Conceptual Spatial Crop Models for Potato Production. (1st June 2017)

• Record Type: Journal Article
• Authors: Chen, H.; Leinonen, I.; Marshall, B.; Taylor, J.A.
• Is Part Of: Advances in animal biosciences. Volume 8:Number 2(2017)
• Page Start: 678
• Page End: 683
• Publication Date: 2017-06-01
• Subjects: crop model spatialisation, -- spatial autocorrelation, -- spatial stratified heterogeneity; Animal nutrition -- Periodicals; Animal breeding -- Periodicals; Animal genetics -- Periodicals; Animal biotechnology -- Periodicals; Environmental sciences -- Periodicals; 636.005
• DOI: 10.1017/S2040470017000851
• Languages: English
• ISSNs: 2040-4700

Abstract

Abstract : Advances in agricultural machinery, information and sensor technology have led to an increasing amount of data that is available spatially 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 precise predictions of yield and quality and an improved field management. This study explores the conceptual spatial models based on the potato crop models that simulate crop physical and physiological processes and predict yields and graded yields at a field-scale. Through exploring the possible spatial scales and model application approaches considering spatial variation an optimal and more effective solution is expected. Issues concerning model quality and uncertainty are also discussed.