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

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. Keywords: Digital soil mapping, multiple linear regression, regression tree, soil-landscape model, soil organic carbon map

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

The Journal of Agricultural Science ◽

10.1017/s002185962100006x ◽

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.

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Rising Stars and Underdogs: The Role Race and Parental Education Play in Predicting Mentorship

Youth & Society ◽

10.1177/0044118x211004637 ◽

2021 ◽

pp. 0044118X2110046

Author(s):

Veronica Fruiht ◽

Jordan Boeder ◽

Thomas Chan

Keyword(s):

Critical Race Theory ◽

Parental Education ◽

Individual Characteristics ◽

Conditional Inference ◽

Youth Of Color ◽

Social Success ◽

Conditional Inference Tree ◽

Tree Models ◽

Nationally Representative ◽

Inference Tree

Research suggests that youth with more financial and social resources are more likely to have access to mentorship. Conversely, the rising star hypothesis posits that youth who show promise through their individual successes are more likely to be mentored. Utilizing a nationally representative sample ( N = 4,882), we tested whether demographic characteristics (e.g., race, SES) or personal resources (e.g., academic/social success) are better predictors of receiving mentorship. Regression analyses suggested that demographic, contextual, and individual characteristics all significantly predicted access to mentorship, specifically by non-familial mentors. However, conditional inference tree models that explored the interaction of mentorship predictors by race showed that individual characteristics mattered less for Black and Latino/a youth. Therefore, the rising star hypothesis may hold true for White youth, but the story of mentoring is more complicated for youth of color. Findings highlight the implications of Critical Race Theory for mentoring research and practice.

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