Academic literature on the topic 'Vegetables – Prices – Forecasting'

Vegetable quantity arrival to market varies every day with which its prices also changes rapidly. This paper analyses the factors that affect the rapid change in prices of vegetables such as demand forecasting, demand supply management, erroneous statistics of vegetables, storage facilities, and supply chain system, etc. Government of India has no control over the production of horticultural and agricultural crops, sometimes under produced and sometimes over produced, which makes demand supply management hard-won. This paper mainly focuses on advantages of demand forecasting and demand supply management of vegetable crops and their effects on farmers and consumers. The Government should find a novel method or a system which gets crop data from farmer, does demand forecasting on day to day basis, control crop acreages, generate accurate statistics and do demand supply management of crops.

Decision-making based on data analysis leads to knowing market trends and anticipating risks and opportunities. These allow farmers to improve their production plan as well as their chances to get an economic success. The aim of this work was to develop a methodology for price forecasting of fruits and vegetables using Queretaro state, MX as a case study. The daily prices of several fruits and vegetables were extracted, from January 2009 to February 2019, from the National System of Market Information. Then, these prices were used to compute the weekly average price of each product and their span commercialization in Q4 and over the median of historical data. Moreover, product characterization was performed to propose a methodology for future price forecasting of multiple agricultural products within the same mathematical model and it resulted in the identification of 18 products that fit the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model. Finally, future price estimation and validation was performed to explain the product price fluctuations between weeks and it was found that the relative error for most of products modeled was less than 10%, e.g., Hass avocado (7.01%) and Saladette tomato (8.09%). The results suggest the feasibility for the implementation of systems to provide information for better decisions by Mexican farmers.

Ukraine is a predominantly agricultural country, and this branch has been recently demonstrating relatively high efficiency. Vegetable growing is a specific branch of crop production, which includes a large set of vegetables grown according to different technologies, with different shelf life of vegetable products, their different cost and production efficiency. The analysis of the situation on the vegetable market of Ukraine showed that there is a certain correlation between production volumes, sales and products sales prices. The price market environment on the vegetable market in recent years is largely determined by the ratio of supply and demand on the market. Thus, sales volumes increase when the supply on the market is the highest and the price level on the market is the lowest. The absence of permanent wholesale distribution channels also leads to an increase in the hidden market for vegetable products. According to experts of the Ukrainian Agrarian Confederation, the hidden market for fruit and vegetables is about $ 14 billion, or about 60% of the total turnover of vegetable products in Ukraine. Due to the moratorium on the sale of agricultural land, businesses are not able to buy land on their own and develop their business in the long-term prospects. Today, government support in the vegetable sector is limited to preferential lending and to individual funding programs, most often in collaboration with international donors. Much of the support for agro-industrial farms goes to grain and pulse plant producers, which significantly limits the opportunity for developing crop producers with higher marginality. The conditions in which the agrarian sector operates have a high degree of changeable uncertainty, and this circumstance requires agricultural producers to find ways to obtain reliable information about the state of the agricultural market, organizational and functional links between the subjects of the agricultural market, prices for agricultural products. etc. The purpose of this study is to: analyze the price of vegetable sales in Ukraine; substantiation of the use of the AGMEMOD partial equilibrium model for forecasting vegetable production in Ukraine; establish dependence of demand and supply of vegetable production on their sales price; determine the point of equilibrium of supply and demand and calculate of the optimal selling price of vegetables in Ukraine; justify the optimal costs for vegetable production; analyze of the price of selling vegetables in Ukraine and determine the optimal price according to supply and demand, as well as the optimal cost of vegetable production. Now, there are 12 key vegetable crops in Ukraine. These are potatoes, cucumbers, tomatoes, cabbage, beets, carrots, onions, garlic, peppers, zucchini, eggplants and pumpkin. Of these 12 cultures, 9 showed an increase in the period 2010-2016, even without taking into account the uncontrolled Crimea and Donbass. This increase has been driven by two crucial factors: - yield increase. This was made possible due to improving the quality of the seed and natural technological progress in the processing and the use of crop protecting agents. - increase in export demand for products. The demand, for example, for Ukrainian carrots and onions has increased, and therefore the opportunities for their cultivation have become greater. Price is a complex economic category, practically the only element of marketing that enables an enterprise to earn real income. Without proper economic justification of the price level, the normal functioning of economic entities and entire sectors of the economy is impossible, which in turn has a significant impact on the material well-being of the population. The level of market price depends on the value of other marketing elements, as well as on the level of competition on the market and the general state of the economy. As a rule, other marketing elements also change (for example, with increasing product differentiation in order to maximize price or at least the difference between price and cost). The price formation strategy allows determining the price level and marginal prices for individual product groups. The price formation should always be carried out taking into account the nomenclature and quality of products, their usefulness, importance and purchasing power of consumers and prices of the competitors. The strategy of price formation management is a set of measures to maintain conditional prices while actually regulating them in accordance with the variety and characteristics of demand, competition in the market. The AGMEMOD model is an example of the partial equilibrium (PE) models used in agriculture. The main advantages of partial equilibrium models are: the simplicity of the implemented algorithms, the operation of which is quite easily traced; relative availability of necessary data; the calculations are amenable to adequate economic interpretation, making it possible to quickly analyze the consequences of making a decision in the agricultural sector. However, partial equilibrium models are not without their disadvantages. In particular, they do not permit to assess macroeconomic effects such as changes in national income or employment levels, the effects that may be obtained from the redistribution of resources (labor, capital, etc.) into more efficient sectors. For national researchers, it is advisable to use these models, because they have a module of Ukraine, but it is necessary to supplement the program with statistics on vegetables. The demand is a function of price changes in the current period, and the supply is a function of price changes in previous periods. Econometric models of supply and demand dependence of vegetable production on the price of their sale are constructed. The equilibrium of the system is observed at the price of 6558 UAH. for 1 ton of vegetables under the given conditions of consumption, the demand is equal to supply and is 9321 thousand tons. Econometric models of price dependence on material costs, labor costs and depreciation have been constructed. By the first model, it can be determined that the content of unaccounted factors is estimated at 99.82 UAH. per hectare; with an increase in material costs by 1 hectare by 1 UAH, selling price increases by 0.9 UAH. per ton. Based on the Fisher's ratio test, the model is adequate, the relationship between the indicators is tight. The relationship between the indicators of the second model is weak, the calculated correlation coefficient can be trusted, but in general, the adequacy of the model conclusion cannot be made. The model shows that with an increase in labor costs by 1 UAH per hectare, the price increases by 10.03 UAH per ton. The third model based on the Fisher's ratio test is adequate, the relationship between the indicators is average. With the increase in depreciation costs per hectare by 1 UAH, the selling price will increase by 12.42 UAH per ton. The value of the linear correlation coefficient other than zero is statistically significant. Based on the calculated models, we will determine the optimal cost per hectare: material – 7144.2 UAH, labor costs – 689.4 UAH, depreciation costs – 543.4 UAH.

