1. Prepared by Durmanov Akmal
TASHKENT INSTITUTE OF IRRIGATION AND AGRICULTURAL MECHANIZATION ENGINEERS
FORECASTING WITH ECONOMIC-MATHEMATICAL MODELING THE COSTS OF THE GREENHOUSE VEGETABLES
Prepared by Durmanov Akmal

2. Agriculture plays an important role in the economy of Uzbekistan
Providing 37% of GDP
About 55% of employment
About 600,000 tons of vegetables or 20 kg per capita per year are currently produced in the agricultural production of protected soil
In recent years, the volume of agricultural production has increased more than twofold. This allowed increasing per capita consumption of meat by 1.3 times, milk - more than 2 times, fruits - almost 4 times
To ensure, according to the medical norm (150 kg per person per year) the country’s agrarians need to produce annually about 1.7 million tons.

3. Greenhouse vegetables production in Uzbekistan
Increasing the production and the competitiveness of greenhouse vegetable production, within the framework of the State Program for , a partial (up to 20%) cost recovery for energy resources are envisaged
At the same time the gross harvest of vegetables in 2018 should reach 720 thousand tons,
and in thousand tons.

4. Purpose
Forecasting a production in food sector for the future with satisfactory reliability level is an essential for sustainable agricultural development.
To solve this problem, this work presented an attempt to use methods of economic-mathematical modeling

5. Materials and methods
This work is based on the actual data for the dynamics of average consumer prices for vegetable greenhouse products in the Republic of Uzbekistan
An econometric model of pricing has been constructed, taking into account the factor of seasonality and its applicability is shown for forecasting the price of vegetable products for subsequent periods.

6. Information base of the research
Official state statistics;
Normative legal acts of government and regional levels;
Data of the Ministry of Agriculture of the Republic of Uzbekistan;
Reference materials of specialized publications on the topic;
Materials received from participants of the vegetable market of protected soil

7. Results
To obtain forecast values for the value of products, including season-factors, an additive model of the time series of the form Y = T + S + E, (2) where Т - trend, S - seasonal and Е - random components of time series.
Dynamics of prices for greenhouse vegetable (in the case of cucumber) by the value of the moving average
Q4,2013
Q1,2014
Q2,2014
Q4,2014
Q1,2015
Q3,2015
Q4,2015
Q1,2016
Q2,2016
Q4,2016
Q1,2017
Q2,2017
Q3,2017
Q4,2017

8. Results Y=-0, 0002x4+0, 0057x3-0, 0626x2+0, 2221x+0, 4127, R2=0.96
Average share of a citizen’s income Uzbekistan, aimed at the acquisition of vegetable greenhouse products
Q4,2013
Q1,2014
Q2,2014
Q4,2014
Q1,2015
Q3,2015
Q4,2015
Q1,2016
Q2,2016
Q4,2016
Q1,2017
Q2,2017
Q3,2017
Q4,2017

9. each index = the actual value for the month / 12 month moving average
Results
Calculation of monthly seasonal indices №
months
Average Ratio
seasonal index
Indices
2013
2014
2015
2016
2017
2018 1
January
167,5
162,8
131,4
200
209,6
164,8
269,6
162,57 2
February
188,4
183,1
184,1
191,9
212,6
271,1
170,75 3
March
155,6
151,3
159,7
170,9
151,6
140,4
216,2
139,80 4
April
132,4
128,7
136,9
135,3
141,5
116
186,75
119,41 5
May
106,1
103,1
117,1
107,5
109,7
91,1
145,95
95,23 6
June
63,7
61,9
70,2
63,3
64,7
56,7
89,05
57,33 7
July
54,1
52,6
49,7
56,9
41,3
68,7
89,35
50,99 8
August 48
46,6
47,8
43,5
36,7
63,9
82,25
45,69 9
September
43,6
42,4 35
32,1
44,6
62,8
85,1
43,27 10
October
66,4
64,6
66,3
60,3
72,1
67,1
103,15
61,49 11
November
92,2
89,6
99,6
88,3
102,4
78,5
129,7
83,08 12
December
116,7
113,4
114,5
97,7
157,5
104,4
183,15
109,54
Total
1234,8
1200
each index = the actual value for the month / 12 month moving average
Skorr.

10. Discussion
Seasonal price increases occur throughout the winter months, and especially at the early spring months.
And, on the contrary, the seasonal decline in prices occurs in the summer and early autumn.
Amplitude of seasonal price fluctuations in the range from the most "expensive" (February) to the "cheapest" month (September) is very significant
The value of the coefficient of determination R2 = 0.94 indicates that the dynamics of prices for greenhouse products in the period

11. Conclusion
For a more accurate price forecast for greenhouse vegetables, it is necessary to consider the qualitative composition of costly and other external factors for the theoretical construction of the response function.
Modeling the pricing of domestic greenhouse products will make it possible, in order to increase the predictability of the demand for greenhouse vegetables, to achieve certain uniformity in their production, sales and the production of a stable profit.