Minimum quantity of urban refuse compost affecting physical and chemical soil properties

Biochar application is reported as a method for improving physical and chemical soil properties, with a still questionable impact on the crop yields and quality. Plant productivity can be affected by biochar properties and soil conditions. High efficiency of biochar application was reported many times for plant cultivation in tropical and arid climates; however, the knowledge of how the biochar affects soils in temperate climate zones exhibiting different properties is still limited. Therefore, a three-year-long field experiment was conducted on a loamy Haplic Luvisol, a common arable soil in Central Europe, to extend the laboratory-scale experiments on biochar effectiveness. A low-temperature pinewood biochar was applied at the rate of 50 t h−1, and maize was selected as a tested crop. Biochar application did not significantly impact the chemical soil properties and fertility of tested soil. However, biochar improved soil physical properties and water retention, reducing plant water stress during hot dry summers, and thus resulting in better maize growth and higher yields. Limited influence of the low-temperature biochar on soil properties suggests the crucial importance of biochar-production technology and biochar properties on the effectiveness and validity of its application in agriculture.

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Optimization of the sampling scheme for maps of physical and chemical properties estimated by kriging

Revista Brasileira de Ciência do Solo ◽

10.1590/s0100-06832013000500002 ◽

2013 ◽

Vol 37 (5) ◽

pp. 1128-1135 ◽

Cited By ~ 10

Author(s):

Gener Tadeu Pereira ◽

Zigomar Menezes de Souza ◽

Daniel De Bortoli Teixeira ◽

Rafael Montanari ◽

José Marques Júnior

Keyword(s):

Soil Properties ◽

Chemical Property ◽

Chemical Properties ◽

Sampling Scheme ◽

Optimal Sampling ◽

Sampling Schemes ◽

Medium Scale ◽

Chemical Soil ◽

Physical And Chemical Property ◽

Physical And Chemical

The sampling scheme is essential in the investigation of the spatial variability of soil properties in Soil Science studies. The high costs of sampling schemes optimized with additional sampling points for each physical and chemical soil property, prevent their use in precision agriculture. The purpose of this study was to obtain an optimal sampling scheme for physical and chemical property sets and investigate its effect on the quality of soil sampling. Soil was sampled on a 42-ha area, with 206 geo-referenced points arranged in a regular grid spaced 50 m from each other, in a depth range of 0.00-0.20 m. In order to obtain an optimal sampling scheme for every physical and chemical property, a sample grid, a medium-scale variogram and the extended Spatial Simulated Annealing (SSA) method were used to minimize kriging variance. The optimization procedure was validated by constructing maps of relative improvement comparing the sample configuration before and after the process. A greater concentration of recommended points in specific areas (NW-SE direction) was observed, which also reflects a greater estimate variance at these locations. The addition of optimal samples, for specific regions, increased the accuracy up to 2 % for chemical and 1 % for physical properties. The use of a sample grid and medium-scale variogram, as previous information for the conception of additional sampling schemes, was very promising to determine the locations of these additional points for all physical and chemical soil properties, enhancing the accuracy of kriging estimates of the physical-chemical properties.

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Comparison of Soil Properties and Organic Components in Infusions According to Different Aerial Appearances of Tea Plantations in Central Taiwan

Sustainability ◽

10.3390/su12114384 ◽

2020 ◽

Vol 12 (11) ◽

pp. 4384

Author(s):

Prapasiri Tongsiri ◽

Wen-Yu Tseng ◽

Yuan Shen ◽

Hung-Yu Lai

Keyword(s):

