Estimation of soil organic carbon losses and counter approaches from organic materials in black soils of northeastern China

2019 ◽  
Vol 20 (3) ◽  
pp. 1241-1252
Author(s):  
Shichao Wang ◽  
Yawen Zhao ◽  
Jinzhou Wang ◽  
Jiajia Gao ◽  
Ping Zhu ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Organic Materials ◽  
Northeastern China ◽  
Carbon Losses

scholarly journals Soil age and soil organic carbon content shape biochemical responses to multiple freeze–thaw events in soils along a postmining agricultural chronosequence

2021 ◽  
Author(s):  
Christoph Rosinger ◽  
Michael Bonkowski
Keyword(s):  
Microbial Biomass ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Content ◽  
Organic Carbon Content ◽  
Freeze Thaw ◽  
Biochemical Responses ◽  
Microbial Responses ◽  
Soil Age ◽  
Carbon Losses

AbstractFreeze–thaw (FT) events exert a great physiological stress on the soil microbial community and thus significantly impact soil biogeochemical processes. Studies often show ambiguous and contradicting results, because a multitude of environmental factors affect biogeochemical responses to FT. Thus, a better understanding of the factors driving and regulating microbial responses to FT events is required. Soil chronosequences allow more focused comparisons among soils with initially similar start conditions. We therefore exposed four soils with contrasting organic carbon contents and opposing soil age (i.e., years after restoration) from a postmining agricultural chronosequence to three consecutive FT events and evaluated soil biochgeoemical responses after thawing. The major microbial biomass carbon losses occurred after the first FT event, while microbial biomass N decreased more steadily with subsequent FT cycles. This led to an immediate and lasting decoupling of microbial biomass carbon:nitrogen stoichiometry. After the first FT event, basal respiration and the metabolic quotient (i.e., respiration per microbial biomass unit) were above pre-freezing values and thereafter decreased with subsequent FT cycles, demonstrating initially high dissimilatory carbon losses and less and less microbial metabolic activity with each iterative FT cycle. As a consequence, dissolved organic carbon and total dissolved nitrogen increased in soil solution after the first FT event, while a substantial part of the liberated nitrogen was likely lost through gaseous emissions. Overall, high-carbon soils were more vulnerable to microbial biomass losses than low-carbon soils. Surprisingly, soil age explained more variation in soil chemical and microbial responses than soil organic carbon content. Further studies are needed to dissect the factors associated with soil age and its influence on soil biochemical responses to FT events.

Effect of cultivation history on soil organic carbon status of arable land in northeastern China

Geoderma ◽  
2019 ◽  
Vol 342 ◽  
pp. 55-64 ◽  
Author(s):  
Yan Wang ◽  
Shuai Wang ◽  
Kabindra Adhikari ◽  
Qiubing Wang ◽  
Yueyu Sui ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Arable Land ◽  
Northeastern China ◽  
Cultivation History

scholarly journals Soil Chemical Properties as Influenced by Long Term Manuring and Nitrogen Fertilization in Bangladesh

2019 ◽  
pp. 1-9
Author(s):  
Fahamida Akter ◽  
Md. Mizanur Rahman ◽  
Md. Ashraful Alam
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Rice Straw ◽  
Green Manure ◽  
Organic Materials ◽  
Chemical Properties ◽  
Soil Chemical Properties ◽  
Organic Fertilizers ◽  
Total N

Organic fertilizers are enriched in plant nutrients which may enhance the soil chemical properties. However, studies on the effect of long term fertilization on soil chemical attributes is yet lacking in Bangladesh. Therefore, an experiment was conducted to assess the changes of soil chemical properties as influenced by long term manuring and nitrogen fertilizer in silt clay loam soil under rice-wheat cropping system. The experimental plot received different organic materials for the last 26 years (1988-2014). Five types of organic materials treatments such as control (no manure), cowdung, compost, green manure and rice straw were applied at the rate of 0, 25, 25, 7.5 and 1.5 t ha-1, respectively in a yearly sequence. Three levels of nitrogen viz. 0, 75 and 100 kg ha-1 for rice and 0, 80 and 120 kg ha-1 for wheat were applied in this study. Long term application of different organic materials positively increased soil organic carbon and total N, P, S and decreased pH and K, Ca and Mg availability. Increase in soil organic carbon was found maximum under green manure and lowest in rice straw applied soil. The green manure contributed to the maximum accumulation of soil nitrogen. N dose of 80 kg ha-1 was found effective in increasing availability of soil nutrients.

