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The application of organic fertilizers induced an increase in soil organic matter contents (data not shown) and significant differences on CO2 losses between organic treatments and Control and NPK treatments. Methane emissions were unaffected by the use of organic fertilizers. When the Global Warming Potential of the cumulative losses was calculated, no differences between treatments were observed.
4. Conclusion Our results show that the greenhouse gas emissions from soil were not affected by the application of organic amendments, considering an end-point evaluation. Thus, the application of composted organic wasted to agricultural soils can be a useful tool to reduce both the waste problem and N requirements for agriculture.
Acknowledgements This work was supported by the Spanish Commission of Science and Technology (MCyT project number AGL2009C02-02). The current study involved the collaborative work between the “Navarre’s Station of Viticulture and Oenology” (EVENA, Spanish acronym) and Public University of Navarre (UPNA, Spanish acronym). M.E. CallejaCervantes held a grant from the Public University of Navarre References Bouwman, A.F. 1990. Exchange of greenhouse gases between terrestrial ecosystems and the atmosphere. In: Bouwman, A.F., (Ed.), Soils and the Green House Effect, Wiley, Chischester, pp. 61-127.
IPCC, 1995. Climate change Scientific and Technical Analysis of Impacts, Adaptions and Mitigation. Contribution of Working Group II to the Second Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, London.
Freibauer, A., Rounsevell, M.D.A., Smith, P. and Verhagen, J. 2004. Carbon sequestration in the agricultural soils of Europe. Geoderma 122, 1-23.
Menéndez, S., López-Bellido, R.J., Benítez-Vega, J., González-Murua, C., López-Bellido, L. and Estavillo, J.M. 2008.
Long-term effect of tillage, crop rotation and N fertilization to wheat on gaseous emissions under rainfed Mediterranean conditions. European Journal of Agronomy 28, 559-569.
Nitrogen Workshop 2012
Coupling long term database with SWAT and STICS models for testing models and simulating nitrogen management scenarios Deneufbourg, M.a, Pugeaux, N.b, Huguet, J.b, Billy, C.b, Duval, J.b, Vandenberghe, C.a, Marcoen, J.M.a, Beaudoin, N.b a Univ. Liège-Gembloux Agro-Bio Tech. Soil Science Unit. Passage des Déportés, 2. B-5030 Gembloux, Belgique b INRA Unité Agro-Impact. Pôle du Griffon, 02000 Barenton-Bugny, France
1. Background & Objectives:
Pollution of surface and ground water by nitrates released from agriculture at European scale (EU, 2010) combined with the global scarcity of raw materials makes better use of nitrogen (N) essential. The impact of changes in agricultural practices on receiving water bodies is difficult to evaluate in the long term because of the lag time of the aquifers and soils.
Modeling nitrate leaching allows a prediction of nitrates concentration below the rooting zone and at the outlet of the watershed in the long term. The European funded project “Sustainable Use of Nitrogen” (SUN) aims to contribute to a sustainable management of N in agriculture in order to reduce the leaks towards water and air.
One objective of this project is to quantify the impact of different tactical or strategic N management scenarios in crop and fodder systems on nitrate leaching by modeling over 50 years the soil-crop-air system with the STICS and SWAT models applied to two watersheds.
This paper is focused on the method and results of the coupling database-models.
2. Materials & Methods:
Study areas The study areas are 2 watersheds (Table 1) in which monitor was carried out over a number of years (since 1990 in Bruyères and 2000 in Arquennes).
Table 1. Main characteristics of the study watersheds.
Name Location Area Soil variability Farming system ReferencesArquennes Southern 78 ha Low (loam/ sandy Mixed crop-livestock Deneufbourg et Belgium loam) farming al., 2010 (Wallonia) Bruyères Northern 160 ha High (loam, sand, Arable crops Beaudoin et al., France loamy clay, sandy 2005 limestony loam Models Two different soil-crop models are being used: an agronomy oriented model, STICS (Brisson et al., 2003), previously spatially distributed (Schnebelen et al., 2004) and a hydrology oriented model, SWAT (Neitsch et al., 2005). The STICS model runs at a plot scale whereas the SWAT model runs at a catchment scale. Inputs of both models are climate, soils properties, land use, topography (only for SWAT) and agronomic practices. Outputs of both models are the crop yield, Leaf Area Index, biomass, water balance and N balance. For STICS, soil water content and soil mineral N content are additional studied outputs. The Arquennes watershed had already been modelled with the SWAT model in a previous study whereas the Bruyères watershed had previously been modelled with the STICS model.
