Search

Your search keyword '"Jordy Salmon-Monviola"' showing total 31 results
Start Over Author "Jordy Salmon-Monviola"
31 results on '"Jordy Salmon-Monviola"'

1. Extracting Soil Water Holding Capacity Parameters of a Distributed Agro-Hydrological Model from High Resolution Optical Satellite Observations Series

Author

Sylvain Ferrant, Vincent Bustillo, Enguerrand Burel, Jordy Salmon-Monviola, Martin Claverie, Nathalie Jarosz, Tiangang Yin, Vincent Rivalland, Gérard Dedieu, Valerie Demarez, Eric Ceschia, Anne Probst, Ahmad Al-Bitar, Yann Kerr, Jean-Luc Probst, Patrick Durand, and Simon Gascoin

Subjects
nitrate contamination, nitrogen excess, agro-hydrological model, spatial calibration, soil water holding capacity, leaf area index, Formosat-2 time series, Sentinel-2 type time series, Science
Abstract

Sentinel-2 (S2) earth observation satellite mission, launched in 2015, is foreseen to promote within-field decisions in Precision Agriculture (PA) for both: (1) optimizing crop production; and (2) regulating environmental impacts. In this second scope, a set of Leaf Area Index (LAI) derived from S2 type time-series (2006–2010, using Formosat-2 satellite) is used to spatially constrain the within-field crop growth and the related nitrogen contamination of surface water simulated at a small experimental catchment scale with the distributed agro-hydrological model Topography Nitrogen Transfer and Transformation (TNT2). The Soil Water Holding Capacity (SWHC), represented by two parameters, soil depth and retention porosity, is used to fit the yearly maximum of LAI (LAX) at each pixel of the satellite image. Possible combinations of soil parameters, defining 154 realistic SWHC found on the study site are used to force spatially homogeneous SWHC. LAX simulated at the pixel level for the 154 SWHC, for each of the five years of the study period, are recorded and hereafter referred to as synthetic LAX. Optimal SWHCyear_I,pixel_j, corresponding to minimal difference between observed and synthetic LAXyear_I,pixel_j, is selected for each pixel, independent of the value at neighboring pixels. Each re-estimated soil maps are used to re-simulate LAXyear_I. Results show that simulated and synthetic LAXyear_I,allpixels obtained from SWHCyear_I,allpixels are close and accurately fit the observed LAXyear_I,allpixels (RMSE = 0.05 m2/m2 to 0.2 and R2 = 0.99 to 0.94), except for the year 2008 (RMSE = 0.8 m2/m2 and R2 = 0.8). These results show that optimal SWHC can be derived from remote sensing series for one year. Unique SWHC solutions for each pixel that limit the LAX error for the five years to less than 0.2 m2/m2 are found for only 10% of the pixels. Selection of unique soil parameters using multi-year LAX and neighborhood solution is expected to deliver more robust soil parameters solutions and need to be assessed further. The use of optical remote sensing series is then a promising calibration step to represent crop growth within crop field at catchment level. Nevertheless, this study discusses the model and data improvements that are needed to get realistic spatial representation of agro-hydrological processes simulated within catchments.

Published
2016
Full Text
View/download PDF

4. A French hydrologist’s research for sustainable agriculture

Author

Rémi Dupas, Ophélie Fovet, Alice H. Aubert, Alain Crave, Jordy Salmon-Monviola, Jérôme Molénat, Sol Agro et hydrosystème Spatialisation (SAS), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Rennes Angers, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Swiss Federal Insitute of Aquatic Science and Technology [Dübendorf] (EAWAG), Géosciences Rennes (GR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'étude des Interactions Sol - Agrosystème - Hydrosystème (UMR LISAH), and Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier

Subjects
Water quality, Climate, [SDE]Environmental Sciences, Agriculture, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, Catchment, Groundwater, Modelling, Water Science and Technology
Abstract

International audience; Here we highlight the career contributions of Chantal Gascuel-Odoux in hydrology, soil science and agronomy. These contributions are divided into four main categories: i) Influence of soil and subsoil properties on hydrological processes; ii) Water, solute and particulate transfer at the catchment scale; iii) Design of integrated approaches to landscapes and territorial management; iv) Contributions to public policies and public debates. We conclude by emphasising Chantal’s human qualities, which we particularly appreciate as former mentees.

Published
2023

7. Reduction of stream nitrate concentrations by land management in contrasted landscapes

Author

Laurène Casal, Nouraya Akkal-Corfini, Jordy Salmon-Monviola, Anne Probst, Patrick Durand, Cyril Benhamou, Françoise Vertès, Sylvain Ferrant, François Laurent, Jean-Luc Probst, AgroParisTech (FRANCE), Centre National d'Études Spatiales - CNES (FRANCE), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Institut National de la Recherche Agronomique - INRA (FRANCE), Institut de Recherche pour le Développement - IRD (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), ARVALIS - Institut du végétal (FRANCE), Centre d'Etudes Spatiales de la Biosphère - CESBIO (Toulouse, France), Sol Agro et hydrosystème Spatialisation (SAS), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, ARVALIS - Institut du végétal [Paris], Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Ecologie Fonctionnelle et Environnement (ECOLAB), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), French National Research Agency (ESCAPADE project in AGROBIOSPHERE program) : ANR-12-AGRO-0003 Agence de l'Eau Adour Garonne (BAG'AGES project, MOSAIC project of the AGROBIOSPHERE program : ANR-12-AGRO-0005, Arvalis, AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées, and Institut National Polytechnique de Toulouse - INPT (FRANCE)

Subjects
Riparian zone, Riparian buffer, [SDE.MCG]Environmental Sciences/Global Changes, Biodiversité et Ecologie, Land management, Soil Science, Context (language use), [SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil study, 010501 environmental sciences, Best management practice, Catchment, 01 natural sciences, Mitigation scenario, chemistry.chemical_compound, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, Nitrate, [SDU.STU.GC]Sciences of the Universe [physics]/Earth Sciences/Geochemistry, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, Nitrogen cycle, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, 2. Zero hunger, Hydrology, geography, geography.geographical_feature_category, [SDE.IE]Environmental Sciences/Environmental Engineering, 04 agricultural and veterinary sciences, 15. Life on land, 6. Clean water, Water Framework Directive, chemistry, 13. Climate action, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Agronomy and Crop Science, Groundwater, Distributed model, Nitrogen cycling
Abstract

International audience; Optimizing management practices at the plot scale is sometimes not sufficient to reach water framework directive objectives for nitrate pollution. Land management measures involving targeted setting aside of croplands is a promising solution, but its efficiency depends on the local context. We used a distributed agro-hydrological model to compare management interventions intended to decrease vertical and lateral nitrate leaching from soil to groundwater and stream water in two contrasted agricultural catchments. The simulated scenarios combined two strategies: optimization of agricultural practices and land-use conversion from agricultural to natural land at different locations within the catchments. Long- term climate, discharge, and nitrate concentrations have been monitored for the two catchments and agricultural practices are well known over the 13-year simulation period (2002-2015). The Kervidy-Naizin site (KN) is subject to intense livestock pressure with mean nitrogen inputs of 257 kg ha(-1) year while the Aurade site (AU) is primarily cereal cultivation with nitrogen inputs of 109 kg ha(-1) year(-1). The results highlight a large nitrogen legacy in KN, resulting in a progressive and long lived (> 10 years) response to changes in management, while in AU, this response is perceptible after only 5-7 years. For both catchments, the most effective scenario involves wide riparian buffer strips in interception position covering about 15% of the catchment area. In KN, this land conversion scenario, simulated with the agro-hydrological model TNT2, created a decrease of nitrate concentration in stream water by 25% versus 15% in AU. Contrastingly, the implementation of best management practices decreased stream nitrate concentration only by 9% for KN and 4% for AU.

