Effect of agriculture and of clear-cut forest harvest on landscape-scale soil organic carbon storage in Saskatchewan

1997 ◽  
Vol 77 (2) ◽  
pp. 211-218 ◽  
Author(s):  
D. J. Pennock ◽  
C. van Kessel

The development of sound management approaches to reduce soil organic carbon (SOC) losses presupposes that we thoroughly understand the sources of these losses. We used a landscape-scale research design to estimate human-induced SOC losses by comparing SOC storage in undisturbed landscapes with comparable landscapes disturbed by clear-cutting of forests in the Mixedwood/Gray Luvisolic zone of central Saskatchewan and by agricultural activity in the Black soil zone. A 14.0% decrease in soil organic carbon storage in the upper 45 cm of the soil (from 57.1 Mg ha−1 in mature Mixedwood sites to 49.1 Mg ha−1 in clear-cut landscapes) occurred due to clear cutting at the research sites in the Mixedwood forest. The dominant soil type at these sites, Gray Luvisolic soils developed in glacial till, experienced a 11% loss in SOC storage; higher losses (36% loss) occurred from sandy Brunisolic inclusions in the sites. Changes in SOC storage at the research sites in the Black soil zone landscapes varied with texture and parent material: sandy glacio-fluvial landscapes experienced slight gains of SOC (from 54.1 to 60.1 Mg ha−1); silt and clay glacio-lacustrine landscapes experienced a 15.3% decrease in SOC (from 145.2 to 122.9 Mg ha−1); and loamy glacial till landscapes underwent a major decrease in SOC storage (from 116.2 to 75.2 Mg ha−1) Our results indicate that attempts to increase SOC storage in Saskatchewan soils should concentrate on agricultural landscapes, especially those dominated by glacial till. Key words: Landscape, soil organic carbon, Chernozemic, Mollisol

2014 ◽  
Vol 94 (4) ◽  
pp. 477-488 ◽  
Author(s):  
A. Brett Campeau ◽  
Peter M. Lafleur ◽  
Elyn R. Humphreys

Campeau, A. B., Lafleur, P. M. and Humphreys, E. R. 2014. Landscape-scale variability in soil organic carbon storage in the central Canadian Arctic. Can. J. Soil Sci. 94: 477–488. Arctic soils constitute a vast, but poorly quantified, pool of soil organic carbon (SOC). The uncertainty associated with pan-Arctic SOC storage estimates – a result of limited SOC and land cover data – needs to be reduced if we are to better predict the impact of future changes to Arctic carbon stocks resulting from climate warming. In this study landscape-scale variability in SOC at a Southern Arctic Ecozone site in the central Canadian Arctic was investigated with the ultimate goal of up-scaling SOC estimates with a land cover classification system. Total SOC was estimated to depths of 30 cm and 50 cm for 76 soil pits, together representing eight different vegetation communities in seven different broad landscape units. Soil organic carbon to 50 cm was lowest for the xerophytic herb community in the esker complex landscape unit (7.2±2.2 SD kg m−2) and highest in the birch hummock terrain in the lowland tundra landscape unit (36.4±2.8 kg m−2), followed by wet sedge and dry sedge communities in the wetland complex (29.8±9.9 and 22.0±2.0 kg m−2, respectively). The up-scaled estimates of mean SOC for the study area (excluding water) were 15.8 kg m−2 (to 50 cm) and 11.6 kg m−2 (to 30 cm). On a landscape scale, soil moisture content was found to have an important influence on SOC variability. Overall, this study highlights the importance of SOC variability at fine scales and its impact on up-scaling SOC in Arctic landscapes.


Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 517
Author(s):  
Sunwei Wei ◽  
Zhengyong Zhao ◽  
Qi Yang ◽  
Xiaogang Ding

Soil organic carbon storage (SOCS) estimation is a crucial branch of the atmospheric–vegetation–soil carbon cycle study under the background of global climate change. SOCS research has increased worldwide. The objective of this study is to develop a two-stage approach with good extension capability to estimate SOCS. In the first stage, an artificial neural network (ANN) model is adopted to estimate SOCS based on 255 soil samples with five soil layers (20 cm increments to 100 cm) in Luoding, Guangdong Province, China. This method is compared with three common methods: The soil type method (STM), ordinary kriging (OK), and radial basis function (RBF) interpolation. In the second stage, a linear model is introduced to capture the regional differences and further improve the estimation accuracy of the Luoding-based ANN model when extending it to Xinxing, Guangdong Province. This is done after assessing the generalizability of the above four methods with 120 soil samples from Xinxing. The results for the first stage show that the ANN model has much better estimation accuracy than STM, OK, and RBF, with the average root mean square error (RMSE) of the five soil layers decreasing by 0.62–0.90 kg·m−2, R2 increasing from 0.54 to 0.65, and the mean absolute error decreasing from 0.32 to 0.42. Moreover, the spatial distribution maps produced by the ANN model are more accurate than those of other methods for describing the overall and local SOCS in detail. The results of the second stage indicate that STM, OK, and RBF have poor generalizability (R2 < 0.1), and the R2 value obtained with ANN method is also 43–56% lower for the five soil layers compared with the estimation accuracy achieved in Luoding. However, the R2 of the linear models built with the 20% soil samples from Xinxing are 0.23–0.29 higher for the five soil layers. Thus, the ANN model is an effective method for accurately estimating SOCS on a regional scale with a small number of field samples. The linear model could easily extend the ANN model to outside areas where the ANN model was originally developed with a better level of accuracy.


Geoderma ◽  
2006 ◽  
Vol 134 (1-2) ◽  
pp. 200-206 ◽  
Author(s):  
Huajun Tang ◽  
Jianjun Qiu ◽  
Eric Van Ranst ◽  
Changsheng Li

Geoderma ◽  
2010 ◽  
Vol 154 (3-4) ◽  
pp. 261-266 ◽  
Author(s):  
Fengpeng Han ◽  
Wei Hu ◽  
Jiyong Zheng ◽  
Feng Du ◽  
Xingchang Zhang

2018 ◽  
Vol 24 (9) ◽  
pp. 4160-4172 ◽  
Author(s):  
Minghua Song ◽  
Yu Guo ◽  
Feihai Yu ◽  
Xianzhou Zhang ◽  
Guangmin Cao ◽  
...  

Author(s):  
Arvind Kumar Rai ◽  
Srinivasan Ramakrishnan ◽  
Nirmalendu Basak ◽  
Parul Sundha ◽  
A. K. Dixit ◽  
...  

2020 ◽  
Vol 96 ◽  
pp. 103146 ◽  
Author(s):  
Xuefeng Zhu ◽  
Hongtu Xie ◽  
Michael D. Masters ◽  
Yu Luo ◽  
Xudong Zhang ◽  
...  

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