- 1IRSTEA,
Environment, Territory and Infrastructure Research Unit (ETBX), 50
Avenue de Verdun, Gazinet, F-33612, Cestas, France.
francoise.vernier@irstea.fr.
- 2IRSTEA, Environment, Territory and Infrastructure Research Unit (ETBX), 50 Avenue de Verdun, Gazinet, F-33612, Cestas, France.
- 3Chambre
régionale d'agriculture Aquitaine-Limousin-Poitou-Charentes,
Agropole-2133 route de Chauvigny-CS, 45002-86550, Mignaloux-Beauvoir,
France.
- 4Environment, Remote Sensing and Spatial
Information, UMR TETIS (Joint Research Unit AgroParisTech-Irstea-Cirad)
Land, 361 rue Jean-François Breton, F-34196, Montpellier, France.
Abstract
Non-point
source pollution is a cause of major concern within the European Union.
This is reflected in increasing public and political focus on a more
sustainable use of pesticides, as well as a reduction in diffuse
pollution. Climate change will likely to lead to an even more intensive
use of pesticides in the future, affecting agriculture in many ways. At
the same time, the Water Framework Directive (WFD) and associated EU
policies called for a "good" ecological and chemical status to be
achieved for water bodies by the end of 2015, currently delayed to
2021-2027 due to a lack of efficiency in policies and timescale of
resilience for hydrosystems, especially groundwater systems. Water
managers need appropriate and user-friendly tools to design
agro-environmental policies. These tools should help them to evaluate
the potential impacts of mitigation measures on water resources, more
clearly define protected areas, and more efficiently distribute
financial incentives to farmers who agree to implement alternative
practices. At present, a number of reports point out that water managers
do not use appropriate information from monitoring or models to make
decisions and set environmental action plans. In this paper, we propose
an integrated and collaborative approach to analyzing changes in land
use, farming systems, and practices and to assess their effects on
agricultural pressure and pesticide transfers to waters. The integrated
modeling of agricultural scenario (IMAS) framework draws on a range of
data and expert knowledge available within areas where a pesticide
action plan can be defined to restore the water quality, French
"Grenelle law" catchment areas, French Water Development and Management
Plan areas, etc. A so-called "reference scenario" represents the actual
soil occupation and pesticide-spraying practices used in both
conventional and organic farming.
A number of alternative scenarios are then defined in cooperation with
stakeholders, including socio-economic conditions for developing
alternative agricultural systems or targeting mitigation measures. Our
integrated assessment of these scenarios combines the calculation of
spatialized environmental indicators with integrated bio-economic
modeling. The latter is achieved by a combined use of Soil and Water
Assessment Tool (SWAT) modeling with our own purpose-built land use
generator module (Generator of Land Use version 2 (GenLU2)) and an
economic model developed using General Algebraic Modeling System (GAMS)
for cost-effectiveness assessment. This integrated approach is applied
to two embedded catchment areas (total area of 360,000 ha) within the
Charente river basin (SW France). Our results show that it is possible
to differentiate scenarios based on their effectiveness, represented by
either evolution of pressure (agro-environmental indicators) or
transport into waters (pesticide concentrations). By analyzing the
implementation costs borne by farmers, it is possible to identify the
most cost-effective scenarios at sub-basin and other aggregated levels
(WFD hydrological entities, sensitive areas). Relevant results and
indicators are fed into a specifically designed database. Data
warehousing is used to provide analyses and outputs at all thematic,
temporal, or spatial aggregated levels, defined by the stakeholders
(type of crops, herbicides, WFD areas, years), using Spatial On-Line
Analytical Processing (SOLAP) tools. The aim of this approach is to
allow public policy makers to make more informed and reasoned decisions
when managing sensitive areas and/or implementing mitigation measures.
KEYWORDS:
Agriculture; Data warehousing; Indicators; Integrated modeling; Pesticides; Scenarios; Water management
