Prospective life cycle assessment of viticulture under climate change scenarios, application on two case studies in France.

Viticulture needs to satisfy consumers' demands for environmentally sound grape and wine production while envisaging adaptation options to diminish the impacts of projected climate change on future productivity. However, the impact of climate change and the adoption of adaptation levers on the environmental impacts of future viticulture have not been assessed. This study evaluates the environmental performance of grape production in two French vineyards, one located in the Loire Valley and another in Languedoc-Roussillon, under two climate change scenarios. First, the effect of climate-induced yield change on the environmental impacts of future viticulture was assessed based on grape yield and climate data sets. Second, besides the climate-induced yield change, this study accounted for the impacts of extreme weather events on grape yield and the implementation of adaptation levers based on the future probability and potential yield loss due to extreme events. The life cycle assessment (LCA) results associated with climate-induced yield change led to opposite conclusions for the two vineyards of the case study. While the carbon footprint of the vineyard from Languedoc-Roussillon is projected to increase by 29 % by the end of the century under the high emissions scenario (SSP5-8.5), the corresponding footprint is projected to decrease in the vineyard from the Loire Valley by approximately 10 %. However, when including the effect of extreme events and adaptation options, the life cycle environmental impacts of grape production are projected to drastically increase for both vineyards. For instance, under the SSP5-8.5 scenario, the carbon footprint for the vineyard of Languedoc-Roussillon is projected to increase fourfold compared to the current footprint, while it will rise threefold for the vineyard from the Loire Valley. The obtained LCA results emphasized the need to account for the impact of both climate change and extreme events on grape production under future climate change scenarios.

Introducing ground cover management in pesticide emission modeling.

Ground cover management (GCM) is an important agricultural practice used to reduce weed growth, erosion and runoff, and improve soil fertility. In the present study, an approach to account for GCM is proposed in the modeling of pesticide emissions to evaluate the environmental sustainability of agricultural practices. As a starting point, we include a cover crop compartment in the mass balance of calculating initial (within minutes after application) and secondary (including additional processes) pesticide emission fractions. The following parameters were considered: (i) cover crop occupation between the rows of main field crops, (ii) cover crop canopy density, and (iii) cover crop family. Two modalities of cover crop occupation and cover crop canopy density were tested for two crop growth stages, using scenarios without cover crops as control. From that, emission fractions and related ecotoxicity impacts were estimated for pesticides applied to tomato production in Martinique (French West Indies) and to grapevine cultivation in the Loire Valley (France). Our results demonstrate that, on average, the presence of a cover crop reduced the pesticide emission fraction reaching field soil by a factor of 3 compared with bare soil, independently of field crop and its growth stage, and cover crop occupation and density. When considering cover exported from the field, ecotoxicity impacts were reduced by approximately 65% and 90%, compared with bare soil for grapevine and tomato, respectively, regardless of the emission distribution used. Because additional processes may influence emission distributions under GCM, such as runoff, leaching, or preferential flow, further research is required to incorporate these processes consistently in our proposed GCM approach. Considering GCM in pesticide emission modeling highlights the potential of soil cover to reduce pesticide emissions to field soil and related freshwater ecotoxicity. Furthermore, the consideration of GCM as common farming practice allows the modeling of pesticide emissions in intercropping systems. Integr Environ Assess Manag 2022;18:274-288. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Life cycle assessment data of French organic agricultural products.

Environmental data on organic products are needed to assess their environmental performance. The purpose of the ACV Bio project reported here was to generate environmental data as life cycle assessment (LCA) data for a sample of French organic production systems including cropping systems (annual crops, intercrops, forages), grassland, wine grapes, cow milk, calves, beef cattle, sheep, pigs, broilers and eggs. LCA was used to estimate environmental impacts of products from these systems. Recommended uses are to characterize part of the diversity of French organic farming systems and some of their environmental impacts, identify areas for improvement, perform eco-design and sensitivity analysis, and/or make system choices in a given context. However, these data do not represent average French organic products and should not be used as such. The MEANS-InOut web application was used to generate life cycle inventories (LCI). Impact assessment was performed using SimaPro v9 software. The Environmental Footprint 2.0 characterisation method was used to generate LCA data. These data were supplemented with three LCA indicators: cumulative energy demand, land competition (CML-IA non-baseline) and biodiversity loss. Three non-LCA indicators were also calculated for certain systems: diversity of crop families (for cropping systems), agro-ecological infrastructure (for sheep) and pesticide treatment frequency index (for grapes). In total, 173 products were modelled. LCA and non-LCA data are available in the Microsoft® Excel file at Data INRAE (https://doi.org/10.15454/TTR25S). LCI data are available in the AGRIBALYSE database and can be accessed using SimaPro and openLCA software. Farmer-practice data are available on demand.