Abstract
Content
- Introduction
- 1. Relevance
- 2. Aims and objectives of the study
- 3. An approach to predictive modeling of weather conditions
- Conclusion
- References
Introduction
Problems of modeling of economic processes are very acute in periods of unstable economic situation of enterprises and the Ukraine as a whole. The use of modeling techniques can improve the economic efficiency of production units, including companies operating in the field of agriculture due to the optimal inventory control, revenue, expenses in relation with the fact that the primary objective of the agricultural enterprise is to maximize the harvest.
In this context, the task of making a variety of industrial and economic decisions based on meteorological information and analytical data of climate for agriculture is especially important. Accurate and timely information on weather forecasts provided by the hydro – meteorological services, provides a sustainable economy and society as a whole. Job services manifests itself in cases where adverse weather conditions and dangerous hydrometeorological phenomena directly affect the population and economy.
Considering the fact that Ukraine is characterized by a temperate continental climate (steadily hot summer, consistently cold winter and low rainfall), we can say that the probability of occurrence of natural dangerous phenomena is high.
Country's agriculture is under a Dangerous phenomenon of nature. Agriculture in Ukraine is one of the most important sectors of the economy. In the GDP structure (Figure 1), the greatest weight in the Ukraine in 2010 has the processing industry – 13%, followed by trade and repairs – 12%, transport and communications – 9% and agriculture – 9% [1]. Almost a third of jobs and a significant portion of the gross state product is provided by agriculture. It is estimated that in 2010 the result from operating activities of agricultural enterprises was 12.8 billion UAH profit. In 2009 the figure was 7.2 billion UAH. The level of profitability was 20.6% and 13.4% in 2010 and 2009, respectively. Profits from the production of agricultural products were 73% of all enterprises. The amount of earnings by an average of one company was 2.3 million UAH (in 2009 – respectively 63% and 1.8 mln.). At the same time 27% of businesses were at a loss. The average loss per company was 1.1 million UAH (in 2009 – respectively 37% and 993.5 thous.).
Fig. 1. Structure of Ukraine’s GDP in 2010
Despite the increase in profitability of crop production, according to research asset management company, "Nico" [2] in 2010 Ukraine’s households received 39.2 million tons of grain, which is 14.8% less than in 2009., but above average for 10 years (36.7 million tons). In particular, wheat production in 2009 decreased by 19.4%, which is destined to decline in grain yields (4.1 t / ha, or 6.9%).
In the Donetsk region in 2011, profitability was 30% compared to 25.4% in 2010 [12]. This means that in 2011 the Donetsk region received a profit of 1.2 billion UAH due to the implementation of crop and livestock production.
Nature dangerous phenomena that have a negative impact on agriculture of Ukraine are as follows: air temperature below – 25C, above 35 C, frozen on the surface of the soil freezing crops rot of winter, the ice crust, drought and hot winds, hail and squally showers with wind, long and heavy rains, dust storms.
All these leads to damage and loss of fruit buds, damage and destruction of winter crops and perennial grasses, the root system of fruit, carbohydrate depletion plants, washing away crops, soil dust drift plants.
1. Relevance
In order to withstand the dangerous phenomena nature agricultural workers spend preventive measures. These include: the withdrawal of frost – resistant varieties of plants, watering plants, plant cover, snow accumulation, snow removal, destruction of the ice cover, anti – hail protection, tillage crops, replanting, soil conservation crop rotations, shelterbelts.
Such events allow agricultural enterprises to reduce economic losses from nature hazards. However, the effectiveness of such measures depends on the timeliness of their execution, so the question of constructing a model of accurate weather forecasts for the performance management of agricultural enterprises is relevant to the economy of Ukrainian farms and the country as a whole.
2. Aims and objectives of the study
The aim is to develop a model predicting the effect of weather conditions on the efficiency of agriculture.
The objectives of the work are:
- the study of existing methods of weather forecasting and detection methods, which combined together, will give the most efficient outcome prediction;
- development of predictive modeling of weather conditions on the efficiency of agricultural production prototype;
- testing the model in relation to enterprises operating in agriculture;
- analysis of the effectiveness of the model.
