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Conceptual Spatial Crop Models for Potato Production

Published online by Cambridge University Press:  01 June 2017

H. Chen ,
I. Leinonen ,
B. Marshall  and
J.A. Taylor
H. Chen*
Affiliation:
School of Agriculture, Food and Rural Development, Newcastle University, NE1 7RU, UK
I. Leinonen
Affiliation:
School of Agriculture, Food and Rural Development, Newcastle University, NE1 7RU, UK
B. Marshall
Affiliation:
Consultant, 5 Muirloch Farm Cottage, Liff, Dundee, DD2 5NQ, UK
J.A. Taylor
Affiliation:
School of Agriculture, Food and Rural Development, Newcastle University, NE1 7RU, UK
*
†Email: [email protected]
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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.

Keywords

crop model spatialisationspatial autocorrelationspatial stratified heterogeneity
Type
Spatial Crop Models
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 678 - 683
Copyright
© The Animal Consortium 2017 

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References

Allaire, SE, Cambouris, AN, Lafond, JA, Lange, SF, Pelletier, B and Dutilleul, P 2014. Spatial Variability of Potato Tuber Yield and Plant Nitrogen Uptake. Agronomy Journal 106, 851–859.Google Scholar
Kabat, E, Marshall, B, van den Broek, BJ, Vas, J and van Keulen, H 1995. Modelling and Parameterisation of the Soil-Plant-Atmosphere System – A Comparison of Potato Growth Models. Wageningen Pers, Wageningen, The Netherlands.Google Scholar
Jefferies, RA and Heilbronn, TD 1991. Water stress as a constraint on growth in the potato crop. 1. Model development. Agricultural and Forest Meteorology 53, 185–196.Google Scholar
MacKerron, DKL, Marshall, B and McNicol, JW 2004. MAPP and the underlying functions that it contains. In D.K.L. MacKerron and A.J. Haverkort (eds) Decision support systems in potato production: bringing models to practice. Wageningen Academic Publishers, The Netherlands.Google Scholar
Matheron, G 1963. Principles of geostatistics. Economic Geology 58, 1246–1266.Google Scholar
Moran, PAP 1950. Notes on continuous stochastic phenomena. Biometrika 37, 17–23.Google Scholar
Ortega, R and Santibanez, O 2007. Determination of management zones in corn (Zea mays L.) based on soil fertility. Computers and Electronics in Agriculture 58 (1), 49–59.Google Scholar
Panday, SG 2014. Use of remote sensing data in a crop growth model to estimate actual crop. http://www.itc.nl/library/papers_2014/msc/nrm/gangarampanday.pdf (retrieved 12/01/2017).Google Scholar
Pedroso, M, Taylor, JA, Tisseyre, B, Charnomordic, B and Guillaume, S 2010. A segmentation algorithm for the delineation of management zones. Computers and Electronics in Agriculture 70, 199–208.Google Scholar
Pringle, MJ, McBratney, AB, Whelan, BM and Taylor, JA 2003. A preliminary approach to assessing the opportunity for site-specific crop management in a field, using a yield monitor. Agricultural Systems 76, 273–292.Google Scholar
Steduto, P 2003. Biomass water-productivity. Comparing the growth-engines of crop models. FAO Expert Meeting on Crop Water Productivity under Deficient Water Supply, Rome. February 26–28.Google Scholar
Taylor, JA, Whelan, BM and McBratney, AB 2007. Establishing management classes for broad acre agriculture. Agronomy Journal 99, 1366–1376.Google Scholar
Whelan, BM and Taylor, JA 2013. Precision Agriculture for Grain Production Systems. CSIRO Publishing: Collingwood, Australia.Google Scholar
Wang, JF, Christakos, G and Hu, MG 2009. Modelling spatial means of surfaces with stratified non-homogeneity. IEEE Transactions on Geoscience and Remote Sensing 47, 4167–4174.Google Scholar
Wang, JF, Zhang, TL and Fu, BJ 2016. A measure of spatial stratified heterogeneity. Ecological Indicators 67, 250–256.Google Scholar