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1. Assessment of Agricultural Practices From Sentinel 1 and 2 Images Applied on Rice Fields to Develop a Farm Typology in the Camargue Region

Author

Laure Hossard, Kamran Irfan, Nicolas Baghdadi, Dominique Courault, Fabrice Flamain, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Innovation et Développement dans l'Agriculture et l'Alimentation (UMR Innovation), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), 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 Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and This work was supported by the French National Center of Spatial Studies (CNES project,funded by the TOSC Acomity) and by INRAE.

Subjects
Atmospheric Science, Soil salinity, 010504 meteorology & atmospheric sciences, Geophysics. Cosmic physics, [SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy, 0211 other engineering and technologies, Context (language use), 02 engineering and technology, Agricultural engineering, 01 natural sciences, Farm typology, Grassland, Crop, High resolution, Computers in Earth Sciences, TC1501-1800, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, 2. Zero hunger, geography.geographical_feature_category, Land use, QC801-809, business.industry, Sowing, Remote sensing, 15. Life on land, Classification, Ocean engineering, Agricultural practices, Agricultural practice, Geography, Agriculture, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Paddy field, Rice, business
Abstract

International audience; In the global change context, an efficient management of the available resources has become one of the most important topics particularly for the sustainable crop development. Many questions concern the evolution of the rice farming systems in Camargue in Southeastern France, which play a crucial role in controlling the soil salinity. Their surface area significantly decreased from 20 000 ha in 2010 to 14 000 ha in 2014. The arrival of the new Sentinel satellites makes it possible to evaluate these crop evolutions. The objectives of this study were to propose operational methodologies to accurately assess the surface areas of the main crops, rice, wheat, and grassland, from classifications based on multispectral data; map agricultural practices (sowing and harvest residue burning); and elaborate a farm typology based on variables computed from remote sensing data to better understand the farming strategies. Dense time series of Sentinel images acquired at high spatial resolution (10 m) were analyzed for 2016 and 2017. A satisfactory accuracy was obtained for land use classification with 88% of correctly classified fields. The accuracy obtained for the estimation of the sowing date varied according to the studied year from 8 to 12 days, and burned areas were correctly identified (80%). The farm typology allowed to cluster farms at the territory level.

Published
2020

2. Spatial and temporal diversity of service plant management strategies across vineyards in the south of France. Analysis through the Coverage Index

Author

Karim Barkaoui, Hugo Fernández-Mena, Audrey Naulleau, Aurélie Metay, Raphaël Metral, Florian Celette, Laure Hossard, Léo Garcia, Christian Gary, Hélène Frey, Agrosystèmes Biodiversifiés (UMR ABSys), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), 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 Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), Agroécologie et Environnement (AGE), Isara, Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Innovation et Développement dans l'Agriculture et l'Alimentation (UMR Innovation), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and This project was funded by INRAE, Division of Environment and Agronomy and Montpellier SupAgro, Institut Agro, and University of Montpellier.

Subjects
0106 biological sciences, F08 - Systèmes et modes de culture, Vignoble, Enquête sur exploitations agricoles, Plant Science, 01 natural sciences, Survey, Cover crop, media_common, 2. Zero hunger, F07 - Façons culturales, biology, Spontaneous vegetation, Environmental resource management, Intercropping, 04 agricultural and veterinary sciences, Vegetation, Geography, [SDE]Environmental Sciences, Service plant, media_common.quotation_subject, Soil Science, Vineyard, Culture intercalaire, Plante de couverture, Quality (business), Ecosystem, Service (business), business.industry, 15. Life on land, Vineyards, biology.organism_classification, services écosystémiques, Water resources, Agronomy, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Plante de culture, business, Agronomy and Crop Science, 010606 plant biology & botany
Abstract

International audience; Highlights:• Spontaneous service plants strategies present a high spatial and temporal diversity.• Strategies can be measured by calculating the Coverage Index (CI) for each vineyard.• Higher CI was linked to quality labels, organic crop protection and low target yields.• No link was found between CI and water and soil resources in vineyards.• Target services by winegrowers are consistent with the CI and period coverage.Abstract: 'Service plants' include spontaneous vegetation or sown species of cover crops associated with perennial crops in the rows or inter-rows with a high potential to provide ecosystem functions and services. In vineyards, service plants target specific services depending on the management strategy implemented by the winegrower, including the plant species, the surface covered, the plant growth control and destruction date. Understanding the management strategies linked to their associated target services at the regional scale is necessary to better help winegrowers, advisers and policy makers regarding an adapted use of service plants. To do this, we conducted a survey in 2016 among 334 winegrowers in Languedoc-Roussillon region in France, enquiring about their service plant management practices during the season 2014-2015. Given the diversity of the strategies of service plant management, we proposed a typology analyzing their spatial and temporal dimensions. Further, we present a Coverage Index (CI), which combines both temporal and spatial dimensions of the service plant management strategies. We conducted a multiple components analysis and clustering to create a vineyard typology and applied linear models to find correlations between the CI and specific vineyard characteristics. Three quarters of interviewed winegrowers sowed or maintained service plants in their vineyards; 41 % used a winter service plant strategy; 8.4 % a semi-permanent and 27.3 % a permanent service plant strategy. The preferred surface coverage strategy was full surface during grapevine dormancy and its reduction to half of the inter-rows after grapevine budburst. However, the diversity of surface coverage strategies during the grapevine vegetative period was remarkable. Lower water resources and specific soil characteristics were not linked to the service plant management strategies. Higher CI was associated with vineyards presenting quality labels (PDO and Organic), independent wine-making and lower target yields, showing that the added value of producing high quality wine plays an important role when implementing service plants in vineyards. Overall, our study showed: i) the popularity of spontaneous service plant strategies; ii) the spatial and temporal diversity of service plant management strategies and iii) the utility of the CI to study the implementation of service plants and to understand the motivations and constraints of their use.

