Improving Estimates of Nitrogen Emissions for Life Cycle Assessment of Cropping Systems at the Scale of an Agricultural Territory.

In life cycle assessment (LCA), simple models are currently used to estimate cropping system nitrogen (N) emissions on farms. At large spatial scales (e.g., countries), these models are valid. At a smaller spatial scale (e.g., territories), these models may be less accurate, since they completely or partially ignore local conditions such as management practices, soil or climate. The purpose of this study was to consider the variability of those factors when estimating N emissions in LCA at the watershed scale. To this end, Syst'N, decision-support software based on a simulation model of crop and soil N dynamics at field and crop-rotation scales, was applied to predict N emissions from cropping systems in a coastal watershed (Lieue de Grève, France). Syst'N predictions were compared to N emissions estimated by AGRIBALYSE, a static site-dependent method at field and single-crop scales. Syst'N was more sensitive to site-specific soil properties than AGRIBALYSE. Estimates of N emissions that include spatial variability in soil and climate therefore become possible in LCA when a simulation model such as Syst'N is used in the inventory phase.

Organic matter characteristics of food processing industry wastewaters affecting their C and N mineralization in soil incubation.

This study aimed at determining food processing wastewater composition factors that regulate their carbon and nitrogen mineralization when added to soil. Twenty three different wastewaters from various food processing industries were characterized by C and N concentrations, liquid and solid physical separation and acid solubility. They were also incubated in a calcareous soil during six months at 28 degrees C. The C and N concentrations were low but covered a wide range. Carbon and nitrogen were variously distributed in the liquid and solid fractions and much C was present in the acid-soluble fraction in which C to N ratios were low. The C and N mineralization measured during soil incubation covered a wide range of decomposition pathways. Carbon mineralization was linked significantly (p=0.05) with the C to N ratio of the acid soluble fraction and C present in the liquid fraction. N mineralization was significantly correlated (p=0.05) with the organic C to organic N ratio and the C to N ratio of the acid soluble fraction. Multiple factor analysis and clustering also enabled defining clusters which partially overlap the various origins of the wastewaters.

Relevance of organic matter fractions as predictors of wastewater sludge mineralization in soil.

Seventeen different wastewater sludges were characterized using both chemical and organic matter fractionation methods (water extraction, Van Soest method, and acid hydrolysis) and 6-mo incubation studies to assess their decomposition in soil. Simple correlation and multiple factor analysis (MFA) were then performed to establish relationships between composition and C and N mineralization of sludges. Carbon and N concentrations covered a wide range of values, but organic carbon (C(o)) to organic nitrogen (N(o)) ratios were relatively low (from 5 to 19). Carbon and N were mainly distributed in the most soluble fractions of the Van Soest method and in the water-insoluble fraction at 100 degrees C. Carbon mineralization varied from 180 to 661 g C kg(-1) organic C added during the 168-d incubation. The addition of sludges led to different inorganic N dynamics: from -3.3 to +120.0 g N kg(-1) sludge organic C mineralized after the 168-d incubation. Fractionation studies showed that the most discriminating method was acid hydrolysis. Carbon mineralization was linked with the proportion of sludge N and C present in the lignin-like fraction (r = -0.68 and -0.65, respectively). Significant relationships were established between N mineralization and N(o) to C(o) ratio (0.88 < r < 0.95) and the C(o) to N(o) ratio of sludges, the C to N ratio of the soluble fraction obtained by the Van Soest method, the water-soluble fraction at 100 degrees C, and the C and N present in the acid-hydrolyzable fraction. Finally, multiple factor analysis also enabled establishing a sludge typology using five clusters based on composition and mineralization characteristics.

Assessing N emissions in surface water at the national level: comparison of country-wide vs. regionalized models.

Many countries are developing models to estimate N emissions in rivers as part of national-scale water quality assessments. Generally, models are applied with national databases, while at the regional scale, more detailed databases are sometimes available. This paper discusses pros and cons of developing regionalized models versus applying countrywide models. A case study is used to support the discussion. The model used, called Nutting-N (NUTrient Transfer modelING-Nitrogen), relies on a statistical approach linking nitrogen sources and watershed land and river characteristics and aims to evaluate the risk of water bodies failing to reach quality objectives defined by national and federal policies. After calibration and evaluation at the national scale (France), the predictive quality of the model was compared with two regionalized models in a crystalline massif (Brittany, western France, 27,000 km(2)) and in a sedimentary basin (Seine, Paris basin, 78,000 km(2)), where detailed regional databases are available. The national-scale model provided robust predictions in most conditions encountered in France (efficiency=0.69). Terrestrial retention was related mainly to specific runoff, and its median value was estimated at 49% of the N surplus, whereas median river retention represented 18% of incoming N discharge. Regionalizing the model generally improved goodness-of-fit, as the root mean squared error was reduced by 6-24%. However, precision of parameter estimates degraded when too few monitoring basins were available or when variability in land and river characteristics was too low in the calibration dataset. Hence, regional-scale models should be advocated only after the trade-off between improvement of fit and degradation of parameter estimates is examined.