Abstract
Advancing models for accurate estimation of food production is essential for policymaking and managing national plans of action for food security. This research proposes two machine learning models for the prediction of food production. The adaptive network-based fuzzy inference system (ANFIS) and multilayer perceptron (MLP) methods are used to advance the prediction models. In the present study, two variables of livestock production and agricultural production were considered as the source of food production. Three variables were used to evaluate livestock production, namely livestock yield, live animals, and animal slaughtered, and two variables were used to assess agricultural production, namely agricultural production yields and losses. Iran was selected as the case study of the current study. Therefore, time-series data related to livestock and agricultural productions in Iran from 1961 to 2017 have been collected from the FAOSTAT database. First, 70% of this data was used to train ANFIS and MLP, and the remaining 30% of the data was used to test the models. The results disclosed that the ANFIS model with generalized bell-shaped (Gbell) built-in membership functions has the lowest error level in predicting food production. The findings of this study provide a suitable tool for policymakers who can use this model and predict the future of food production to provide a proper plan for the future of food security and food supply for the next generations.
Highlights
Introduction iationsClimate change, natural hazards, drought, uncertainty in recourses, and population growth are increasingly threatening the food security of the global nations [1]
The results of the testing phase of the multilayer perceptron (MLP) model were in accordance with the results of the training phase as the MLP model with ten neurons had the highest accuracy for predicting livestock production because the RMSE of this model was equal to 265,590,099.2, which was lower than other models with different neurons
The results showed that the adaptive network-based fuzzy inference system (ANFIS) model with the generalized bell-shaped (Gbell) membership function, due to the lower RMSE, had a better predictive performance in both agricultural and livestock production forecasting compared to the ANFIS model with other membership functions, and it had a higher predictability power on the current data compared to the MLP model
Summary
Natural hazards, drought, uncertainty in recourses, and population growth are increasingly threatening the food security of the global nations [1]. It is estimated that the world’s population will exceed 9.7 billion by 2050, which will encourage worldwide hunger and food insecurity [2]. There are two means of the food supply, i.e., domestic production and imports [3]. Awareness of a region’s potential for producing food provides the foundation for developing informed policies for food security. Reliable food prediction models can be used by policymakers to reconsider the annual food import volumes and prices [5]. Insight into the food production value to better manage the poverty and support vulnerable groups exposed to food insecurity [6]. Conventional time series and mathematical models had Licensee MDPI, Basel, Switzerland
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