Published
2019

8. Reduced phosphorus loads from the Loire and Vilaine Rivers were accompanied by increasing eutrophication in Vilaine Bay (South Brittany, France)

Author

Widya Ratmaya, Dominique Soudant, Jordy Salmon-Monviola, Nathalie Cochennec-Laureau, Evelyne Goubert, Françoise Andrieux-Loyer, Laurent Barillé, and Philippe Souchu

Abstract

The evolution of eutrophication (i.e., phytoplankton biomass) during recent decades was examined in the coastal waters of Vilaine Bay (VB, France) in relation to those in their main external nutrient sources, the Loire and Vilaine Rivers. Dynamic Linear Models, corroborated by the Mann-Kendall test, were used to study long-term trends and seasonality of dissolved inorganic nutrient and chlorophyll a concentrations (Chl a) in rivers and coastal waters between 1980 and 2013. The reduction in dissolved riverine inorganic phosphorus concentrations (DIP) from the early 1990s led to the decrease in their Chl a levels. However, dissolved inorganic nitrogen concentrations (DIN) decreased only slightly in the Vilaine and actually increased in the Loire, especially during summer. Simultaneously, phytoplankton in the VB has undergone profound changes with: (1) increase in biomass, (2) change in the position of the annual peak from spring to summer, and (3) increase in diatom:dinoflagellate ratios, especially in summer. The increase in phytoplankton biomass in VB, particularly in summer, was probably due to increased DIN loads from the Loire, sustained by internal regeneration of DIP and dissolved silicate from sediments. This long-term ecosystem-scale analysis reports the consequence of nutrient management scenarios focused solely on P reduction. Freshwater ecosystems upstream reveal successful recoveries through the control of P alone, while eutrophication continues to increase downstream, especially during the period of N limitation. Therefore, nutrient management strategies, paying particular attention to diffuse N-sources, are required to control eutrophication in receiving coastal waters.

Published
2018

10. A data warehouse to explore multidimensional simulated data from a spatially distributed agro-hydrological model to improve catchment nitrogen management

Author

Marie-Odile Cordier, Pierre Moreau, Jordy Salmon-Monviola, Tassadit Bouadi, Ren Quiniou, Chantal Gascuel-Odoux, Declarative & Reliable management of Uncertain, user-generated Interlinked Data (DRUID), GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Large Scale Collaborative Data Mining (LACODAM), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Sol Agro et hydrosystème Spatialisation (SAS), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, ANR Systerra project ACASSYA [ANR-08-STRA-01], ANR-08-STRA-0001,ACASSYA,ACcompagner l'évolution Agro-écologique deS SYstèmes d'élevAge dans les bassins versants côtiers(2008), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), AGROCAMPUS OUEST, and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)

Subjects
Environmental Engineering, Computer science, Nitrogen, Interface (computing), 010501 environmental sciences, Space (commercial competition), USable, computer.software_genre, 01 natural sciences, Catchment, Task (project management), Data warehouse, Dimension (data warehouse), Multidimensional modeling, 0105 earth and related environmental sciences, 2. Zero hunger, OLAP, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Ecological Modeling, Online analytical processing, Distributed agro-hydrological model, 04 agricultural and veterinary sciences, 15. Life on land, Data science, [SDE.ES]Environmental Sciences/Environmental and Society, Water quality, 13. Climate action, 040103 agronomy & agriculture, Simulation data, 0401 agriculture, forestry, and fisheries, Data mining, Cropping, computer, Software
Abstract

Spatially distributed agro-hydrological models allow researchers and stakeholders to represent, understand and formulate hypotheses about the functioning of agro-environmental systems and to predict their evolution. These models have guided agricultural management by simulating effects of landscape structure, farming system changes and their spatial arrangement on stream water quality. Such models generate many intermediate results that should be managed, analyzed and transformed into usable information. We describe a data warehouse (N-Catch) built to store and analyze simulation data from the spatially distributed agro-hydrological model TNT2. We present scientific challenges to and tools for building data warehouses and describe the three dimensions of N-Catch: space, time and an original hierarchical description of cropping systems. We show how to use OLAP to explore and extract all kinds of useful high-level information by aggregating the data along these three dimensions and how to facilitate exploration of the spatial dimension by coupling N-Catch with GIS. Such tool constitutes an efficient interface between science and society, simulation remaining a research activity, exploration of the results becoming an easy task accessible for a large audience. A data warehouse (DW) as a tool to explore simulated agro-environmental data.N-Catch as an example of a DW for analyzing N emissions across a catchment.DWs for catchment N management.

Published
2017

11. Extracting soil water holding capacity parameters of a distributed agro-hydrological model from high resolution optical satellite observations series

Author

Valérie Demarez, Sylvain Ferrant, Eric Ceschia, Anne Probst, Nathalie Jarosz, Vincent Rivalland, Enguerrand Burel, Gérard Dedieu, Simon Gascoin, Ahmad Al-Bitar, Patrick Durand, Yann Kerr, Martin Claverie, Vincent Bustillo, Jean-Luc Probst, Jordy Salmon-Monviola, Tiangang Yin, Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées, Site Auch, Institut Universitaire Technologique Paul Sabatier (IUT Paul Sabatier), Sol Agro et hydrosystème Spatialisation (SAS), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, NASA Goddard Space Flight Center (GSFC), Centre National de la Recherche Scientifique (CNRS), Ecole Nationale Supérieure Agronomique de Toulouse, Laboratoire Ecologie Fonctionnelle et Environnement (ECOLAB), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Institut National Polytechnique (Toulouse) (Toulouse INP), European Spatial Agency (ESA), research program BAG'AGES - Agence de l'Eau Adour-Garonne (AEAG), CNRS-INSU Observatoire Spatial Regional (OSR) funds, ANR ESCAPADE, ANR RUEdesSOLS, CNES (the French Space Agency), ESA, AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), École nationale supérieure agronomique de Toulouse [ENSAT], Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Mutlu Ozdogan, Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Toulouse (UT), Institut Universitaire de Technologie - Paul Sabatier (IUT Paul Sabatier), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT), École nationale supérieure agronomique de Toulouse (ENSAT), Laboratoire Ecologie Fonctionnelle et Environnement (LEFE), Université de Toulouse (UT)-Université de Toulouse (UT)-Observatoire Midi-Pyrénées (OMP), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP)

Subjects
010504 meteorology & atmospheric sciences, Mean squared error, Sentinel-2 type time series, Science, [SDV]Life Sciences [q-bio], 0208 environmental biotechnology, spatial calibration, Soil science, 02 engineering and technology, 01 natural sciences, Formosat-2 time series, Calibration, Leaf area index, soil water holding capacity, 0105 earth and related environmental sciences, Remote sensing, 2. Zero hunger, Soil map, Pixel, leaf area index, nitrate contamination, 15. Life on land, 020801 environmental engineering, nitrogen excess, agro-hydrological model, Transformation (function), General Earth and Planetary Sciences, Environmental science, Satellite, Precision agriculture
Abstract

Sentinel-2 (S2) earth observation satellite mission, launched in 2015, is foreseen to promote within-field decisions in Precision Agriculture (PA) for both: (1) optimizing crop production; and (2) regulating environmental impacts. In this second scope, a set of Leaf Area Index (LAI) derived from S2 type time-series (2006-2010, using Formosat-2 satellite) is used to spatially constrain the within-field crop growth and the related nitrogen contamination of surface water simulated at a small experimental catchment scale with the distributed agro-hydrological model Topography Nitrogen Transfer and Transformation (TNT2). The Soil Water Holding Capacity (SWHC), represented by two parameters, soil depth and retention porosity, is used to fit the yearly maximum of LAI (LAX) at each pixel of the satellite image. Possible combinations of soil parameters, defining 154 realistic SWHC found on the study site are used to force spatially homogeneous SWHC. LAX simulated at the pixel level for the 154 SWHC, for each of the five years of the study period, are recorded and hereafter referred to as synthetic LAX. Optimal SWHCyear_I,pixel_j, corresponding to minimal difference between observed and synthetic LAX(year_I,pixel_j), is selected for each pixel, independent of the value at neighboring pixels. Each re-estimated soil maps are used to re-simulate LAX(year_I). Results show that simulated and synthetic LAX(year_I,allpixels) obtained from SWHCyear_I,allpixels are close and accurately fit the observed LAX(year_I,allpixels) (RMSE = 0.05 m(2)/m(2) to 0.2 and R-2 = 0.99 to 0.94), except for the year 2008 (RMSE = 0.8 m(2)/m(2) and R-2 = 0.8). These results show that optimal SWHC can be derived from remote sensing series for one year. Unique SWHC solutions for each pixel that limit the LAX error for the five years to less than 0.2 m(2)/m(2) are found for only 10% of the pixels. Selection of unique soil parameters using multi-year LAX and neighborhood solution is expected to deliver more robust soil parameters solutions and need to be assessed further. The use of optical remote sensing series is then a promising calibration step to represent crop growth within crop field at catchment level. Nevertheless, this study discusses the model and data improvements that are needed to get realistic spatial representation of agro-hydrological processes simulated within catchments.

