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Household food security is a major issue in developing countries like Pakistan. Despite significant breakthroughs in grain production within the country, the problem of food availability and utilization persists. Diet is one of the most potent determinants of nutritional condition. The dietary intake method has been utilized to determine the food security status of households, which depends on various factors. There are no automatic and user-friendly methods available to decide food security status, which is generally determined by manually calculating calorie intakes. Due to its high performance and precision, machine learning holds major significance. In this paper, the status of food security has been examined by applying machine learning algorithms, namely, support vector machine, naïve Bayes, k-nearest neighbors, random forest, logistic regression, and neural network, on survey data of households for best predicting the status. A food analysis (FA) app has been developed to automatically predict the FAO status of a household’s food security by implementing the random forest model that found higher precision among algorithms. Additionally, the proposed mobile app will also be helpful for collecting the households’ data. Furthermore, the objective of the study was to enhance food security awareness among individuals.
Abdul Razzaq; Umar Ijaz Ahmed; Sarfraz Hashim; Aamir Hussain; Salman Qadri; Sami Ullah; Ali Nawaz Shah; Ali Imran; Attika Asghar. An Automatic Determining Food Security Status: Machine Learning based Analysis of Household Survey Data. International Journal of Food Properties 2021, 24, 726 -736.
AMA StyleAbdul Razzaq, Umar Ijaz Ahmed, Sarfraz Hashim, Aamir Hussain, Salman Qadri, Sami Ullah, Ali Nawaz Shah, Ali Imran, Attika Asghar. An Automatic Determining Food Security Status: Machine Learning based Analysis of Household Survey Data. International Journal of Food Properties. 2021; 24 (1):726-736.
Chicago/Turabian StyleAbdul Razzaq; Umar Ijaz Ahmed; Sarfraz Hashim; Aamir Hussain; Salman Qadri; Sami Ullah; Ali Nawaz Shah; Ali Imran; Attika Asghar. 2021. "An Automatic Determining Food Security Status: Machine Learning based Analysis of Household Survey Data." International Journal of Food Properties 24, no. 1: 726-736.
Umar Ijaz Ahmed. Impact of balance use of fertilizers on wheat efficiency in cotton wheat cropping system of Pakistan. 2020, 1 .
AMA StyleUmar Ijaz Ahmed. Impact of balance use of fertilizers on wheat efficiency in cotton wheat cropping system of Pakistan. . 2020; ():1.
Chicago/Turabian StyleUmar Ijaz Ahmed. 2020. "Impact of balance use of fertilizers on wheat efficiency in cotton wheat cropping system of Pakistan." , no. : 1.
Climate change is a serious threat to agriculture in many developing countries including Pakistan. Changing the pattern of climate and its extreme conditions have already led to a decline in crop productivity. However, farmers in developing countries experience risks beyond just climate change, many of which are related to policy, strategy, and factor endowments. The impact of these risks have serious implications for food security, rural livelihood, farm households’ wellbeing, and, above all, their motive to adapt to these changes in the long-term. To have an in-depth knowledge of farmers’ perceptions about the changing climate, this study investigates various aspects such as the determinants of perception about various risk sources and the relevant mitigation and adaptation options. To do so, 480 farmers from agriculture-dominated Punjab provinces were randomly selected in order for us to evaluate their awareness levels, socioeconomic dynamics that influence their perceptions, and various factors that influence their perceptions to achieve the desired findings. We applied the principle factor analysis approach to ascertain major sources and strategies based on farmers’ perception and planned/practiced options. Further, regression analysis was done to evaluate the factors influencing the perception levels of farmers about risk sources. The results showed that majority of the farmers faced various risks, and were trying to adapt crop husbandry practices towards these perceived risks. Change in agricultural policies (3.96) was placed as the highest risk source, while the need for small dams/turbine schemes was the top priority for risk management strategy (mean value of 4.39). By observing the effect of farm and farmer’s characteristics on risk sources and risk management strategies, it was revealed that these characteristics ominously provoked farmers’ perspectives about risk sources and management strategies. The findings imply the need for coherent environmental policy that encompasses price stability, community-led adaptation campaigns, and easy/uninterrupted flows of information that enables the farming community to facilitate sustainable decision processes.
