/AnMtgsAbsts2009.52618 Relationships Among Soil Mineralizable Nitrogen, Soil Properties and Climatic Indices.

Wednesday, November 4, 2009: 10:00 AM
Convention Center, Room 320, Third Floor

Jacynthe Dessureault-Rompré1, Bernie Zebarth1, David Burton2, Mehdi Sharifi3, Julia Cooper4, Cynthia Grant5 and Craig Drury6, (1)Potato Research Centre, Agriculture and Agri-Food Canada, Fredericton, NB, Canada
(2)Deptartment of Environmental Science, CANADA, Nova Scotia Agricultural College, Truro, NS, Canada
(3)Plant and Animal Sciences, Nova Scotia Agricultural College, Truro, NS, Canada
(4)Nafferton Ecological Farming Group, Newcastle Univ., Northumberland, United Kingdom
(5)Agriculture and Agri-Food Canada, Brandon, MB, CANADA
(6)Agriculture and Agri-Food Canada, Harrow, ON, CANADA
Abstract:
Soil nitrogen (N) mineralization is an important N contributor to crop uptake, however the soil and climatic controls on soil mineralizable N are poorly understood. Samples from 58 sites across Canada were used to evaluate the degree to which variation in size of soil mineralizable N pools can be explained through simple soil properties and climatic indices. Mineralizable N was determined using a 24 wk aerobic incubation at 25 ¢ªC. Potentially mineralizable N (N0) was estimated by curve-fitting using N mineralized from to 24 wk and Pool I, a labile mineralizable N pool, was determined as the N mineralized in the first 2-wk period. Soil properties were relatively effective predictors of mineralizable N, with sand and soil organic N both explaining 34% of the variability in N0. Particulate organic matter N and particulate organic matter carbon explained 18 and 14% of the variability in Pool I. Simple climate normals such as mean annual temperature and total annual precipitation were generally poor predictors for N0, explaining at best 11% of its variability, whereas potential evapotranspiration predicted 23% of the variability in Pool I. The use of re_clim indices, which combine information on soil moisture and soil temperature, improved the prediction capacity of climatic data compared with simple climate normals alone, and explained up to 31% of the variability for N0. Including soil and climatic parameters in a multiple regression model explained about two-third and one-third of the variability in N0 and Pool I, respectively. This study demonstrated that a large proportion of the variation in N0, which is relatively stable over time, can be explained using simple soil properties in combination with re_clim indices whereas the labile Pool I, which is sensitive to recent management, is more difficult to predict.