Predicting soil nitrogen supply from soil properties

2015 ◽  
Vol 95 (1) ◽  
pp. 63-75 ◽  
Author(s):  
Jacynthe Dessureault-Rompré ◽  
Bernie J. Zebarth ◽  
David L. Burton ◽  
Alex Georgallas
Keyword(s):  
Soil Properties ◽  
Growing Season ◽  
Nitrogen Supply ◽  
Regression Equation ◽  
First Order ◽  
Mineralizable N ◽  
Prediction Function ◽  
Prediction Functions ◽  
Satisfactory Prediction ◽  
Soil Nitrogen Supply

Dessureault-Rompré, J., Zebarth, B. J., Burton, D. L. and Georgallas, A. 2015. Predicting soil nitrogen supply from soil properties. Can. J. Soil Sci. 95: 63–75. 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=0.17–0.18) 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=0.41–0.49) 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.

Factors affecting soil nitrogen supply in tobacco fields in Southwest Hubei

2010 ◽  
Vol 18 (6) ◽  
pp. 1157-1162
Author(s):  
Shu-Jun ZHAO ◽  
Jia-Fu YUAN ◽  
Xin-Ran ZHANG ◽  
Xiang-Yu XU ◽  
You-Sheng XIONG ◽  
...  
Keyword(s):  
Soil Nitrogen ◽  
Nitrogen Supply ◽  
Factors Affecting ◽  
Soil Nitrogen Supply

scholarly journals Soil nitrogen supply of peat grasslands estimated by degree days and soil organic matter content

2020 ◽  
Vol 117 (3) ◽  
pp. 351-365
Author(s):  
J. Pijlman ◽  
G. Holshof ◽  
W. van den Berg ◽  
G. H. Ros ◽  
J. W. Erisman ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Soil Nitrogen ◽  
Organic Matter Content ◽  
Matter Content ◽  
Nitrogen Supply ◽  
Degree Days ◽  
Soil Organic Matter Content ◽  
Soil Nitrogen Supply

Log landings are methane emission hotspots in managed forests

2021 ◽  
Author(s):  
Juliana Vantellingen ◽  
Sean C. Thomas
Keyword(s):  
Organic Matter ◽  
Soil Properties ◽  
Management Practices ◽  
Emission Rate ◽  
Woody Debris ◽  
Organic Matter Content ◽  
Growing Season ◽  
Matter Content ◽  
Emission Rates ◽  
Managed Forests

Log landings are areas within managed forests used to process and store felled trees prior to transport. Through their construction and use soil is removed or redistributed, compacted, and organic matter contents may be increased by incorporation of wood fragments. The effects of these changes to soil properties on methane (CH<sub>4</sub>) flux is unclear and unstudied. We quantified CH<sub>4</sub> flux rates from year-old landings in Ontario, Canada, and examined spatial variability and relationships to soil properties within these sites. Landings emitted CH<sub>4</sub> throughout the growing season; the average CH<sub>4</sub> emission rate from log landings was 69.2 ± 12.8 nmol m<sup>-2</sup> s<sup>-1</sup> (26.2 ± 4.8 g CH<sub>4</sub> C m<sup>-2</sup> y<sup>-1</sup>), a rate comparable to CH<sub>4</sub>-emitting wetlands. Emission rates were correlated to soil pH, organic matter content and quantities of buried woody debris. These properties led to strong CH<sub>4</sub> emissions, or “hotspots”, in certain areas of landings, particularly where processing of logs occurred and incorporated woody debris into the soil. At the forest level, emissions from landings were estimated to offset ~12% of CH<sub>4</sub> consumption from soils within the harvest area, although making up only ~0.5% of the harvest area. Management practices to avoid or remediate these emissions should be developed as a priority measure in “climate-smart” forestry.

Use of hot KCl-NH4-N to estimate fertilizer N requirements

1997 ◽  
Vol 77 (2) ◽  
pp. 161-166 ◽  
Author(s):  
C. A. Campbell ◽  
Y. W. Jamel ◽  
A. Jalil ◽  
J. Schoenau
Keyword(s):  
N Mineralization ◽  
Growth Models ◽  
Growing Season ◽  
Order Rate Constant ◽  
Weather Data ◽  
N Availability ◽  
Fertilizer N ◽  
Available N ◽  
Mineralizable N ◽  
Potentially Mineralizable N

We need an easy-to-use chemical index for estimating the amount of N that becomes available during the growing season, to improve N use efficiency. This paper discusses how producers may, in future, use crop growth models that incorporate indices of soil N availability, to make more accurate, risk-sensitive estimates of fertilizer N requirements. In a previous study, we developed an equation, using 42 diverse Saskatchewan soils, that related potentially mineralizable N (N0) to NH4N extracted with hot 2 M KCl (X), (i.e., N0 = 37.7 + 7.7X, r2 = 0.78). We also established that the first order rate constant (k) for N mineralization at 35°C is indeed a constant for arable prairie soils (k = 0.067 wk−1). We modified the N submodel of CERES-wheat to include k and N0 (values of N0 were derived from the hot KCl test). With long-term weather data (precipitation and temperature) as input, this model was used to estimate probable N mineralization during a growing season and yield of wheat (grown on fallow or stubble), in response to fertilizer N rates at Swift Current. The model output indicated that the amount of N mineralized in a growing season for wheat on fallow was similar to that for wheat on stubble, as we hypothesized. Further the model indicated that rate of fertilizer N had only minimal effect on N mineralized. We concluded that, despite the importance of knowing the Nmin capability of a soil, it is available water, initial levels of available N and rate of fertilizer N that are the main determinants of yield in this semiarid environment. The theoretical approach we have proposed must be validated under field conditions before it can be adopted for use. Key words: N mineralization, Hot KCl-NH4-N, potentially mineralizable N, CERES-wheat model

scholarly journals Time series prediction using a recursive algorithm of a combination of genetic programming and constant optimization