Published
2021

3. Assessing the resilience of farming systems on the Saïs plain, Morocco

Author

Laure Hossard, Hatem Belhouchette, Aziz Fadlaoui, Elsa Ricote, Innovation et Développement dans l'Agriculture et l'Alimentation (UMR Innovation), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), 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 Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre Régional de la Recherche Agronomique de Meknès (INRA - Meknès), Institut National de la Recherche Agronomique (INRA), Institut Agronomique et Vétérinaire Hassan II - IAV (MOROCCO) (IAV), Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM), Agrosystèmes Biodiversifiés (UMR ABSys), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), ANR-16-ARM2-0007,SEMIARID,Sustainable and Efficient Mediterranean farming systems: Improving Agriculture Resilience through Irrigation and Diversification.(2016), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, 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 [Maroc] (INRA Maroc), Institut Agronomique et Vétérinaire Hassan II (IAV Hassan II), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, This study was carried out within the SEMI-ARID project (ARIMNET2 research program) founded by the French Agency of Research (ANR) and MESRSFC (Morocco). We thank all farmers and actors who kindly participated in this study., and European Project: 618127,EC:FP7:KBBE,FP7-ERANET-2013-RTD,ARIMNET2(2014)

Subjects
010504 meteorology & atmospheric sciences, Water scarcity, EAU DISPONIBLE, media_common.quotation_subject, Performance, APPROCHE PARTICIPATIVE, Participatory approach, [SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy, Public policy, Climate change, 010501 environmental sciences, EXPLOITATION AGRICOLE, 01 natural sciences, CLASSIFICATION, [SDV.SA.STA]Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture, Environmental planning, 0105 earth and related environmental sciences, media_common, 2. Zero hunger, Farm type, Global and Planetary Change, MAROC, business.industry, STRUCTURE AGRICOLE, Citizen journalism, SYSTEME DE PRODUCTION, RESILIENCE, Water resources, Hotspot (Wi-Fi), Geography, 13. Climate action, Agriculture, CHANGEMENT CLIMATIQUE, Scale (social sciences), Diversification, Psychological resilience, business, PENURIE
Abstract

International audience; Highlights:• A participatory approach was used to study farm resilience after a shock.• Shocks differed according to farm type, but were all related to water.• Mental models were used with local actors to design future farming systems.• Performance was assessed according to eight complementary indicators.• According to local actors, all farm types would adapt, suggesting a good resilience.Abstract:The Mediterranean region is expected to be a hotspot for climate change, making the resilience of farming systems a major challenge. Some studies have used quantitative models at the farm scale to analyze the resilience of farming systems but with little involvement of stakeholders. We used a participatory approach with local actors on the Saïs plain of Morocco to design possible future states and qualitatively assess the resilience of typical farm types (FTs) experiencing major change. Our approach combined individual interviews of farmers and local actors, mainly public, with participatory collective meetings to identify representative FTs and their performance, project their evolutions and future performance in the face of change, and evaluate their resilience. Performance, defined according to literature, interviews and meetings, included different types of capital, income, yields, markets, support of public policies, and water access. Four FTs were considered: highly irrigated horticulture (FT1), rainfed cereals (FT2), partly irrigated cereal-legumes (FT3), and mostly irrigated fruit-tree/horticulture (FT4). The primary driver for FT2 and FT3 was climate change; for FT1 and FT4, it was limiting access to water resources. Participants designed more diversified systems for all FTs in relation to those changes. Rankings of FT performance did not change between current and future states. Performance did not evolve significantly, but FT4 was seen as the most resilient and FT2 the least. These qualitative results differ somewhat from other studies mobilizing quantitative approaches, but they highlight the potential of local adaptation to limit the impacts of global change on vulnerable agriculture.

Published
2021

4. Covid-19 management by farmers and policymakers in Burkina Faso, Colombia and France: lessons for climate action

Author

Laure Hossard, Nadine Andrieu, P. Guerra, Nina Graveline, Ngonidzashe Chirinda, Patrick Dugué, Innovation et Développement dans l'Agriculture et l'Alimentation (UMR Innovation), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), 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 Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), International Center for Tropical Agriculture [Colombie] (CIAT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Département Environnements et Sociétés (Cirad-ES), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), and Mohammed VI Polytechnic University [Marocco] (UM6P)

Subjects
010504 meteorology & atmospheric sciences, Natural resource economics, F08 - Systèmes et modes de culture, Cash crop, Enquête sur exploitations agricoles, [SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy, 01 natural sciences, Greenhouse gas emission, 11. Sustainability, Climate change, 2. Zero hunger, Prise de décision, 1. No poverty, Secteur agricole, 04 agricultural and veterinary sciences, réduction des émissions, P02 - Pollution, P40 - Météorologie et climatologie, Agricultural model, Context (language use), Agricultural sector, Évaluation impact sur environnement, Production (economics), émissions de gaz à effet de serre, atténuation des effets du changement climatique, Crise économique, E10 - Économie et politique agricoles, 0105 earth and related environmental sciences, Consumption (economics), business.industry, COVID-19, [SDV.SA.AEP]Life Sciences [q-bio]/Agricultural sciences/Agriculture, economy and politics, 15. Life on land, Climate change mitigation, Action (philosophy), 13. Climate action, Agriculture, Greenhouse gas, Système d'exploitation agricole, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Animal Science and Zoology, business, Agronomy and Crop Science
Abstract

CONTEXT During crises, adaptation or recovery measures or plans at local or national scales may not necessarily address longer-term or structural problems such as climate change mitigation. OBJECTIVE This article describes farmers and policymakers' responses to mitigate the adverse effects of Covid-19 on the agricultural sector. We then assess the responses' possible effects on greenhouse gas (GHG) emissions. METHODS The study is based on surveys conducted with farmers, traders, and extension staff in Burkina Faso, Colombia, and France, and literature. We used the Cool Farm Tool calculator to assess GHG emissions associated with fertilizer production, crop production and produce transportation to international markets for the three main cash crops in the three countries. RESULTS AND CONCLUSIONS We identified contrasting responses by the agricultural sector mostly driven by changes in the consumption patterns at local or international levels. We also identified contrasting state responses to mitigate Covid-19. These responses at farm and policy scales led to similar trends in decreasing carbon dioxide (CO2) emissions across the studied countries. However, none of the studied countries linked Covid-19 response measures to long-term climate change mitigation actions. Therefore, an opportunity to sustain Covid-19 induced short-term decreases in GHG emissions was overlooked. SIGNIFICANCE Analyzing the impacts that Covid-19 had on agricultural systems and the decision taken by policymakers to handle its direct and indirect effects can help society draw lessons on how to improve climate action.