The object of study – ways of improving the efficiency of agricultural enterprises.
Purpose of the study – models and methods for predicting the effect of weather conditions on the efficiency of agriculture.
It is assumed that the development and implementation of a prototype created by the forecast model will improve the efficiency of agricultural enterprises in Ukraine.
3. An approach to predictive modeling of weather conditions
Development of predictive modeling of weather conditions on the efficiency of agricultural production prototype involves several steps.
Step one: selection the input data for prediction.
When selecting the input data should be considered that poor weather phenomena occur in the atmospheric front. Atmospheric front is formed when a mass of cold and warm air converge.
Thus, the main indicator for the simulation of weather conditions is temperature.
In addition to temperature data, humidity indicators should be used, because the convergence of air masses increase horizontal gradients of humidity.
These two indicators will be key, with which forecast model will output data on weather conditions. However, if the indicators are highly correlated with each other, the main data set must be extended with new, for example, indicators of pressure and wind speed.
Step Two – Analysis of output data.
Model outputs will be information about possible adverse weather phenomena. Also, the model will give recommendations on necessary protective measures to preserve the harvest. Such information can significantly affect the economic position of enterprises operating in agriculture, by reducing the damage to crops from extreme weather events.
It is assumed that the development and implementation of a prototype created by the forecast model will improve the efficiency of agricultural enterprises in Ukraine.
Fig.2 – Diagram of the predictive model
(animation: 7 frames, 3 cycles, 40 Kb)
Conclusions
The problem of prediction of adverse natural phenomena that can significantly reduce the efficiency of agriculture, is a relevant and widely studied, as evidenced by, first, on – going research to improve the effectiveness of forecasting methods, and secondly, exposure to agricultural land in Ukraine adverse natural phenomena.
Four basic approaches (synoptic, statistical, hydrodynamic and space.) can be used in a study of weather. Each of their approaches reveals the essential features of macro meteorological processes.
Hydrodynamic models of atmospheric circulation have improved the effectiveness of short – and medium – term forecasts, synoptic situation and weather over the past decade. System of hydrodynamic equations that reflect the fundamental physical laws, can effectively predict the state of the atmosphere for a period of 5 – 10 days.
In the synoptic methods of long – term weather forecast for the study of atmospheric macro processes weather maps and maps of baric topography are used.
In the synoptic methods of long – term weather forecast for the study of atmospheric macro processes are used weather maps and maps of baric topography, as well as a number of special maps that reflect the structure of the thermobaric field and the nature of the atmospheric circulation. Compiling weather forecast precedes the atmospheric circulation.
Statistical methods for forecasting emerged almost simultaneously with the synoptic. However, their development hampered by the complexity of the implementation of a large volume of calculations. Use of a computer eliminates this obstacle, and the accumulation of more material extends the application of statistical methods.
The second group includes the methods of statistical regression, correlation, discriminant analysis, etc. Using this approach, values of meteorological variables and of the anomalies are predicted.
Space weather forecast can significantly improve the methods of operational control of the crops and crop forecast, both in regional and local scales, to solve other problems in various branches of agriculture.
A positive feature of the statistical method is a gradual increase in forecast accuracy due to the accumulated knowledge base. Due to the generalization of the real cases of atmospheric circulation, these methods predict the weather, which is close to normal. Since the weather conditions on Ukraine’s territory in the locations of agricultural are mild, application of the statistical method of forecasting the weather conditions should be considered as appropriate.
However, to improve the skill of forecasts obtained, it is necessary to combine methods of numerical and statistical forecasting.
Thus, to solve the problem of constructing the model prediction of dangerous phenomena of nature is necessary to use a statistical model that is applied to the values of the predictors, the are calculated by numerical model.
The goal is to create predictive models of the effect of weather conditions on the efficiency of agricultural enterprises, a brief overview of the main methods used in solving this problem is provided, the initial requirements for the prototype model are set – use of the knowledge base of geographic and time – temperature indicators.
This abstract is based on the uncompleted master's work. Date of completion of master's work is December 2012.
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