Nitrogen turnover in the soil-crop system; comparison of fourteen simulation models

Willigen, P. de

doi:10.1007/978-94-011-3434-7_1

Cited by 68 publications

(47 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The DAISY code (Hansen et al, 1990Abrahamsen and Hansen, 2000) has been validated at several occasions (e.g. Shaffer et al, 2001) and has always been evaluated favourable, in particular with regard to simulation of nitrogen and carbon transformations in soil (de Willigen, 1991;Vereecken et al, 1991;Diekkru¨ger et al, 1995;Smith et al, 1997). This paper presents the concept of the newly developed DOM module in the DAISY model, which was tested with field data from a study on DOM mobilization and transport after cultivating grassclover swards (Vinther et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Challenges in modelling dissolved organic matter dynamics in agricultural soil using DAISY

Gjettermann

Styczen

Hansen

et al. 2008

Soil Biology and Biochemistry

View full text Add to dashboard Cite

Because dissolved organic matter (DOM) plays an important role is terrestrial C-, N-and P-balances and transport of these three components to aquatic environments, there is a need to include it in models. This paper presents the concept of the newly developed DOM modules implemented in the DAISY model with focus on the quantification of DOM sorption/desorption and microbial-driven DOM turnover. The kinetics of DOM sorption/desorption is described by the deviation of the actual DOM concentration in solution from the equilibrium concentration, C eq . The C eq is soil specific and estimated from pedotransfer functions taking into account the soil content of organic matter, Al and Fe oxides. The turnover of several organic matter pools including one DOM pool are described by first-order kinetics.The DOM module was tested at field scale for three soil treatments applied after cultivating grass-clover swards. Suction cups were installed at depths 30, 60 and 90 cm and soil solution was sampled for quantification of dissolved organic C (DOC) and dissolved organic N (DON). In the topsoil, the observed fluctuations in DOC were successfully simulated when the sorption/desorption rate coefficient k was low. In the subsoil, the observed concentrations of DOC were steadier and the best simulations were obtained using a high k. The model shows that DOC and DON concentrations are levelled out in the subsoils due to soil buffering. The steady concentration levels were based on the C eq for each horizon and the kinetic concept for sorption/desorption of DOC appeared a viable approach. If C eq was successfully estimated by the pedotransfer function it was possible to simulate the DOC concentration in the subsoil. In spite of difficulties in describing the DOC dynamics of the topsoil, the DOM module simulates the subsoil concentration level of DOC well, and also-but with more uncertainty-the DON concentration level. r

show abstract

Section: Introductionmentioning

confidence: 99%

Challenges in modelling dissolved organic matter dynamics in agricultural soil using DAISY

Gjettermann

Styczen

Hansen

et al. 2008

Soil Biology and Biochemistry

View full text Add to dashboard Cite

show abstract

“…The agricultural management model allows for building complex management 145 scenarios (Hansen, 2002). The model has been validated in a number of major comparative tests (Diekkrüger 146 et al, 1995; Hansen et al, 1991a,b; Jensen et al, 1997; Smith et al, 1997;Svendsen et al, 1995; Vereecken 147 et al, 1991;De Willigen, 1991). 148…”

mentioning

confidence: 99%

“…In addition also an analysis of the 75 implemented process descriptions and the mutual comparison of models provides information on the predictive 76 power. Several model comparison studies have been conducted in which nitrate leaching models were compared 77 (De Willigen and Neeteson, 1985;Vereecken et al, 1991;De Willigen, 1991;Diekkrüger et al, 1995; Moreels 78 et al, 2003; Kersebaum et al, 2007;Jabro et al, 2012 It is widely recognised that despite the deterministic nature of process oriented models they often have a limited 83 validity range for certain climatic, pedological, hydrological and agronomic circumstances characterised by high 84 inputs. It is not clear whether the models are able to produce relatively reliable predictions for low input 85 conditions.…”

mentioning

confidence: 99%

Performance assessment of nitrate leaching models for highly vulnerable soils used in low-input farming based on lysimeter data

