Predicting soil nitrogen supply from soil properties

Abstract

. 2015. Predicting soil nitrogen supply from soil properties. Can. J. Soil Sci. 95: 63Á75. Prediction functions based on simple kinetic models can be used to estimate soil N mineralization as an aid to improved fertilizer N management, but require long-term incubations to obtain the necessary parameters. Therefore, the objective of this study was to examine the feasibility of predicting the mineralizable N parameters necessary to implement prediction functions and in addition to verify their efficiency in modeling soil N supply (SNS) over a growing season. To implement a prediction function based on a first-order (F) kinetic model, a regression equation was developed using a data base of 92 soils, which accounted for 65% of the variance in potentially mineralizable N (N 0 ) using soil total N (STN) and Pool I, a labile mineralizable N pool. However, the F prediction function did not provide satisfactory prediction (R 2 00.17Á0.18) of SNS when compared with a field-based measure of SNS (PASNS) if values of N 0 were predicted from the regression equation. We also examined a two-pool zero-plus first-order (ZF) prediction function. A regression model was developed including soil organic C and Pool I and explained 66% of the variance in k S , the rate constant of the zero-order pool. In addition, a regression equation was developed which explained 86% of the variance in the size of the first-order pool, N L , from Pool I. The ZF prediction function provided satisfactory prediction of SNS (R 2 00.41Á0.49) using both measured and predicted values of k S and N L . This study demonstrated a simple prediction function can be used to estimate SNS over a growing season where the mineralizable N parameters are predicted from simple soil properties using regression equations.Key words: Arable crops, kinetic equations, mineralizable N pools, prediction, regression models Dessureault-Rompre´, J., Zebarth, B. J., Burton, D. L. et Georgallas, A. 2015. Pre´vision des re´serves d'azote a`partir des proprie´te´s du sol. Can. J. Soil Sci. 95: 63Á75. On peut recourir aux fonctions de pre´vision reposant sur de simples mode`les cine´tiques pour estimer la mine´ralisation du N dans le sol et faciliter la gestion des amendements azote´s, mais obtenir les parame`tres requis pour cela ne´cessite de longues incubations. L'e´tude devait e´tablir s'il est possible de pre´voir les parame`tres du N mine´ralisable essentiels a`l'application des fonctions de pre´vision, puis d'en ve´rifier l'efficacite´en mode´lisant les re´serves d'azote du sol au cours d'une pe´riode ve´ge´tative. Pour appliquer une fonction de pre´vision qui reposait sur un mode`le cine´tique du premier degre´(F), les auteurs ont e´labore´une e´quation de re´gression en recourant a`une base de donne´es sur 92 sols qui expliquait 65 % de la variance du N susceptible d'eˆtre mine´ralise´(N 0 ) a`partir de la concentration totale de N dans le sol et du bassin I, une re´serve de N mine´ralisable labile. La fonction F ne permet pas de pre´voir de fac¸on satisfaisante (R 2 0 0.17Á0....

Depth distribution of mineralizable nitrogen pools in contrasting soils in a semi-arid climate

“…As a result, it may be possible to use these simple soil properties to estimate the depth distribution of soil mineralizable N pools needed to predict net soil N mineralization. Furthermore, the very similar relationship between N L and Pool I from this study as well as for surface soils in the New Brunswick study (Dessureault-Rompré et al 2015) suggests that this relationship is applicable across a wide range of soils, climatic zones, and cropping systems. …”

“…1A, Table 6). Using the same three parameters and the data set from Dessureault-Rompré et al (2015) from New Brunswick, the regression equation obtained could explain up to 67% of the k S variability (Fig. 1B, Table 6).…”

“…1. Relationship between predicted and measured values of k S using the prediction equations shown in Table 6 and (A) the present study data set and (B) the data set from Dessureault-Rompré et al (2015). (2015) found SOC and STN explained 42% and 53%, respectively, of the variation in k S for soils across New Brunswick.…”

“…properties, Pool I, and CaCl 2 -N for the 0-15 and 30-60 cm depths (Table 5) Recently, Dessureault-Rompré et al (2015) observed that 58% of the variation in the k S parameter could be explained by a regression model including STN and Pool I using surface (0-15 cm) soils from across New Brunswick, Canada. In the present study, a regression model including STN, Pool I, and soil pH explained 66% of the variability in k S across all sites and depths (Fig.…”

“…Dessureault-Rompré et al (2013) found that a two-pool zero-plus first-order model generally fit experimental data from incubation experiments better than first-order models with one or two pools. In the present study, the following zero-plus first-order model was used (Dessureault-Rompré et al 2015):…”