Published
2021

5. STICS crop model and Sentinel-2 images for monitoring rice growth and yield in the Camargue region

Author

Dominique Courault, Françoise Ruget, Laure Hossard, Valérie Demarez, Nicolas Baghdadi, Hélène Dechatre, Fabrice Flamain, Kamran Irfan, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Innovation et Développement dans l'Agriculture et l'Alimentation (UMR Innovation), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, 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), Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), 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), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), CNES Centre national d'études spatiales (projet TOSCA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), 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 Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), 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), and 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)

Subjects
Production mapping, Environmental Engineering, 010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], [SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy, 01 natural sciences, Yield (wine), Leaf area index, Paddy, 0105 earth and related environmental sciences, Remote sensing, 2. Zero hunger, Farm scale, business.industry, 04 agricultural and veterinary sciences, 15. Life on land, LAI, Remote sensing (archaeology), Agriculture, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Paddy field, Spatial variability, Scale (map), business, Agronomy and Crop Science, Cropping
Abstract

International audience; The assessment of rice yield at territory level is important for strategic economic decisions. Assessing spatial and temporal yield variability at regional scale is difficult because of the numerous factors involved, including agricultural practices, phenological calendars, and environmental contexts. New remote sensing data acquired at decametric resolution (Sentinel missions) can provide information on this spatial variability. The study objective was thus to evaluate the potential of Sentinel-2 images for monitoring rice cropping systems and yield from farm to region scales. The approach considered both observations and modeling. In-depth farmers surveys were carried out in the Camargue region, Southeastern France. The novelty was to use operational tools (BVNET and PHENOTB) to compute leaf area index, to daily interpolate this biophysical variable from 44 images acquired in 2016 and 2017 for each rice field, and to derive key phenological parameters from the analysis of the temporal profiles. The STICS crop model was spatially used, considering the biophysical variables derived from remote sensing. We tested four simulation strategies, differing in the integration intensity of remote sensing information into the model. Results have shown that (1) Sentinel-2 data allowed distinguishing early and late rice varieties. (2) The phenological stages mapped at the regional level allowed to better understand the agricultural practices of farmers. (3) The assimilation of remote sensing data to the STICS crop model significantly improved yield estimation and provided useful information on the spatial variability observed at regional scale. It was the first time that Sentinel-2 data are used with STICS crop model to assess rice yield at both farm and regional scale in the Camargue area. The proposed method is based on free open data and free access model, easily reproducible in other environmental contexts.

Published
2021

6. Lack of evidence for a decrease in synthetic pesticide use on the main arable crops in France

Author

Laure Hossard, Céline Pelosi, Laurence Guichard, David Makowski, Innovation et Développement dans l'Agriculture et l'Alimentation (UMR Innovation), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), 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), Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Université Paris-Saclay, Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), and AgroParisTech-Institut National de la Recherche Agronomique (INRA)

Subjects
Environmental Engineering, [SDV]Life Sciences [q-bio], Biodiversity, Context (language use), 010501 environmental sciences, 01 natural sciences, Crop, Agricultural science, agricultural policy, Environmental Chemistry, Agricultural policy, crop, Waste Management and Disposal, 0105 earth and related environmental sciences, 2. Zero hunger, water pollution, business.industry, Environmental engineering, 04 agricultural and veterinary sciences, Pesticide, Pollution, Geography, 13. Climate action, Agriculture, impact, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Water quality, Arable land, business, pesticide indicator
Abstract

The frequent, widespread use of pesticides in agriculture adversely affects biodiversity, human health, and water quality. In 2008, the French government adopted an environmental policy plan, “Ecophyto 2018”, to halve pesticide use within 10 years. Trends in synthetic pesticide sales and use in France were described, through three different indicators: the number of unit doses (NUD), the quantity of active ingredient (QAI), and the treatment frequency index (TFI). Changes in pesticide use on seven of the principal arable crops in France since the implementation of this policy plan were analyzed, together with the impact of changes in pesticide use on water quality. No evidence was found for a decrease in pesticide sales at national level between 2008 and 2013. In terms of the TFI values for individual crops, the only decrease in pesticide use observed since 2001 was for soft wheat. This decrease was very slight, and pesticide use did not decline more rapidly after 2006 than before. Changes in pesticide use differed between French regions and crops. Water pollution did not decrease during the period studied. Possible explanations for the lack of effectiveness of the French environmental plan are considered in the context of European legislation.

Published
2017

7. Comparison of two modeling approaches to simulate rice production in the Camargue region using Sentinel 2 data

Author

Fabrice Flamain, Emile Ndikumana, Valérie Demarez, D. Ho Tong Minh, Dominique Courault, Françoise Ruget, Laure Hossard, Nicolas Baghdadi, Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), 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), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées, Innovation et Développement dans l'Agriculture et l'Alimentation (UMR Innovation), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), 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), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Santé Végétale (SV), Institut National de la Recherche Agronomique (INRA)-École Nationale d'Ingénieurs des Travaux Agricoles - Bordeaux (ENITAB), and IEEE Geoscience and Remote Sensing Society (GRSS). USA.

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 010504 meteorology & atmospheric sciences, 0211 other engineering and technologies, 02 engineering and technology, Agricultural engineering, 01 natural sciences, lai, stics, High spatial resolution, Production (economics), Lack of knowledge, Leaf area index, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, 2. Zero hunger, region, business.industry, 15. Life on land, yield, simulation, Remote sensing (archaeology), Agriculture, Environmental science, Scale (map), business, Cropping, farm
Abstract

International audience; The assessment of yield variability at the territory scale is often difficult due to the lack of knowledge on various factors involved, e.g., the agricultural practices of farmers and phenological calendars. Remote sensing can help to provide precise and timely information on crops particularly with the unprecedented amount of free Sentinel data within the Copernicus programme. This study focuses on the evaluation of the contribution of the new Sentinel 2 data to provide phenological information on rice cropping systems in the Camargue region in the South-Eastern France. Dense time series of data acquired at high spatial resolution (10m) were analyzed for 2016 and 2017 and were used to map rice, compute Leaf Area Index. Various methods combining remote sensing information were compared with two different crop models (STICS and SAFY) to assess the yields. The performances are discussed according to surveys made with farmers.