Published
2016

12. Uncertainty assessment of a dominant-process catchment model of dissolved phosphorus transfer

Author

Philip M. Haygarth, Keith Beven, Michael Hollaway, Patrick Durand, Rémi Dupas, Chantal Gascuel-Odoux, Jordy Salmon-Monviola, Sol Agro et hydrosystème Spatialisation (SAS), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Lancaster Environment Centre, Lancaster University, and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
lcsh:GE1-350, Hydrology, Biogeochemical cycle, Propagation of uncertainty, lcsh:T, [SDV]Life Sciences [q-bio], 0208 environmental biotechnology, lcsh:Geography. Anthropology. Recreation, Biogeochemistry, 02 engineering and technology, 15. Life on land, lcsh:Technology, lcsh:TD1-1066, 6. Clean water, 020801 environmental engineering, lcsh:G, 13. Climate action, Soil water, Environmental science, Spatial variability, lcsh:Environmental technology. Sanitary engineering, GLUE, Water content, lcsh:Environmental sciences, Groundwater
Abstract

Hydrol. Earth Syst. Sci.ISI Document Delivery No.: EF6IITimes Cited: 0Cited Reference Count: 77Dupas, Remi Salmon-Monviola, Jordy Beven, Keith J. Durand, Patrick Haygarth, Philip M. Hollaway, Michael J. Gascuel-Odoux, ChantalAgence de l'Eau Loire Bretagne via the "Trans-P project"; ORE AgrHySThis work was funded by the "Agence de l'Eau Loire Bretagne" via the "Trans-P project". Long-term monitoring in the Kervidy-Naizin catchment is supported by "ORE AgrHyS".Copernicus gesellschaft mbhGottingen; We developed a parsimonious topography-based hydrologic model coupled with a soil biogeochemistry sub-model in order to improve understanding and prediction of soluble reactive phosphorus (SRP) transfer in agricultural headwater catchments. The model structure aims to capture the dominant hydrological and biogeochemical processes identified from multiscale observations in a research catchment (Kervidy-Naizin, 5 km(2)). Groundwater fluctuations, responsible for the connection of soil SRP production zones to the stream, were simulated with a fully distributed hydrologic model at 20m resolution. The spatial variability of the soil phosphorus content and the temporal variability of soil moisture and temperature, which had previously been identified as key controlling factors of SRP solubilization in soils, were included as part of an empirical soil biogeochemistry sub-model. The modelling approach included an analysis of the information contained in the calibration data and propagation of uncertainty in model predictions using a generalized likelihood uncertainty estimation (GLUE) "limits of acceptability" framework. Overall, the model appeared to perform well given the uncertainty in the observational data, with a Nash-Sutcliffe efficiency on daily SRP loads between 0.1 and 0.8 for acceptable models. The role of hydrological connectivity via groundwater fluctuation and the role of increased SRP solubilization following dry/hot periods were captured well. We conclude that in the absence of near-continuous monitoring, the amount of information contained in the data is limited; hence, parsimonious models are more relevant than highly parameterized models. An analysis of uncertainty in the data is recommended for model calibration in order to provide reliable predictions.

Published
2016

13. Modelling spatial dynamics of cropping systems to assess agricultural practices at the catchment scale

Author

François Oehler, Luc Sorel, Fabien Ferchaud, Jordy Salmon-Monviola, Patrick Durand, Sol Agro et hydrosystème Spatialisation (SAS), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Agrosystèmes et impacts environnementaux carbone-azote (Agro-Impact), Institut National de la Recherche Agronomique (INRA), National Institute of Water and Atmospheric Research [Auckland] (NIWA), Brittany Region ('Bretagne Eau Pure' Project), UR 1158 Agroressources et impacts environnementaux, Institut National de la Recherche Agronomique (INRA)-Agroressources et impacts environnementaux (AgroImpact)-Environnement et Agronomie (E.A.)-Biologie et Amélioration des Plantes (BAP), and National Institute of Water and Atmospheric Research [Hamilton] (NIWA)

Subjects
010504 meteorology & atmospheric sciences, Stochastic modelling, [SDV]Life Sciences [q-bio], water, Ecological succession, Agricultural engineering, Horticulture, rotation, 01 natural sciences, nitrogen, nitrogen budgets, evolution, Cropping system, Temporal scales, Cover crop, 0105 earth and related environmental sciences, 2. Zero hunger, algorithm, denitrification, Agroforestry, business.industry, hydrological impact, crop management system, methodology, Forestry, 04 agricultural and veterinary sciences, landscape, 15. Life on land, simulation, Field (geography), Computer Science Applications, knapsack, 13. Climate action, Agriculture, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, stochastic modelling, business, Agronomy and Crop Science, Cropping, crop succession
Abstract

CT3 ; EnjS 4 ; avec travaux de conception d'ingénierie : oui : outil pour simuler des systèmes de culture (successions de cultures et ITK) sur un bassin versant; Assessing the environmental impacts of agricultural practices at the catchment scale increasingly involves the use of spatially distributed models that include cropping systems as input. Use of these models for diagnosis and evaluation requires large datasets at large spatial and temporal scales. The description of spatial dynamics of cropping systems at a fine resolution (i.e. field level) is particularly needed. As these data are scarce or not available, our objective was to propose a model that first reconstructs spatial dynamics of past cropping systems from available data, and second constructs alternative spatial cropping systems, corresponding to agricultural practice scenarios. Classification systems for farms and fertilisation practices were defined to classify farm-level organisation for crop-succession and crop management strategies, in particular nitrogen fertilisation. Winter cover crops and multiple strategies per crop at the farm level can be represented with this model. Cropping systems are modelled in three steps: we modelled crop cover succession in summer with Markov chains based on empirical data and in winter with rules based on expert agronomic knowledge: lastly a Knapsack-based algorithm was used to allocate a crop management system to fields with constraints on several crop management strategies per crop type. Results of a case study performed on the Fremeur catchment (western France) show that this model respects the main constraints for cropping system modelling to assess agricultural practices at the catchment scale. From a scenario discussed with stakeholders and regional authorities characterised by stable areas of summer crops, the decrease in bare soil area in winter and overall reduction in total nitrogen fertilisation for different types of crops and farms we demonstrate the model's ability to simulate coherent cropping systems. Thus, if faced with a lack of data or knowledge about the local factors that drive changes in cropping systems, the model can construct the spatial dynamics of cropping systems, which can be used as input to distributed nitrate transfer models to assess agricultural practices at the catchment scale.

