期刊
CROP & PASTURE SCIENCE
卷 64, 期 5, 页码 435-441出版社
CSIRO PUBLISHING
DOI: 10.1071/CP13009
关键词
critical interval; calibration curve; errors of observation in both x and y in regression; wrong-way regression; data truncation
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 = Y-0/Y-max *100, where Y-0 is the yield without applied nutrient, and Y-max 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 Y-max (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. We describe a mathematical approach for obtaining critical soil test values and associated confidence intervals for anticipated grain yield responses to major nutrients, derived from diverse datasets contained in the BFDC National Database. The approach makes use of mathematical transforms of the data on each axis to provide a linear relationship between soil test and relative grain yield. We describe the strengths and limitations of the procedure for researchers, advisors, and farmers who use the calibrations.
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