
• Wilson GT, Armitage P (2012). Box-Jenkins Seasonal Models. Research Gate 2: 156. doi: 10.1007/978-0-85729-974-1_8.
• Wilson GT (2016). Time Series Analysis: Forecasting and Control.5th Edition. by George E. P. Box. Gwilym M. Jenkins. Gregory C. Reinsel and Greta M. Ljung. 2015. Published by John Wiley and Sons Inc.. Hoboken. New Jersey. pp. 712. ISBN: 978-1-118-67502-1. Journal of Time Series Analysis, Published online in Wiley Online Library (wileyonlinelibrary.com) doi: 10.1111/jtsa.12194.
• Wu G (2019). Important roles of animal protein in human nutrition and health. Texas Ave M University Department of Animal Science. (access date: 20 Ocak 2024).
• Yıldırım AE (2023). Strategy based on imports in meat is very risky. 24 Ekim 2023 Salı. https://www.ekonomim.com/kose-yazisi/ette-ithalata-dayali-strateji-cok-riskli/712747.
• Yüksel D (2015). The Analysis of Import and Export Quantities Between The Near and Middle East Countries Which Are Affected From the Arab Spring and Turkey with ARIMA Models. Master’s Thesis, Dokuz Eylül University (Unpublished).
• Zhao D, Zhang R, Zhang H, He S (2022). Prediction of global omicron pandemic using ARIMA. MLR. and Prophet models. Scientific Reports 12: 18138. https://doi.org/10.1038/s41598-022-23154-4.