Published
2012

14. Simulating the effect of technical and environmental constraints on the spatio-temporal distribution of herbicide applications and stream losses

Author

Florent Tortrat, Chantal Gascuel-Odoux, Frédérick Garcia, Jordy Salmon-Monviola, Véronique Masson, Ronan Trépos, and Marie-Odile Cordier

Subjects
Agricultural catchment, Ecology, business.industry, Distribution (economics), Time distribution, Decision rule, Agricultural engineering, Working time, Agriculture, Western europe, Environmental science, Animal Science and Zoology, Water quality, business, Agronomy and Crop Science
Abstract

Technical and environmental constraints on agricultural practices can spatially and temporally concentrate or spread herbicide applications, thereby modifying herbicide losses in stream water. This study analyses the effect of such constraints on spatio-temporal patterns of sowing and weeding activities and, consequently, on herbicide losses, focusing on weeding operations in maize. Machine availability and farmer working time were considered technical constraints, while weather and soil conditions were considered environmental constraints. Simulated experiments were performed with the SACADEAU model, which combines a decision submodel with crop-growth and herbicide-transfer submodels. The decision submodel designed in conjunction with agricultural experts contains decision rules that distribute crop sowing and weeding operations among fields. The model was applied on an agricultural catchment in western Europe, and the results were analysed over nine spring periods. Simulations suggest that, in addition to reducing overall herbicide-application rates, strategies that modify spatial distribution of herbicide applications by reducing herbicide applications in bottomlands could be particularly effective. Temporally distributing herbicide applications, for example via collective machine management, also is effective. Finally, spatial strategies that focus activities on a given area appear to be more efficient than temporal ones that spread activities over time because the former are less dependent on weather conditions.

Published
2011

15. Modelling the interplay between nitrogen cycling processes and mitigation options in farming catchments

Author

Laurent Ruiz, Patrick Durand, Chantal Gascuel-Odoux, Jordy Salmon-Monviola, Françoise Vertès, Pierre Moreau, Sol Agro et hydrosystème Spatialisation (SAS), AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), ANR ACASSYA, Lannion-Tr, Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
Pollution, media_common.quotation_subject, [SDV]Life Sciences [q-bio], agricultural system, 0207 environmental engineering, hydrology, 02 engineering and technology, STREAMS, environmental impact, Grassland, riparian zone, Genetics, 020701 environmental engineering, Nitrogen cycle, hedgerow, media_common, Riparian zone, geography, geography.geographical_feature_category, model, business.industry, Ecology, Environmental resource management, Simulation modeling, Stakeholder, 04 agricultural and veterinary sciences, 15. Life on land, 6. Clean water, 13. Climate action, Agriculture, agricultural landscape, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, nitrogen pollution, Animal Science and Zoology, catchment system, business, Agronomy and Crop Science
Abstract

SUMMARYQuantitative assessment of mitigation measures for nitrogen (N) pollution requires adequate models, good knowledge of catchment functioning and a thorough understanding of agricultural systems and stakeholder constraints. The current paper analyses a set of results from simulations, with two models, of agricultural changes in two catchments in different contexts with different constraints. The results show that reducing N inputs and increasing grassland areas are the most efficient measures, not only because they reduce N fluxes in streams but also because they enhance N use by agriculture and the whole catchment system. Introducing catch crops, hedgerows and riparian buffers are interesting complementary measures but of limited impact when implemented alone. These results are sensitive to the way mitigation measures are translated into model inputs, and their operational implications are discussed.

Published
2015

16. Improved Environmental Life Cycle Assessment of Crop Production at the Catchment Scale via a Process-Based Nitrogen Simulation Model

Author

Jordy Salmon-Monviola, Wenjie Liao, Hayo Van der Werf, UMR SAS INRAE, Sol Agro et hydrosystème Spatialisation (SAS), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), European Project: 267196,EC:FP7:PEOPLE,FP7-PEOPLE-2010-COFUND,AGREENSKILLS(2012), and AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA)

Subjects
Crops, Agricultural, Biogeochemical cycle, Process (engineering), Nitrogen, [SDV]Life Sciences [q-bio], chemistry.chemical_element, n2o emissions, engineering.material, Soil, nitrate, Environmental Chemistry, Computer Simulation, Fertilizers, uncertainty, Life-cycle assessment, climate, 2. Zero hunger, Nitrates, denitrification, business.industry, Crop yield, Simulation modeling, Environmental engineering, Agriculture, General Chemistry, dynamics, Models, Theoretical, fertilizer, 6. Clean water, Crop Production, biofuels, chemistry, 13. Climate action, network, engineering, impact, Environmental science, Fertilizer, France, business
Abstract

Environ. Sci. Technol.ISI Document Delivery No.: CR5WTTimes Cited: 0Cited Reference Count: 50Liao, Wenjie van der Werf, Hayo M. G. Salmon-Monviola, JordyEuropean Union Marie-Curie FP7 COFUND People Program through AgreenSkills fellowship [267196]W.L. received support from the European Union Marie-Curie FP7 COFUND People Program through the award of an AgreenSkills fellowship (grant agreement no. 267196).Amer chemical socWashington; One of the major challenges in environmental life cycle assessment (LCA) of crop production is the nonlinearity between nitrogen (N) fertilizer inputs and onsite N emissions resulting from complex biogeochemical processes. A few studies have addressed this nonlinearity by combining process-based N simulation models with LCA, but none accounted for nitrate (NO3-) flows across fields. In this study, we present a new method, TNT2-LCA, that couples the topography-based simulation of nitrogen transfer and transformation (TNT2) model with LCA, and compare the new method with a current LCA method based on a French life cycle inventory database. Application of the two methods to a case study of crop production in a catchment in France showed that, compared to the current method, TNT2-LCA allows delineation of more appropriate temporal limits when developing data for on-site N emissions associated with specific crops in this catchment. It also improves estimates of NO3- emissions by better consideration of agricultural practices, soil-climatic conditions, and spatial interactions of NO3- flows across fields, and by providing predicted crop yield. The new method presented in this study provides improved LCA of crop production at the catchment scale.

Published
2015

17. Contrasting the spatial management of nitrogen and phosphorus for improved water quality: Modelling studies in New Zealand and France

Author

Philippe Leterme, Richard W. McDowell, Pierre Moreau, Philippe Merot, Patrick Durand, Jordy Salmon-Monviola, Invermay Agricultural Centre, AgResearch Ltd, Sol Agro et hydrosystème Spatialisation (SAS), AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), New Zealand Ministry for Business, Innovation and Employment under the Clean Water, Productive Land programme [C10X1006], French National Research Agency (ANR) through the ACASSYA project [ANR-08-STRA-01], Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
Critical source area(s), Cost effectiveness, [SDV]Life Sciences [q-bio], Drainage basin, Soil Science, chemistry.chemical_element, Plant Science, Grassland, Mitigation strategy, 2. Zero hunger, Hydrology, geography, geography.geographical_feature_category, Land use, Phosphorus, Water quality modelling, 15. Life on land, Nitrogen, 6. Clean water, Agronomy, chemistry, Environmental science, Cost-effectiveness, Water quality, Agronomy and Crop Science, Buffer
Abstract

Critical source areas (CSAs) define areas of a farm or catchment that emit the majority of water quality contaminants but account for a minority of the area at a field, farm or catchment scale. Using process based modelling we tested the hypothesis that the definition and management of CSAs would decrease losses of phosphorus (P) in two New Zealand catchments and nitrogen (N) in a French catchment. In the New Zealand catchment, CSAs of P loss were isolated to small areas within fields commensurate with surface flow pathways, while in the French catchment, CSAs for N loss were influenced by factors (inputs and land use) relevant at a field (or multiple field) scale. Scenarios were tested that examined the management of CSAs versus whole field management for P, and decreasing N loss within CSAs by increasing the proportion of grassland fields and changing their location relative to cropland. The results showed that N losses decreased by up to 25% as more grassland was incorporated into the catchment, especially in wet areas near valley bottoms due to a longer growth period and better utilisation/storage of N than cropland. For P, focusing mitigation on CSAs decreased catchment losses to a similar degree as mitigations applied across the whole catchment, but was on average 6–7 times more cost-effective. Therefore, the definition and management of CSAs at an appropriate scale is recommended to improve water quality and minimise the impact on farm profitability.

