Use of natural heterogeneity in a small field site to explore the influence of the soil matrix on nitrogen mineralisation and nitrification

Soil Research ◽  
1997 ◽  
Vol 35 (3) ◽  
pp. 579 ◽  
Author(s):  
D. T. Strong ◽  
P. W. G. Sale ◽  
K. R. Helyar
Keyword(s):  
Soil Properties ◽  
Multiple Regression ◽  
Multiple Regression Equation ◽  
Regression Equations ◽  
Small Field ◽  
Undisturbed Soil ◽  
Soil Matrix ◽  
Ph Buffering ◽  
Functional Relationships ◽  
Physical Scale

The influence of soil properties on microbiological processes is often examined by comparing the behaviour of taxonomically disparate soils. One of the limitations of this approach is that the results can be confounded by the unmeasured properties which vary between soils of different type or between soils which have had different climatic and management histories. This study tested the hypothesis that the heterogeneity between 100 small contiguous undisturbed soil cubes (about 1·7 cm3), sampled from the surface of a very small field plot (14 by 14 cm), was sufficiently large to use for the exploration of how soil properties influence biological processes. After incubation of the soil for 35 days, the coefficients of variation for nitrate (NO3), ammonium (NH4), gravimetric water content (θg), bulk density (BD), pH buffering capacity (pHBC), and pH were 28, 39, 27, 10, 13, and 2%, respectively. A multiple regression equation predicting nitrate concentration had an r2 value of 0·89 and significantly included 4 predictor variables, with only pH being non-significant. These analyses confirmed the hypothesis. When the values of measured soil properties of adjoining soil cubes were meaned to estimate values for larger soil volumes, the multiple regression equations for predicting NO3 concentration explained more of the variation (r2 values as high as 0·99). However, information concerning the influence of certain soil properties on N mineralisation and nitrification was lost, with only pHBC and BD remaining significant in the regression model. It was concluded that at a given physical scale of investigation, the structure of the spatial variability may determine whether or not a relationship between 2 variables is observed. Smaller samples are more likely to identify functional relationships which may exist between measured variables at the microscale.

The influence of the soil matrix on nitrogen mineralisation and nitrification. I. Spatial variation and a hierarchy of soil properties

Soil Research ◽  
1998 ◽  
Vol 36 (3) ◽  
pp. 429 ◽  
Author(s):  
D. T. Strong ◽  
P. W. G. Sale ◽  
K. R. Helyar
Keyword(s):  
Soil Properties ◽  
Water Content ◽  
Organic N ◽  
Small Field ◽  
N Mineralisation ◽  
Mineral N ◽  
Conceptual Tool ◽  
Soil Matrix ◽  
Total N ◽  
Soil Variables

Natural heterogeneity of soil properties was used to explore their influence on nitrogen (N) mineralisation and nitrification in undisturbed small soil volumes (soil cells; c. 1 · 7 cm3 ) sampled from a small field plot (2 m by 3 m). Soil cells (840) were randomly ascribed to 1 of 6 treatments in which soils were retained continuously moist (M10 and M30 treatments) and amended with organic N from clover (Cl10 and Cl30 treatments), dried and rewetted (DW10), or treated with urea (Ur10) (subscripts indicate soil incubation at matric potential - 10 or - 30 kPa). After 20 days of incubation at 24C, each soil cell was analysed for NO-3 -N, NH + 4 -N, pH, bulk density (BD), volumetric water content (θv), water content at - 490 kPa (θv490), and pH buffer capacity (pHBC). On 25 soil cells from each treatment, % clay, % silt, % sand, total N (% N), organic carbon (% C), and 7 cations and anions were also determined. Net N mineralisation and net nitrification occurred in all treatments, and the total mineral N at the end of the incubation was 497, 81, 73, 31, 27, and 31 µg N/g in the Ur10 Cl10, Cl30, M10, M30, and DW10 treatments, respectively. Net N mineralisation in the M30 treatment was 84% of that in the M10 treatment, and net N mineralisation in the Cl30 treatment was 86% of that in the Cl10 treatment. Fluctuations in soil pH varied markedly between treatments and over time, and it was apparent that alkaline processes were occurring in all soil cells. The heterogeneity between soil samples was substantial for all of the soil variables. Soil variables were classified in a hierarchy from the least to the most fundamental based on their stability through time. This ranking provides a conceptual tool for understanding interrelationships between soil properties and for interpreting results of regression analyses. The sampling approach adopted in this study was designed to harness the natural heterogeneity of soil properties in the small field site while keeping other properties and environmental factors, that usually vary over larger distances, constant. Both the extent of heterogeneity of soil properties and the nature of their correlations with NO-3 -N suggested that this technique would be useful in the exploration of how soil properties influence N mineralisation and nitrification.

