appendix - ademe

Appendix

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Futu re

EAppendix

312

• E1 - Glossary

• E2 - The women and the men of Climator

Adaptation (to climate change)Adjustment of natural or man-made systems in the face of changing climate. The adaptation can be by anticipation or by reaction, public or private, planned or unplanned.

AdvancementNumber of days of advancement of a phenological stage between a reference period and a later period. This can be “natural”, i.e. due to increasing temperatures, or “anthropogenic”, i.e. due to a change in practices, such as varietal choice or sowing date.

Analysis of varianceA statistical test to compare the variability* which exists between the different levels of a factor, called “inter”, with the variability within the levels, called “intra”. This test is able to determine whether a qualitative factor produces statistically significant effects at a given probability thresh-old on a given variable.

ANO (method of anomalies)See Downscaling methods*.

Aquifer rechargeThe amount of water from rainfall not used by the plant canopy or the natural environment. In natural conditions (rainfed crops), it is the sum of drainage (water percolating below the soil layer explored by roots) and surface runoff. On the scale of the catchment basin, hydrologists often call it “effective rainfall”, an ambiguous term because it can be confused with the “effective rainfall” of agronomists (rainfall-runoff). In artificial conditions (as in irrigated agriculture), aquifer recharge must include the irrigation applied, counted as negative, since this water is abstracted from the environment.

ARPÈGESimulation model (see Simulator*) of the behaviour of the atmosphere, used by Météo-France to predict the climate* from now until the end of the century. In CLIMATOR, it is coupled with downscaling* methods to obtain regional climate scenarios. It is one of the IPCC global climate models.

Available daysThe number of days on which a technical operation can be carried out during the appropriate phase of the crop or cover/catch crop. For example a variation in the rainfall or its distribution in the autumn with climate change* could alter the number of days available for soil tillage or sowing.

Available water reserve (AWR)For a soil, the storage capacity for water extractable by plant roots. It is determined mainly by the volume explored by the roots and the density and nature of the soil constituents. For a fixed rooting depth, it is a characteristic parameter of a soil and almost constant. In CLIMATOR, the AWR is assumed to be an intrinsic and constant property of each soil, i.e., the same value for all crops until reaching the soil/subsoil boundary. The results of simulations of the water balance* are very sensitive to it.

AvoidanceA way of avoiding the effects of drought by increasing the possibilities of water entry (e.g. a strong root system) or by reducing the possibilities of its loss (less foliage, lower planting density, size, natural or, in extreme cases, artificial defoliation). As opposed to escape*, which is to do with timing, avoidance is to do with space, by way of morphology.

1EGlossary

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Glossary

A1BSee Emission scenarios*.

A2See Emission scenarios*.

Blue water The fraction of the rainwater which is returned to the aquifer*. In the temperate zone, it is about one third of the rainfall (in France, generally, 300mm/yr)

Boxplot Graphical representation of a sample and its distribution. Also called a “box and whisker plot”, a boxplot is a box around a central value extended by vertical lines. The central value is the 50% quantile*, the bottom and top of the box are the 20% and 80% quantiles respectively. The lines connect the sides of the box to the minimum and maximum values in the sample.

B1See Emission scenarios*.

Carbon fertilisationIncrease in carbon fixation (hence elaboration of biomass) under the effect of increased carbon dioxide (CO2) concentration in the atmosphere. Carbon fertilisation, a means of increasing yields, is one of the positive effects of climate change on cultivated systems.

Climate changeModification of certain climate* parameters by an external cause. The difference between two thirty-year periods which can be explained simply by natural variability* is not a change. In CLI-MATOR, this external cause is the modification of the atmospheric greenhouse gas and aerosol concentrations resulting from human activities. Climate change can only be evaluated over pe-riods of at least 30 years, which is in keeping with the slow change in the external causes of the change studied here.

ClimateQuantitative description of the statistics of atmospheric and surface variables over a 30-year pe-riod, without regard to particular years. To speak of the climate of summer 2003 in this context is a nonsense. The climate is described by parameters, including the central values (mean, median), but also by the variability* (variance, extremes, cycles) and the spatiotemporal and intervariable linkages.

Climatic model (or climate simulator)The climate* is usually calculated from the available meteorological observations. For the distant past and possible futures, one has to make of use of physical theories. A climate simulator acts like a meteorological prediction model, except that one is not interested in the predicted chro-nology but only uses the statistical distribution of the variables. To simulate the climate, one has to represent the globe and the ocean-atmosphere interactions. Once the global simulation is established, one can use downscaling methods* for the impact studies*.

Climatic water requirement.This term is the counterpart of the supply, represented by the rainfall. It corresponds to the po-tential evapotranspiration ET0.

Climatic weather recordA set of weather records for a site covering a long enough period to define the climate* (30 years as a general rule).

