Nitrogen Dynamics and Indices to Predict Soil Nitrogen Supply in Humid Temperate Soils

Luce, Mervin St.; Whalen, Joann K.; Ziadi, Noura; Zebarth, Bernie J.

doi:10.1016/b978-0-12-385538-1.00002-0

Cited by 144 publications

(55 citation statements)

References 258 publications

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“…The calibrated model gave similar statistics with cross-validation and independent validation tests (Table 2), except for the fine-textured set. Since soil N supply does not have a theoretical basis for NIRS prediction, the predictions are derived from relationships with primary properties such as texture, total N, and organic C, which are known to impact soil N supply significantly (St. Luce et al, 2011). Soil N supply was always more related to total N than organic C (data not shown), showing a better relationship with total N in the coarse-textured subset (r = 0.30, P < 0.001, data not shown) than the total set {r = 0.17, P < 0.01, data not shown) and the fine-textured subset {r = 0.08, P > 0.05, data not shown).…”

Section: Prediction For Soil Nitrogen Supplymentioning

confidence: 99%

“…It is well known that soil N supply is related to both the quantity and quality (phyisco-chemical forms) of organic N in soils. Many studies tried to relate labile soil organic N fractions to soil N supply with mixed results (St. Luce et al, 2011). The labile fraction of soil organic N, having a turnover rate of days to a few years contributes significantly to the soil N supply (Haynes, 2005).…”

Section: Prediction For Soil Nitrogen Supplymentioning

confidence: 99%

“…Previous work using NIRS in Canada primarily focused on total N, total C, organic C, and soil texture, and involved a limited number of soil samples (« < 220) collected from three or less sites and years, respectively (Malley et al, 1999;Martin et al, 2002;Nduwamungu et al, 2009b;Xie et al, 2011). Since these parameters are correlated with soil N supply (St. Luce et al, 2011), this holds promise for developing a NIRS-based model of soil N supply.…”

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confidence: 99%

“…Soil texture (clay content) is a major factor influencing soil biochemical activity (St. Luce et al, 2011). A recent study showed improved prediction of soil N supply by several chemical and biological N availability indices after grouping soils based on texture (Nyiraneza et al, 2012).…”

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confidence: 99%

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Near Infrared Reflectance Spectroscopy Prediction of Soil Nitrogen Supply in Humid Temperate Regions of Canada

Luce

Ziadi

Nyiraneza

et al. 2012

Soil Science Society of America Journal

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Near infrared reflectance spectroscopy (NIRS) holds promise for rapid assessment of soil total N and organic C contents, but its ability to predict soil N supply in soils with contrasting physio-chemical properties needs to be evaluated. The objectives of this study were to: (i) evaluate NIRS predictions of measured soil parameters (total N, organic C, and C/N ratio) and as an indirect indicator of soil N supply, namely corn (Zea mays L.) N uptake and (ii) assess the effect of sample set heterogeneity on NIRS predictions. Soil samples (n = 282 in the total set) were collected between 2000 and 2009 from 52 sites across four Canadian provinces and were allocated to fine-(>350 g clay kg-\ n = 101) and coarse-textured subsets (<350 g clay kg~n = 181). Prediction models were developed using modified partial least squares regression. Prediction precision was assessed using the coefficient of determination {R^) and ratio of prediction to deviation (RPD = ratio of standard error of prediction corrected for bias to standard deviation of the reference data used in the validation). For the total set, predictions were reliable for total N, organic C and C/N ratio (0.7 < R^ < 0.9, 1.75 < RPD < 3), and less reliable for soil N supply {R^ < 0.7, RPD < 1.75). Prediction precision for total N, organic C, C/N ratio, and soil N supply increased with set homogeneity (i.e., better in the texture subsets than the total set), resulting in reliable predictions for soil N supply (RPD > 2.00) in the texture subsets. This study demonstrated the possibility of developing reliable NIRS predictive models for total N, organic C, and C/N ratio for soils with contrasting physiochemical properties, as well as reliable NIRS predictive models for soil N supply within homogeneous texture sets.Abbreviations: CV, coefficient of variation; Math treat, math treatment; n^ number of samples in calibration; n^, number of samples invalidation; NIRS, near infrared reflectance spectroscopy; organic C, organic carbon; R^, coefficient of determination; RPD, ratio of prediction to deviation; SD, standard deviation; SEC, standard error of calibration; SECV, standard error of cross-validation; SEP, standard error of prediction; SEP(C), standard error of prediction corrected for bias; total N, total nitrogen; SNVD, standard normal variate and detrending; T-Outliers, outliers discarded during calibration.