Published
2019

8. Assessment of agricultural practices from sentinel 1 & 2 images applied on rice fields to get a farm typology in the Camargue region

Author

Dinh Ho Tong Minh, Fabrice Flamain, Emile Ndikumana, Laure Hossard, Valérie Demarez, Dominique Courault, Nicolas Baghdadi, Institut National de la Recherche Agronomique (INRA), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), Centre d'études spatiales de la biosphère (CESBIO), Centre National d'Études Spatiales [Toulouse] (CNES)-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Météo France-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Université Fédérale Toulouse Midi-Pyrénées-Météo France-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Innovation et Développement dans l'Agriculture et l'Alimentation (UMR Innovation), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), 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 recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), 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), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées, and IEEE Geoscience and Remote Sensing Society (GRSS). USA.

Subjects
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Soil salinity, 010504 meteorology & atmospheric sciences, sowing date, Context (language use), 01 natural sciences, Grassland, Crop, 0105 earth and related environmental sciences, 2. Zero hunger, residue management, geography.geographical_feature_category, Agroforestry, business.industry, land use, OCCITANIE, Global change, 04 agricultural and veterinary sciences, 15. Life on land, Geography, Remote sensing (archaeology), Agriculture, CAMARGUE, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Paddy field, FARM TYPOLOGY, business, random forest
Abstract

International audience; In the global change context, an efficient management of the available resources has become one of the most important topic particularly for sustainable crop development. Many questions concern the evolution of the rice farming systems in Camargue, in the Southeastern France, which play a crucial role to control the soil salinity and whose the surfaces significantly decreased these last years from 20 000 ha in 2010 to around 14000 ha in 2014. The arrival of the new satellites Sentinel makes it possible to evaluate these crop evolutions. The objectives of this study were to propose operational methodologies to: 1) accurately assess the surfaces of the main crops, rice, wheat and grassland in 2016 and 2017 from classifications based on multispectral data, (2) map some agricultural practices (sowing and harvest residue burning), and (3) elaborate a farm typology for the Camargue region based on variables computed from remote sensing data to better understand the farmer strategies.

Published
2019

9. Modelling agricultural changes and impacts at landscape scale: A bibliometric review

Author

Pierre Chopin, Laure Hossard, Innovation et Développement dans l'Agriculture et l'Alimentation (UMR Innovation), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), 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 tropicaux (ASTRO), Institut National de la Recherche Agronomique (INRA), Department of Crop Production Ecology, and Swedish University of Agricultural Sciences (SLU)

Subjects
Topic model, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Environmental Engineering, 010504 meteorology & atmospheric sciences, Biodiversity, 010501 environmental sciences, Biodiversity conservation, 01 natural sciences, European studies, Integrated assessment, Environmental planning, 0105 earth and related environmental sciences, 2. Zero hunger, business.industry, Ecological Modeling, Corporate governance, Scale (chemistry), Water quantity, 15. Life on land, Geography, Water quality, 13. Climate action, Salient, Agriculture, business, Software, Topic modelling
Abstract

International audience; Understanding the range of approaches available for assessing the impacts of agricultural changes at landscape scale is important when addressing local to global issues. Using a topic modelling approach, we reviewed the literature on impact modelling of agricultural landscapes. A search in Web of Science using the keywords model, agricultural systems and landscape yielded 1,975 hits, of which 514 papers met our selection criteria. The most salient terms fell within six groups: change, scale, pollution, biodiversity, practices and terms on biophysical/regulatory conditions. We identified four main topics: water quality, water quantity/energy crops, biodiversity and Integrated Assessment. Water management issues were more likely to be covered in North American researches, while issues related to Integrated Assessment were mainly covered in European studies. We found no relationship between topic and model type. We conclude that future integrated studies should consider the diversity of agricultural systems in governance of water and biodiversity issues.

Published
2019

10. Applying deep learning for agricultural classification using multitemporal SAR Sentinel-1 for Camargue, France

Author

Dominique Courault, Nicolas Baghdadi, Laure Hossard, Emile Ndikumana, Dinh Ho Tong Minh, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA), ANR-10-EQPX-20, Society of Photographic Instrumentation Engineers (SPIE). Cardiff, GBR., and Ndikumana, Emile

Subjects
010504 meteorology & atmospheric sciences, Computer science, 0211 other engineering and technologies, 02 engineering and technology, LAND COVER MAP, 01 natural sciences, forêt aléatoire, RECURRENT NEURAL NETWORK, law.invention, k-nearest neighbors algorithm, classification biologique, law, Radar imaging, couverture du sol, conservation à long terme, FRANCE SUD, Radar, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, 2. Zero hunger, business.industry, Deep learning, Pattern recognition, agricultural land cover map, recurrent neural network, long-short term memory, gated recurrent unit, k nearest neighbors, random forest, vector support machines, Random forest, Agricultural sciences, Support vector machine, SENTINEL-1, Recurrent neural network, CAMARGUE, MULTI-TEMPORAL, Metric (mathematics), [SDE]Environmental Sciences, Artificial intelligence, business, Sciences agricoles, SAR
Abstract

International audience; The aim of this paper is to provide a better understanding of potentialities of the new Sentinel-1 radar images for mapping the different crops in the Camargue region in the South France. The originality relies on deep learning techniques. The analysis is carried out on multitemporal Sentinel-1 data over an area in Camargue, France. 50 Sentinel-1 images processed in order to produce an intensity radar data stack from May 2017 to September 2017. We revealed that even with classical machine learning approaches (K nearest neighbors, random forest, and support vector machine), good performance classification could be achieved with F-measure/Accuracy greater than 86 % and Kappa coefficient better than 0.82. We found that the results of the two deep recurrent neural network (RNN)-based classifiers clearly outperformed the classical approaches. Finally, our analyses of Camargue area results show that the same performance was obtained with two different RNN-based classifiers on the Rice class, which is the most dominant crop of this region, with a F-measure metric of 96 %. These results thus highlight that in the near future, these RNN-based techniques will play an important role in the analysis of remote sensing time series.

