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Higher‐Order Analysis of Nutrient Accumulation Data
Author(s) -
Sadler E. John,
Karlen Douglas L.
Publication year - 1994
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj1994.00021962008600010006x
Subject(s) - interpolation (computer graphics) , nutrient , mathematics , constant (computer programming) , zea mays , linear interpolation , biomass (ecology) , statistics , growth rate , sample (material) , order (exchange) , soil science , econometrics , environmental science , biological system , agronomy , mathematical analysis , computer science , chemistry , biology , geometry , ecology , economics , chromatography , animation , computer graphics (images) , finance , polynomial , programming language
Biomass and nutrient accumulation data have often been obtained to determine rates of nutrient uptake. Traditionally calculated as the difference in accumulation divided by elapsed time, rate values thus obtained are slopes of linear interpolations between points on the accumulation curve. That implies an assumption of constant uptake rate during the observation period. Our objective was to illustrate a higherorder interpolant that is not subject to such assumptions. With it, one obtains smooth curves consistent with the assumption that daily uptake rates are somewhat related. The abrupt changes in rates determined with linear interpolation are consistent with daily rates that are unrelated. Analyses of historical and recent data showed that additional information may be obtained from higher‐order analysis methods. Cubic interpolation methods were applied to the accumulation curve to obtain continuous, smooth nutrient uptake curves. The programs used are described, and two sample data sets of corn ( Zea mays L.) growth and N accumulation illustrate the strengths, weaknesses, and inherent assumptions of this analytical technique. In general, this technique can be used if the objective is to analyze intraseasonal variation in growth or uptake rates determined from sparse data.