Database Data from field measurements in both watersheds, initially stored in spreadsheets, were sorted, standardized and inserted in a database managed with PostgreSQL (Duval et al., 2010).
Coupling database-models An interface was developed to retrieve the required data in the database and to adapt their format to the input files of the STICS model (ESPIA project). Input files are obtained from field measurements and expert relation rules between simulation units. Agricultural Nitrogen Workshop 2012 management input files of the SWAT model were also generated automatically but no interface was built.
3. Results & Discussion:
Preliminary results were obtained for crop yield and soil nitrogen content simulated with STICS and SWAT models on several parcels (from 1 to 10 ha) in Arquennes watershed from 2000 to 2007 (the Bruyères watershed still need to be fully modelled with the SWAT model).
These parcels are considered to be representative of the context of the watershed. Up to now, we focused on sugar beet and potato crops. First results are presented in Table 2, as example.
Preliminary results allowed to reveal some differences between STICS and SWAT models and to test the coupling between database and models. Coupling database and models is a worthwhile approach which makes the use and comparison of several models easier.
References Beaudoin, N., Saad, J., Van Laethem, C., Maucorps, J., Machet, J.M. and Mary, B. 2005. Nitrate leaching in intensive arable agriculture in Northern France: effect of farming practices, soils and crop rotations. Agriculture, Ecosystem and Environment 111, 292-310.
Brisson, N., Gary C., Justes, E., et al. 2003. An overview of the crop model STICS. European Journal of Agronomy 18, 309-332.
Deneufbourg M., Vandenberghe, C. and Marcoen J.M. 2010. Mise en œuvre du Programme de Gestion Durable de l’Azote et évaluation d’impact à l’échelle d’un bassin versant agricole (Arquennes, Belgique). Biotechnology, Agronomy, Society and Environment 14 (S1), 27-38.
Duval, J., Constantin, J. and Beaudoin, N., 2010. Interfaçage d’une base de données POSTGRESQL d’essais de longue durée avec le modèle STICS. Poster- Séminaire STICS ; 16-18 mars, Sorèze (F81).
Neitsch, S.L., Arnold, J.G., Kiniry, J.R. and Williams, J.R., 2005. Soil and Water Assessment Tool Theoretical Documentation, Version 2005. Temple, Texas: USDA-ARS Grassland, Soil and Water Research Laboratory.
Schnebelen, N., Nicoullaud, B., Bourennane, H., Couturier, A., Verbeque, B., Revalier, C., Bruand, A. and Ledoux E. 2004. The STICS model to predict nitrate leaching following agricultural practices. Agronomie 24, 423–435.
UE, 2010 : Mise en œuvre de la directive 91/676/CEE du Conseil concernant la protection des eaux contre la pollution par les nitrates à partir de sources agricoles. Synthèse des rapports établis par les États membres pour la période 2004-2007. Commission Européenne. 13 pages.
Aknowledgements We thank INTERREG EU (SUN), Picardie region (ESPIA) and Walloon Public Service.
Nitrogen Workshop 2012
Determination of nitrogen concentration in pig slurries using NIR spectroscopy Fuccella, R.a, Cabassi, G.b, Marino Gallina, P.a a Di.Pro.Ve. Department of Plant Production, Università degli studi di Milano, Milan, Italy b CRA-FLC Centro di ricerca per le Produzioni Foraggere e Lattiero-Casearie di Lodi, Lodi, Italy
Spectra were acquired in diffuse reflectance mode using samples either as they were after thawing or mixed with silica sand. The silica sand was pre-treated at 550 °C. Samples were warmed at 20 °C on a Dubnoff bath just before scanning. At least three average spectra, obtained from three different fillings, were obtained for each sample. Chemometric analysis of the NIR spectra were performed using Matlab™ R2009b software (The Mathworks, Inc.) and PLS Toolbox (Eigenvector Research, Inc.). The following pre-treatments were applied to spectra: Extended Multiplicative Scattering Correction, Savitsky-Golay function and mean center. Outlier detection was obtained by principal components analysis (PCA) and by Q and Hotelling’s T2 test (95% confidence limit). Partial least squares (PLS) regressions models were developed and a random subset cross-validation with 8 numbers of data splits and 4 numbers of iterations was used for model development and validation.
