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.

Soil C and N models that integrate microbial diversity.

Industrial agriculture is yearly responsible for the loss of 55-100 Pg of historical soil carbon and 9.9 Tg of reactive nitrogen worldwide. Therefore, management practices should be adapted to preserve ecological processes and reduce inputs and environmental impacts. In particular, the management of soil organic matter (SOM) is a key factor influencing C and N cycles. Soil microorganisms play a central role in SOM dynamics. For instance, microbial diversity may explain up to 77 % of carbon mineralisation activities. However, soil microbial diversity is actually rarely taken into account in models of C and N dynamics. Here, we review the influence of microbial diversity on C and N dynamics, and the integration of microbial diversity in soil C and N models. We found that a gain of microbial richness and evenness enhances soil C and N dynamics on the average, though the improvement of C and N dynamics depends on the composition of microbial community. We reviewed 50 models integrating soil microbial diversity. More than 90 % of models integrate microbial diversity with discrete compartments representing conceptual functional groups (64 %) or identified taxonomic groups interacting in a food web (28 %). Half of the models have not been tested against an empirical dataset while the other half mainly consider fixed parameters. This is due to the difficulty to link taxonomic and functional diversity.

Tillage intensity and pasture in rotation effectively shape soil microbial communities at a landscape scale.

Soil microorganisms are essential to agroecosystem functioning and services. Yet, we still lack information on which farming practices can effectively shape the soil microbial communities. The aim of this study was to identify the farming practices, which are most effective at positively or negatively modifying bacterial and fungal diversity while considering the soil environmental variation at a landscape scale. A long-term research study catchment (12 km2 ) representative of intensive mixed farming (livestock and crop) in Western Europe was investigated using a regular grid for soil sampling (n = 186). Farming systems on this landscape scale were described in terms of crop rotation, use of fertilizer, soil tillage, pesticides treatments, and liming. Molecular microbial biomass was estimated by soil DNA recovery. Bacterial and fungal communities were analyzed by 16S and 18S rRNA gene pyrosequencing. Microbial biomass was significantly stimulated by the presence of pasture during the crop rotation since temporary and permanent pastures, as compared to annual crops, increased the soil microbial biomass by +23% and +93% respectively. While soil properties (mainly pH) explained much of the variation in bacterial diversity, soil tillage seemed to be the most influential among the farming practices. A 2.4% increase in bacterial richness was observed along our gradient of soil tillage intensity. In contrast, farming practices were the predominant drivers of fungal diversity, which was mainly determined by the presence of pastures during the crop rotation. Compared to annual crops, temporary and permanent pastures increased soil fungal richness by +10% and +14.5%, respectively. Altogether, our landscape-scale investigation allows the identification of farming practices that can effectively shape the soil microbial abundance and diversity, with the goal to improve agricultural soil management and soil ecological integrity.

Microbial Diversity Indexes Can Explain Soil Carbon Dynamics as a Function of Carbon Source.

Mathematical models do not explicitly represent the influence of soil microbial diversity on soil organic carbon (SOC) dynamics despite recent evidence of relationships between them. The objective of the present study was to statistically investigate relationships between bacterial and fungal diversity indexes (richness, evenness, Shannon index, inverse Simpson index) and decomposition of different pools of soil organic carbon by measuring dynamics of CO2 emissions under controlled conditions. To this end, 20 soils from two different land uses (cropland and grassland) were incubated with or without incorporation of 13C-labelled wheat-straw residue. 13C-labelling allowed us to study residue mineralisation, basal respiration and the priming effect independently. An innovative data-mining approach was applied, based on generalized additive models and a predictive criterion. Results showed that microbial diversity indexes can be good covariates to integrate in SOC dynamics models, depending on the C source and the processes considered (native soil organic carbon vs. fresh wheat residue). Specifically, microbial diversity indexes were good candidates to help explain mineralisation of native soil organic carbon, while priming effect processes seemed to be explained much more by microbial composition, and no microbial diversity indexes were found associated with residue mineralisation. Investigation of relationships between diversity and mineralisation showed that higher diversity, as measured by the microbial diversity indexes, seemed to be related to decreased CO2 emissions in the control soil. We suggest that this relationship can be explained by an increase in carbon yield assimilation as microbial diversity increases. Thus, the parameter for carbon yield assimilation in mathematical models could be calculated as a function of microbial diversity indexes. Nonetheless, given limitations of the methods used, these observations should be considered with caution and confirmed with more experimental studies. Overall, along with other studies on relationships between microbial community composition and SOM dynamics, this study suggests that overall measures of microbial diversity may constitute relevant ways to include microbial diversity in models of SOM dynamics.

Hydrochemical buffer assessment in agricultural landscapes: from local to catchment scale.

Non-point-source pollution of surface and groundwater is a prominent environmental issue in rural catchments, with major consequences on water supply and aquatic ecosystem quality. Among surface-water protection measures, environmental or landscape management policies support the implementation and the management of buffer zones. Although a great number of studies have focused on buffer zones, quantification of the buffer effect is still a recurring question. The purpose of this article is a critical review of the assessment of buffer-zone functioning. Our objective is to provide land planners and managers with a set of variables to assess the limits and possibilities for quantifying buffer impact at the catchment scale. We first consider the scale of the local landscape feature. The most commonly used empirical method for assessing buffers is to calculate water/nutrient budgets from inflow-outflow monitoring at the level of landscape structures. We show that several other parameters apart from mean depletion of flux can be used to describe buffer functions. Such parameters include variability, with major implication for water management. We develop a theoretical framework to clarify the assessment of the buffer effect and propose a systematic analysis taking account of temporal variability. Second, we review the current assessment of buffer effects at the catchment scale according to the theoretical framework established at the local scale. Finally, we stress the limits of direct empirical assessment at the catchment scale and, in particular, we emphasize the hierarchy in hydrological processes involved at the catchment scale: The landscape feature function is constrained by other factors (climate and geology) that are of importance at a broader spatial and temporal scale.