Groenendijk

Heinen

Klammler

et al. 2014

Science of The Total Environment

View full text Add to dashboard Cite

Elsevier Groenendijk, P.; Heinen, M.; Klammler, G.;Kupfersberger, H.; Pisinaras, V.; Gemitzi, A.... (2014). Performance assessment of nitrate leaching models for highly vulnerable soils used in low-input farming based on lysimeter data. Science of the Total Environment. 499: 463-480. doi:10.1016/j.scitotenv.2014.07.002 . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 2 The agricultural sector faces the challenge of ensuring food security without an excessive burden on the 24 environment. Simulation models provide excellent instruments for researchers to gain more insight into relevant 25 processes and best agricultural practices and provide tools for planners for decision making support. The extent 26 to which models are capable of reliable extrapolation and prediction is important for exploring new farming 27 systems or assessing the impacts of future land and climate changes. 28A performance assessment was conducted by testing six detailed state-of-the-art models for simulation of nitrate 29 leaching (ARMOSA, COUPMODEL, DAISY, EPIC, SIMWASER/STOTRASIM, SWAP/ANIMO) for 30 lysimeter data of the Wagna experimental field station in Eastern Austria, where the soil is highly vulnerable to 31 nitrate leaching. 32Three consecutive phases were distinguished to gain insight in the predictive power of the models: 1) a blind test 33 for 2005 -2008 in which only soil hydraulic characteristics, meteorological data and information about the 34 agricultural management were accessible; 2) a calibration for the same period in which essential information on 35 field observations was additionally available to the modellers; and 3) a validation for 2009 -2011 with the 36 corresponding type of data available as for the blind test. A set of statistical metrics (mean absolute error, root 37 mean squared error, index of agreement, model efficiency, root relative squared error, Pearson's linear 38 correlation coefficient) was applied for testing the results and comparing the models. 39None of the models performed good for all of the statistical metrics. Models designed for nitrate leaching in high 40 input farming systems had difficulties in accurate predicting leaching in low input farming systems that are 41 strongly influenced by the retention of nitrogen in catch crops and nitrogen fixation by legumes. An accurate 42 calibration does not guarantee a good predictive power of the model. Nevertheless all models were able to 43 identify years and crops with high and low leaching rates. 44 Keywords 45Lysimeter, model comparison, nitrate leaching, performance assessment, predictive power, simula...

show abstract

“…Previous studies in the Middle Rio Grande have focused on analyzing the spatial and temporal patterns of nutrient concentrations using monitoring and discharge data [Anderholm et al, 1995;Passell et al, 2004;2005]. Several studies have looked at how nutrient load patterns change downstream and over time [Moore and Anderholm, 2002;Oelsner et al, 2007;Passell et al, 2004;2005].…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have looked at how nutrient load patterns change downstream and over time [Moore and Anderholm, 2002;Oelsner et al, 2007;Passell et al, 2004;2005]. Results from these studies suggest that wastewater treatment plants are important point sources of nutrients to the river [Oelsner et al, 2007;Passell et al, 2004] and agricultural diversions can remove nitrogen and improve water quality downstream of wastewater treatment plant point sources [Oelsner et al, 2007].…”

Section: Introductionmentioning

confidence: 99%

Use of a dynamic simulation model to understand nitrogen cycling in the middle Rio Grande, NM.

Meixner

Tidwell²,

Oelsner

et al. 2008

View full text Add to dashboard Cite

Water quality often limits the potential uses of scarce water resources in semiarid and arid regions. To best manage water quality one must understand the sources and sinks of both solutes and water to the river system. Nutrient concentration patterns can identify source and sink locations, but cannot always determine biotic processes that affect nutrient concentrations. Modeling tools can provide insight into these large-scale processes. To address questions about large-scale nitrogen removal in the Middle Rio Grande, NM, we created a system dynamics nitrate model using an existing integrated surface watergroundwater model of the region to evaluate our conceptual models of uptake and denitrification as potential nitrate removal mechanisms.We modeled denitrification in groundwater as a first-order process dependent only on concentration and used a 5% denitrification rate. Uptake was assumed to be proportional to transpiration and was modeled as a percentage of the evapotranspiration calculated within the model multiplied by the nitrate concentration in the water being transpired. We modeled riparian uptake as 90% and agricultural uptake as 50% of the respective evapotranspiration rates. Using these removal rates, our model results suggest that riparian uptake, agricultural uptake and denitrification in groundwater are all needed to produce the observed nitrate concentrations in the groundwater, conveyance channels, and river as well as the seasonal concentration patterns. The model results indicate that a total of 497 metric tons of nitrate-N are removed from the Middle Rio Grande annually. Where river nitrate concentrations are low and there are no large nitrate sources, nitrate behaves nearly conservatively and riparian and agricultural uptake are the most important removal mechanisms. Downstream of a large wastewater nitrate source, denitrification and agricultural uptake were responsible for approximately 90% of the nitrogen removal.

show abstract

Nitrogen turnover in the soil-crop system; comparison of fourteen simulation models

Cited by 68 publications

References 5 publications

Challenges in modelling dissolved organic matter dynamics in agricultural soil using DAISY

Challenges in modelling dissolved organic matter dynamics in agricultural soil using DAISY

Performance assessment of nitrate leaching models for highly vulnerable soils used in low-input farming based on lysimeter data

Use of a dynamic simulation model to understand nitrogen cycling in the middle Rio Grande, NM.

Contact Info

Product

Resources

About