Modeling interannual variability of global soil respiration from climate and soil properties

2010 ◽  
Vol 150 (4) ◽  
pp. 590-605 ◽  
Author(s):  
Shutao Chen ◽  
Yao Huang ◽  
Jianwen Zou ◽  
Qirong Shen ◽  
Zhenghua Hu ◽  
...  
2002 ◽  
Vol 8 (8) ◽  
pp. 800-812 ◽  
Author(s):  
James W. Raich ◽  
Christopher S. Potter ◽  
Dwipen Bhagawati

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 544
Author(s):  
Jetse J. Stoorvogel ◽  
Vera L. Mulder

Despite the increased usage of global soil property maps, a proper review of the maps rarely takes place. This study aims to explore the options for such a review with an application for the S-World global soil property database. Global soil organic carbon (SOC) and clay content maps from S-World were studied at two spatial resolutions in three steps. First, a comparative analysis with an ensemble of seven datasets derived from five other global soil databases was done. Second, a validation of S-World was done with independent soil observations from the WoSIS soil profile database. Third, a methodological evaluation of S-world took place by looking at the variation of soil properties per soil type and short distance variability. In the comparative analysis, S-World and the ensemble of other maps show similar spatial patterns. However, the ensemble locally shows large discrepancies (e.g., in boreal regions where typically SOC contents are high and the sampling density is low). Overall, the results show that S-World is not deviating strongly from the model ensemble (91% of the area falls within a 1.5% SOC range in the topsoil). The validation with the WoSIS database showed that S-World was able to capture a large part of the variation (with, e.g., a root mean square difference of 1.7% for SOC in the topsoil and a mean difference of 1.2%). Finally, the methodological evaluation revealed that estimates of the ranges of soil properties for the different soil types can be improved by using the larger WoSIS database. It is concluded that the review through the comparison, validation, and evaluation provides a good overview of the strengths and the weaknesses of S-World. The three approaches to review the database each provide specific insights regarding the quality of the database. Specific evaluation criteria for an application will determine whether S-World is a suitable soil database for use in global environmental studies.


1995 ◽  
Vol 59 (5) ◽  
pp. 1430-1435 ◽  
Author(s):  
M. A. Liebig ◽  
A. J. Jones ◽  
J. W. Doran ◽  
L. N. Mielke

2019 ◽  
Author(s):  
Xia Zhao ◽  
Yuanhe Yang ◽  
Haihua Shen ◽  
Xiaoqing Geng ◽  
Jingyun Fang

Abstract. Surface soils interact strongly with both climate and biota and provide fundamental ecosystem services that maintain food, climate, and human security. However, the quantitative linkages between soil properties, climate, and biota at the global scale remain unclear. By compiling a comprehensive global soil database, we mapped eight major soil properties (bulk density; clay, silt, and sand fractions; soil pH; soil organic carbon [SOC] density; soil total nitrogen [STN] density; and soil C : N mass ratios) in the surface (0–30 cm) soil layer based on machine learning algorithms, and demonstrated the quantitative linkages between surface soil properties, climate, and biota at the global scale (i.e., global soil-climate-biome diagram). On the diagram, bulk density increased significantly with higher mean annual temperature (MAT) and lower mean annual precipitation (MAP); soil clay fraction increased significantly with higher MAT and MAP; Soil pH decreased with higher MAP and lower MAT, and the critical MAP for the transition from alkaline to acidic soil decreased with decreasing MAT; SOC density and STN density both were jointly affected by MAT and MAP, showing an increase at lower MAT and a saturation tendency towards higher MAP. Surface soil physical and chemical properties also showed remarkable variations across biomes. The soil-climate-biome diagram suggests the co-evolution of the soil, climate, and biota under global environmental change.


2018 ◽  
Author(s):  
Lorenzo Menichetti ◽  
Alberto Tonda

Genetic Programming is a powerful optimization technique, able to deliver high-quality results in several real-world problems. One of its most successful applications is symbolic regression, where the objective is to find a suitable expression to model the underlying relationship between data points, with no aprioristic assumptions. In this paper, we propose the application of a Genetic Programming technique to a dataset on soil respiration and soil properties, in order to investigate possible influences of soil properties on soil respiration through symbolic regression. The best candidate models obtained by the technique are then studied to determine possible differences in the relationships related to environmental factors. Recurring patterns in the best solutions proposed by the search algorithm are identified, and the suitability of symbolic regression in soil science is evaluated and discussed. Genetic Programming proves to be an extremely promising data mining technique for soil scientists, as it is able to uncover relationships that could otherwise remain hidden, while remaining completely neutral and bias-free. We suggest its application for routine data analysis, as the technique presents particular interest for environmental modeling and development of pedotransfer functions.


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