Interpolation and Interpretation for Field Scale Decision Making

Yield mapping combine harvesters are capable of the continuous mapping of cereal yields. Explanations for the observed variability are frequently based upon the interpolation of soil data from a limited number of point samples. The point soil data and yield surface are integrated using the algorithms provided within mapping software and geographical information systems (GIS) that are increasingly available to the nonspecialist in spatial analyses. The surfaces and correlations produced are particularly sensitive to the choice and parameterisation of the algorithms, much of which is done within a ‘black-box’ environment. Furthermore, it is rarely established that spatial auto-correlation exists at the scale of sampling. Hence, interpolation may not always be valid. This can have a significant impact on the interpretation of the data and the process of field scale decision making. This paper discusses the consequences of using different interpolation algorithms and methods of visualisation, using examples from agricultural field consultancy within the United Kingdom. The authors conclude that a number of treatment classes should be defined for a few zones, rather than attempting to continuously vary application rates in response to an inaccurate model of the soil nutrient surface.