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Section: Prediction For Soil Nitrogen Supplymentioning

confidence: 99%

Section: Prediction For Soil Nitrogen Supplymentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Near Infrared Reflectance Spectroscopy Prediction of Soil Nitrogen Supply in Humid Temperate Regions of Canada

Luce

Ziadi

Nyiraneza

et al. 2012

Soil Science Society of America Journal

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“…N mineralization is a biological process, the rate of which depends on the substrate concentration, microbial activity, and environmental factors such as soil moisture, aeration, temperature, pH, and availability of other nutrients (Griffin 2008;St. Luce et al 2011).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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A better understanding of the depth distribution of soil mineralizable nitrogen (N) pools is important to improve prediction of net soil N mineralization. However, our understanding of the depth distribution of these N pools under the semi-arid conditions of western Canada is limited. This study examined the depth distribution of soil mineralizable N pools (k S , the rate constant of a nondepleting zero-order stable N pool, and N L , the size of a depleting first-order labile N pool) of six sites in western Canada chosen to vary with respect to soil zone, soil texture, and cropping system. The depth distribution of mineralizable N pools varied substantially among sites, indicating that this distribution needs to be considered in making predictions of net soil N mineralization. A single regression equation including soil total nitrogen (STN), Pool I (a labile mineralizable N pool determined through a 14-day aerobic incubation), and soil pH explained 67% of the variation in k S across sites and soil depths. In addition, 95% of the variation in N L was explained by a regression model with Pool I. Thus, although the depth distribution of soil mineralizable N pools can vary substantially among sites, the mineralizable N parameters can be adequately predicted across sites and soil depths from simple soil properties. Comparison with a study using surface soils under humid conditions in New Brunswick suggests that the relationship between N L and Pool I is applicable across a wide range of soils, climatic zones, and cropping systems, whereas the regression model to predict k S varied with climatic zone, perhaps reflecting different pedogenic processes stabilizing the organic matter in these climatic zones.Key words: climatic zone, nitrogen, regression analysis, soil properties.Résumé : Il importe de mieux comprendre la manière dont les réserves de N minéralisable sont réparties selon la profondeur si l'on veut mieux prévoir la minéralisation nette de cet élément dans le sol. Malheureusement, on saisit mal comment ces réserves sont distribuées en profondeur dans les régions semi-arides de l'Ouest canadien. La présente étude devait établir la répartition des réserves de N minéralisable d'après leur profondeur dans le sol, (à savoir, établir les paramètres k S , c.-à-d. la constante d'une réserve stable de N du premier degré ne diminuant pas, et N L , une réserve labile de N du premier degré qui va décroissant) à six endroits de l'ouest du Canada, qui différaient par la zone de sol, la texture du sol et le régime agricole. La répartition en profondeur des réserves de N varie sensiblement d'un site à l'autre, signe qu'on doit tenir compte de ce facteur quand on formule des prévisions sur la minéralisation nette du N. Une équation de régression simple incluant la concentration totale d'azote dans le sol, la réserve I (réserve de N minéralisable labile déterminée après une incubation de 14 jours dans des conditions aérobies) et le pH du sol, explique 67 % de la variation du paramètre k S d'un site et d'une profondeu...

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Does potentially mineralizable nitrogen predict maize yield in newly cropped soil?

Gebremedhin

Sarr

Coyne

et al. 2020

Agrosystems Geosci & Env

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Fallow soils converted to crop use have unpredictable soil organic nitrogen (SON) availability during the growing season, which incentivizes over‐applying fertilizer N by way of compensation. Potentially mineralizable nitrogen (PMN) is a soil health indicator, and its increase is a management strategy to meet crop N demands. How well does PMN predict yield in transition, periods from fallow to crop, especially when conservation practices are imposed simultaneously? In a short‐term study, conducted in Madison County, Kentucky (37.85 N, 84.29 W) from 2015 to 2017, we tested the hypothesis that implementing conservation practices (use of cover crops and manure) during the transition period would mitigate adverse effects on N availability by increasing PMN and significantly benefit maize (Zea mays L.) grain yield. Six treatments were used: (a) an unamended control; (b) cover crops; (c) horse manure; (d) cover crops and horse manure; (e) chemical fertilizer; and (f) cover crops and chemical fertilizer. Soils were sampled after cover crop termination, before manure or fertilizer application in spring, and at fall harvest each year and analyzed for PMN. The seasonal PMN and maize grain yield were influenced by cover crops and manure (p < .05). Maize grain yield had a positive response to cover crops but not manure. The correlation between PMN and grain yield, though positive, was very weak (R2 = .03). We conclude that conservation practices, particularly cover crops, in the transition period can benefit PMN and crop yield, but PMN is not a good predictor of yield during transition stages.

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Nitrogen Dynamics and Indices to Predict Soil Nitrogen Supply in Humid Temperate Soils

Cited by 144 publications

References 258 publications

Near Infrared Reflectance Spectroscopy Prediction of Soil Nitrogen Supply in Humid Temperate Regions of Canada

Near Infrared Reflectance Spectroscopy Prediction of Soil Nitrogen Supply in Humid Temperate Regions of Canada

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

Does potentially mineralizable nitrogen predict maize yield in newly cropped soil?

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