Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-03T00:06:24.751Z Has data issue: false hasContentIssue false
  • English
  • Français

How significantly different are your within field zones?

Published online by Cambridge University Press:  01 June 2017

B. Tisseyre  and
C. Leroux
B. Tisseyre*
Affiliation:
UMR ITAP, Montpellier SupAgro, Irstea, 2 place Viala, 34060, Montpellier, France
C. Leroux
Affiliation:
UMR ITAP, Montpellier SupAgro, Irstea, 2 place Viala, 34060, Montpellier, France SMAG Company, Le Terra Verde, Parc Eurêka, 55 Rue Euclide, 34000 Montpellier, France
*
†E-mail: [email protected]
Get access

Abstract

A classical approach in precision agriculture consists in validating within field zones defined from high spatial resolution observations by agronomic information (AI). Zones validation generally involves a two-step process. First, AI are obtained on a regular grid or following a target sampling strategy inside the field. Then, a statistical test, most often an ANOVA, is used to determine if the management zones created with the high spatial resolution auxiliary data explain differences in the AI values. Unfortunately, in precision agriculture, many of the works using such an approach omit a necessary condition for the implementation of the aforementioned ANOVA test, i.e. the observations need to be independent from each other. This condition is unfortunately seldom satisfied since AI are often spatially auto-correlated. In order to highlight this problem, simulated datasets with different and known AI spatial autocorrelation were used. Results show that as AI are more and more spatially auto-correlated, ANOVA tests almost always conclude that the management zones obtained with auxiliary data are significant whatever the zoning, i.e. even a completely random one. To overcome this problem, the paper introduces two methods directly inspired from published works in the field of ecology. Two cases were considered: the first one applies when large AI dataset (n>40) is available and the other one applies for small AI dataset (n<40). Both methods are implemented on a real precision viticulture example.

Keywords

ANOVAeffective sample sizerandomization methodsspatial autocorrelation
Type
Data analysis and Geostatistics
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. 620 - 624
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Clifford, P, Richardson, S and Hemon, D 1989. Testing the association between two spatial processes. Biometrics 45, 123–134.CrossRefGoogle ScholarPubMed
Cressie, NA 1991. Statistics for Spatial Data. John Wiley & Sons, New York, USA.Google Scholar
Dale, MRT and Fortin, M-J 2002. Spatial autocorrelation and statistical tests in ecology. Eco Science 9, 162–167.Google Scholar
Dutilleul, P 1993. Modifying the t-test for assessing the correlation between two spatial processes. Biometrics 49, 305–314.CrossRefGoogle Scholar
Goovaerts, P 1997. Geostatistics for Natural Ressources Evaluation, Applied Geostatistics Series. Oxford University Press, New York.CrossRefGoogle Scholar
Legendre, P and Legendre, L 1998. Numerical Ecology, 2nd English edition. Elsevier, Amsterdam, The Netherlands.Google Scholar
Taylor, J, Tisseyre, B, Bramley, R, Reid, A and Stafford, J 2005. A comparison of the spatial variability of vineyard yield in European and Australian production systems. Precision Agriculture 5, 907–914.Google Scholar
Taylor, JA and Bates, TR 2013. A discussion on the significance associated with Pearson’s correlation in precision agriculture studies. Precision Agriculture 14, 558–564.CrossRefGoogle Scholar
Kitchen, NR, Sudduth, KA, Myers, DB, Drummond, ST and Hong, SY 2005. Delineating productivity zones on claypan soil fields using apparent soil electrical conductivity. Computers and Electronics in Agriculture 46, 285–308.CrossRefGoogle Scholar
Peralta, NR, Costa, JL, Balzarini, M, Franco, MC, Córdoba, M and Bullock, D 2015. Delineation of management zones to improve nitrogen management of wheat. Computers and Electronics in Agriculture 110, 103–113.CrossRefGoogle Scholar