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147117

PREDICTING TOMATO CROP PRODUCTION

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Last updated: 04 Jan 2025

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Abstract

abstract The study aimed to predict the production of the tomato crop in its three plantings using time series for the production of the tomato crop during the period of (1999- 2014). The test of ADF showed unit root and Normality test showed that the time series data of the study does not significantly differ from the normal distribution of the random variable .The use of modern analysis procedures based on Box-Jenkins methodology showed efficient prediction of tomato crop production. The model of ARIMA (0,0,1) was the best model among the proposed models in this study to predict the winter tomato crop where production is forecasted at 4165, 4238, 4312, 4385 and 4458 thousand tons during the period of (2016- 2020), respectively. The proposed model to predict summer tomato crop is the model ARIMA (2,0,1), where production is forecasted at 4801, 5058, 5209, 5258 and 5246 thousand tons during the period of (2016- 2020), respectively. The model of ARIMA (0,0,1) was proposed to predict the indigo tomato crop where production is estimated at 769, 747, 726, 705 and 684 thousand tons during the period of (2016- 2020), respectively.The study recommends using models that have been reached to predict the production of the tomato crop and the adoption of forecasts given by the proposed models to put the future plans to develop the tomato manufacturing and to export it processed and fresh. Using methodology of Box-Jenkins in the conclusion and the development of standard models to predict other crops, according to the actual development of the time series subjected to the study.

DOI

10.21608/ejar.2017.147117

Keywords

predicting, Tomato Crop

Authors

First Name

SAHAR A.

Last Name

IBRAHIM

MiddleName

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Affiliation

Cent. Lab. for Design and Stat. Analysis Res., ARC.

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Orcid

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First Name

RANIA F.

Last Name

MAHMOUD

MiddleName

-

Affiliation

Cent. Lab. for Design and Stat. Analysis Res., ARC.

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City

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Orcid

-

Volume

95

Article Issue

1

Related Issue

21304

Issue Date

2017-03-01

Receive Date

2016-11-07

Publish Date

2017-03-01

Page Start

475

Page End

494

Print ISSN

1110-6336

Online ISSN

2812-4936

Link

https://ejar.journals.ekb.eg/article_147117.html

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https://ejar.journals.ekb.eg/service?article_code=147117

Order

28

Type

Original Article

Type Code

1,041

Publication Type

Journal

Publication Title

Egyptian Journal of Agricultural Research

Publication Link

https://ejar.journals.ekb.eg/

MainTitle

PREDICTING TOMATO CROP PRODUCTION

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Article

Created At

22 Jan 2023