سید محمدرضا حاج سیدجوادی

This research analyzes fertility attitudes and childbearing behaviors in Iran using both linear and non-linear models on a sample of 1,900 units collected through a survey method. The study enriches the cognitive perspective beyond structuralist and rationalist critiques by emphasizing indicators such as "the meaning of life," "the vague shadow of the future," "existential and economic uncertainty," and "behavioral changes in taste and preference." The study utilizes multi-layer analysis and hybrid models like the random forest model. The sample predominantly consists of individuals in their 30s with a university education. Findings reveal a higher desire for children compared to actual childbearing behavior, with women showing less desire than men, particularly in poorer classes where fertility rates are below replacement level. The desire to have four or more children among married individuals is predicted to be less than two percent. The study highlights the increasing correlation between women's education and realistic awareness of fertility dynamics.

One of the important concerns of policymakers in Iran is the management of changes and crises created in the foreign exchange market. This issue is more felt in countries dependent on the import of intermediate goods and production inputs. Considering the effect of the exchange rate on the trade of agricultural inputs on the one hand, and the close relationship between the import of agricultural inputs and food security on the other hand, knowing the effects and relationship of the exchange rate with the trade of agricultural inputs is of particular importance for proper policy making in this sector. Iran's foreign exchange policies for the import of different groups of goods were varied and the type of allocated exchange rate for the import of each group of goods was different in different years. Over recent years, the import of basic goods, including livestock and poultry inputs, has been carried out at the official exchange rate. Considering that the exchange rate of the free market has experienced a much higher level than the official exchange rate, it has caused the import of livestock inputs more than usual during the past years. In this regard, this study aimed at investigating the effect of the gap of free and official exchange rates on the import volume of livestock inputs using the Markov Switching approach during the years 1993-2021.

In this study, the relationship between the "free and official exchange rate gap" index and the import volume variable of each of the livestock inputs of soybean meal, corn and barley was modeled as follows:In the above equation, LIM is the logarithm of input import volume, LPW is the logarithm of global input price, LRG is the logarithm of the gap between the free exchange rate and the official exchange rate, LTA is the input import tariff rate, and LYE is the amount of domestic production. According to the import demand function, it was expected that the effect of world price, import tariff rate and domestic production of each input on the imports volume would be negative and the variable effect of the gap between the free and official exchange rates on the volume of imports would be positive. In this study, the Markov Switching model was used to achieve the research goal. The studied time period included the annual data of 1993-2021.

The estimation results of the Markov Switching model for each of the equations of corn, barley and soybean meal showed that the effect of the world price of corn, barley and soybean meal on their import volume was negative and significant. In addition, the effect of the corn import tariff variable on its import volume was negative and significant, while the import tariff of barley and soybean meal did not have a significant effect on their import volume. An increasing in the domestic production of barley and soybean meal would result in decreasing their import volume, while changes in the domestic production of corn would not have a significant effect on its import volume. As expected, the effect of the gap between the free and official exchange rates on the import volume of corn, barley, and soybean meal was also positive and significant, so that as the gap between the free and official exchange rates increased, the demand for importing these products would also increase.

Based on the study results, an increase in the gap between the free and official exchange rates cause an increased demand for corn, barley, and soybean meal imports as well. Therefore, to reduce the free and official exchange rates, applying appropriate policies, timely purchase of inputs from global markets and a revision in the business process of livestock and poultry institutions are suggested.

The agricultural sector is one of the most basic and vital component in the social and economic structures of any country. Today, with increasing in the world's population and needing to provide food on the other hand, and increasing in the price fluctuations of agricultural products on the other hand, traditional agriculture is no longer responsible for the sustainable food security of the world population. In recent years, the occurrence of two incidents of the spread of the corona virus and the outbreak of war in Ukraine, have made the price of agricultural products extremely unstable. Today, even many farmers and agricultural associations in developing countries are not aware of the changes in market prices and the latest technological developments in the field of agricultural product prices, and they do not have the ability to discover the optimal price for selling their products. In such a situation, the use of intelligent models in order to accurately forecast the price of agricultural goods is vitally important for farmers and agricultural sector activists.Smart agriculture is an emerging concept that involves the integration of advanced technologies to collect and analyze data in order to solve the challenges and problems of the agricultural sector. In the meantime, forecasting the price of agricultural products involves with some basic challenges; including: 1) Data of agricultural product price is mostly non-linear, unstable, non-normal, and noisy and follows chaotic behavior, 2) There is uncertainty in the forecasted data obtained from different models, 3) In the studies related to price forecasting, the "publishable base model" is not provided in order to provide the forecasted price values. Therefore, the aim of this study is to provide a non-linear hybrid intelligent model for accurate forecasting of the future price of pistachios in the field of smart agriculture through managing the multidimensional nature of data, considering uncertainty in the forecasting data and finally building a publishable base model in the field of product price prediction.The hybrid model proposed in this study has the following innovations; 1) the deep learning neural network model and the Auto-Encoder network have been used to forecast the agricultural product price and determine the optimal lag of price as an input variable simultaneously, 2) The Monte Carlo method has been used as a non-parametric method to provide a confidence interval and calculate the most likely price that can happen, 3) The practical application of price forecasting models, i.e., "publishable base model" is presented in order to provide forecasted price values.

