Lime requirement of acidic Queensland soils. I. Relationships between soil properties and pH buffer capacity

Soil Research ◽  
1990 ◽  
Vol 28 (5) ◽  
pp. 695 ◽  
Author(s):  
RL Aitken ◽  
PW Moody ◽  
PG Mckinley
Keyword(s):  
Linear Regression ◽  
Organic Carbon ◽  
Soil Properties ◽  
Multiple Linear Regression ◽  
Multiple Regression ◽  
Buffer Capacity ◽  
Clay Content ◽  
Exchangeable Al ◽  
Ph Buffer ◽  
Exchange Acidity

The pH buffer capacity of 40 acidic surface soils (pHw <6.5) was determined from soil-CaCO3- moist incubations. Buffer capacity values ranged from 02 to 5.4 g CaCO3 kg-1 soil unit-1 pH increase. Organic carbon, clay content, ECEC, 1M KCl extractable acidity and Al, and the change in CEC with pH (�CEC) were measured and correlated with pH buffer capacity. Step-up multiple linear regression indicated that the effect of �CEC on buffer capacity was highly significant (r2 = 0.77, P <0.001), whereas that of exchangeable Al or exchange acidity was not. This suggests that deprotonation reactions, compared with exchangeable Al or exchange acidity, are considerably more important in determining buffer capacity. The major soil property affecting �CEC in our soils was the organic carbon content and, when step-up multiple linear regression was used, �CEC could be best estimated by organic carbon plus clay content plus ECEC (R2 = 0.77, P < 0.001). To ascertain whether exchangeable Al (or exchange acidity) would contribute to buffer capacity in soils with less variable charge, soils of relatively low organic carbon (<2.5%) were considered. For the 33 soils with <2.5% organic carbon, �CEC was still the major determinant of buffer capacity (r2 = 0.76, P <0.001), although inclusion of exchange acidity in a multiple regression with �CEC significantly increased the variance accounted for (R2 = 0.80, P < 0.001). Of the soil properties that could be routinely measured, a multiple regression equation combining organic carbon, clay content and exchange acidity accounted for 85% of the variance in buffer capacity, with organic carbon being the most important.

Relationships between extractable Al, selected soil properties, pH buffer capacity and lime requirement in some acidic Queensland soils

Soil Research ◽  
1992 ◽  
Vol 30 (2) ◽  
pp. 119 ◽  
Author(s):  
RL Aitken
Keyword(s):  
Organic Matter ◽  
Organic Carbon ◽  
Soil Properties ◽  
Buffer Capacity ◽  
Clay Content ◽  
Ph Buffering ◽  
Ph Buffer ◽  
Mineral Soils ◽  
Chloride Salts ◽  
Hydrolysis Of

The objectives of this study were to examine (1) interrelationships between various forms of extractable A1 and selected soil properties, (2) the contribution of extractable A1 to pH buffer capacity, and (3) investigate the use of extractable A1 to predict lime requirement. Aluminium was extracted from each of 60 Queensland soils with a range of chloride salts: 1 M KCl (AlK), 0.5 M CuCl2 (AlCu), 0.33 M LaCl3 (AlLa) and 0.01 M CaCl2 (AlCa). The amounts of A1 extracted were in the order AlCu > AlLa > Alk > AlCa. Little or no A1 was extracted by KC1 or Lac13 in soils with pHw values greater than 5.5 , whereas CuCl2 extracted some A1 irrespective of soil pH. The greater amounts of A1 extracted by CuCl2 were attributed mainly to A1 from organic matter, even though all of the soils were mineral soils (organic carbon 54.7%). Both AlCu and AlLa, were significantly (P < 0.001) correlated with organic carbon, whereas none of the extractable A1 measures was correlated with clay content. AlK and A~L, were poorly correlated to pH buffer capacity. The linear relationship between AlCu and pH buffer capacity (r2 = 0.49) obtained in this study supports the view of previous researchers that the hydrolysis of A1 adsorbed by organic matter is a source of pH buffering in soils. However, the change in CEC with pH accounted for 76% of the variation in pH buffer capacity, indicating that other mechanisms such as deprotonation of organic groups and variable charge minerals are also involved in pH buffering. The ability of CuCl2 and LaCl3extractable Al to estimate lime requirement depended on the target pH. The results suggest that lime requirements based on neutralization of AlLa would be sufficient to raise pHw to around 5.5, whereas requirements based on neutralization of AlCu substantially overestimated the actual lime requirement to pHw 5.5, but gave a reasonable estimation of the lime requirement to pHw 6 5.