Published
2014

18. Agro-hydrology and multi-temporal high-resolution remote sensing: toward an explicit spatial processes calibration

Author

Eric Ceschia, Valérie Demarez, Amanda Veloso, Vincent Bustillo, G. Dedieu, Vincent Rivalland, Simon Gascoin, Martin Claverie, Jean-Luc Probst, Jordy Salmon-Monviola, Patrick Durand, Sylvain Ferrant, Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Sol Agro et hydrosystème Spatialisation (SAS), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Department of Geographical Sciences, University of Maryland [College Park], University of Maryland System-University of Maryland System, NASA Goddard Space Flight Center (GSFC), Laboratoire Ecologie Fonctionnelle et Environnement (ECOLAB), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées, CNES (Centre National d'Etudes Spatiales), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire Ecologie Fonctionnelle et Environnement (LEFE), Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT), Ferrant, Sylvain, Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), Agrocampus Ouest (FRANCE), Centre National d'Études Spatiales - CNES (FRANCE), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Institut National de la Recherche Agronomique - INRA (FRANCE), Institut de Recherche pour le Développement - IRD (FRANCE), National Aeronautics and Space Administration - NASA (USA), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), University of Maryland (USA), and Laboratoire Ecologie fonctionnelle et Environnement - EcoLab (Toulouse, France)

Subjects
[SDV]Life Sciences [q-bio], lcsh:Technology, lcsh:TD1-1066, Sciences de la Terre, Modelling, Hydrology (agriculture), TNT2, Leaf area index, Logistic function, lcsh:Environmental technology. Sanitary engineering, lcsh:Environmental sciences, Hydrologie, Remote sensing, 2. Zero hunger, Hydrology, lcsh:GE1-350, Discharge, lcsh:T, lcsh:Geography. Anthropology. Recreation, 15. Life on land, Field (geography), Agro-hydrologye, LAI, lcsh:G, 13. Climate action, Temporal resolution, Environmental science, Satellite, Seeding, Sentinel-2
Abstract

The growing availability of high-resolution satellite image series offers new opportunities in agro-hydrological research and modeling. We investigated the possibilities offered for improving crop-growth dynamic simulation with the distributed agro-hydrological model: topography-based nitrogen transfer and transformation (TNT2). We used a leaf area index (LAI) map series derived from 105 Formosat-2 (F2) images covering the period 2006–2010. The TNT2 model (Beaujouan et al., 2002), calibrated against discharge and in-stream nitrate fluxes for the period 1985–2001, was tested on the 2005–2010 data set (climate, land use, agricultural practices, and discharge and nitrate fluxes at the outlet). Data from the first year (2005) were used to initialize the hydrological model. A priori agricultural practices obtained from an extensive field survey, such as seeding date, crop cultivar, and amount of fertilizer, were used as input variables. Continuous values of LAI as a function of cumulative daily temperature were obtained at the crop-field level by fitting a double logistic equation against discrete satellite-derived LAI. Model predictions of LAI dynamics using the a priori input parameters displayed temporal shifts from those observed LAI profiles that are irregularly distributed in space (between field crops) and time (between years). By resetting the seeding date at the crop-field level, we have developed an optimization method designed to efficiently minimize this temporal shift and better fit the crop growth against both the spatial observations and crop production. This optimization of simulated LAI has a negligible impact on water budgets at the catchment scale (1 mm yr−1 on average) but a noticeable impact on in-stream nitrogen fluxes (around 12%), which is of interest when considering nitrate stream contamination issues and the objectives of TNT2 modeling. This study demonstrates the potential contribution of the forthcoming high spatial and temporal resolution products from the Sentinel-2 satellite mission for improving agro-hydrological modeling by constraining the spatial representation of crop productivity.

Published
2014

19. CASIMOD’N: An agro-hydrological distributed model of catchment-scale nitrogen dynamics integrating farming system decisions

Author

Patrick Durand, Laurent Ruiz, Luc Delaby, Christine Baratte, Pierre Moreau, Philippe Faverdin, Chantal Gascuel-Odoux, Benoit Piquemal, Jordy Salmon-Monviola, Françoise Vertès, Eric Ramat, Sol Agro et hydrosystème Spatialisation (SAS), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), UPRES EA 4029, Université du Littoral Côte d'Opale (ULCO), ANR ACASSYA, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Laboratoire d'Informatique Signal et Image de la Côte d'Opale (LISIC)

Subjects
Integrated farming, Nitrogen, [SDV]Life Sciences [q-bio], Ecological farming, Diffuse pollution, Agricultural engineering, 010501 environmental sciences, 01 natural sciences, Farming system, 0105 earth and related environmental sciences, 2. Zero hunger, Land use, Agroforestry, business.industry, Distributed element model, 04 agricultural and veterinary sciences, 15. Life on land, Manure, Agriculture, 8. Economic growth, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Systems design, Animal Science and Zoology, Scale (map), business, Agro-hydrological modelling, Agronomy and Crop Science
Abstract

This work presents the new integrative model CASIMOD’N (Catchment and Agricultural Systems Integrated MODel for Nitrogen), which assesses effects of farming systems on nitrogen (N) dynamics at the catchment level. Its main innovation is the consideration of the level of the farming system through production strategies, farmer decisions and the expression of decisions as management practices, along with the link between these farming systems, their practices and water pollution. CASIMOD’N integrates farming systems at the farm level and N transfers and transformations at the field, farm and catchment levels. It was built by adapting and combining three models: the catchment-scale biophysical model TNT2 and two farm-scale models, TOURNESOL and FUMIGENE, for the allocation of land use and manure, respectively. The intrinsic logic behind farming system design and function was represented by ensuring agreement between livestock-feeding and manure-management strategies under specific farm constraints (land fragmentation, distance between fields and farmyards) and agronomic rules. The model is able to simulate management practices (crop, manure and mineral fertiliser allocation). An assessment of the farming system modelling was performed by comparing the management practices simulated with CASIMOD’N with (i) observed data from a livestock-oriented catchment and (ii) a reference dataset of management practices reconstructed with a Markov chain and Knapsack-based algorithm. Then, the spatial distributions of the main N fluxes at the sub-catchment scale simulated with CASIMOD’N and based on the reconstructed management practices are discussed. Simulations of the two options had few differences in spatial distribution of the main N compartments, organic and mineral fertilisation and N flux at the outlets both at catchment and sub-catchment levels. However, CASIMOD’N was more accurate for simulating farming systems than the reconstructed reference dataset of management practices. This suggests that CASIMOD’N can be used to conceive, implement and assess prospective scenarios involving farming system redesign.

Published
2013

20. An approach for global sensitivity analysis of a complex environmental model to spatial inputs and parameters: A case study of an agro-hydrological model

Author

Pierre Moreau, Patrick Durand, Virginie Parnaudeau, Jordy Salmon-Monviola, Valérie Viaud, Sol Agro et hydrosystème Spatialisation (SAS), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), French National Research Agency (ACASSYA program) [ANR-STRA-08-01-01], and AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA)

Subjects
Environmental Engineering, 010504 meteorology & atmospheric sciences, Computer science, [SDV]Life Sciences [q-bio], 0207 environmental engineering, 02 engineering and technology, Spatial distribution, computer.software_genre, 01 natural sciences, GeneralLiterature_MISCELLANEOUS, Spatial sensitivity analysis, Econometrics, Morris method, Sensitivity (control systems), 020701 environmental engineering, 0105 earth and related environmental sciences, Elementary effects method, Ecological Modeling, Sampling (statistics), Fractional factorial design, 15. Life on land, Field (geography), Water quality, Agro-hydrological distributed model, Spatial ecology, Data mining, computer, Software
Abstract

A methodology is presented to assess the sensitivity of a complex model involved in integrated assessment and modeling approaches to spatial factors. The application considers the spatially-distributed agro-hydrological model TNT2, and its sensitivity to soil characteristics and their spatial distribution (soil pattern). The final goal is to identify soil input data that require more accurate description (measurement) and the relevant spatial resolution for soil information. Based on methods commonly used for non-spatially-distributed models (Morris method and a fractional factorial design with ANOVA), the proposed approach is innovative in the way that spatial input factors are considered in the global sensitivity analysis. The global sensitivity analysis is performed in three steps (i) screening among soil input data to identify those that most affect model outputs, (ii) quantifying the sensitivity of TNT2 to the dominant soil input factors and their interactions when considering a single soil and (iii) incorporating the soil pattern into the global sensitivity as an explicit input factor. The results indicate differences in the hierarchy of influential input factors between the screening and quantitative methods. The model's low sensitivity to spatial patterns provides recommendations for further field sampling campaigns. The hierarchical approach developed in this paper is based on sensitivity analysis methods with relatively low computational demand. The approach is generic and applicable to any complex spatial model. A methodology is applied to analyze the sensitivity of a model to spatial inputs.Influence of the spatial distribution of soil properties on model outputs is discussed.Results provide insights for further field sampling campaigns and model development.

