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Integrating Soil and Weather Data to Describe Variability in Plant Available Nitrogen
Author(s) -
Kay B. D.,
Mahboubi A. A.,
Beauchamp E. G.,
Dharmakeerthi R. S.
Publication year - 2006
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj2005.0039
Subject(s) - environmental science , growing season , cropping , soil water , spatial variability , regression analysis , soil carbon , hydrology (agriculture) , agronomy , ecology , mathematics , soil science , statistics , agriculture , biology , geotechnical engineering , engineering
Although there are economic and environmental reasons to manage fertilizer‐nitrogen (N) more effectively in variable landscapes, the impact of weather and its interaction with soil properties/landscape attributes or management practices has received little attention. The objectives of this study were to assess the magnitude of temporal and spatial variability in soil and plant N in a variable landscape under different management practices and to assess the dependence of their temporal variability on readily available weather variables such as air temperature and rainfall. The experiment was conducted from 1997 to 2003 on a simple slope under three maize ( Zea mays L.) based cropping systems. Soil and shoot N were measured through the growing season and the sum used as a measure of plant available N (PAN). Values of PAN varied with year, treatment, landscape position, and year × treatment and year × treatment × position interaction terms. The effects were quantified for each management treatment using multiple regression analyses to relate PAN to soil organic carbon (OC), cumulative degree days (CDD), and cumulative rainfall (CRF) in different periods within the growing season. Plant Available Nitrogen was most strongly influenced by rainfall early in the growing season and exhibited a nonlinear response to OC and CRF. The regression model predicted spatial patterns that were generally stable when applied to historical weather data; PAN increased with OC in 12 of the 15 yr. The analyses illustrate the feasibility of combining soils and weather data to predict N dynamics in variable landscapes.