2002 ◽  
Vol 15 (2) ◽  
pp. 265-279 ◽  
Author(s):  
Witthaya Panyaworayan ◽  
Georg Wuetschner
Keyword(s):  
Time Series ◽  
Genetic Programming ◽  
Recursive Algorithm ◽  
Time Series Prediction ◽  
Prediction Function ◽  
Prediction Functions

In this paper we present a prediction process of Time Series using a combination of Genetic Programming and Constant Optimization. The Genetic Programming will be used to evolve the structure of the prediction function, whereas the Constant Optimization will determine the numerical parameters of the prediction function. The prediction process is applied recursively. In each recursion step, a sub-prediction function is evolved. At the end of the iteration all sub-prediction functions form the final prediction function. The avoiding of a major problem in the prediction called over-fitting is also described in this article.

Combined effects of site preparation, soil properties, and sowing date on the establishment of Pinus sylvestris and Picea abies from seeds

2003 ◽  
Vol 33 (5) ◽  
pp. 931-945 ◽  
Author(s):  
Michelle de Chantal ◽  
Kari Leinonen ◽  
Hannu Ilvesniemi ◽  
Carl Johan Westman
Keyword(s):  
Soil Moisture ◽  
Picea Abies ◽  
Pinus Sylvestris ◽  
Soil Properties ◽  
Seedling Emergence ◽  
Growing Season ◽  
Sowing Date ◽  
Site Preparation ◽  
Winter Mortality ◽  
Frost Heaving

The aim of this study is to determine the effect of site preparation on soil properties and, in turn, the emergence, mortality, and establishment of Pinus sylvestris L. (Scots pine) and Picea abies (L.) Karst. (Norway spruce) seedlings sown in spring and summer along a slope with variation in soil texture and moisture. Three site preparation treatments of varying intensities were studied: exposed C horizon, mound (broken L–F–H–Ae–B horizons piled over undisturbed ground), and exposed Ae–B horizons. Seedling emergence was higher in the moist growing season than in the dry one. During a dry growing season, mounds and exposed C horizon had negative effects on soil moisture that increased mortality. Moreover, frost heaving was an important cause of winter mortality on mounds and exposed C horizon, whereas frost heaving was low on exposed Ae–B horizons, even though soil moisture and the content of fine soil particles (<0.06 mm) were high. Frost heaving mortality was higher for summer-sown than for spring-sown seedlings and for P. abies than for P. sylvestris. Growing season mortality was high following a winter with frost heaving, suggesting that roots were damaged, thereby making seedlings more susceptible to desiccation.

scholarly journals Schrenk spruce leaf litter decomposition varies with snow depth in the Tianshan Mountains

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Lu Gong ◽  
Xin Chen ◽  
Xueni Zhang ◽  
Xiaodong Yang ◽  
Yanjiang Cai
Keyword(s):  
Soil Properties ◽  
Snow Cover ◽  
Litter Decomposition ◽  
Growing Season ◽  
Temporal Variations ◽  
Tianshan Mountains ◽  
Cover Depth ◽  
C And N ◽  
Thaw Period ◽  
Snow Cover Depth

Abstract Seasonal snowfall, a sensitive climate factor and the main form of precipitation in arid areas, is important for forest material circulation and surface processes and profoundly impacts litter decomposition and element turnover. However, how the thickness and duration of snow cover affect litter decomposition and element release remain unclear. Thus, to understand the effects of snow on litter decomposition, fiber degradation and their relationships with soil properties, a field litterbag experiment was conducted under no, thin, medium, and thick snow cover in a Schrenk spruce (Picea schrenkiana) forest gap in the Tianshan Mountains. The snow cover period exhibited markedly lower rates of decomposition than the snow-free period. The litter lignin, cellulose and N concentrations in the pregrowing season and middle growing season were significantly higher than those in the deep-freeze period, and the litter C and P concentrations were significantly higher during the onset of the freeze–thaw period, deep-freeze period and thaw period than in the late growing season. The litter cellulose, C and N concentrations were significantly higher under thick snow cover than under no snow cover in most stages. Moreover, the correlations among litter mass, cellulose, lignin/cellulose and soil bulk density varied with snow cover depth. The temporal variations and snow cover depth affected the decomposition process significantly. The former affected lignin, cellulose and P, and the latter affected cellulose, C and N and changed the litter-soil properties relationship. These differences provide references for understanding how winter conditions affect material cycling and other ecological processes under climate change.

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