Published
2018

11. Deep Recurrent Neural Network for agricultural classification using multitemporal SAR Sentinel-1 for Camargue, France

Author

Dominique Courault, Laure Hossard, Emile Ndikumana, Dinh Ho Tong Minh, Nicolas Baghdadi, Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Innovation et Développement dans l'Agriculture et l'Alimentation (UMR Innovation), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), 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), ANR-10-EQPX-20, and Minh, Dinh Ho Tong

Subjects
010504 meteorology & atmospheric sciences, Computer science, IMAGE SATELLITE, 0211 other engineering and technologies, Camargue, 02 engineering and technology, 01 natural sciences, law.invention, long-short term memory, remote sensing, law, Radar, lcsh:Science, agriculture, 2. Zero hunger, Random forest, classification, [SDE]Environmental Sciences, France, SAR, Synthetic aperture radar, agricultural land cover map, gated recurrent unit, Radar imaging, active learning, cartography, K nearest neighbors, TELEDETECTION, Spatial analysis, APPRENTISSAGE ACTIF, CARTOGRAPHIE, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, sentinel-1, recurrent neural network, K nearest neighbor, random forest, vector support machine, camargue, france, business.industry, Deep learning, rice, Sentinel-1, vector support machines, Speckle noise, 15. Life on land, Support vector machine, General Earth and Planetary Sciences, lcsh:Q, Artificial intelligence, business, RIZ, radar
Abstract

International audience; The development and improvement of methods to map agricultural land cover are currently major challenges, especially for radar images. This is due to the speckle noise nature of radar, leading to a less intensive use of radar rather than optical images. The European Space Agency Sentinel-1 constellation, which recently became operational, is a satellite system providing global coverage of Synthetic Aperture Radar (SAR) with a 6-days revisit period at a high spatial resolution of about 20 m. These data are valuable, as they provide spatial information on agricultural crops. The aim of this paper is to provide a better understanding of the capabilities of Sentinel-1 radar images for agricultural land cover mapping through the use of deep learning techniques. The analysis is carried out on multitemporal Sentinel-1 data over an area in Camargue, France. The data set was processed in order to produce an intensity radar data stack from May 2017 to September 2017. We improved this radar time series dataset by exploiting temporal filtering to reduce noise, while retaining as much as possible the fine structures present in the images. We revealed that even with classical machine learning approaches (K nearest neighbors, random forest, and support vector machines), good performance classification could be achieved with F-measure/Accuracy greater than 86% and Kappa coefficient better than 0.82. We found that the results of the two deep recurrent neural network (RNN)-based classifiers clearly outperformed the classical approaches. Finally, our analyses of the Camargue area results show that the same performance was obtained with two different RNN-based classifiers on the Rice class, which is the most dominant crop of this region, with a F-measure metric of 96%. These results thus highlight that in the near future these RNN-based techniques will play an important role in the analysis of remote sensing time series.This article belongs to the Special Issue High Resolution Image Time Series for Novel Agricultural Applications

Published
2018

12. Estimating inter-annual variability in winter wheat sowing dates from satellite time series in Camargue, France

Author

Pietro Alessandro Brivio, Luigi Ranghetti, Giacinto Manfron, Laure Hossard, Sylvestre Delmotte, Mirco Boschetti, Lorenzo Busetto, IREA Istituto per il Rilevamento Elettromagnetico dell’Ambiente, Consiglio Nazionale delle Ricerche, Innovation et Développement dans l'Agriculture et l'Alimentation (UMR Innovation), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), ANR-10-LABEX-0001-01, Projet Scenarice (no. 1201-008, Agropolis Fondation - Cariplo Fondation), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), 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
010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], 0211 other engineering and technologies, Time Series, 02 engineering and technology, Management, Monitoring, Policy and Law, 01 natural sciences, Crop, seasonal distribution, weather condition, Temperate climate, Computers in Earth Sciences, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Earth-Surface Processes, 2. Zero hunger, Global and Planetary Change, modis, business.industry, Crop yield, Sowing, durum wheat, Vernalization, time series analyses, Geography, Agronomy, Agriculture, Crop simulation model, preceding crop, business, Cropping
Abstract

Crop simulation models are commonly used to forecast the performance of cropping systems under different hypotheses of change. Their use on a regional scale is generally constrained, however, by a lack of information on the spatial and temporal variability of environment-related input variables (e.g., soil) and agricultural practices (e.g., sowing dates) that influence crop yields. Satellite remote sensing data can shed light on such variability by providing timely information on crop dynamics and conditions over large areas. This paper proposes a method for analyzing time series of MODIS satellite data in order to estimate the inter-annual variability of winter wheat sowing dates. A rule-based method was developed to automatically identify a reliable sample of winter wheat field time series, and to infer the corresponding sowing dates. The method was designed for a case study in the Camargue region (France), where winter wheat is characterized by vernalization, as in other temperate regions. The detection criteria were chosen on the grounds of agronomic expertise and by analyzing high-confidence time-series vegetation index profiles for winter wheat. This automatic method identified the target crop on more than 56% (four-year average) of the cultivated areas, with low commission errors (11%). It also captured the seasonal variability in sowing dates with errors of ±8 and ±16 days in 46% and 66% of cases, respectively. Extending the analysis to the years 2002–2012 showed that sowing in the Camargue was usually done on or around November 1st (±4 days). Comparing inter-annual sowing date variability with the main local agro-climatic drivers showed that the type of preceding crop and the weather conditions during the summer season before the wheat sowing had a prominent role in influencing winter wheat sowing dates.