Published
2013

21. Effect of climate change and increased atmospheric CO2 on hydrological and nitrogen cycling in an intensive agricultural headwater catchment in western France

Author

Pierre Moreau, Patrick Durand, François Oehler, Chantal Gascuel-Odoux, Philippe Merot, Cyril Benhamou, Jordy Salmon-Monviola, Sol Agro et hydrosystème Spatialisation (SAS), AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
2. Zero hunger, Hydrology, Atmospheric Science, Global and Planetary Change, geography, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], Global warming, Climate change, Wetland, Groundwater recharge, 15. Life on land, 010501 environmental sciences, 01 natural sciences, 6. Clean water, Water balance, 13. Climate action, Evapotranspiration, Environmental science, Climate model, Nitrogen cycle, 0105 earth and related environmental sciences
Abstract

Climate change and increased atmospheric CO2 concentration can impact hydrological and nitrogen cycling at the catchment scale. The objective of this study is to assess these impacts in an intensive agricultural headwater catchment in western France. A calibrated and validated agro-hydrological model was driven by output of the climate model ARPEGE under the A1B emission scenario over 30-year simulation periods. Our study indicated that with climate warming and increased atmospheric CO2, the main trends in water balance were a decrease in annual actual evapotranspiration (AET), a decrease in annual discharge and wetland extent, and a decrease in spring and summer of groundwater recharge and soil-water content. Not considering the effects of increased atmospheric CO2 in the agro-hydrological model led to overestimating discharge decrease and underestimating AET decrease and wetland extent. Climate change could influence N cycling by increasing soil N mineralisation, increasing soil denitrification in wetlands and upstream areas, and decreasing NO3–N load to streams. Since wetlands appear to be sensitive to climate change, improving modelling to better predict their responses is an important issue, especially to help plan sustainable management of these vulnerable areas.

Published
2013

22. Modeling the interaction between fields and a surrounding hedgerow network and its impact on water and nitrogen flows of a small watershed

Author

C. Benhamou, Ph. Merot, Catherine Grimaldi, Jordy Salmon-Monviola, Patrick Durand, Sol Agro et hydrosystème Spatialisation (SAS), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA)

Subjects
Watershed, Water flow, [SDV]Life Sciences [q-bio], Soil Science, 010501 environmental sciences, 01 natural sciences, Hedge, Temperate climate, 0105 earth and related environmental sciences, Earth-Surface Processes, Water Science and Technology, Transpiration, 2. Zero hunger, Hydrology, Interaction hedge-crop, 04 agricultural and veterinary sciences, Spatially distributed model, 15. Life on land, Rural watershed, Water quality, Tree pruning, Soil water, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Agronomy and Crop Science, Pruning
Abstract

The rural landscape in Western Europe, as in many regions of the world, is structured by networks of woody hedgerows that surround agricultural fields. Although many studies at local scale have shown the strong impact of hedgerows on soil water and nitrogen balances, few have quantified the impact of hedgerows at the watershed scale. This study estimated via modeling the impact of hedgerow networks on soil water and nitrogen balances of a small watershed in a temperate climate. The spatially explicit model TNT2 (topography-based nitrogen transfer and transformation), to which a new sub-model of hedgerow functioning was added, was used to perform the study. The unique character of this sub-model is to consider "double-cover" cells (crop plus hedge) to take into account competition for light, water, and nitrogen. The effect of hedge management by branch pruning was also studied. The model was used to simulate a small experimental watershed in western Europe over 17 years with the hedgerow network that existed in 1999 (48 m ha(-1)), or without hedgerows. On average, predictions of hedge transpiration and hedge N uptake were consistent with observations from the bibliography. On double-cover cells, the hydro-chemical impact of the hedge resulted from the complex combination of processes, which can have opposite effects. At the watershed scale, the hedgerow network decreased predicted water flow at the outlet by 4.5% and nitrogen flow by 3.3%, respectively, compared to those flows when the watershed had no hedgerows. Finally, hedgerow pruning has a low effect on water and nitrogen flow at the outlet of watershed. (c) 2013 Elsevier B.V. All rights reserved.

Published
2013

23. Mining simulation data by rule induction to determine critical source areas of stream water pollution by herbicides

Author

Chantal Gascuel-Odoux, Véronique Masson, Ronan Trépos, Jordy Salmon-Monviola, Marie-Odile Cordier, Unité de Biométrie et Intelligence Artificielle (ancêtre de MIAT) (UBIA), Institut National de la Recherche Agronomique (INRA), Diagnosing, Recommending Actions and Modelling (DREAM), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Sol Agro et hydrosystème Spatialisation (SAS), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, project 'Sacadeau' set up project 'APPEAU' in the French national research agency programme 'Agriculture and sustainable development' (ADD), Unité de Biométrie et Intelligence Artificielle (UBIA), Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA), and Salmon-Monviola, Jordy

Subjects
Decision support system, 0207 environmental engineering, 02 engineering and technology, 010501 environmental sciences, Horticulture, computer.software_genre, Machine learning, Symbolic learning, Decision support, Tree structure, Modelling, Catchment, Pesticide, 01 natural sciences, Plot (graphics), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Set (abstract data type), Decision aids, 020701 environmental engineering, 0105 earth and related environmental sciences, business.industry, Rule induction, Forestry, Computer Science Applications, Tree (data structure), Inductive logic programming, Environmental science, Artificial intelligence, Data mining, business, Agronomy and Crop Science, computer
Abstract

International audience; Modelling in environmental sciences is becoming increasingly complex because ever-increasing numbers of processes are combined, thus making model-based decision aids both more relevant but more difficult to develop. Our approach focused on water quality and aimed to identify spatial tree patterns that represent surface flow and pollutant pathways from plot to plot involved in water pollution by herbicides. First, a simulation model predicted herbicide transfer rate, the proportion of applied herbicide that reaches water courses, based on the spatial and temporal distribution of weed-control activities. These predictions were used as a set of learning examples for symbolic learning techniques to induce rules based on qualitative and quantitative attributes and explain two classes of transfer rate. In this study we compared two automatic symbolic learning techniques applied to a set of simulations: (1) a relational- inductive method using the inductive logic programming (ILP) approach to induce spatial tree patterns; and (2) an attribute-value method to induce aggregated attributes of the trees. Twenty-eight and thirty-three rules were learnt by the ILP and attribute-value approaches which explained 81% and 88% of the examples, respectively. The ILP approach provided relevant indicators of plot-to-plot connectivity. The integrated attribute-value approach is simpler and quicker, but the ILP patterns are more useful for stakeholders.

