scholarly journals Effect of in-situ Incorporation Green Manures on Soil Organic Carbon, pH, Bulk Density and Economics Involved in Its Incorporation

Author(s):  
Ghous Ali ◽  
Ch. Pulla Rao ◽  
A.S. Rao ◽  
Y. Ashoka Rani
2016 ◽  
Vol 20 (9) ◽  
pp. 3859-3872 ◽  
Author(s):  
William Alexander Avery ◽  
Catherine Finkenbiner ◽  
Trenton E. Franz ◽  
Tiejun Wang ◽  
Anthony L. Nguy-Robertson ◽  
...  

Abstract. The need for accurate, real-time, reliable, and multi-scale soil water content (SWC) monitoring is critical for a multitude of scientific disciplines trying to understand and predict the Earth's terrestrial energy, water, and nutrient cycles. One promising technique to help meet this demand is fixed and roving cosmic-ray neutron probes (CRNPs). However, the relationship between observed low-energy neutrons and SWC is affected by local soil and vegetation calibration parameters. This effect may be accounted for by a calibration equation based on local soil type and the amount of vegetation. However, determining the calibration parameters for this equation is labor- and time-intensive, thus limiting the full potential of the roving CRNP in large surveys and long transects, or its use in novel environments. In this work, our objective is to develop and test the accuracy of globally available datasets (clay weight percent, soil bulk density, and soil organic carbon) to support the operability of the roving CRNP. Here, we develop a 1 km product of soil lattice water over the continental United States (CONUS) using a database of in situ calibration samples and globally available soil taxonomy and soil texture data. We then test the accuracy of the global dataset in the CONUS using comparisons from 61 in situ samples of clay percent (RMSE  =  5.45 wt %, R2  =  0.68), soil bulk density (RMSE  =  0.173 g cm−3, R2  =  0.203), and soil organic carbon (RMSE  =  1.47 wt %, R2  =  0.175). Next, we conduct an uncertainty analysis of the global soil calibration parameters using a Monte Carlo error propagation analysis (maximum RMSE  ∼  0.035 cm3 cm−3 at a SWC  =  0.40 cm3 cm−3). In terms of vegetation, fast-growing crops (i.e., maize and soybeans), grasslands, and forests contribute to the CRNP signal primarily through the water within their biomass and this signal must be accounted for accurate estimation of SWC. We estimated the biomass water signal by using a vegetation index derived from MODIS imagery as a proxy for standing wet biomass (RMSE  <  1 kg m−2). Lastly, we make recommendations on the design and validation of future roving CRNP experiments.


2016 ◽  
Author(s):  
William Alexander Avery ◽  
Catherine Finkenbiner ◽  
Trenton E. Franz ◽  
Tiejun Wang ◽  
Anthony L. Nguy-Robertson ◽  
...  

Abstract. The need for accurate, real-time, reliable, and multi-scale soil water content (SWC) monitoring is critical for a multitude of scientific disciplines trying to understand and predict the earth's terrestrial energy, water, and nutrient cycles. One promising technique to help meet this demand is fixed and roving cosmic-ray neutron probes (CRNP). However, the relationship between observed low-energy neutrons and SWC is affected by local soil and vegetation calibration parameters. This effect may be accounted for by a calibration equation based on local soil type and the amount of standing biomass. However, determining the calibration parameters for this equation is labor and time intensive, thus limiting the full potential of the roving CRNP in large surveys and long transects, or its use in novel environments. In this work, our objective is to develop and test the accuracy of using globally available datasets (clay weight percent, soil bulk density, and soil organic carbon) to support the operability of the CRNP. Here, we develop a 1 km product of soil lattice water over the CONtinental United States (CONUS) using a database of in-situ calibration samples and globally available soil taxonomy and soil texture data. We then test the accuracy of the global dataset in the CONUS using comparisons from 61 in-situ samples of clay percent (RMSE = 5.45 wt. %, R2 = 0.68), soil bulk density (RMSE = 0.173 g/cm3, R2 = 0.203), and soil organic carbon (RMSE = 1.47 wt. %, R2 = 0.175). Next, we conduct an uncertainty analysis of the global soil calibration parameters using a Monte Carlo error propagation analysis (maximum RSME ~0.035 cm3/cm3 at a SWC = 0.40 cm3/cm3). In terms of vegetation, fast growing crops (i.e. maize and soybeans) contribute to the CRNP signal primarily through the water within their biomass and this signal must be minimized for accurate estimation of SWC. We estimated the biomass water signal by using a vegetation index derived from MODIS imagery as a proxy for standing wet biomass (RMSE < 1 kg/m2). Lastly, we make recommendations on the design and validation of future roving CRNP experiments.


2016 ◽  
Author(s):  
Christopher Poeplau ◽  
Cora Vos ◽  
Axel Don

Abstract. Estimation of soil organic carbon (SOC) stocks requires estimates of the carbon content, bulk density, stone content and depth of a respective soil layer. However, different application of these parameters could introduce a considerable bias. Here, we explain why three out of four frequently applied methods overestimate SOC stocks. In stone rich soils (> 30 Vol. %), SOC stocks could be overestimated by more than 100 %, as revealed by using German Agricultural Soil Inventory data. Due to relatively low stone content, the mean systematic overestimation for German agricultural soils was 2.1–10.1 % for three different commonly used equations. The equation ensemble as re-formulated here might help to unify SOC stock determination and avoid overestimation in future studies.


ael ◽  
2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Sumit Sharma ◽  
Tracy Wilson ◽  
Tyson Ochsner ◽  
Jason G. Warren

2006 ◽  
Vol 28 (2) ◽  
pp. 115 ◽  
Author(s):  
S. H. Roxburgh ◽  
B. G. Mackey ◽  
C. Dean ◽  
L. Randall ◽  
A. Lee ◽  
...  

A woodland–open forest landscape within the Brigalow Belt South bioregion of Queensland, Australia, was surveyed for soil organic carbon, soil bulk density and soil-surface fine-litter carbon. Soil carbon stocks to 30 cm depth across 14 sites, spanning a range of soil and vegetation complexes, ranged from 10.7 to 61.8 t C/ha, with an overall mean of 36.2 t C/ha. Soil carbon stocks to 100 cm depth ranged from 19.4 to 150.5 t C/ha, with an overall mean of 72.9 t C/ha. The standing stock of fine litter ranged from 1.0 to 7.0 t C/ha, with a mean of 2.6 t C/ha, and soil bulk density averaged 1.4 g/cm3 at the soil surface, and 1.6 g/cm3 at 1 m depth. These results contribute to the currently sparse database of soil organic carbon and bulk density measurements in uncultivated soils within Australian open forests and woodlands. The estimates of total soil organic carbon stock calculated to 30 cm depth were further partitioned into resistant plant material (RPM), humus (HUM), and inert organic matter (IOM) pools using diffuse mid-infrared (MIR) analysis. Prediction of the HUM and RPM pools using the RothC soil carbon model agreed well with the MIR measurements, confirming the suitability of RothC for modelling soil organic carbon in these soils. Methods for quantifying soil organic carbon at landscape scales were also explored, and a new regression-based technique for estimating soil carbon stocks from simple field-measured soil attributes has been proposed. The results of this study are discussed with particular reference to the difficulties encountered in the collection of the data, their limitations, and opportunities for the further development of methods for quantifying soil organic carbon at landscape scales.


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