The agricultural sector in Afghanistan faces many challenges in general that have directly affected the production of crops. Especially wheat crop because of its great importance to the population sector as it is the first source of food in Afghanistan. Problem of this study due to wheat production in Afghanistan is insufficient for domestic consumption. Therefore, the Afghan government is relying on foreign markets to cover the gap between production and consumption. The study aims to assess the current situation of wheat production and consumption in Afghanistan, as well as to understand the farmers' perceptions and attitudes towards the problems facing them. The agricultural sector in Afghanistan faces many challenges in general that have directly affected the production of crops. Especially wheat crop because of its great importance to the population sector as it is the first source of food in Afghanistan. The current study applied simple regression analysis in estimating the general trends to determine the productive and economic indicators of Wheat crop. Also, we use Analysis of variance (One Way ANOVA) to understand the farmers' perceptions and attitudes towards the problems facing them. The results showed that wheat productivity averaged 1.77 tons per ha and ranged between a minimum of 1.23 tons per ha in 2008 and a maximum of 2.20 tons per ha in 2015. On the other hand, the estimated regression equation indicates that productivity of wheat crop followed an increase trend, at an annual rate of 0.047 ton per ha and a statistically significant rate of change amounting to 2.66% of the study period’s average productivity.
Wheat is considered the main food crops in Afghanistan, whether to use it for majority of the population consumption or to use it in some industries and others. Problem: Afghanistan suffers from a large gap between production and consumption, so the current research investigates the problem arising from a shortage of wheat production to meet self-sufficiency of the population. Methods: The time series analysis can provide short-run forecast for sufficiently large amount of data on the concerned variables very precisely. In univariate time series analysis, the ARIMA models are flexible and widely used. The ARIMA model is the combination of three processes: (i) Autoregressive (AR) process, (ii) Differencing process and (iii) Moving-Average (MA) process. These processes are known in statistical literature as main univariate time series models and are commonly used in many applications. Where, Estimation of future wheat requirement is one of the essential tools that may help decision-makers to determine wheat needs and then developing plans that help reduce the gap between production and consumption. A solid strategy that widely applying of improved seeds and fertilizers, an effective research and extension system for better crop management is necessary to eliminate this gap for self-sufficiency in wheat production, besides providing the necessary financial sums for that. Where most prediction methods are valid for one-year prediction. However, moving prediction methods have been found to measure and predict the future movement of the dependent variable. Aims: The current research aims to prediction for Area, Productivity, Production, Consumption and Population over the period (2002-2017), to estimate the values of these variables in the period of (2018-2030). Results: The results showed that through the drawing of the historical data for Planted area, Productivity, Production, Consumption and Population of wheat crop it was evident that the series data is not static due to an increasing or a decreasing of general trend, which means the instability of the average, by using Auto-correlation function (ACF) and Partial Correlation Function to detect the stability of the time series, The results showed also, the significance of Autocorrelation coefficient and partial correlation coefficient values, which indicates that the time series is not static.
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).
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