Published
2012

24. Modeling the potential benefits of catch-crop introduction in fodder crop rotations in a Western Europe landscape

Author

Chantal Gascuel-Odoux, Laurent Ruiz, Thierry Raimbault, Patrick Durand, Jordy Salmon-Monviola, Véronique Masson, Françoise Vertès, Marie-Odile Cordier, Pierre Moreau, Sol Agro et hydrosystème Spatialisation (SAS), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA), Diagnosing, Recommending Actions and Modelling (DREAM), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), French National Research Agency (ANR) through the ACASSYA project [ANR-08-STRA-01], AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec, Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), and ANR-08-STRA-0001,ACASSYA,ACcompagner l'évolution Agro-écologique deS SYstèmes d'élevAge dans les bassins versants côtiers(2008)

Subjects
Crops, Agricultural, Environmental Engineering, Context (language use), STREAMS, 010501 environmental sciences, Models, Biological, 01 natural sciences, Stream water quality, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Rivers, Humans, Environmental Chemistry, Computer Simulation, Nitrogen mass balance, Waste Management and Disposal, 0105 earth and related environmental sciences, Catch-crop, 2. Zero hunger, business.industry, Agroforestry, Crop yield, Agriculture, 04 agricultural and veterinary sciences, Nitrogen Cycle, 15. Life on land, Crop rotation, Pollution, 6. Clean water, Europe, Work (electrical), 13. Climate action, Agro-hydrological distributed model, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Catch crop, Edible Grain, business, Eutrophication, Water Pollutants, Chemical
Abstract

International audience; Among possible mitigation options to reduce agricultural-borne nitrate fluxes to water bodies, introduction of catch crop before spring crops is acknowledged as a cost-efficient solution at the plot scale, but it was rarely assessed at the catchment level. This study aims to evaluate a set of catch crop implantation scenarios and their consequences in a coastal catchment prone to eutrophication. The objectives are (i) to discuss the potential benefits of catch crop introduction taking into account the limitations due to the physiographic and agricultural context of the area (ii) to propose a multicriteria classification of these scenarios as a basis for discussion with stakeholders. We used the distributed agro-hydrological model TNT2 to simulate 25 scenarios of catch crop management, differing in length of catch crop growing period, place in the crop rotation and residue management. The scenarios were classified considering the variations in main crop yields and either nitrogen fluxes in stream or the global nitrogen mass balance at the catchment level. The simulations showed that in the catchment studied, little improvement can be expected from increasing the catch crop surface. Catch crop cultivation was always beneficial to reduce nitrogen losses, but led to adverse effects on main crop yields in some cases. Among the scenarios involving additional catch crop surface, introducing catch crop between two winter cereals appeared as the most promising. The classification of scenarios depended on the chosen criteria: when considering only the reduction of nitrogen fluxes in streams, exporting catch crop residues was the most efficient while when considering the global nitrogen mass balance, soil incorporation of catch crop residues was the most beneficial. This work highlights the interest, while using integrated models, of assessing simulated scenarios with multicriteria approach to provide stakeholder with a picture as complete as possible of the consequences of prospective policies.

Published
2012

25. Understanding nitrogen transfer dynamics in a small agricultural catchment: Comparison of a distributed (TNT2) and a semi distributed (SWAT) modeling approaches

Author

Anne Probst, Jordy Salmon-Monviola, Sylvain Ferrant, Laurent Ruiz, Jean-Luc Probst, Eric Justes, Patrick Durand, José-Miguel Sánchez-Pérez, François Oehler, Philippe Dugast, Laboratoire Ecologie Fonctionnelle et Environnement (ECOLAB), Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées, National Institute of Water and Atmospheric Research [Wellington] (NIWA), Sol Agro et hydrosystème Spatialisation (SAS), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, GPN, Institut National Polytechnique (Toulouse) (Toulouse INP), Agrocampus Ouest (FRANCE), Centre National de la Recherche Scientifique - CNRS (FRANCE), GPN (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Institut National de la Recherche Agronomique - INRA (FRANCE), National Institute of Water and Atmospheric Research - NIWA (NEW ZEALAND), and Université Toulouse III - Paul Sabatier - UT3 (FRANCE)

Subjects
generic model, coastal plain, Denitrification, 010504 meteorology & atmospheric sciences, Biodiversité et Ecologie, [SDV]Life Sciences [q-bio], 0207 environmental engineering, Soil science, 02 engineering and technology, Spatial distribution, 01 natural sciences, water quality, inca, soil, [SHS]Humanities and Social Sciences, Océan, Atmosphère, scale, small catchement modeling, source pollution, [INFO]Computer Science [cs], SWAT model, 020701 environmental engineering, 0105 earth and related environmental sciences, Water Science and Technology, agriculture, Hydrology, nitrogen loss, biogeochemical model, Distributed element model, Agriculture, Mineralization (soil science), 15. Life on land, 6. Clean water, Humus, nitrate concentration, Small catchment modeling, 13. Climate action, Nitrogen losses, [SDE]Environmental Sciences, impact, Soil horizon, Environmental science, Water quality
Abstract

ORE Agrhys - Agrohydrologie; International audience; The coupling of an hydrological and a crop model is an efficient approach to study the impact of the interactions between agricultural practices and catchment physical characteristics on stream water quality. We analyzed the consequences of using different modeling approaches of the processes controlling the nitrogen (N) dynamics in a small agricultural catchment monitored for 15 years. Two agro-hydrological models were applied: the fully distributed model TNT2 and the semi-distributed SWAT model. Using the same input dataset, the calibration process aimed at reproducing the same annual water and N balance in both models, to compare the spatial and temporal variability of the main N processes. The models simulated different seasonal cycles for soil N. The main processes involved were N mineralization and denitrification. TNT2 simulated marked seasonal variations with a net increase of mineralization in autumn, after a transient immobilization phase due to the burying of the straw with low C:N ratio. SWAT predicted a steady humus mineralization with an increase when straws are buried and a decrease afterwards. Denitrification was mainly occuring in autumn in TNT2 because of the dynamics of N availability in soil and of the climatic and hydrological conditions. SWAT predicts denitrification in winter, when mineral N is available in soil layers. The spatial distribution of these two processes was different as well: less denitrification in bottom land and close to ditches in TNT2, as a result of N transfer dynamics. Both models simulate correctly global trend and inter-annual variability of N losses in small agricultural catchment when a sufficient amount data is available for calibration. However. N processes and their spatial interactions are simulated very differently, in particular soil mineralization and denitrification. The use of such tools for prediction must be considered with care, unless a proper calibration and validation of the different N processes is carried out.

Published
2011

27. Nitroscape: An integrated model of nitrogen fluxes and transformation at the landscape scale

Author

Sylvia Duretz, Jean-Louis Drouet, Patrick Durand, Hutchings, N. J., Theobald, M. R., Dragosits, U., Jordy Salmon-Monviola, Olivier Maury, Sutton, M., Pierre Cellier, Environnement et Grandes Cultures (EGC), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Sol Agro et hydrosystème Spatialisation (SAS), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Dept of Agroecology, Facullty of Agricultural Sciences, Aarhus University [Aarhus], Dept of chemistry and agricultural analysis, Universidad Politécnica de Madrid (UPM), Centre for Ecology and Hydrology, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Institut National de la Recherche Agronomique (INRA)-AgroParisTech

Subjects
[SDV]Life Sciences [q-bio], MODELE NITROSCAPE, AGRONOMIE
Abstract

absent

Published
2009

28. A decision-oriented model to evaluate the effect of land use and agricultural management on herbicide contamination in stream water

Author

Véronique Masson, Pierre Aurousseau, Patrick Durand, Chantal Gascuel-Odoux, Frédérick Garcia, Ronan Trépos, Florent Tortrat, Marie-Odile Cordier, Jordy Salmon-Monviola, Sol Agro et hydrosystème Spatialisation (SAS), AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), AGROCAMPUS OUEST, Diagnosing, Recommending Actions and Modelling (DREAM), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Unité de Biométrie et Intelligence Artificielle (UBIA), Institut National de la Recherche Agronomique (INRA), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), and Unité de Biométrie et Intelligence Artificielle (ancêtre de MIAT) (UBIA)

Subjects
Pollution, Environmental Engineering, media_common.quotation_subject, 0207 environmental engineering, Drainage basin, 02 engineering and technology, 010501 environmental sciences, 01 natural sciences, Stream water quality, surface flow, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], herbicide, decision oriented modelling, catchment, 020701 environmental engineering, Water pollution, Subsurface flow, pesticide, 0105 earth and related environmental sciences, media_common, agriculture, 2. Zero hunger, Hydrology, geography, subsurface flow, geography.geographical_feature_category, Land use, business.industry, Ecological Modeling, 15. Life on land, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 6. Clean water, 13. Climate action, Agriculture, Environmental science, Catchment area, Water quality, business, Software
Abstract

International audience; Modelling stream water pollution by herbicides in agricultural areas is a critical issue since numerous and incompletely known processes are involved. A decision-oriented model, SACADEAU-Transf, which represents water and pesticide transfer in medium-sized catchments (10~50 km2) is presented. This model aims at evaluating the effect of land use, agricultural practice and landscape on the contamination of stream water in rural catchments. The processes are represented in an easily understandable way with a moderate amount of information, producing semi-quantitative and spatialized outputs. Modelling focuses on the first few months after herbicide application when high levels of contamination are generally observed, by considering transfer through the catchment area via surface and subsurface flow. The surface flow, based on a tree plot network representation of the catchment, is controlled by soilsurface properties and saturated conditions. The subsurface flow based on Topmodel concepts is controlled by the topography. Herbicide transfer is coupled to water transfer by taking into account the main characteristics of the chemicals. The model simulates the daily water and herbicide outflow at the outlets of the farmers~ fields as well as from the catchment. Preliminary results on maize herbicide transfer are presented for an agricultural catchment with an area of 17 km2 located in north-western France. The relevance of SACADEAU-Transf model is discussed in view of the qualities required for the decision-oriented models developed for improving agro-environmental management.