Variation in nutrient content of feedingstuffs rich in protein and reassessment of the chemical method for metabolizable energy estimation for poultry

1976 ◽  
Vol 86 (2) ◽  
pp. 293-303 ◽  
Author(s):  
G. N. Lodhi ◽  
Daulat Singh ◽  
J. S. Ichhponani
Keyword(s):  
Multiple Regression ◽  
Crude Protein ◽  
Regression Equation ◽  
Metabolizable Energy ◽  
Multiple Regression Equation ◽  
Regression Equations ◽  
Ether Extract ◽  
Crude Fibre ◽  
Energy Estimation ◽  
Short Period

SummaryA series of five metabolism trials was made to determine apparent nitrogen digestibility and metabolizable energy (ME) contents of protein rich feedingstuffs. The mean nitrogen digestibilities of fish meal, groundnut, mustard, sesame and cottonseed cakes were 66, 69, 68, 57 and 40%, respectively. Corresponding values for metabolizable energy values were 1820, 2460, 2330, 1870 and 1530 kcal/kg, respectively. The metabolizable energy contents of coconut cake, niger cake and blood meal were 1190, 2360 and 2190 kcal/kg, respectively. The quantity of protein, its digestibility and crude fibre content in the cakes are the prime factors for this trend in MB. Simple and multiple regression equations were derived from biologically assayed metabolizable energy and chemically analysed energy-yielding nutrient contents of the feedingstuffs. The simple regression equation is:ME kcal/kg = 32·95 (% crude protein + % ether extract × 2·25+ % available carbohydrate)–29·20.The multiple regression equation is:ME kcal/kg = 370·29 + (24·47 × % crude protein)+ (65·77 × % ether extract)+ (44·07 × % available carbohydrate)- (8·15 × % crude fibre).The correlation coefficients of simple and multiple regression equations were 0·72 and 0·73, respectively, indicating that there is very little advantage for prediction in using the multiple regression equation. The usefulness of the equation for routine checking of poultry feeds for ME is apparent since the nutrients required to predict metabolizable energy can be analysed within a short period of time.

Sample Size and the Accuracy of Predictions Made from Multiple Regression Equations

1982 ◽  
Vol 7 (2) ◽  
pp. 91-104 ◽  
Author(s):  
Richard Sawyer
Keyword(s):  
Sample Size ◽  
Multiple Regression ◽  
Normal Population ◽  
Absolute Error ◽  
Multiple Regression Equation ◽  
Regression Equations ◽  
Multivariate Normal ◽  
Rules Of Thumb ◽  
Multivariate Normal Population ◽  
The Mean

Some rules of thumb are given for estimating the accuracy of predictions based on a multiple regression equation developed from a random sample of a multivariate normal population. The distribution of the prediction error in this case can be approximated usefully by a normal distribution. Formulas are given for the moments of the distribution and for other parameters such as the mean absolute error (MAE). The approximate inflation in MAE (over its asymptotic value) due to estimating the regression coefficients is a simple function of the base sample size and the number of predictors.

The influence of the soil matrix on nitrogen mineralisation and nitrification III. Predictive utility of traditional variables and process location within the pore system

Soil Research ◽  
1999 ◽  
Vol 37 (1) ◽  
pp. 137 ◽  
Author(s):  
D. T. Strong ◽  
P. W. G. Sale ◽  
K. R. Helyar
Keyword(s):  
Soil Ph ◽  
Organic N ◽  
Organic Substrates ◽  
Small Field ◽  
N Mineralisation ◽  
Field Plot ◽  
Pore System ◽  
Undisturbed Soil ◽  
Soil Matrix ◽  
Drying And Rewetting