ABSTRACTChili is one of the leading commodities of vegetables which has strategic value at national and regional levels.An unexpected increase in chili prices often results a surge of inflation and economic turmoil. Study and modeling ofchili production are needed as a planning and evaluation material for policy makers. One of the most frequently usedmethods in modeling and forecasting time series data is Autoregressive Integrated Moving Avarage (ARIMA). Theresults of ARIMA modeling on chili production data found that the data were unstationer conditions of the mean sothat must differenced while the data on the production of small chilli carried out the stages of data transformation anddifferencing due to the unstationer of data on variants and the mean. The best ARIMA model that can be appliedbased on the smallest AIC and MSE criteria for data on the amount of chili and small chilli production in CentralKalimantan Province is ARIMA (3,1,0).Keywords: modeling of chilli, forecasting of chilli, Autoregresive Integrated Moving Avarage, ARIMA, Box-Jenkins.

ABSTRACTChili is one of the leading commodities of vegetables which has strategic value at national and regional levels.An unexpected increase in chili prices often results a surge of inflation and economic turmoil. Study and modeling ofchili production are needed as a planning and evaluation material for policy makers. One of the most frequently usedmethods in modeling and forecasting time series data is Autoregressive Integrated Moving Avarage (ARIMA). Theresults of ARIMA modeling on chili production data found that the data were unstationer conditions of the mean so thatmust differenced while the data on the production of small chilli carried out the stages of data transformation anddifferencing due to the unstationer of data on variants and the mean. The best ARIMA model that can be applied basedon the smallest AIC and MSE criteria for data on the amount of chili and small chilli production in Central KalimantanProvince is ARIMA (3,1,0).Keywords: modeling of chilli, forecasting of chilli, Autoregresive Integrated Moving Avarage, ARIMA, Box-Jenkins.

Vegetable producers and marketers make business decisions based on supply estimates. The U.S. Dept. of Agriculture provides estimates of planting intentions for field crops but not for most vegetable crops. This study developed models that can be used to forecast vegetable crop plantings. Multiple linear regression analysis was used to determine the factors that influence plantings of potatoes and onions. Field crop planting intentions, industry structure, lagged values of plantings, prices received, price volatility, and the price of sugar beets were found to be significant factors. The models and/or methods used in this study should be useful to those interested in forecasting vegetable plantings.

In the study of vegetable price forecast, as the price is subject to various uncertain factors (weather, supply and demand, etc.), it has attributes such as high nonlinear, randomness and high noise, which would lead to the difficulty in forecasting. But grasping the law of price development and understanding the development trend of price, would help farmers grow the vegetable reasonably, and reduce unbalanced supply and demand. Therefore, we will make use of the characteristics of neural networks such as self-adapt，self-study and high fault tolerance, to build up the model of BP neural network with the training function of L-M for forecasting the vegetable prices. Finally, numerical example proves that the method is effective.

This paper is concerned with time series data for vegetable prices, which have a great impact on human’s life. An accurate forecasting method for prices and an early-warning system in the vegetable market are an urgent need in people’s daily lives. The time series price data contain both linear and nonlinear patterns. Therefore, neither a current linear forecasting nor a neural network can be adequate for modeling and predicting the time series data. The linear forecasting model cannot deal with nonlinear relationships, while the neural network model alone is not able to handle both linear and nonlinear patterns at the same time. The linear Hodrick-Prescott (H-P) filter can extract the trend and cyclical components from time series data. We predict the linear and nonlinear patterns and then combine the two parts linearly to produce a forecast from the original data. This study proposes a structure of a hybrid neural network based on an H-P filter that learns the trend and seasonal patterns separately. The experiment uses vegetable prices data to evaluate the model. Comparisons with the autoregressive integrated moving average method and back propagation artificial neural network methods show that our method has higher accuracy than the others.