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Conceptual Spatial Crop Models for Potato Production

Lookup NU author(s): Dr Hongyan Chen, Dr Ilkka Leinonen, Dr James Taylor

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by Cambridge University Press, 2017.

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Abstract

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.

Publication metadata

Author(s): Chen H, Leinonen I, Marshall B, Taylor JA

Publication type: Article

Publication status: Published

Journal: Advances in Animal Biosciences

Year: 2017

Volume: 8

Issue: 2

Pages: 678-683

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

URL: https://doi.org/10.1017/S2040470017000851

DOI: 10.1017/S2040470017000851

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