scholarly journals Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data

2020 ◽  
Vol 12 (7) ◽  
pp. 1115 ◽  
Author(s):  
Shuai Wang ◽  
Qianlai Zhuang ◽  
Xinxin Jin ◽  
Zijiao Yang ◽  
Hongbin Liu
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Quality ◽  
Vegetation Index ◽  
Forest Ecosystems ◽  
Remotely Sensed ◽  
Northeastern China ◽  
Boosted Regression Trees ◽  
Land Policy ◽  
The Government

Forest ecosystems play an important role in regional carbon and nitrogen cycling. Accurate and effective monitoring of their soil organic carbon (SOC) and soil total nitrogen (STN) stocks provides important information for soil quality assessment, sustainable forestry management and climate change policy making. In this study, a geographical weighted regression (GWR) model, a multiple stepwise regression (MLSR) model, and a boosted regression trees (BRT) model were compared to obtain the best prediction of SOC and STN stocks of the forest ecosystems in northeastern China. Five-hundred and thirteen topsoil (0–30 cm) samples (10.32 kg m−2 (±0.53) for SOC, 1.21 kg m−2 (±0.32) for STN), and 9 remotely-sensed environmental variables were collected and used for the model development and verification. By comparing with independent verification data, the best model (BRT) achieved R2 = 0.56 and root mean square error (RMSE) = 00.85 kg m−2 for SOC stocks, R2 = 0.51 and RMSE = 0.22 kg m−2 for STN stocks. Of all the remotely-sensed environment variables, soil adjusted vegetation index (SAVI) and normalized difference vegetation index (NDVI) are of the highest relative importance in predicting SOC and STN stocks. The spatial distribution of the predicted SOC and STN stocks gradually decreased from northeast to southwest. This study provides an attempt to rapidly predict SOC and STN stocks in the dense vegetation covered area. The results can help evaluate soil quality and facilitate land policy and regulation making by the government in the region.

The quantity and stability of soil organic carbon following vegetation degradation in a salt-affected region of Northeastern China

CATENA ◽  
2022 ◽  
Vol 211 ◽  
pp. 105984
Author(s):  
Pujia Yu ◽  
Yixuan Li ◽  
Shiwei Liu ◽  
Zhi Ding ◽  
Aichun Zhang ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Northeastern China ◽  
Vegetation Degradation

Residue cover and rainfall intensity effects on runoff soil organic carbon losses

CATENA ◽  
2009 ◽  
Vol 78 (1) ◽  
pp. 81-86 ◽  
Author(s):  
K. Jin ◽  
W.M. Cornelis ◽  
D. Gabriels ◽  
M. Baert ◽  
H.J. Wu ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Rainfall Intensity ◽  
Carbon Losses

Spatial distribution of soil organic carbon in the ecologically fragile Horqin Grassland of northeastern China

Geoderma ◽  
2018 ◽  
Vol 325 ◽  
pp. 102-109 ◽  
Author(s):  
Yuqiang Li ◽  
Xuyang Wang ◽  
Yayi Niu ◽  
Jie Lian ◽  
Yongqing Luo ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Northeastern China

Soil crusting impact on soil organic carbon losses by water erosion

CATENA ◽  
2013 ◽  
Vol 107 ◽  
pp. 26-34 ◽  
Author(s):  
S. Maïga-Yaleu ◽  
I. Guiguemde ◽  
H. Yacouba ◽  
H. Karambiri ◽  
O. Ribolzi ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Water Erosion ◽  
Soil Crusting ◽  
Carbon Losses
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