To judge the quality of NIR calibrations we followed the guidelines proposed by Malley et al.
(2004) based on three indices: i) R2 (coefficient of determination during validation); ii) RER (range error ratio; the ratio between the range of measured values and the RMSEP, Root Mean Squared Error of Prediction); iii) RDP (ratio of performance to deviation; the ratio between the standard deviation of the measured values and the RMSEP).
4. Conclusions The Buchi bench top spectroscope showed good predictive capabilities for TKN and NH4-N concentration of pig slurries. The portable instrument LAB PODTM (Polycromix) was less accurate but still adequate and useful to improve current management of pig slurries, because the rates of application are normally calculated assuming categorized mean slurry N concentrations.
References De Ferrari G., Marino Gallina P., Cabassi G., Bechini L. and Maggiore T. 2007. Near infrared spectral analysis of cattle slurries from Lombardy breeding farms, In: Proceedings of the 12th International Conference Auckland, New Zealand 10-15/04/2005. In: Burling-Claridge G.R, Holroyd S.E., Sumner R.M.W. (eds).
Malley D.F., Martin P.D. and Ben-Dor E. 2004. Application in Analysis of soils. American Society of Agronomy, Crop Science Society of America, Soil Science Society of America. Madison, Wisconsis, USA. Near Infrared Spettroscopy in Agriculture. Agronomy Monograph 44, 729-736 Sørensen, L. K., Sørensen, P. and Birkmose T. S. 2007. Application of reflectance near infrared spectroscopy for animal slurry analyses. Soil Science Society of America Journal 71, 1398-1405
Nitrogen Workshop 2012
Differentiating sewage and manure derived nitrate within surface waters Fenech, C.a, Rock, L.b, Nolan, K.c, Tobin, J.a, Morrissey, A. d a School of Biotechnology, Dublin City University, Dublin 9, Ireland.
b Environmental Engineering Research Centre, SPACE, Queen’s University Belfast, N. Ireland.
c School of Chemistry, Dublin City University, Dublin 9, Ireland.
d OSCAIL, Dublin City University, Dublin 9, Ireland.
1. Background & Objectives Nitrate is naturally found within the environment as part of the nitrogen cycle. However, anthropogenic sources have greatly increased nitrate loads within ground and surface waters. This has had a severe impact on aquatic ecosystems and has given rise to health considerations in humans and livestock. Therefore, efforts in nitrate sources determination have increased. Nitrate sources can be determined on the basis of the nitrogen (N) and oxygen (O) isotopic compositions (δ15N, δ18O) of nitrate. However, sewage and manure have overlapping δ15N and δ18O values (Figure 1), making their differentiation on this basis impossible. Hence, co-occurring discriminators of nitrate sources are required to differentiate between sewage and manure nitrate sources.
Figure 1. A general depiction of the normal range of δ18O and δ15N values for the dominant sources of nitrate Adapted from (Kendall, 1998).
In the present study, human and veterinary specific chemical markers are being assessed as markers of sewage and manure contamination respectively. Pharmaceuticals and related compounds such as metabolites and food additives have been detected within surface waters. They make ideal chemical markers as pharmaceuticals and related compounds are relatively water-soluble and non-volatile, and their natural background levels are low (Benotti and Brownawell, 2007). Furthermore, they are commonly persistent in order to avoid the substance becoming inactive before having a curing effect (Christensen, 1998; Halling-Sørensen et al., 1998; Jjemba, 2006; Enick and Moore, 2007).
Therefore, through the careful choice of a suite of pharmaceuticals differentiation between sewage and manure nitrate sources may be possible.
2. Materials & Methods A suite of 10 chemical markers suitable for the differentiation of sewage and manure within surface waters was identified. The chemical markers selected provide specific information about the sample being analysed. Apart from giving information about the source of nitrate being human (sewage) or veterinary (manure), further information can be elucidated from their presence or absence, such as whether the input is raw or treated sewage. An SPE-LC-MS method for their simultaneous
Nitrogen Workshop 2012
determination within surface waters has subsequently been developed. Method development initially consisted of identification and optimisation of the separate SPE, HPLC and MS portions, followed by combination to a single SPE-LC-MS method and its validation. This was used to analyse samples from two sites influenced by sewage or manure inputs over a 6 month period which could be integrated to isotopic data.