Statistical models for forecasting pigeonpea yield in Varanasi region

被引：0

作者：

Pritykumari ^{[1
,3
]}

Mishra, G. C. ^{[1
]}

Srivastava, C. P. ^{[2
]}

机构：

[1] Banaras Hindu Univ, Inst Agr Sci, Dept Farm Engn, Sect Agr Stat, Varanasi 221005, Uttar Pradesh, India

[2] Banaras Hindu Univ, Inst Agr Sci, Dept Entomol & Agr Zool, Varanasi 221005, Uttar Pradesh, India

[3] Gujarat Agr Univ, Coll Hort, Anand 388110, Gujarat, India

来源：

JOURNAL OF AGROMETEOROLOGY | 2016年 / 18卷 / 02期

关键词：

Artificial neural network (ANN); autoregressive integrated moving average (ARIMA) model; regression model andpigeonpea yield; NETWORKS; DISTRICT;

D O I：

暂无

中图分类号：

S3 [农学（农艺学）];

学科分类号：

0901 ;

摘要：

Present study deals with different linear and non-linear statistical models like multiple linear regression (MLR) model, autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) for forecastingpigeon pea yield grown in Varanasi region of Uttar Pradesh using 27 years of data (1985-86 to 2011-12). The performance of the model was assessed by root mean squared error (RMSE). On the basis of empirical studies, ANN was found to be best suitable model having lowest RMSE with forecasted yield during the year 2012-13 for Varanasi region.

引用

页码：306 / 310

页数：5

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