Published
2017

13. Quantifying the impact of crop protection practices on pesticide use in wine-growing systems

Author

Marie Thiollet-Scholtus, Christian Gary, Laure Hossard, Jean Marc Barbier, Florine Mailly, Innovation et Développement dans l'Agriculture et l'Alimentation (UMR Innovation), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Agence de l'Environnement et de la Maîtrise de l'Energie (ADEME), Agro-Systèmes Territoires Ressources Mirecourt (ASTER Mirecourt), Institut National de la Recherche Agronomique (INRA), Fonctionnement et conduite des systèmes de culture tropicaux et méditerranéens (UMR SYSTEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), INRA, ADEME, PSPE SCEP DEPHY, French Environment and Energy Management Agency (ADEME), French National Research Agency (ANR) : ANR-10-EQPX-17, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), 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), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), and Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)

Subjects
0106 biological sciences, assessment, [SDV]Life Sciences [q-bio], Soil Science, Context (language use), Plant Science, 01 natural sciences, vitis vinifera l, herbicide, fungicide, 2. Zero hunger, business.industry, Mechanical weed control, food and beverages, 04 agricultural and veterinary sciences, 15. Life on land, treatment frequency index, Weed control, Crop protection, Fungicide, Agronomy, 13. Climate action, Agriculture, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, PEST analysis, business, Agronomy and Crop Science, Cropping, 010606 plant biology & botany
Abstract

Reducing pesticide use is a major challenge in agriculture, and farmers are encouraged to develop integrated practices. The aim of this study was to describe, analyse and assess the current contribution of various options to pest, disease and weed management on the reduction of fungicides, herbicides and insecticides by quantifying both their use and their effect on pesticide use intensity, within the context of different wine-growing regions. We conducted a statistical analysis of pesticide use and crop management over two seasons (2006 and 2010) for 11 French wine-growing regions (10,000 data). We used the Treatment Frequency Index (TFI) indicator to characterise the intensity of pesticide use. We analysed three crop management options: timing of first fungicide spray, type of soil cover cropping, and type of weed control. For each management option, we compared alternative or low-input practices to normal chemical-based practices, e.g., mechanical weed control (alternative option) vs. herbicide (normal chemical option). To strengthen the results, two quantitative variables completed the analysis: fungicide mean sprayed dose in the field and number of fungicides sprayed in the field. Results showed that each alternative or low input practice had an impact on TFI, but that this impact differed between wine-growing regions and between the management options under consideration. Regarding fungicides, our results showed that late timing of first fungicide spray (compared to regional reference) contributed to a reduction up to 50% of the part of the TFI due to fungicides, as compared to fields sprayed at median or early timing. Regarding herbicides, results of low-input/alternative practices were more homogeneous in terms of their relative impact on TFI reduction. The differences in use and impact of crop protection practices were more important between regions than between the two studied years. Our results may help policy makers to target methods for decreasing pesticide use, based on particularities of wine-growing regions and locally realistic practices.

Published
2017

14. From stakeholders narratives to modelling plausible future agricultural systems: integrated assessment of scenarios for Camargue, Southern France

Author

Jean-Claude Mouret, Vincent Couderc, Laure Hossard, Santiago Lopez-Ridaura, Sylvestre Delmotte, Jean-Marc Barbier, Innovation et Développement dans l'Agriculture et l'Alimentation (UMR Innovation), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), CCAFS Climate Change, Agriculture and Food Security, International Maize and Wheat Improvement Center (CIMMYT), Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR), Pytrik Reidsma, Marie-Hélène Jeuffroy, 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
010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], Soil Science, Context (language use), Plant Science, 01 natural sciences, multi-criteria assessment, [SHS]Humanities and Social Sciences, greenhouse gas emission, 11. Sustainability, participatory approach, bio-economic model, Agricultural productivity, 0105 earth and related environmental sciences, 2. Zero hunger, Food security, business.industry, Environmental resource management, 04 agricultural and veterinary sciences, 15. Life on land, Participatory development, Geography, climate change, 13. Climate action, Agriculture, Scale (social sciences), Sustainability, 040103 agronomy & agriculture, Organic farming, 0401 agriculture, forestry, and fisheries, business, Agronomy and Crop Science
Abstract

International audience; European farmers are facing challenges that call for important transformations on their agricultural production systems, including an increasing number of regulations aimed at reducing environmental impacts from farming practices. Climate change is also expected to affect agricultural production in most European regions, and in Southern Europe this effect is expected to negatively impact yields. In this study, we present the application of an innovative participatory approach to assess the potential of innovative agricultural systems to reconcile environmental sustainability with economic viability while contributing to local and global food security. Our approach consisted of combining (1) the participation of local stakeholders in the design of narrative scenarios, and (2) an integrated assessment of scenarios through the calculation of indicators at different scale with a bio-economic model. We tested our approach with a case study situated in the Camargue region of Southern France. Rice is currently the main crop in this region, but farmers there face adverse economic conditions linked to the recent reform of European Common Agriculture Policy. After identifying the main drivers of change, local stakeholders developed narrative scenarios and described how farmers would adapt within the context of those changes. These elements were then translated into model inputs. At the regional level, the four scenarios led to variations in farmland acreage (28,000–33,000 ha), as well as the proportion of rice crops (19–75%) and areas cultivated under organic farming standards (8–43%). The four scenarios also led to different values for indicators of agricultural economic welfare, food production, and environmental impacts. Trade-offs between these indicators and the associated objectives assigned to agriculture were identified and discussed with the stakeholders. We end with a discussion of the limitations and advantages of our approach to the participatory development and assessment of locally developed narrative scenarios.

Published
2017

15. 'Contextualized VGI' creation and management to cope with uncertainty and imprecision

Author

Simone Sterlacchini, Luca Frigerio, Pietro Alessandro Brivio, Gloria Bordogna, Giacinto Manfron, Laure Hossard, Tomáš Kliment, Bordogna, Gloria, IREA Istituto per il Rilevamento Elettromagnetico dell’Ambiente, Consiglio Nazionale delle Ricerche, Consiglio Nazionale delle Ricerche (CNR), Innovation et Développement dans l'Agriculture et l'Alimentation (UMR Innovation), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), 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
Volunteered geographic information, Knowledge management, 010504 meteorology & atmospheric sciences, Exploit, Computer science, media_common.quotation_subject, [SDV]Life Sciences [q-bio], Geography, Planning and Development, 0211 other engineering and technologies, contextualized Volunteered Geographic Information (VGI), lcsh:G1-922, Context (language use), 02 engineering and technology, vgi, Ontology (information science), level-based approximate reasoning, 01 natural sciences, Filter (software), [SHS]Humanities and Social Sciences, Earth and Planetary Sciences (miscellaneous), Citizen science, Quality (business), Computers in Earth Sciences, Representation (mathematics), uncertainty, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, media_common, fuzzy ontology, business.industry, smart app, contextualized volunteered geographic information, Imprecision, business, imprecision, lcsh:Geography (General)
Abstract

International audience; This paper investigates the causes of imprecision of the observations and uncertainty of the authors who create Volunteer Geographic Information (VGI), i.e., georeferenced contents enerated by volunteers when participating in some citizen science project. Specifically, various aspects of imprecision and uncertainty of VGI are outlined and, to cope with them, a nowledge-based approach is suggested based on the creation and management of “contextualized VGI”. A case study example in agriculture is reported where contextualized VGI can be created bout in situ crops observations by the use of a smart app that supports volunteers by means of both an ontology and the representation of the context of the geo-localization. Furthermore, n approach to cope with both ill-defined knowledge and volunteer’s uncertainty or imprecise observations is defined based on a fuzzy ontology with uncertainty level-based approximate easoning. By representing uncertainty and imprecision of VGI, users, i.e., consumers, can exploit quality checking mechanisms to filter VGI based on their needs.

