Quantifying impact of droughts on barley yield in North Dakota, USA using Multiple Linear Regression and Artificial Neural Network

Title:

Quantifying impact of droughts on barley yield in North Dakota, USA using Multiple Linear Regression and Artificial Neural Network

Creator:

Odabas, Mehmet Serhat , Leelaruban , Navaratnam, Simsek, Halis, and Padmanabhan , G.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:b1250d60-6daf-4be0-b159-ed0c811b623a
uuid:b1250d60-6daf-4be0-b159-ed0c811b623a
doi:10.14311/NNW.2014.24.020

Subject:

Barley yield, multiple linear regression, artificial neural network, and drought impact

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

This research investigated the effect of different drought conditions on Barley (Hordeum vulgare L.) yield in North Dakota, USA, using Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) methods. Though MLR method is widely used, the ANN method has not been used in the past to investigate the effect of droughts on barley yields to the best of authors’ knowledge. It is found from this study that the ANN model performs better than MLR in estimating barley yield. In this paper, the ANN is proposed as a viable alternative method or in combination with MLR to investigate the impact of droughts on crop yields.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2014 Volume:24 | Number:4

Harvested from:

CDK

Metadata only:

false