Published
2009

29. Modelling denitrification at the catchment scale

Author

François Oehler, Jordy Salmon-Monviola, Zakaria Saadi, Patrick Durand, Paul Bordenave, Laboratoire Ecologie Fonctionnelle et Environnement (ECOLAB), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées, Sol Agro et hydrosystème Spatialisation (SAS), AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), AGROCAMPUS OUEST, and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA)

Subjects
Environmental Engineering, Watershed, Denitrification, 010504 meteorology & atmospheric sciences, Nitrogen, [SDV]Life Sciences [q-bio], 0207 environmental engineering, 02 engineering and technology, 01 natural sciences, modelling, cycle de l'azote, chemistry.chemical_compound, Nitrate, CATCHMENT, Water Movements, nitrogen cycle, Environmental Chemistry, TNT2, Computer Simulation, 020701 environmental engineering, Nitrogen Compounds, Waste Management and Disposal, Nitrogen cycle, 0105 earth and related environmental sciences, Riparian zone, watershed, modélisation, Hydrology, geography, geography.geographical_feature_category, HYDROLOGIE, Biogeochemistry, DENITRIFICATION, 15. Life on land, Pollution, 6. Clean water, bassin versant, chemistry, Models, Chemical, Soil water, Environmental science, Spatial variability, France, Water Pollutants, Chemical
Abstract

The objective of this work was to evaluate the importance of heterotrophic denitrification in the fate of nitrogen surpluses at the catchment scale. For that purpose we modified the denitrification module of TNT2 model and calibrated the model on a small catchment where denitrification measurements had been performed in different locations. The main interest of the TNT2 model is its ability to simulate the dynamics of the zones where soil and shallow water table interact, making it possible to spatialize the denitrification process. Daily water and nitrogen flux at the outlet were relatively well simulated (Nash of 0.85 and 0.77). In average, the model correctly simulates the denitrification measurements (R = 0.68). Nitrogen flux towards the atmosphere, at the catchment scale (4.70 g N m(-2) year(-1)), is of the same order of magnitude as the soluble N flux in the stream. The model was able to reproduce the distribution of denitrification in the riparian (mean of 9.26 g N m(-2) year(-1)) and hillslope (mean of 3.45 g N m(-2) year(-1)) domains of the catchment. The results confirm the importance of riparian denitrification, but show also that hillslope soils contribute significantly (60%) to the whole catchment denitrification. The variations of denitrification rates, and also of nitrate concentrations in stream were not very well simulated by the model, highlighting the complexity of the spatial and temporal controls of nitrogen dynamics in areas with high inputs of nitrogen fertilizers, especially under organic forms. (C) 2008 Elsevier B.V. All rights reserved.

Published
2009

30. Des modèles et des outils pour des projets de territoire

Author

Patrick Durand, Pierre Moreau, Laurent Ruiz, Jordy Salmon-Monviola, Francoise Vertès, Christine Baratte, Cyril Benhamou, Tassadit Bouadi, Marie-Odile Cordier, Samuel Corgne, Pauline Dusseux, Philippe Faverdin, Chantal Gascuel, Laurence Hubert-Moy, Virginie Parnaudeau, Eric Ramat, Thierry Raimbault, Valérie Viaud, Sol Agro et hydrosystème Spatialisation (SAS), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Elevage [Rennes] (PEGASE), AGROCAMPUS OUEST-Institut National de la Recherche Agronomique (INRA), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Declarative & Reliable management of Uncertain, user-generated Interlinked Data (DRUID), GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-CentraleSupélec, Diagnosing, Recommending Actions and Modelling (DREAM), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-Université de Rennes 1 (UR1), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-Université de Nantes (UN)-École pratique des hautes études (EPHE)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS), Université du Littoral Côte d'Opale (ULCO), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Brest (UBO)-École pratique des hautes études (EPHE)-Université de Nantes (UN)-Université d'Angers (UA)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
[SDV]Life Sciences [q-bio], Casimod'N, TNT2
Abstract

absent

31. A machine learning approach for evaluating the impact of land use and management practices on streamwater pollution by pesticides

Author

Cordier, M. O., Garcia, F., Gascuel-Odoux, C., Masson, V., Jordy Salmon-Monviola, Tortrat, F., Trepos, R., Diagnosing, Recommending Actions and Modelling (DREAM), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Unité de Biométrie et Intelligence Artificielle (ancêtre de MIAT) (UBIA), Institut National de la Recherche Agronomique (INRA), Sol Agro et hydrosystème Spatialisation (SAS), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Centre Armoricain de Recherche en Environnement (CAREN), Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-Ecole Nationale Supérieure Agronomique de Rennes-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS), Modelling and Simulation Society of Australia and New Zealand Inc. (MSSANZ). Canberra, AUS., Modelling and Simulation Society of Australia and New Zealand, Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec, Unité de Biométrie et Intelligence Artificielle (UBIA), Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Ecole Nationale Supérieure Agronomique de Rennes-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)

Subjects
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; Streamwater pollution by pesticides is a critical environmental issue in farmed catchment areas. Many important factors are involved in this pollution phenomenon, like weather, area topology, land use and crop management practices, which all influence streamwater quality. The purpose of the ongoing study presented in this paper is to evaluate the impact of land use and management practices on streamwater pollution. We use modelling, simulation and machine learning techniques for acquiring knowledge about this complex domain. Our main objective is to learn qualitative rules relating the pollution factors to the temporal distribution of the stream pesticide concentration. The study area is the farmed catchment of Fremeur (~17 km2), located in Brittany, France. Our approach relies on a simulation model, called SACADEAU, based on two main components: a transfer model and a management model. The biophysical transfer model is the core of the model. It aims at simulating on a daily basis the pesticide transfer through the catchment area from application locations on maize parcels to the river. The management model simulates farmers\\\' operations like tillage, sowing, weeding (pesticide treatments described by dates, molecules, quantities) on maize crop parcels. A climate model, which provides daily weather data such as temperature and rainfall amount and a spatial model, which distributes in space the agricultural activities according to the fields and catchment area topology, are the two other components of the SACADEAU model. Using the outputs of these three sub-models, a biophysical transfer model determines herbicide outflow, modelling transfer from application locations, through the catchment area, to the stream. This simulation model is used for generating a large number of trajectories of the catchment system, considering different weather series or spatial distributions of land use and agricultural activities. The complexity of the model inputs and outputs makes difficult interpretation of results. Qualitative trajectories, or scenarios, are thus obtained by using machine learning techniques on these simulation data. ICL, an inductive logic programming software, is used for learning a set of rules which summarizes the scenarios. The Sacadeau model is not already fully implemented and first results have been obtained with a simplified model. We were able to check the coherence and the feasibility of our approach, and to build a first view of the role of some attributes in stream-water quality. Thanks to the complete model, more specific rules declined in terms of temporal and spatial variations should be established in the future.

Catalog

Books, media, physical & digital resources
See catalog results