Soil organic carbon storage by shaded and unshaded coffee systems and its implications for climate change mitigation in China

2021 ◽  
pp. 1-8
Author(s):  
Ziwei Xiao ◽  
Xuehui Bai ◽  
Mingzhu Zhao ◽  
Kai Luo ◽  
Hua Zhou ◽  
...  
Keyword(s):  
Climate Change ◽  
Linear Regression ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Multiple Linear Regression ◽  
Carbon Storage ◽  
Soil Ph ◽  
Soil Bulk Density ◽  
Soil N ◽  
And Storage

Abstract Shaded coffee systems can mitigate climate change by fixation of atmospheric carbon dioxide (CO2) in soil. Understanding soil organic carbon (SOC) storage and the factors influencing SOC in coffee plantations are necessary for the development of sound land management practices to prevent land degradation and minimize SOC losses. This study was conducted in the main coffee-growing regions of Yunnan; SOC concentrations and storage of shaded and unshaded coffee systems were assessed in the top 40 cm of soil. Relationships between SOC concentration and factors affecting SOC were analysed using multiple linear regression based on the forward and backward stepwise regression method. Factors analysed were soil bulk density (ρb), soil pH, total nitrogen of soil (N), mean annual temperature (MAT), mean annual moisture (MAM), mean annual precipitation (MAP) and elevations (E). Akaike's information criterion (AIC), coefficient of determination (R2), root mean square error (RMSE) and residual sum of squares (RSS) were used to describe the accuracy of multiple linear regression models. Results showed that mean SOC concentration and storage decreased significantly with depth under unshaded coffee systems. Mean SOC concentration and storage were higher in shaded than unshaded coffee systems at 20–40 cm depth. The correlations between SOC concentration and ρb, pH and N were significant. Evidence from the multiple linear regression model showed that soil bulk density (ρb), soil pH, total nitrogen of soil (N) and climatic variables had the greatest impact on soil carbon storage in the coffee system.

scholarly journals Relationship of soil properties to fractionation, bioavailability and mobility of lead and zinc in soil

2008 ◽  
Vol 53 (No. 5) ◽  
pp. 225-238 ◽  
Author(s):  
N. Finžgar ◽  
P. Tlustoš ◽  
D. Leštan
Keyword(s):  
Organic Matter ◽  
Regression Analysis ◽  
Soil Organic Matter ◽  
Soil Properties ◽  
Multiple Regression ◽  
Contaminated Soils ◽  
Organic Matter Content ◽  
Clay Content ◽  
Exchange Capacity ◽  
Matter Content

Sequential extractions, metal uptake by <i>Taraxacum officinale</i>, Ruby&rsquo;s physiologically based extraction test (PBET) and toxicity characteristic leaching procedure (TCLP), were used to assess the risk of Pb and Zn in contaminated soils, and to determine relationships among soil characteristics, heavy metals soil fractionation, bioavailability and leachability. Regression analysis using linear and 2nd order polynomial models indicated relationships between Pb and Zn contamination and soil properties, although of small significance (<i>P</i> < 0.05). Statistically highly significant correlations (<i>P</i> < 0.001) were obtained using multiple regression analysis. A correlation between soil cation exchange capacity (CEC) and soil organic matter and clay content was expected. The proportion of Pb in the PBET intestinal phase correlated with total soil Pb and Pb bound to soil oxides and the organic matter fraction. The leachable Pb, extracted with TCLP, correlated with the Pb bound to carbonates and soil organic matter content (<i>R</i><sup>2</sup> = 69%). No highly significant correlations (<i>P</i> < 0.001) for Zn with soil properties or Zn fractionation were obtained using multiple regression.

scholarly journals Multiple linear regression and random forest to predict and map soil properties using data from portable X-ray fluorescence spectrometer (pXRF)

