Browse by author
Lookup NU author(s): Dr Hongyan Chen,
Dr Ilkka Leinonen,
Dr James Taylor
This is the authors' accepted manuscript of an article that has been published in its final definitive form by Cambridge University Press, 2017.
For re-use rights please refer to the publisher's terms and conditions.
Advances in agricultural machinery, information and sensor technology has led to an explosion in the amount and type of data that is readily available both pre and within season. The case is compelling for the spatialisation of existing, non-spatial (field-scale) crop models that can accommodate this "big data" and lead to more spatially precise predictions of yield and quality and more effective, within field management. This study explores the conceptual spatial models based on the non-spatial potato crop models, which simulates crop physical and physiological processes and predicts yield and graded yields at a field-scale. Through exploring the possible spatial scales and approaches where models can be applied while considering spatial variation an optimal and more effective solution is expected. Issues concerning model quality and uncertainty are also discussed.
Author(s): Chen H, Leinonen I, Marshall B, Taylor JA
Publication type: Article
Publication status: Published
Journal: Advances in Animal Biosciences
Print publication date: 01/07/2017
Online publication date: 01/06/2017
Acceptance date: 10/02/2017
Date deposited: 25/07/2017
ISSN (print): 2040-4700
ISSN (electronic): 2040-4719
Publisher: Cambridge University Press
Altmetrics provided by Altmetric