Published
2016

16. A meta-analysis of maize and wheat yields in low-input vs. conventional and organic systems

Author

Laure Hossard, Marie-Hélène Jeuffroy, Gregg R. Sanford, David W. Archer, Chris Nilsson, Erik Steen Jensen, Caroline Colnenne-David, David Makowski, Maria Ernfors, Michel Bertrand, Nicolas Munier-Jolain, Sieg Snapp, Philippe Debaeke, Innovation et Développement dans l'Agriculture et l'Alimentation (UMR Innovation), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Agricultural Research Service, United States Department of Agriculture, UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Department of Biosystems and Technology, Swedish University of Agricultural Sciences (SLU), Agroécologie [Dijon], Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Department of Agronomy, University of Wisconsin, Deparment of Plant, Soil and Microbial Sciences, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Programme GloFoodS, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), 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)-AgroParisTech, USDA-ARS : Agricultural Research Service, 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, University of Wisconsin-Madison, Innovation et Développement dans l'Agriculture et l'Alimentation ( UMR Innovation ), Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ) -Centre international d'études supérieures en sciences agronomiques ( Montpellier SupAgro ) -Institut National de la Recherche Agronomique ( INRA ) -Centre de Coopération Internationale en Recherche Agronomique pour le Développement ( CIRAD ), Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, Swedish University of Agricultural Sciences ( SLU ), Institut National de la Recherche Agronomique ( INRA ) -Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, and Michigan State University

Subjects
0106 biological sciences, organic system, [SDV]Life Sciences [q-bio], Pesticide application, Winter wheat, maize, 01 natural sciences, low-imput system, wheat, Organic systems, Cropping system, Mathematics, 2. Zero hunger, [ SDV ] Life Sciences [q-bio], business.industry, Crop yield, Low input, food and beverages, 04 agricultural and veterinary sciences, conventional system, yield, Agronomy, Agriculture, comparison, Yield (chemistry), 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, business, Agronomy and Crop Science, 010606 plant biology & botany
Abstract

Organic and low-input systems are proposed as ways to reduce the environmental impacts of agriculture. Previous studies have shown that yields of organic systems can be ∼19 to 25% lower than conventional systems. An intermediary, low-input system could be less damaging for the environment than conventional systems, while reducing yield losses in comparison with organic systems. In this study, we performed a meta-analysis to compare low-input systems to conventional and organic systems. Our analysis is based on data of cropping system experiments conducted in Europe and North America, and focuses on two important crops, maize (Zea mays L.) and soft winter wheat (Triticum aestivum L.). Pesticide use was greatly reduced for low-input systems as compared with conventional for the two crops (50% for maize, 70% for wheat on average). Mean mineral N use was also reduced by 36% for maize and 28% for wheat in low-input relative to conventional. Maize yields in low-input systems were not different from those in conventional systems, and were higher than yields in organic systems (yield ratio of low-input vs. organic = 1.24). Wheat yields in low-input systems were lower than yields in conventional systems (yield ratio of low-input vs. conventional = 0.88), but were substantially higher than yields in organic systems (yield ratio of low input vs. organic = 1.43). This is one of the first meta-analyses to assess performance in terms of pesticide use intensity, and yields, with clear evidence emerging that low-input systems can markedly reduce pesticide application, without strongly reducing crop yields, relative to conventional systems.

Published
2016

17. Linking cropping system mosaics to disease resistance durability

Author

Marie Gosme, Veronique Souchere, Marie-Hélène Jeuffroy, Laure Hossard, Agronomie, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Sciences pour l'Action et le Développement : Activités, Produits, Territoires (SADAPT), and AgroParisTech-Institut National de la Recherche Agronomique (INRA)

Subjects
0106 biological sciences, cultivar resistance, [SDV]Life Sciences [q-bio], Population, Biology, Plant disease resistance, 01 natural sciences, Cultivar, Cropping system, landscape metric, education, buffer size, 2. Zero hunger, education.field_of_study, Resistance (ecology), Agroforestry, business.industry, Ecological Modeling, fungi, food and beverages, spatially explicit model, 04 agricultural and veterinary sciences, 15. Life on land, Tillage, Agriculture, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, cropping practice, business, Cropping, 010606 plant biology & botany
Abstract

Cultivar resistance plays a major role in current disease management strategies, but its efficacy is usually short-lived unless resistance deployment strategies to ensure resistance durability can be designed. Using a spatially explicit model, we evaluated cropping system mosaics that were designed by stakeholders involved in field agronomic practices to manage phoma stem canker of winter oilseed rape. We simulated pathogen population adaptation to a newly introduced major resistance gene (RlmX) to estimate the durability of the resistance under various scenarios of cropping system mosaics within a small region. Our objective was first to find descriptors of agricultural landscape that are relevant for resistance management and then to study the relationship between cropping practices applied in nearby fields and the genetic structure of the pathogen population in fields cropped with RlmX-cultivars. Key cropping practices were characterized with different metrics for several buffer sizes (100–2000 m) around target fields; and these indicators were used in linear models to predict pathogen evolution. Indicators describing local cultivar composition were very informative; adding information on tillage, but not nitrogen fertilization or fungicide treatment, could marginally increase the goodness of fit. The effects of cropping practices on resistance durability could be shown when the landscape was characterized within 500 m around RlmX-fields. We conclude that, in order to study and ultimately design landscapes promoting resistance durability against phoma stem canker, it is sufficient to take into account a relatively small portion of the landscape around RlmX-cultivars, focusing on cultivar choice and tillage practices of RlmX cultivated fields.