Muhammad Iqbal; Azhar Abbas; Syed Naqvi; Muhammad Rizwan; Abdus Samie; Umar Ahmed. Drivers of Farm Households’ Perceived Risk Sources and Factors Affecting Uptake of Mitigation Strategies in Punjab Pakistan: Implications for Sustainable Agriculture. Sustainability 2020, 12, 9895 .
AMA StyleMuhammad Iqbal, Azhar Abbas, Syed Naqvi, Muhammad Rizwan, Abdus Samie, Umar Ahmed. Drivers of Farm Households’ Perceived Risk Sources and Factors Affecting Uptake of Mitigation Strategies in Punjab Pakistan: Implications for Sustainable Agriculture. Sustainability. 2020; 12 (23):9895.
Chicago/Turabian StyleMuhammad Iqbal; Azhar Abbas; Syed Naqvi; Muhammad Rizwan; Abdus Samie; Umar Ahmed. 2020. "Drivers of Farm Households’ Perceived Risk Sources and Factors Affecting Uptake of Mitigation Strategies in Punjab Pakistan: Implications for Sustainable Agriculture." Sustainability 12, no. 23: 9895.
Umar Ijaz Ahmed. Comparative Analysis of Wheat and Brassica in Terms of Relative Profitability: A Case Study of District Chakwal, Punjab, Pakistan. 2017, 1 .
AMA StyleUmar Ijaz Ahmed. Comparative Analysis of Wheat and Brassica in Terms of Relative Profitability: A Case Study of District Chakwal, Punjab, Pakistan. . 2017; ():1.
Chicago/Turabian StyleUmar Ijaz Ahmed. 2017. "Comparative Analysis of Wheat and Brassica in Terms of Relative Profitability: A Case Study of District Chakwal, Punjab, Pakistan." , no. : 1.
Umar Ijaz Ahmed. Adoption of off-farm diversification income sources in managing agricultural risks among cotton farmers in Punjab Pakistan. 2016, 1 .
AMA StyleUmar Ijaz Ahmed. Adoption of off-farm diversification income sources in managing agricultural risks among cotton farmers in Punjab Pakistan. . 2016; ():1.
Chicago/Turabian StyleUmar Ijaz Ahmed. 2016. "Adoption of off-farm diversification income sources in managing agricultural risks among cotton farmers in Punjab Pakistan." , no. : 1.
Umar Ijaz Ahmed. Access to output market by small farmers: The case of Punjab, Pakistan. 2016, 1 .
AMA StyleUmar Ijaz Ahmed. Access to output market by small farmers: The case of Punjab, Pakistan. . 2016; ():1.
Chicago/Turabian StyleUmar Ijaz Ahmed. 2016. "Access to output market by small farmers: The case of Punjab, Pakistan." , no. : 1.
Umar Ijaz Ahmed. Determinants of Off-farm Activity Participation among Cotton Farmers in Punjab, Pakistan. 2015, 1 .
AMA StyleUmar Ijaz Ahmed. Determinants of Off-farm Activity Participation among Cotton Farmers in Punjab, Pakistan. . 2015; ():1.
Chicago/Turabian StyleUmar Ijaz Ahmed. 2015. "Determinants of Off-farm Activity Participation among Cotton Farmers in Punjab, Pakistan." , no. : 1.
Umar Ijaz Ahmed. An econometric estimation of post-harvest losses of kinnow in Pakistan. 2015, 1 .
AMA StyleUmar Ijaz Ahmed. An econometric estimation of post-harvest losses of kinnow in Pakistan. . 2015; ():1.
Chicago/Turabian StyleUmar Ijaz Ahmed. 2015. "An econometric estimation of post-harvest losses of kinnow in Pakistan." , no. : 1.
Umar Ijaz Ahmed. Food insecurity and copping strategies of micro growers in Punjab, Pakistan. 2015, 1 .
AMA StyleUmar Ijaz Ahmed. Food insecurity and copping strategies of micro growers in Punjab, Pakistan. . 2015; ():1.
Chicago/Turabian StyleUmar Ijaz Ahmed. 2015. "Food insecurity and copping strategies of micro growers in Punjab, Pakistan." , no. : 1.