Amino Acids ◽

Soil Properties ◽

Free Amino Acids ◽

Free Amino ◽

Data Set ◽

Organic Components ◽

Chemical Soil ◽

Tea Plantations ◽

Central Taiwan ◽

Physical And Chemical

The soil properties, climate, type of management, and fermentation process critically affect the productivity and quality of tea. In this study, tender tea leaves were collected from central Taiwan, and organic components in their infusions as well as physical and chemical soil properties differentiated using aerial photographs where good (G) and bad (B) growth exhibitions were determined. Eleven physical and chemical soil properties as well as five compounds in tea infusions were analyzed to determine the main factor that affects the growth of these tea trees. The Fleiss’ kappa statistic results revealed that the wet aggregate stability, pH, and exchangeable potassium content exhibit the most significant effect, with scores of 0.86, 0.64, and 0.62, respectively. Soil quality calculated using the mean weight diameter based on 11 soil properties revealed that ~67% of the total score of G is greater than that of B. Generally, contents of total polyphenols (51.67%) and catechins (51.76%) in the infusions of B were greater than those of G. In addition, significant positive correlations between the free amino acids content and soil properties, including pH and copper content, were observed. However, a negative correlation between the free amino acids and flavone contents and most of the soil properties was observed. The survey data set obtained from this study can provide useful information for the improved management of tea plantations.

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Correlation Between Physical and Chemical Soil Properties and Growth of Red Meranti in Haurbentes Forest Research

Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) ◽

10.29244/jpsl.9.1.68-74 ◽

2019 ◽

Vol 9 (1) ◽

pp. 68-74

Author(s):

Exze Erizilina ◽

◽

Prijanto Pamoengkas ◽

Darwo Darwo ◽

◽

...

Keyword(s):

Soil Properties ◽

Forest Research ◽

Chemical Soil ◽

Physical And Chemical

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Comparing Physical and Chemical Soil Properties Measurement: CNAL vs. NSSC

Soil Horizons ◽

10.2136/sh2010.1.0022 ◽

2010 ◽

Vol 51 (1) ◽

pp. 22

Author(s):

E.J. Neafsey ◽

Stephen D. DeGloria ◽

Matthew W. Havens ◽

William D. Philpot ◽

Patrick J. Sullivan

Keyword(s):

Soil Properties ◽

Chemical Soil ◽

Physical And Chemical

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Occurrence and intensity of wild boar disturbances, effects on the physical and chemical soil properties of alpine grasslands

Plant and Soil ◽

10.1007/s11104-013-1784-z ◽

2013 ◽

Vol 373 (1-2) ◽

pp. 243-256 ◽

Cited By ~ 30

Author(s):

C. Guillermo Bueno ◽

José Azorín ◽

Daniel Gómez-García ◽

Concepción L. Alados ◽

David Badía

Keyword(s):

Soil Properties ◽

Wild Boar ◽

Alpine Grasslands ◽

Chemical Soil ◽

Physical And Chemical

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Soybean yield modeling using bootstrap methods for small samples

Spanish Journal of Agricultural Research ◽

10.5424/sjar/2016143-8635 ◽

2016 ◽

Vol 14 (3) ◽

pp. e0207 ◽

Cited By ~ 5

Author(s):

Gustavo H. Dalposso ◽

Miguel A. Uribe-Opazo ◽

Jerry A. Johann

Keyword(s):

Regression Model ◽

Soil Properties ◽

Confidence Intervals ◽

Great Influence ◽

Multiple Linear Regression Model ◽

Model Parameters ◽

Bootstrap Methods ◽

Soybean Yield ◽

Chemical Soil ◽

Physical And Chemical

One of the problems that occur when working with regression models is regarding the sample size; once the statistical methods used in inferential analyzes are asymptotic if the sample is small the analysis may be compromised because the estimates will be biased. An alternative is to use the bootstrap methodology, which in its non-parametric version does not need to guess or know the probability distribution that generated the original sample. In this work we used a set of soybean yield data and physical and chemical soil properties formed with fewer samples to determine a multiple linear regression model. Bootstrap methods were used for variable selection, identification of influential points and for determination of confidence intervals of the model parameters. The results showed that the bootstrap methods enabled us to select the physical and chemical soil properties, which were significant in the construction of the soybean yield regression model, construct the confidence intervals of the parameters and identify the points that had great influence on the estimated parameters.

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