Regression analysis was used to examine the importance of organic nitrogen (%N), soil water content (θv), soil pH, and C: N ratio for predicting N mineralisation in a small field plot. Undisturbed soil cubes (c. 1·7 cm3) were collected from the soil surface and received treatments of drying and rewetting, urea, substrate derived from clover leachate, or no amendment, and were incubated at either –10 or –30 kPa for 20 days. The data confirm the hypothesis that within a small field plot, θv and %N explain most of the variation in net N mineralisation and nitrification. The pore size classes of 0·6–10 and 10–30 µm made disproportionately small and large contributions to N mineralisation, respectively, apparently due to non-uniform distribution of organic N through the pore system. When soluble N substrate was added to the soils, both these pore classes appeared to support mineralisation. We concluded that prior to sampling, the microbial biomass had been more active in the pores 0·6–10 µm, and had nearly exhausted the organic substrates in this pore class, whereas this was not so for the 10–30 µm pore class. Drying and rewetting increased the importance of %N as a predictor of N mineralisation, probably because this treatment disrupted physical protective mechanisms of organic N. Soil pH was generally not a useful predictor of N mineralisation and often seemed to be a dependent rather than an independent variable in relation to nitrification. Neither was C: N ratio a useful predictor of N transformation processes, and this was probably related to physical regulatory mechanisms in the soil.

Use of the neutron method in assessing the changes in soil strength of undisturbed and ameliorated transitional red-brown earths during soil drying cycles

Soil Research ◽  
1990 ◽  
Vol 28 (2) ◽  
pp. 167
Author(s):  
NS Jayawardane ◽  
J Blackwell
Keyword(s):  
Bulk Density ◽  
Soil Strength ◽  
Wheat Crop ◽  
Regression Equations ◽  
Experimental Site ◽  
Undisturbed Soil ◽  
Soil Matrix ◽  
Penetrometer Resistance ◽  
Neutron Count ◽  
Neutron Method

The relationships between penetrometer resistance (qp) and volumetric moisture content (�v) measured using the neutron method in an undisturbed transitional red-brown earth and after an~elioration by application of surface gypsum and slotted gypsum were examined. A very highly significant (P < 0.001) negative correlation was obtained between qp and �v in all treatments. The low r2 values of the regressions were attributed to heterogeneity in strength characteristics of the soil matrix, due to presence of cracks and macropores and the associated wetting patterns. The qp at any given e, was significantly reduced in the slots with lower bulk density compared to the undisturbed soil. The differences in qp- �v relationship of the undisturbed part of the soil under different ameliorative practices were attributed to changes in the swelling characteristic, and hence in the bulk density at any given �v of the undisturbed soil, caused by the presence of gypsum and the slots. Regression equations between qp and neutron count rate (n) for the undisturbed soil and for the slots were developed by combining the qp on �v relationships with the neutron meter calibration for �v measurements. The use of these regression equations and measured n values to predict changes in soil strength profiles during a wheat crop drying cycle in an undisturbed and ameliorated transitional red-brown earth was evaluated on another experimental site. There were no significant differences between the predicted and measured qp values in the non-ameliorated soil and the gypsum-slotted soil. Significant differences were observed between the predicted and measured qp values in the surface gypsum applied soil. The study shows the potential for using the neutron method as a convenient in-situ field technique to predict qp profile changes, preferably using qp on n relationships developed at the experimental site.

Estimation de la production de jeunes plantations de pin rouge et de pin gris du sud du Québec

1976 ◽  
Vol 6 (4) ◽  
pp. 478-486 ◽  
Author(s):  
H. A. Bolghari
Keyword(s):  
Multiple Regression ◽  
Jack Pine ◽  
Red Pine ◽  
Pine Plantations ◽  
Regression Equations ◽  
Pine Plantation ◽  
South Shore ◽  
Pine Stands ◽  
Natural Stands ◽  
St Lawrence River

Multiple regression equations have been developed to predict yield from young red pine and jack pine plantations. Data from 446 sample plots representing young red pine and jack pine stands located on the south shore of the St. Lawrence River between Quebec and Montreal were analysed. The red pine plantation yielded more than the jack pine. However, in plantation both species yield more than in natural stands. Taking into account the age and spacing of the sampled plantations, the equation obtained can provide information on yield of red pine and jack pine stands the maximum spacing of which is 3 × 3 m, up to the age of 45 and 35 years respectively. The equations will allow the construction of preliminary yield tables for both species.

