ARIMA versus trend-agrometeorological wheat yield modelling
KrishiKosh
View Archive InfoField | Value | |
Title |
ARIMA versus trend-agrometeorological wheat yield modelling
|
|
Creator |
Roy, Ramanath
|
|
Contributor |
Verma, Urmil
|
|
Subject |
ARIMA, Versus trend-agrometeorological, Wheat, Yield modelling
|
|
Description |
Crop yield forecasting is one of the most important aspects of agricultural statistics system. An efficient crop forecasting infrastructure is a pre-requisite for information system about food supply, especially export–import policies, procurement and price-fixation. The present study has been conducted in Hisar, Ambala, Rohtak and Gurgaon districts of Haryana for wheat yield forecasting. The study was broadly categorized into two parts i.e. ARIMA and Trend-agromet wheat yield modelling. The long time-series data for the period 1959-60 to 1999-2000 have been used for fitting ARIMA models. Trend agromet models have been developed on the basis of 1978-79 to 1999-2000 time series data on wheat yield and meteorological data on maximum temperature, minimum temperature and rainfall for the same period in all the districts. Trend yield alongwith average maximum temperature, average minimum temperature and accumulated rainfall integrated over eleven fortnights (Wheat growth period) have been used as regressors in trend-agromet models. ARIMA (0,1,1) for Hisar and Ambala, ARIMA (1,1,1) for Rohtak and ARIMA (1,1,0) for Gurgaon districts have been fitted for yield forecasting of Rabi wheat 2000-01, 01-02, 02-03, 03-04 and 04-05 in all the four districts. The deviation of the predicted yield from the Department of Agriculture and Remote Sensing estimates was very low, favouring the use of ARIMA models for short-term forecasting. Secondly, different trend-agromet models have been developed for wheat yield prediction 2000-01, 01-02, 02-03 and 03-04 in all the four districts. The relative deviation of predicted yield with DOA/RS estimates are within tolerable limits advocating the use of trend-agromet models for obtaining reliable and timely yield forecasts. Finally, the forecasting performances of both the models have been compared. The estimates obtained using both the models are found to be equally good for yield estimation. Realizing the limitation of availability of meteorological data during the crop growth season, it is emphasized that ARIMA models are the best alternative to have pre-harvest forecast yield in the absence of meteorological informations. The ARIMA estimates bear almost equal accuracy as trend-agromet based yield estimates. |
|
Date |
2016-11-24T09:55:51Z
2016-11-24T09:55:51Z 2005 |
|
Type |
Thesis
|
|
Identifier |
http://krishikosh.egranth.ac.in/handle/1/87345
|
|
Language |
en
|
|
Format |
application/pdf
|
|
Publisher |
CCSHAU
|
|