2017 ◽  
Vol 41 (6) ◽  
pp. 648-664 ◽  
Author(s):  
Sérgio Henrique Godinho Silva ◽  
Anita Fernanda dos Santos Teixeira ◽  
Michele Duarte de Menezes ◽  
Luiz Roberto Guimarães Guilherme ◽  
Fatima Maria de Souza Moreira ◽  
...  
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Soil Properties ◽  
Multiple Linear Regression ◽  
Low Cost ◽  
High Accuracy ◽  
Important Variable ◽  
X Ray ◽  
Element Contents ◽  
Fluorescence Spectrometer

ABSTRACT Determination of soil properties helps in the correct management of soil fertility. The portable X-ray fluorescence spectrometer (pXRF) has been recently adopted to determine total chemical element contents in soils, allowing soil property inferences. However, these studies are still scarce in Brazil and other countries. The objectives of this work were to predict soil properties using pXRF data, comparing stepwise multiple linear regression (SMLR) and random forest (RF) methods, as well as mapping and validating soil properties. 120 soil samples were collected at three depths and submitted to laboratory analyses. pXRF was used in the samples and total element contents were determined. From pXRF data, SMLR and RF were used to predict soil laboratory results, reflecting soil properties, and the models were validated. The best method was used to spatialize soil properties. Using SMLR, models had high values of R² (≥0.8), however the highest accuracy was obtained in RF modeling. Exchangeable Ca, Al, Mg, potential and effective cation exchange capacity, soil organic matter, pH, and base saturation had adequate adjustment and accurate predictions with RF. Eight out of the 10 soil properties predicted by RF using pXRF data had CaO as the most important variable helping predictions, followed by P2O5, Zn and Cr. Maps generated using RF from pXRF data had high accuracy for six soil properties, reaching R2 up to 0.83. pXRF in association with RF can be used to predict soil properties with high accuracy at low cost and time, besides providing variables aiding digital soil mapping.

scholarly journals Multiple linear and spatial regressions to estimate the influence of Latosol properties on black pepper productivity

2019 ◽  
pp. 857-862
Author(s):  
Waylson Zancanella Quartezani ◽  
Julião Soares de Souza Lima ◽  
Talita Aparecida Pletsch ◽  
Evandro Chaves de Oliveira ◽  
Sávio da Silva Berilli ◽  
...  
Keyword(s):  
Linear Regression ◽  
Physical Properties ◽  
Multiple Linear Regression ◽  
Multiple Regression ◽  
Spatial Information ◽  
Spatial Regression ◽  
Chemical Properties ◽  
Fine Sand ◽  
Black Pepper ◽  
Physical And Chemical

There is little knowledge available on the best techniques for transferring spatial information such as stochastic interpolation and multivariate analyses for black pepper. This study applies multiple linear and spatial regression to estimate black pepper productivity based on physical and chemical properties of the soil. A multiple linear regression including all properties of a Latosol was performed and followed by variance analysis to verify the validity of the model. The adjusted variograms and data interpolation by kriging allowed the use of spatial multiple regression with the properties that were significant in the multiple linear regression. The forward stepwise method was used and the model was validated by the F-test. The influence of the Latosol properties was greater than the residual on the prediction of productivity. The model was composed by the physical properties fine sand (FS), penetration resistance (PR), and Bulk density (BD), and by the chemical properties K, Ca, and Mg (except for Mg in the spatial regression). The physical properties were of greater relevance in determining productivity, and the maps estimated by ordinary kriging and predicted by the spatial multiple regression were very similar in shape.

scholarly journals THE EFFECT OF JOB CONDITION, INTERPERSONAL COMMUNICATION, AND PERCEIVED ORGANIZATIONAL SUPPORT ON EMPLOYEE PERFORMANCE OF PDAM COMPANY SURABAYA, INDONESIA

2020 ◽  
Vol 7 (01) ◽  
Author(s):  
Purwanti Purwanti
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Multiple Regression ◽  
Interpersonal Communication ◽  
Organizational Support ◽  
Perceived Organizational Support ◽  
Working Condition ◽  
Employee Performance ◽  
Population Data ◽  
Test Result

