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4. Conclusion The minimum tillage can substitute conventional management. Application of a stable organic matter such as compost is valuable in fertile, aerated soils, but should be avoided in low fertility, anoxic soils as crop yields can be depleted. Preservation of organic matter oxidation through minimum tillage maintained crop production both in fertile and non-fertile soils, but its fit is better in anoxic soils where SOC sequestration is higher. Our findings confirm that there is no unique solution to environmental issues, but a series of options that need to be evaluated in the specific pedo-climatic and farming system conditions.
References Erhart, E., Hartl, W. and Putz, B. 2005. Biowaste compost affects yield, nitrogen supply during the vegetation period and crop quality of agricultural crops. European Journal of Agronomy 23, 305-314.
Fagnano, M., Adamo, P., Zampella, M. and Fiorentino, N. 2011. Environmental and agronomic impact of fertilization with composted organic fraction from municipal solid waste. Agriculture Ecosystems & Environment 141, 100-107.
Lal, R. 2004. Agricultural activities and the global carbon cycle. Nutrient Cycling in Agroecosystems 70, 103-116.
Nitrogen Workshop 2012
Comparison of APSIM and DNDC for simulating nitrogen transformation and N2O emissions from urine patches Vogeler, I.a, Cichota, R.a, Giltrap, Db, Snow, Vc a AgResearch, Grasslands Research Centre, Palmerston North, New Zealand b Landcare Research, Palmerston North, New Zealand c AgResearch, lincoln Research Centre, Lincoln, New Zealand
1. Background & Objectives Nitrogen transformation rates and nitrous oxide (N2O) emissions from urine patches are notoriously variable, both in space and time, due to the variability of controlling environmental factors. Thus annual N2O losses are often made up by a few emission peaks. Effective mitigation of N2O emissions from pastoral systems requires better understanding of the factors that control the interconnected N cycling processes, including nitrification, denitrification and gaseous emissions.
Computer simulation models provide a cost effective method of estimating N2O emissions from soils and for evaluating how heterogeneity in climate and soil affect these emissions. Various simulation approaches are in use or being developed to predict N2O emissions. The models vary in the level of detail or number of nitrogen pools and transformation processes considered, as well as on how the processes are described. Other processes within the models, such as water and heat transport within the soil also affect the modelled N transformations and losses. And while most models have been tested and validated for certain aspects, there is a lack of information on how models compare in other aspects. The objective of this paper is to compare the APSIM (Agricultural Production Systems Simulator; (Keating et al., 2003),) and DNDC (DeNitrification DeComposition; (Li et al., 1992)) model for simulating N transformation processes and N2O emissions from urine patches.
2. Materials & Methods N transformations and N2O emissions from urine patches from the two different simulation approaches, APSIM and DNDC, were compared by setting up simulations comprising two different regions of NZ, two different soils, 4 different N deposition times, (Spring, summer, autumn and winter), and four different N deposition loads (250, 500, 750, and 100 kg N/ha). The simulations were run for 3 months and simulation output included cumulative and daily values of nitrification, denitrification, volatilisation, and N2O emissions. Simulation results were also compared to different datasets comprising N2O emissions from urine patches.
3. Results & Discussion Simulated N transformation rates as dependent on environmental conditions were quite different for the two models, APSIM and DNDC. APSIM simulated denitrification in a silt loam in the Waikato region of NZ increases nearly linear with increasing N load (Figure 1), whereas denitrification simulated by DNDC reaches a plateau at an N load of 250 kg ha-1 and thereafter remains almost constant. DNDC also shows little seasonal affect to denitrification, whereas APSIM predicts much higher denitrification in autumn compared to summer and spring. This model difference is partly due to the higher sensitivity of denitrification in APSIM to soil water content, and of DNDC on soil temperature. Simulated N2O emissions by APSIM show a similar trend to denitrification, whereas those simulated by DNDC show a linear increase over the entire range of N load simulated. This suggests that in DNDC at high N loads nitrification becomes a major source for N2O emissions.
