Time series analysis of cow milk production at Andassa dairy farm, West Gojam Zone, Amhara Region, Ethiopia

Taye, Birhan Ambachew; Alene, Alemayehu Amsalu; Nega, Ashenafi Kalayu; Yirsaw, Bantie Getnet

doi:10.1007/s40808-020-00946-z

Cited by 9 publications

(7 citation statements)

References 12 publications

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“…The stationarity of the gathered data is assessed to apply the ARIMA model, which is an extension of the autoregressive (AR) and moving average (MA) models. As per the ARIMA model, the analyzed time-series data can be described as a linear combination of their previous values and unpredictable disturbances [29][30][31]. This model is characterized by three key components: p (representing the autoregressive order), d (indicating the order of differencing required to achieve stationarity), and q (denoting the moving average order).…”

Section: Methodsmentioning

confidence: 99%

Enhancing Sustainable Dairy Industry Growth through Cold-Supply-Chain-Integrated Production Forecasting

Kashyap,

Shukla,

Jha

et al. 2023

Sustainability

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Cold supply chains (CSCs) are critical for preserving the quality and safety of perishable products like milk, which plays a vital role in the daily lives of a vast population, especially in countries like India. This research centers on sustainable milk production in Northern India, with priorities of ensuring efficiency and waste reduction within the cold supply chain. Leveraging data from a prominent North India-based dairy company, Company ‘X’, an ARIMA model is applied for predicting monthly milk production trends. Utilizing the Statistical Package for the Social Sciences (IBM SPSS STATISTICS 20) software, the study forecasts Company ‘X’s monthly milk production and identifies four distinct ARIMA models based on the autocorrelation function (ACF) and the partial autocorrelation function (PACF). By comparing predicted and actual milk production values (April–October 2021), sustainability metrics are integrated into ARIMA forecasts. Implications for the dairy sector’s sustainability and alignment with the Sustainable Development Goals (SDGs) are assessed through error terms such as R squared (R2) and mean absolute percentage error (MAPE). The study promotes sustainable milk production practices in Northern India’s dairy sector, resonating with the SDGs to optimize demand–supply dynamics and foster a more environmentally conscious dairy industry.

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Section: Methodsmentioning

confidence: 99%

Enhancing Sustainable Dairy Industry Growth through Cold-Supply-Chain-Integrated Production Forecasting

Kashyap,

Shukla,

Jha

et al. 2023

Sustainability

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“…The modeling and forecasting of the COVID-19 pandemic is formulated as a typical univariate time-series problem using the autoregressive integrated moving average (ARIMA) technique, wherein it is assumed that the current or future values of the diagnosed cases/recoveries/deaths are functions of their lagged (past) values. ARIMA models are typically used to model and forecast processes that yield a time-series as output, and have been used in varied areas ranging from weather forecasting (Wanishsakpong and Owusu 2020), transportation forecasting (Ediger and Akar 2007), fuel energy demand forecasting (Andreoni and Postorino 2006), milk production forecasting (Taye et al 2020), groundwater level forecasting (Abuamra et al 2020) to Stock price prediction (Ariyo et al 2014).…”

Section: Modeling and Forecasting Of Covid-19 In Indiamentioning

confidence: 99%

Mathematical modeling and a month ahead forecast of the coronavirus disease 2019 (COVID-19) pandemic: an Indian scenario

Ganiny

Nisar

2021

Model. Earth Syst. Environ.

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India, the second-most populous country in the world is witnessing a daily surge in the COVID-19 infected cases. India is currently among the worst-hit nations worldwide due to the COVID-19 pandemic and ranks just behind Brazil and the USA. The prediction of the future course of the pandemic is thus of utmost importance in order to prevent further worsening of the situation. In this paper, we develop models for the past trajectory (March 01, 2020-July 25, 2020) and also make a month-long (July 26, 2020-August 24, 2020) forecast of the future evolution of the COVID-19 pandemic in India by using an autoregressive integrated moving average (ARIMA) model. We determine the most optimal ARIMA model (ARIMA(7,2,2)) based on the statistical parameters viz. root-mean-squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and the coefficient of determination (R 2). Subsequently, the developed model is used to obtain a one month-long forecast for the cumulative cases, active cases, recoveries, and the number of fatalities. According to our forecasting results, India is likely to have 3800,989 cumulative infected cases, 1634,142 cumulative active cases, 2110,697 cumulative recoveries, and 56,150 cumulative deaths by August 24, 2020, if the current trend of the pandemic continues to prevail. The implications of these forecasts are that in the upcoming month, the infection rate of COVID-19 in India is going to escalate, while the rate of recovery and the case-fatality rate is likely to reduce. In order to avert these possible scenarios, the administration and health-care personnel need to formulate and implement robust control measures, while the general public needs to be more responsible and strictly adhere to the established and newly formulated guidelines in order to slow down the spread of the pandemic and prevent it from transforming into a catastrophe.

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“…A wide number of time series forecasting models are available, be that as it may the ARIMA (Auto regressive integrated moving average) model has emerged as one of the most popular models. Paul and Das (2010) used Box-Jenkins models to forecast fishery dynamics, Lohano and Soomro (2006) and Taye et al (2020) conducted similar investigations using autoregressive models in Pakistan and Ethiopia respectively, Sankar and Vijayalakshmi (2017) also used the univariate ARIMA model for analyzing ghee production in Tamil Nadu. Forecasting of milk production has been a popular topic and will continue Trend Analysis of Milk Production in India to remain so due to its role in social, economic and nutritional security.…”

Section: Introductionmentioning

confidence: 99%

Trend Analysis of Milk Production in India

Lyngkhoi

Singh

et al. 2022

AJDFR

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Background: Dairy sector is a thriving enterprise in Indian agriculture showing colossal growth responsible for placing the country at the top position worldwide in milk production. This sector has seen a rising growth; however, there seems to be demand supply gap over the next few years. Under this backdrop, the study has been taken with the twin objectives of studying the trend and growth rate of production and per capita availability of milk and to forecast the production of milk through ARIMA modelling. Methods: The study is based on secondary data of milk production from 1989 to 2019. The growth was examined by estimating compound annual growth rate and the Autoregressive integrated moving average (ARIMA) methodology was applied for modeling and forecasting of milk production of India. Augmented dickey-fuller (ADF) test was used to determine the stationarity of the model after which it was used to forecast the future production. Result: The CAGR of milk production was higher in comparison to per capita availability (4.34 and 2.71 per cent per annum respectively). The forecast from the fitted ARIMA model show that the milk production is expected to be 244.7 million tonnes in 2024.

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Time series analysis of cow milk production at Andassa dairy farm, West Gojam Zone, Amhara Region, Ethiopia

Cited by 9 publications

References 12 publications

Enhancing Sustainable Dairy Industry Growth through Cold-Supply-Chain-Integrated Production Forecasting

Enhancing Sustainable Dairy Industry Growth through Cold-Supply-Chain-Integrated Production Forecasting

Mathematical modeling and a month ahead forecast of the coronavirus disease 2019 (COVID-19) pandemic: an Indian scenario

Trend Analysis of Milk Production in India

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