The implementation of the proposed hybrid model in this study includes the steps of "data preparation", "data feature engineering", "training and testing the final deep learning neural network model", "building the optimal base model", "creating the most likely price scenarios" using the Monte Carlo method and "inferring new prices or making out-of-sample forecasting" with new data sets” by feeding new price data into the deep learning neural network model. In the proposed hybrid model, data mining techniques are used, including Wavelet Transform (WT), Long-Short Term Memory (LSTM), Auto-Encoder network (AE), Monte Carlo-Markov chain (MCMC) simulation method and the concept of "inferring new prices".In the data preparation stage, using methods such as data smoothing, data rebuilding, correction of duplicate data in several consecutive days, and correction of missing data, the continuous set of pistachio future price time series is prepared to enter the primary model. Also, the wavelet transform function has been used for de-noising the data, the Auto-Encoder network has been used to determine the optimal lag, the Monte Carlo-Markov chain simulation has been used to create the most probable price scenarios, and the deployment concept has been used for out-of-sample forecasting with new data sets. The data used in this study is the time series of the daily price of pistachio futures on the Iran Commodity Exchange in the period from 10/13/2019 to 12/14/2021 in Rials per kilogram.

The results of this study showed that 1) by using the wavelet theory to de-noise the data, the error rate of the price data was reduced and the data had a stable trend, 2) the results of the implementation of the Auto-Encoder network showed that the optimal lag of one can be used as an input variable to forecast the future price of pistachios, 3) The outcomes derived from employing Monte Carlo-Markov chain simulation, coupled with out-of-sample forecasting using the new dataset, reveal compelling insights into the future pricing of pistachios on the Iranian Commodity Exchange. According to the analysis, the most probable and sanguine projection places the future price at the price ceiling of 213 thousand Tomans. Impressively, the forecasted price exhibits a minimal variance of merely 0.7% from the actual observed price, attesting to the precision of the proposed model. The overall accuracy of the model stands commendably high at approximately 93%.

Based on the results, firstly, the forecasted price has a small error with the actual price and this small error shows the power of the built model in forecasting the future price trend of pistachios. Secondly, the alignment of the price resulting from the Monte Carlo simulation with the new price can also be used as a confidence index in risk management for traders and market participants. Thirdly, the process set is the most complete value chain in the production of price forecasting models. Therefore, the use of the proposed hybrid model and the use of the components used in it, i.e. wavelet transform function, Auto-Encoder network, deep learning neural network, Monte Carlo simulation and the concept of inferring new prices; are suggested.

The real exchange rate is considered a key variable due to its interrelationship with other macroeconomic variables and plays an effective role in the export and import of agricultural goods and inputs. Iran's agricultural sector is one of the most dependent sectors on the exchange rate, and as a result, fluctuations in the real exchange rate can have significant effects on the export and import of agricultural inputs such as chemical fertilizers. Considering the effect of the Covid-19 crises and the Ukraine war on real exchange rate fluctuations and the importance of chemical fertilizers trade in current conditions, as well as observing the non-linear behavior of variables, the purpose of this study is to investigate the non-linear causality of real exchange rates and the value of import and export of chemical fertilizers Using monthly data from April 2015 to September 2022, using the nonlinear hybrid wavelet-Markov switching approach. The results of data denoising using wavelet transformation showed that the error rate flowed in a relatively turbulent domain and the data contained some kind of noise. The results of the Markov switching model also showed that the relationship between the real exchange rate and the value of export and import of chemical fertilizers is non-linear. Also, there is a one-way causality relationship from the real exchange rate to the export value of chemical fertilizers, and only when the import value increases, there is a one-way causality relationship from the real exchange rate to the import value of chemical fertilizers. Therefore, in order to reduce real exchange rate fluctuations, it is suggested to diversify the import sources and export destinations of chemical fertilizers, as well as create bilateral and constructive interactive relations with the target countries in order to reach a win-win agreement.

