Accumulation and Crop Uptake of Soil Mineral Nitrogen as Influenced by Tillage, Cover Crops, and Nitrogen Fertilization

2007 ◽  
Vol 99 (3) ◽  
pp. 682-691 ◽  
Author(s):  
Upendra M. Sainju ◽  
Bharat P. Singh ◽  
Wayne F. Whitehead ◽  
Shirley Wang
Keyword(s):  
Cover Crops ◽  
Nitrogen Fertilization ◽  
Mineral Nitrogen ◽  
Soil Mineral ◽  
Soil Mineral Nitrogen ◽  
Crop Uptake

Effects of cover crops on soil mineral nitrogen and on the yield and nitrogen content of maize

2009 ◽  
Vol 31 (2) ◽  
pp. 103-109 ◽  
Author(s):  
Branko Kramberger ◽  
Anastazija Gselman ◽  
Marjan Janzekovic ◽  
Mitja Kaligaric ◽  
Brigita Bracko
Keyword(s):  
Nitrogen Content ◽  
Cover Crops ◽  
Mineral Nitrogen ◽  
Soil Mineral ◽  
Soil Mineral Nitrogen

Link between paddy soil mineral nitrogen release and iron and manganese reduction examined in a rice pot growth experiment

Geoderma ◽  
2018 ◽  
Vol 326 ◽  
pp. 9-21 ◽  
Author(s):  
Masuda Akter ◽  
Heleen Deroo ◽  
Eddy De Grave ◽  
Toon Van Alboom ◽  
Mohammed Abdul Kader ◽  
...  
Keyword(s):  
Paddy Soil ◽  
Mineral Nitrogen ◽  
Nitrogen Release ◽  
Growth Experiment ◽  
Soil Mineral ◽  
Soil Mineral Nitrogen ◽  
Manganese Reduction ◽  
Iron And Manganese

Soil mineral nitrogen and nitrate leaching losses in soil tillage systems combined with a catch crop

1999 ◽  
Vol 50 (2) ◽  
pp. 115-125 ◽  
Author(s):  
Maria Stenberg ◽  
Helena Aronsson ◽  
Börje Lindén ◽  
Tomas Rydberg ◽  
Arne Gustafson
Keyword(s):  
Nitrate Leaching ◽  
Mineral Nitrogen ◽  
Soil Tillage ◽  
Catch Crop ◽  
Soil Mineral ◽  
Soil Mineral Nitrogen ◽  
Tillage Systems ◽  
Leaching Losses

scholarly journals Application of the Bayesian calibration methodology for the parameter estimation in CoupModel

2009 ◽  
Vol 21 ◽  
pp. 13-24 ◽  
Author(s):  
Y. Conrad ◽  
N. Fohrer
Keyword(s):  
Soil Water ◽  
Parameter Space ◽  
Mineral Nitrogen ◽  
Soil Mineral ◽  
Soil Mineral Nitrogen ◽  
Bayesian Calibration ◽  
Soil Temperatures ◽  
Simulation Results ◽  
Water Contents ◽  
Soil Water Contents

Abstract. This study provides results for the optimization strategy of highly parameterized models, especially with a high number of unknown input parameters and joint problems in terms of sufficient parameter space. Consequently, the uncertainty in model parameterization and measurements must be considered when highly variable nitrogen losses, e.g. N leaching, are to be predicted. The Bayesian calibration methodology was used to investigate the parameter uncertainty of the process-based CoupModel. Bayesian methods link prior probability distributions of input parameters to likelihood estimates of the simulation results by comparison with measured values. The uncertainty in the updated posterior parameters can be used to conduct an uncertainty analysis of the model output. A number of 24 model variables were optimized during 20 000 simulations to find the "optimum" value for each parameter. The likelihood was computed by comparing simulation results with observed values of 23 output variables including soil water contents, soil temperatures, groundwater level, soil mineral nitrogen, nitrate concentrations below the root zone, denitrification and harvested carbon from grassland plots in Northern Germany for the period 1997–2002. The posterior parameter space was sampled with the Markov Chain Monte Carlo approach to obtain plot-specific posterior parameter distributions for each system. Posterior distributions of the parameters narrowed down in the accepted runs, thus uncertainty decreased. Results from the single-plot optimization showed a plausible reproduction of soil temperatures, soil water contents and water tensions in different soil depths for both systems. The model performed better for these abiotic system properties compared to the results for harvested carbon and soil mineral nitrogen dynamics. The high variability in modeled nitrogen leaching showed that the soil nitrogen conditions are highly uncertain associated with low modeling efficiencies. Simulated nitrate leaching was compared to more general, site-specific estimations, indicating a higher leaching during the seepage periods for both simulated grassland systems.

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