Published
2015

18. Effects of halving pesticide use on wheat production

Author

Guy Richard, Nathalie G. Munier-Jolain, Laure Hossard, David Makowski, Philippe Debaeke, Michel Bertrand, Marie-Hélène Jeuffroy, Caroline Colnenne-David, Aurore Philibert, Agronomie, AgroParisTech-Institut National de la Recherche Agronomique (INRA), Agrosystèmes Cultivés et Herbagers (ARCHE), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Agroécologie [Dijon], Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Département Environnement et Agronomie (DEPT EA), Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure Agronomique de Toulouse-Institut National Polytechnique (Toulouse) (Toulouse INP), Département Environnement et Agronomie (DPT_EA), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Institut National de la Recherche Agronomique ( INRA ) -AgroParisTech, Agrosystèmes Cultivés et Herbagers ( ARCHE ), Institut National Polytechnique [Toulouse] ( INP ) -Institut National de la Recherche Agronomique ( INRA ) -Ecole Nationale Supérieure Agronomique de Toulouse, Institut National de la Recherche Agronomique ( INRA ) -Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Département Environnement et Agronomie ( DPT_EA ), and Institut National de la Recherche Agronomique ( INRA )

Subjects
Crops, Agricultural, Cost-Benefit Analysis, Yield (finance), [SDV]Life Sciences [q-bio], Environment, Article, Toxicology, Pesticide use, Humans, Soil Pollutants, Production (economics), Pesticides, Cropping system, Triticum, 2. Zero hunger, Organic Agriculture, Models, Statistical, Multidisciplinary, [ SDV ] Life Sciences [q-bio], business.industry, Crop yield, food and beverages, Pesticide, Biotechnology, 13. Climate action, Organic farming, Environmental science, France, Seasons, business, Cropping
Abstract

Pesticides pose serious threats to both human health and the environment. In Europe, farmers are encouraged to reduce their use, and in France a recent environmental policy fixed a target of halving the pesticide use by 2018. Organic and integrated cropping systems have been proposed as possible solutions for reducing pesticide use, but the effect of reducing pesticide use on crop yield remains unclear. Here we use a set of cropping system experiments to quantify the yield losses resulting from a reduction of pesticide use for winter wheat in France. Our estimated yield losses resulting from a 50% reduction in pesticide use ranged from 5 to 13% of the yield obtained with the current pesticide use. At the scale of the whole country, these losses would decrease the French wheat production by about 2 to 3 millions of tons, which represent about 15% of the French wheat export.

Published
2014

19. Characterizing potential flexibility in grassland use : An application to the French Aubrac area

Author

Jean-Pierre Rellier, J. P. Theau, Michel Duru, Laure Hossard, Guillaume Martin, E. Josien, Olivier Therond, Roger Martin-Clouaire, Pablo Cruz, AGroécologie, Innovations, teRritoires (AGIR), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), Mutations des activités, des espaces et des formes d'organisation dans les territoires ruraux (METAFORT), Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF)-ENITA Clermont-AgroParisTech-Institut National de la Recherche Agronomique (INRA), Mathématiques et Informatique Appliquées (MIA-Paris), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Mutations des activités des espaces et des formes d'organisation dans les territoires ruraux (UMR METAFORT), Ecole Nationale d'Ingénieurs des Travaux Agricoles de Clermont-Ferrand (ENITAC)-Institut National de la Recherche Agronomique (INRA)-AgroParisTech-Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Unité de Biométrie et Intelligence Artificielle (UBIA), Institut National de la Recherche Agronomique (INRA), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Université de Toulouse (UT)-Université de Toulouse (UT), and Unité de Biométrie et Intelligence Artificielle (ancêtre de MIAT) (UBIA)

Subjects
0106 biological sciences, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Environmental Engineering, MOUNTANOUS AREAS, Computer science, FRANCE, 010603 evolutionary biology, 01 natural sciences, Grassland, ORGANIZATIONAL FLEXIBILITY, ANALYSIS FRAMEWORK, massif central, midi pyrénées, TIMING FLEXIBILITY, Ease of Access, GRASSLAND USE, 2. Zero hunger, Sustainable development, Flexibility (engineering), AUBRAC, geography, geography.geographical_feature_category, MOUNTAINOUS AREA, business.industry, Ecology, Simulation modeling, Environmental resource management, FLEXIBILITE, 04 agricultural and veterinary sciences, 15. Life on land, aveyron, Agricultural sciences, ORGANIZATIONAL FLEXIBILITY TIMING FLEXIBILITY, Unexpected events, PRAIRIE, Agriculture, PRATIQUE DES AGRICULTEURS, Sustainability, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, business, Agronomy and Crop Science, ZONE DE MONTAGNE, Sciences agricoles
Abstract

International audience; Farmers increasingly need to adjust their management practices to accommodate unexpected events such as drought and preserve the longevity of their production project. Applied to grassland use e.g. grazing or cutting, such an exploitation of flexibility requires some background knowledge about where and when freedom of choice can be exercised. To this end, we design an analysis framework for grassland-based farming systems in mountainous or less-favored areas. An expert-based discrimination tree characterizes organizational flexibility by determining the range of possible types of grassland use under various topographic and farming constraints such as suitability for mechanization and ease of access to a field. A set of time windows evaluates the timing flexibility in grassland use, each associated with a combination of a grassland community type and a type of grassland use. Compared with field data obtained in the French Aubrac region, the outputs of the discrimination tree match for 139 of 165 grassland fields. For a particular type of grassland use, the set of time windows proves that timing flexibility in grassland use between grassland community types can increase by 15 days over a 37 days time range. When applying the two components of the analysis framework to a farm case, it shows that 24% of the farm area offers organizational flexibility, with several possibilities for grassland use. Timing flexibility for bringing forward or delaying the use of the grassland fields is unused in the farm. Most of the dates of grassland first use are similar irrespective of the diversity of grassland communities. The application of the analysis framework offers a sound evaluation of the potential flexibility to establish where and when it is possible to adjust management practices to cope with unexpected events. It can also be helpful in setting up coherent alternatives to the observed management strategies that can then be expanded in dynamic simulation models enabling deeper analysis.

Published
2009

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