The aims of this study is to examine the effect of working condition, Interpersonal Communication and Perceived Organizational Support on performance employment of PDAM  company, Surabaya, Indonesia. Methode used in this research is descriptive Explanatory which is a method that explains causal relationships between the variables observed. This research is limited by data collected from a sample of the population to represent the whole population. Data analyzed by multiple linear regression to, T-test, and F test, with SPSS program. The test result of multiple regression show that every increasing Working condition, Interpersonal Communicationa and perceived organizational support will increase performance of the employes. The results of Hyphothesis thest shows that as a simultaniously there were significant effect between Working condition, Interpersonal Communicationa and perceived organizational support to employee performance, eventhough as a partially that Working condition, and Interpersonal Communicationa are significant effect to employee performance but Perceived Organizational Support has no significant effect to employee performance.

scholarly journals COMPARISON OF THREE MODELS FOR PREDICTING THE SPATIAL DISTRIBUTION OF SOIL ORGANIC CARBON IN BOALEMO REGENCY, SULAWESI

2016 ◽  
Vol 18 (1) ◽  
pp. 42 ◽  
Author(s):  
Eloise Mason ◽  
Yiyi Sulaeman
Keyword(s):  
Linear Regression ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Multiple Linear Regression ◽  
Regression Tree ◽  
Digital Soil Mapping ◽  
Conditional Inference ◽  
Conditional Inference Tree ◽  
Soil Landscape ◽  
Inference Tree

<p><em>Information on the spatial distribution of soil organic carbon content is required for sustainable land management. But, creating this map is time consuming and costly. Digital soil mapping methodology make use legacy soil data to create provisional soil organic carbon map. This map helps soil surveyors in allocating next soil observation. This study aimed: (i) to develop predictive statistical soil organic carbon models for Sulawesi, and (ii) to evaluate the best model between the three obtained models. Boalemo Regeny in Gorontalo Province (Sulawesi) was selected as studying area due to abundant legacy soil data. The study covered dataset preparation, model development, and model comparison. Dataset of soil organic carbon at 6 different depths as target was established from 176 soil profiles and 7 terrain parameters were selected as predictors. Soil-landscape models for each soil depth were created using regression tree, conditional inference tree, and multiple linear regression technique.  Result showed that model performance differed among 3 modelling techniques and soil depths. The tree models were better than the multiple linear regression model as they have the lowest RMSE index. The best model in the mountanious area seems to be the regression tree model, whereas in the plains it may be the conditional inference tree. In creating provisional map, several model should be developed and the median of predicted value is used as provisional map.</em></p><p><em> </em></p><p><em>Keywords: Digital soil mapping, multiple linear regression, regression tree, soil-landscape model, soil organic carbon map</em></p>

A Measure of the Information Loss for Inspection Point Reduction

2009 ◽  
Vol 131 (5) ◽  
Author(s):  
Kristina Wärmefjord ◽  
Johan S. Carlson ◽  
Rikard Söderberg
Keyword(s):  
Cluster Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Multiple Regression ◽  
Automotive Industry ◽  
Information Loss ◽  
Small Subset ◽  
Efficiency Measure ◽  
Point Reduction ◽  
Highly Correlated

Since the vehicle program in the automotive industry gets more and more extensive, the costs related to inspection increase. Therefore, there are needs for more effective inspection preparation. In many situations, a large number of inspection points are measured, despite the fact that only a small subset of points is needed. A method, based on cluster analysis, for identifying redundant inspection points has earlier been successfully tested on industrial cases. Cluster analysis is used for grouping the variables into clusters, where the points in each cluster are highly correlated. From every cluster only one representing point is selected for inspection. In this paper the method is further developed, and multiple linear regression is used for evaluating how much of the information is lost when discarding an inspection point. The information loss can be quantified using an efficiency measure based on linear multiple regression, where the part of the variation in the discarded variables that can be explained by the remaining variables is calculated. This measure can be illustrated graphically and that helps to decide how many clusters that should be formed, i.e., how many inspection points that can be discarded.

scholarly journals Prediction of Phenytoin Dosage in Epilepsy Using Multiple Linear Regression

1982 ◽  
Vol 19 (3) ◽  
pp. 141-144 ◽  
Author(s):  
S P Gray
Keyword(s):  
Body Weight ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Multiple Regression ◽  
Therapeutic Range ◽  
Drug Dosage ◽  
The Body ◽  
Clinical Value ◽  
Number Of Patients ◽  
The Relationship

Analysis of plasma phenytoin in a group of patients treated for epilepsy showed that only 36% had values in the therapeutic range. The relationship between plasma phenytoin, body weight, and daily dosage of the drug were explored, and the data were analysed by multiple regression. The resultant equation, relating all three factors, was used to optimise drug dosage, and the importance of using the body weight of the patient before starting a phenytoin regimen is emphasised. An increase in the number of patients with plasma phenytoin in the therapeutic range was achieved, and the clinical value of being in that range is shown.