4. Conclusion Simulated denitrification and N2O emissions over 3 months for different N loads and seasons were quite different, indicating higher sensitivity of APSIM to soil water content, while DNDC shows a stronger influence of temperature, with denitrification triggered by rainfall. APSIM also shows a much higher seasonal variation in both denitrification and N2O emissions, suggesting higher sensitivity of APSIM to environmental conditions compared with DNDC.
5. Acknowledgments This project is jointly funded by the New Zealand Agricultural Green House Gas Research Centre (NZAGRC) under “Integrated Systems” and MAF under “Sustainable Land Management Mitigation & Adaptation to Climate Change”.
References Keating, B., et al. 2003, An overview of APSIM, a model designed for farming systems simulation: European Journal of Agronomy 18, 267-288.
Li, C., Frolking, S. and Frolking, T.A., 1992, A model of nitrous oxide evolution from soil driven by nitrous oxide evolution from soil driven by rainfall events. Model structure and sensitivity.: Journal of Geophysical Research 9, 9776-9799.
Nitrogen Workshop 2012 Determination of denitrification capacity of small headwater catchments in Flanders.
Van Overtveld, K.a, Tits, M.b, Elsen, A.b, Van De Vreken P.a, Van Orshoven, J.a, Vanderborght, J.a, Diels, J.a, Batelaan, O.a a Department of Earth and Environmental Sciences, K.U. Leuven, Heverlee, Belgium.
b Soil Service of Belgium, Heverlee, Belgium.
1. Background & Objectives Pollution of surface water bodies with nitrates is a major problem in Flanders, Belgium. The nitrate (NO3-) concentration in many surface water bodies exceeds the maximum concentration of 50 mg NO3- L-1 set in the EU Nitrates Directive (91/676/EEC). Although water quality is steadily improving, in 2010 still 28% of the surface water sampling points of the Manure Action Plan water quality network (MAP) exceeded the Nitrates Directive limit at least once a year (VMM, 2010). An important cause of this pollution is leaching from agricultural parcels due to intensive manure application and high nitrogen leftover in soils after harvest of the crops. Over the winter period nitrate largely leaches out of the root zone and may ultimately reach surface water bodies via tile drains or the aquifer. However, during transport through soil and groundwater, denitrification processes may occur, resulting in lower nitrate loading in surface water bodies. Knowledge about the fraction of nitrate that is thus denitrified in Flanders is scarce. Such knowledge is important for policy makers, since it could contribute to delineate zones that are more vulnerable to surface water pollution with nitrate. This way, efforts to improve water quality can be focussed on these regions.
In this study the environmental variables controlling the denitrification capacity of small headwater catchments in Flanders are investigated and a regional differentiation of this denitrification capacity is defined.
2. Materials & Methods For all 794 surface water sampling points of the MAP-network, each individual catchment area was delineated using the ArcSwat GIS-software (Neitsch et al., 2009). A subset of 50 sampling points and their corresponding catchments was selected for further analysis. Selection was based on homogeneity of each catchment regarding soil granulometrical class and hydrogeological properties. The selected catchments were not affected by pollution from residential sewage. For all parcels (agricultural and other land use types) within each catchment, the nitrate leached from the root zone was modelled for 4 subsequent years, by means of an analytical solution of the convection dispersion equation, and the mean nitrate concentration of the leachate below the 90 cm depth plane (rootable depth) for each catchment was calculated. The mean nitrate concentration in the surface water sampling points was calculated as the sum of the monthly measured concentrations, weighed by the ratio of monthly discharge over the total annual discharge. The ratio of area-averaged nitrate concentration of the leachate in each catchment over the weighted mean nitrate concentration in the corresponding surface water sampling point, is interpreted as the denitrification capacity per catchment. This ratio is defined as the process factor (PF) for nitrate (Herelixka et al., 2002). For low values of the process factor (between 1 and 1.5) almost no denitrification occurs. The larger the process factor, the more nitrate denitrification occurs.
