22 August 2013 Methodology for online biometric analysis of soil test–crop response datasets
C. B. Dyson, M. K. Conyers
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Comprehensive data on grain yield responsiveness to applications of the major nutrients nitrogen, phosphorus, potassium, sulfur in Australian cropping experiments have been assembled in the Better Fertiliser Decisions for Cropping (BFDC) National Database for scrutiny by the BFDC Interrogator. The database contains the results of individual field experiments on nutrient response that need to be collectively integrated into a model that predicts probable grain yield response from soil tests. The potential degree of grain yield responsiveness (relative yield, RY%) is related to nutrient concentration in the soil (soil test value, STV) across a range of experimental sites and conditions for each nutrient. The RY% is defined as RY = Y0/Ymax *100, where Y0 is the yield without applied nutrient, and Ymax is the yield which could be attained through adequate application of the nutrient, given sufficiency of all other nutrients. The raw data for RY and STV are transformed so that a linear regression model can be applied. The BFDC Interrogator uses the arcsine-log calibration curve (ALCC) algorithm to estimate a critical soil test value (CSTV) for a given nutrient. The CSTV is defined as the value that would, on average for the broad agronomic circumstances of the incoming crop, lead to a specified percentage of Ymax (e.g. RY = 90%) without any application of that nutrient. This paper describes the ALCC algorithm, which has been developed to ensure that such estimated CSTVs, with safeguards, are reliable and to as high a precision as is realistic.

© CSIRO 2013
C. B. Dyson and M. K. Conyers "Methodology for online biometric analysis of soil test–crop response datasets," Crop and Pasture Science 64(5), 435-441, (22 August 2013). https://doi.org/10.1071/CP13009
Received: 7 January 2013; Accepted: 1 July 2013; Published: 22 August 2013
calibration curve
critical interval
data truncation
errors of observation in both x and y in regression
wrong-way regression
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