Dynamics of selected chemical and microbiological properties changes in soils after application of ultra-fine powdered calcium carbonate – incubation studies

2020 ◽  
Author(s):  
Karolina Woźnica ◽  
Michał Gąsiorek ◽  
Justyna Sokołowska ◽  
Agnieszka Józefowska ◽  
Tomasz Zaleski
Keyword(s):  
Calcium Carbonate ◽  
Organic Carbon ◽  
Dissolved Organic Carbon ◽  
Soil Properties ◽  
Carbon Content ◽  
Buffer Capacity ◽  
Microbial Biomass Carbon ◽  
Soil Samples ◽  
Organic Carbon Content ◽  
Biomass Carbon

&lt;p&gt;Soil acidification is a serious problem on a global scale, about 30% of land surface is occupied by acidic soils (pH&amp;#8804; 5.5). Recent research indicates, that more than 50% of arable soils in Poland have too low pH. Acid soils are characterised the ability to mobilize toxic metals and increased plant uptake as well as decreased microbial activity in the soil. Progressive acidification leads to degradation of soils and caused that they are marginal for agricultural production. Soil acidification is a naturally occurring process, but only when natural factors are supported by intensive human activity, especially by nitrogen fertilisers application, intensive degradation is observed. Traditionally method to increase soil pH is the application of lime materials e.g. calcite, burnt lime or dolomite. The liming efficiency depends on lime material type (primarily chemical form of calcium compounds), the neutralising value, lime application method, soil properties and the particle size distribution of lime. The aim of this research was to determine the rate of action and influence of ultra-fine powdered calcium carbonate on selected chemical and microbiological soil properties.&lt;/p&gt;&lt;p&gt;The incubation studies were conducted on the three soils (G1, G2 &amp;#8211; silt loam and G3 &amp;#8211; sandy loam). Soil samples were taken from the 0-20 cm layer. Soil properties were measured after 7, 14, 30, 60 and 120 days of incubation. The liming factor was ultra-fine powdered calcium carbonate with particle size distribution &lt; 0.08 mm. The application dose was calculated for 0.5 soil hydrolytic acidity. In the soil samples pH&lt;sub&gt;KCl&lt;/sub&gt;, buffer capacity, microbial biomass carbon and dissolved organic carbon content were measured.&lt;/p&gt;&lt;p&gt;Application of lime caused an increase of pH value in all studied soils. The highest increase of the pH&lt;sub&gt;KCl &lt;/sub&gt;was noted between 0 to 7&lt;sup&gt;th&lt;/sup&gt; day of incubation. Afterward, the pH&lt;sub&gt;KCl &lt;/sub&gt;decreased slowly for the soil G1 and G2. However, in the soil G3 significantly decreased just after 7&lt;sup&gt;th&lt;/sup&gt; to 14&lt;sup&gt;th &lt;/sup&gt;day, and afterward, the pH&lt;sub&gt;KCl&lt;/sub&gt; decreased slowly to the end of the incubation period. As a result of liming long-term changes in soil buffer capacity were not noted. The studied soils were characterised by the higher buffer capacity in alkaline than in acidic range. The microbial biomass carbon content was varied during the incubation in all studied soils. The dissolved organic carbon content increased during the incubation, starting from the 7&lt;sup&gt;th&lt;/sup&gt; to the 120&lt;sup&gt;th&lt;/sup&gt; day of incubation for G2 and G3 soils and from 14&lt;sup&gt;th &lt;/sup&gt;to last day of incubation for G1 soil. Application of lime caused an increase of the dissolved organic carbon content in all studied soils. These studies show that application of ultra-fine powdered calcium carbonate is an effective and fast way to improve soil properties.&lt;/p&gt;

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