Relationship Between Soil Properties and Nitrogen Mineralization in Undisturbed Soil Cores from California Agroecosystems

2018 ◽  
Vol 50 (1) ◽  
pp. 77-92 ◽  
Author(s):  
Kenneth Miller ◽  
Brenna J. Aegerter ◽  
Nicholas E. Clark ◽  
Michelle Leinfelder-Miles ◽  
Eugene M. Miyao ◽  
...  
Keyword(s):  
Soil Properties ◽  
Nitrogen Mineralization ◽  
Soil Cores ◽  
Undisturbed Soil ◽  
Undisturbed Soil Cores

FAT THICKNESS MEASURED WITH THREE ULTRASONIC INSTRUMENTS ON LIVE RAM LAMBS AS PREDICTORS OF CUTABILITY

1980 ◽  
Vol 60 (4) ◽  
pp. 857-867 ◽  
Author(s):  
ANDRÉ FORTIN
Keyword(s):  
Multiple Regression ◽  
Live Weight ◽  
Optimal Location ◽  
Regression Equations ◽  
Longissimus Muscle ◽  
Fat Thickness ◽  
Ram Lambs

Fat thickness at four locations over the longissimus muscle was measured ultrasonically on 33 live ram lambs ranging in live weight from 16.0 to 37.0 kg. Simple and multiple regression equations were developed to assess the effectiveness of fat thickness as measured by three different ultrasonic instruments (Krautkrämer USM #2, Scanoprobe Model 731A and Scanogram Model 722) to predict cutability. Weight of trimmed or boneless cuts (shoulder + loin + rack + leg) was predicted with more precision than percentage of cuts. Fat thickness alone or combined with weight at scanning was of no significant value (P > 0.05) in the prediction of percentage of trimmed cuts. Percentage of boneless cuts was predicted more efficiently from weight at scanning alone than from fat thickness alone or combined with weight at scanning. Weight of cuts (trimmed or boneless) was also estimated from the fat measurement (P < 0.01), the weight at scanning (P < 0.01) or a combination of both variables. For the latter, fat thickness did not contribute significantly (P > 0.05). The optimal location of the fat measurement depended on the ultrasonic instrument used. Fat thickness measured with the Krautkrämer was more efficient in its prediction of cutability than fat thickness measured with the Scanoprobe or Scanogram. However, over the range of liveweights studied, the usefulness of fat thickness measured on live ram lambs to predict cutability is questionable.

scholarly journals Microbial soil characteristics of grassland and arable soils linked to thermogravimetry data: correlations, use and limits

2021 ◽  
Author(s):  
Helena Doležalová-Weissmannová ◽  
Stanislav Malý ◽  
Martin Brtnický ◽  
Jiří Holátko ◽  
Michael Scott Demyan ◽  
...  
Keyword(s):  
Soil Properties ◽  
Soil Parameters ◽  
Organic Carbon Content ◽  
Regression Equations ◽  
Arable Soils ◽  
Grassland Soils ◽  
Simple Method ◽  
Organic C ◽  
Mass Losses ◽  
C And N

Abstract. Thermogravimetry (TG) is a simple method that enables rapid analysis of soil properties such as the content of total organic C, nitrogen, clay and C fractions with different stability. However, the possible link between TG data and microbiological soil properties has not been systematically tested yet and limits TG application for soil and soil organic matter assessment. This work aimed to search and to validate relationships of thermal mass losses (TML) to total C and N contents, microbial biomass C and N, basal and substrate-induced respiration, extractable organic carbon content, anaerobic ammonification, urease activity, short-term nitrification activity, specific growth rate, and time to reach the maximum respiration rate for two sample sets of arable and grassland soils. Analyses of the training soil set revealed significant correlations of TML with basic soil properties such as carbon and nitrogen content with distinguishing linear regression parameters and temperatures of correlating mass losses for arable and grassland soils. In a second stage the equations of significant correlations were used for validation with an independent second sample set. This confirmed applicability of developed equations for prediction of microbiological properties mainly for arable soils. For grassland soils was the applicability lower, which was explained as the influence of rhizosphere processes. Nevertheless, the application of TG can facilitate the understanding of changes in soil caused by microorganism’s activity and the different regression equations between TG and soil parameters reflect changes in proportions between soil components caused by land use management.

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