3. Results & Discussion Process factor values ranged from 0.9 to 104.4. The soil granulometrical class of the catchment and the redox potential of the underlying aquifer proved to be the main significantly explanatory variables of the process factor. A predictive regression model for the process factor was constructed with these two variables by means of a stepwise regression analysis. The original process factors (PF) were transformed with a Box-Cox transformation to get a set of normally distributed
transformed process factors (PFt),:
Figure 1. Predicted process factor for surface water in Flanders, Belgium.
White zones correspond with residential areas or zones with no soil data.
4. Conclusion This study investigated factors determining the denitrification capacity of small headwater catchments in Flanders. Results suggest that soil texture and redox potential of the aquifer are the main explanatory variables. A predictive model allowed for a regional differentiation of the denitrification capacity in Flanders. The resulting predictive map of the process factor could be used as a tool to evaluate the vulnerability of surface waters to nitrate pollution.
References DOV, 2011. Databank Ondergrond Vlaanderen, available at: https://dov.vlaanderen.be/dov/DOVInternet/startup.jsp Herelixka, E., Librecht, I., Oorts, K., D'Haene, K., Coppens, F., Vogels, N., Rombauts, S., Merckx, R., Vanongeval, L., Sammels, L., De Neve, S., Wellens, J., Verstraeten, W., Salomez, J., El Sadek, A., Boeckx, P., Geypens, M., Van Orshoven, J., Van Cleemput, O. and Feyen, J. 2002. Eindrapport van de onderzoeksopdracht "N-(eco)²: Bepaling van de hoeveelheid minerale stikstof in de bodem als beleidsinstrument (Besteknummer2000/1)". I.o.v. de Vlaamse Landmaatschappij, Afdeling Mestbank.
Neitsch S. L., Arnold J. G., Kiniry J. R. and Williams J. R. 2009. Soil and Water Assessment Tool. Theoretical Documentation. Version 2009. Grassland, Soil and Water Research Laboratory, Blackland Research Center, Temple, Texas, USA.
VMM. 2010. Annual Report Water. Flemish Environmental Agency (VMM). 78pp.
Nitrogen Workshop 2012
Differentiation between fungi and bacteria as a source of N2O formation in soil Rohe, L.a, Well, R.a, Wrage, N.b, Anderson, T.-H.a and Flessa, H.a a Institute of Agricultural Climate Research, Johann Heinrich von Thünen Institute, Federal Research Institute of Rural Areas, Forestry and Fisheries, Braunschweig, Germany b Faculty of Life Sciences, Agricultural Sciences, Rhine-Waal University of Applied Sciences, Kleve, Germany
1. Background & Objectives N2O emissions of agricultural soils result predominantly from microorganisms, particularly produced during nitrification and denitrification. However, which part microbial groups contribute to N2O formation is not sufficiently investigated yet. Understanding of N2O sources and sinks is an important requirement for evaluating mitigation strategies of N2O emissions. Pure culture studies showed that most fungi in soil lack N2O reductase (Shoun et al., 1992) and that N2O from bacterial and fungal denitrification exhibit different isotopomer ratios (e.g. Sutka et al., 2006, Sutka et al., 2008, Frame and Casciotti, 2010). Studies which combine 15N site preferences of N2O (SP = difference between δ15N of the central and terminal N-position of the asymmetric N2O molecule (Well et al., 2006)) and the analysis of N2O production by different microbial communities in soil to distinguish between bacterial and fungal N2O are lacking so far. The objectives of this study are a) to determine the importance of fungal N2O formation in a sandy arable soil, b) to verify, if the contribution of bacteria and fungi to N2O emission can be assessed by analyzing SP, and c) to determine the effect of N2O reduction on SP. To this end, we used the same approach as in substrate-induced respiration with selective inhibition (SIRIN) (Anderson and Domsch, 1975).