Forecasting of Economic Indicators (Production, Consumption, Population) of Wheat Crop (A Case Study)
Wheat is the most important food crop in Afghanistan, whether consumed by the bulk of the people or used in various sectors. The problem is that Afghanistan has a significant shortfall of wheat between domestic production and consumption. Thus, the present study looks at the issue of meeting self-sufficiency for the whole population due to wheat shortages. To do so, we employ time series analysis, which can produce a highly exact short-run prediction for a significant quantity of data on the variables in question. The ARIMA models are versatile and widely utilised in univariate time series analysis. The ARIMA model combines three processes: I the auto-regressive (AR) process, (ii) the differencing process, and (iii) the moving average (MA) process. These processes are referred to as primary univariate time series models in statistical literature and are widely employed in various applications. Where predicting future wheat requirements is one of the most important tools that decision-makers may use to assess wheat requirements and then design measures to close the gap between supply and consumption. The present study seeks to forecast Production, Consumption, and Population for the period 2002-2017 and estimate the values of these variables between 2002 and 2017. (2018-2030).
Abid, S., 2019. Trends and variability of wheat crop in Pakistan: J. 1005/2018.8.2/1005.2.153.159. https://doi.org/10.18488/.
Box. GEP, Jenkins GM., 1970. Times Series Analysis Forecasting and Control, Holden-Day San Francisco.
C. Chatfield., 2016. The Analysis of Time Series: An Introduction, CRC Press.
Central Statistics Organization of Afghanistan. Annual Trade Bulletin. Kabul,
Chabot P, & Tondel, F., 2011. A regional view of wheat markets and food security in Central Asia: International Development Famine Early Warning Systems Network.
Costa S. J., 2014. Reducing Food Losses in Sub-Saharan Africa - An Action Research evaluation trail from Uganda and Burkinafaso. http://documents.wfp.org/stellent/groups/public/documents/special_initiatives/WFP2652 05.pdf.
Dreisigacker S., 2019. Tracking the Adoption of Bread Wheat Varieties in
Afghanistan Using DNA Fingerprinting: BMC Gene. 20, 1–13.
Goswami, S. N., & Challa, O. (2006). Socio-economic factors affecting land use in India. Agri. Situ. India, 60(10), 615-623. view at Goole scholar / view at publisher.
J. Frain., 1992. Lecture Notes on Univariate Time Series Analysis and Box Jenkins Forecasting, Economic Analysis: Research and Publications.
Kazimi, Z., 2018. Wheat Market Instability in Afghanistan: A Case Study of Kabul, Mazar-e-Sharif, Bamyan and Ghor Provinces, 122–127.
Khapedia, H. L., Sharma, S. K., Sikarwar, R., & Mridha, I. S, 2018. Forecasting Wheat Productivity and Production of Madhya Pradesh , India , Using Autoregressive Integrated Moving Average Models, (7), 4693–4705.
Kirchgässner G, Wolters J, Hassler U., 2013. Univariate stationary processes, in Introduction to Modern Time Series Analysis, Springer, Berlin, Heidelberg.;27-93.
Leao. I, Ahmed, M., and Kar A, 2018. Jobs from Agriculture in Afghanistan: International Development in Focus. Washington, DC: World Bank.
Ministry of Agriculture Irrigation and Livestock., 2013. Agriculture Prospect http://mail.gov.af/Content/files/mail_Agriculture_Prospects_Report_2013%20DECEMBER (1).pdf.
Rout Bob., 2008. How the Water Flows, A Typology of Irrigation Systems in Afghanistan: Afghanistan Research and Evaluation Unit Issue Paper Series. Kabul, Afghanistan.
Saharawat Yashpal, 2017. Genotype × environment interaction and identification of high yielding wheat genotypes for Afghanistan: J. Exp. Biol. & Agric. Sci. 5, 25–34.
Singh, Netajit, L., Darji, V. B., Parmar, D. J., Vbdarji, 2015. Forecasting of Wheat Production and Productivity of Ahmedabad Region of Gujarat State by Using ARIMA Models. Ind. J. of Econ and Dev. 3(6), 2015.
USDA, 2012. Wheat Production Forecast at Near-Record Levels: Commodity intelligence report Afghanistan.
US Department of Agriculture, Foreign Agricultural Service., 2008. Afghanistan Severe Drought Causes Major Decline in Wheat Production: Commodity Intelligence Report.
Waziri A, Habibi A., Manan A R, 2013. Making Afghanistan wheat secure by 2022: Wheat, J. Inf. Ser. 116, 12-14.
World Bank, 2011. Missing Food, the Case of post-harvest Grain Losses in Sub-Saharan African: WB (Vol. 60371-AFR). http://siteresources.worldbank.org/INTARD/Resources/MissingFoods10_web.pdf.
W. L. Young., 1977. The Box-Jenkins Approach to Time Series Analysis and Forecasting: Principles and Applications, RAIRO-Operations Research Recherche Opérationnelle. 11, 129-143. https://doi.org/10.1051/ro/1977110201291.
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