The price forecasting and its changes trend is one of the most important factors in decision making and formulating strategies related to agricultural products. This study aimed at presenting a hybrid data mining model for accurate price forecasting of agricultural products, including nonlinear models of wavelet transform, genetic algorithm, deep neural network and Monte Carlo technique. This proposed model involved a two-stage hybrid model and the base model of nonlinear-nonlinear. In this proposed model, the genetic algorithm for determining the optimal lag of price time series, the wavelet function for the de-noising of price data, the deep neural network for price forecasting, the Monte Carlo method for simulating the most probable price probability and finally, the complex soft calculations for "out-of-sample forecasting with new data set" were used. Results of comparison of the proposed model including "Genetic Algorithm-Wavelet Transform-Deep Neural Network-Monte Carlo", through evaluation criteria, with three competing models of "Genetic Algorithm-Deep Neural Network-Monte Carlo", "Genetic Algorithm-Wavelet Transform-Neural Network-Monte Carlo" and "Genetic Algorithm-Neural Network-Monte Carlo" showed that the proposed model had the better performance in forecasting of future price of saffron compared to the three competing models. Also, the use of deep neural network compared to neural network, the application of wavelet theory for de-noising and also the use of Monte Carlo technique to simulate the predicted prices, increase the forecasting accuracy of future price of saffron. In addition, the use of soft calculations showed that the proposed model had the necessary efficiency and high accuracy for short-term forecasting of the future price of saffron. Therefore, the present study has a good position in achieving the index of maximum accuracy, scenario making of future price trends, sensitivity analysis of components affecting the price and finally, forecasting the future price. Accordingly, the use of the proposed model to forecast the price of agricultural products is recommended.

In recent years, the Iran Commodity Market has always been associated with price destabilizing fluctuations. Thus, it is need to use machine Learnings in the form of forecasting methods to recognize early events and prevent damage caused by these fluctuations. Considering the important position of the pistachio trading ring in the Iran Commodity and also the need to use appropriate tools to correctly diagnosis the future price, The purpose of this study is to design and build a suitable hybrid model based on XGBoost and compare its performance with other machine learning models in order to accurately forecast the future price of pistachio. In this study, software’s of Matlab, Rapid Miner and Scikit learn were used to build the proposed hybrid model. The results of applying the wavelet theory showed that the error value of price data was reduced and the data had a stable trend (white noise). Also, the results of the Performance of Auto-Encoder network and the Genetic algorithm showed that the optimal lag of one is the best input variable for forecasting the future price of pistachios in the period under review. Based on goodness of fit indices, the proposed model of this study, Wavelet-XGBoost in comparing to other data mining models, had a better performance in forecasting the future price of pistachios. Also, out-of-sample forecasting with the selected model showed that the forecasted new prices have little difference with the real data, which indicates the efficiency and accuracy of the selected hybrid model. According to the obtained results, it is strongly recommended to use the proposed model based on XGBoost algorithm to forecast the price of other agricultural products.

Saffron, as the most expensive agricultural and medicinal product in the world, has a special place in buying and selling related to the Iranian agricultural commodity bourse. The saffron commodity bourse, like any other market, has always faced limitations and structural problems, and most of these problems are related to its price fluctuations. Price forecasting using appropriate models can be a great help in reducing the price risk of futures market of saffron. The main question is that in in confronting with various price forecasting methods, which methods should be chosen to forecast the future price of saffron? The purpose of this study is to design the most appropriate hybrid model for forecasting the future price of Negin saffron in the agricultural commodity bourse, which consists of a set of nonlinear models of genetic algorithm, deep neural network, random forest, support vector machine and Monte Carlo method. In this hybrid model, genetic algorithm is used to determine the optimal lag of price time series, deep neural network, random forest model and support vector machine are used to forecast the price time series, and Monte Carlo method is used to simulate the most probable price probability. The results of this study showed that the forecasting accuracy of the hybrid model of "Genetic Algorithm-Deep Neural Network-Monte Carlo" is higher than the two models of "Genetic Algorithm-Random Forest-Monte Carlo" and "Genetic Algorithm-Support Vector Machine -Monte Carlo". Therefore, using a deep neural network and calculating the most probable price probability by the Monte Carlo method, provide the most accurate saffron price prediction with a high degree of reliability and minimal risk. Thus, it is suggested that that the management of the commodity bourse, stock market participants and researchers can use the advantages of this proposed model in forecasting the price of agricultural products.