Closed-Skew Normal distributionProbability distribution possessing an asymmetry parameter, derived from the normal or Gaus-sian law and retaining its useful mathematical properties. This distribution is used in the stochas-tic weather* generator to reproduce asymmetry in climatic variables.

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1EGlossary

Conditional probabilityA probability is said to be conditional when the event concerned cannot occur if a complemen-tary event has already occurred. The probability of the second event is thus conditioned by that of the first.

Cover (and catch) cropsIn a crop rotation, if the time interval between two crops is long enough and the soil and weather conditions suitable, a so-called “cover (or “catch”) crop” can be grown, with particular aims:- as a plant cover to trap mineral nitrogen and limit soil erosion (example of non-harvested cover

crops: mustard after rape in a maize-wheat-rape rotation; example of a harvested cover crop: ryegrass after wheat in a maize-wheat rotation).

- a harvested crop helping to control weeds (example: early sunflower after wheat in a maize-wheat rotation).

By bringing forward harvest dates, climate change* should increase the possibilities of growing cover and catch crops.

Crop model (or crop simulator)Mathematical and computer representation of all the mechanisms involved in the absorption of light, water and mineral elements, the production of biomass and grain by a plant canopy, in interaction with the soil and weather.

Crop protocol (or cultural practices)A logical and methodical series of technical operations (e.g., sowing, fertilisation etc.) carried out on a crop during the course of its life to achieve a production objective. Climate change* may modify either the timing of technical operations (depending on their feasibility) or else the yield potential sought.

Crop rotationDescribes the succession of different crops which are grown on a given field. This succession is defined both by the species present and by their order of appearance over time.

Cropping systemFor a given field, all the crop (and catch/cover crop) successions which take place there, together with their associated cultural practices (or crop protocols*). Climate change* can, for example, modi-fy the timing of the occupation of a plot by each crop and hence the feasibility* of a crop succession, both for reasons of date and for the number of days available* to carry out a technical operation.

C3 versus C4In the processes of plant photosynthesis there are two types of CO2 fixation. The terms “C4” and “C3” identify these two types of CO2 fixation and refer to the number of carbon atoms in the first organic molecule formed. These mechanisms are associated with different ecophysiological behaviour. The C3 species, which are the commonest, react more markedly to the increase in CO2, whereas photosynthesis in C4 plants, already optimised, is less reactive. C4 plants, mainly of tropical origin (maize, sorghum etc.) make better use of water.

DevelopmentAll the qualitative changes which occur over the course of the life of a plant. It consists of the formation of the different plant organs (organogenesis). The phenological stages are the markers which punctuate the development of the plant. Governed mainly (but not entirely) by tempera-ture, development will be affected by climate change* as acceleration, also called advancement*.

Distant future (DF)A 30-year period, from 2070 to 2099, considered as the distant time horizon which indicates to us what would happen if we were not to introduce any adaptation measures to climate change*. The climatic data produced for this period are all the result of simulations, coupling models of general circulation (ARPÈGE and other IPCC models) and downscaling* methods. As for all the periods, the results for the DF are produced in the form of statistics (mean, median, standard deviation, deciles) indicating the year-to-year variability within the period and allowing one to judge the impacts* of climate change*. “Average years” for the period were also calculated to represent typical yearly patterns (e.g. of growth*, irrigation). The results can also be analysed in the form of projections, i.e. the difference from the reference period, RP.

1E


Glossary

Downscaling methods (QQ, WT, ANO)Methods for reducing the results of climate models* from the planetary scale to that of the obser-vations. They play a double role of spatial interpolation and of correcting certain distortions. One distinguishes between statistical downscaling methods, which are based on model-observation relations for the present climate, and dynamic downscaling methods which use physical laws, as-sumed to be unaffected by climate change, for the interpolation part. See also Quantile-quantile.

Emission scenarios (A1B, A2, B1)Climate simulations over the 21st century (and beyond) require emission scenarios for anthropo-genic greenhouse gas emissions, which correspond to different probable trajectories for world socio-economic development. The A1B scenario is intermediate in the range of imaginary sce-narios (A2 is more pessimistic and B1 more optimistic). It represents a world using both fossil and non-fossil energy resources. It predicts a CO2 concentration of 500ppm in 2050 and 700mm in 2100, and a world average warming of 2.8ºC by 2100 (probability range: 1.7- 4.4 ºC).

Escape A way of avoiding suffering from drought (or a heat or biotic stress) by shifting the growing peri-od as far as possible outside the dry period (or period of high temperatures or disease incidence). In the temperate zone, the earlier a variety or species is, the more it will “escape” the summer drought. As opposed to avoidance*, which is concerned with space, escape is to do with time.

Extreme eventAn event, instantaneous or not, in the course of which a variable quantifying it falls outside its statistical distribution: for example below the 1% quantile* or above the 99% quantile*. In CLIMA-TOR, climatic statistical distributions apply to the recent past* and one of the stations* studied. According to this definition, a temperature considered extreme in the recent past* may no longer be extreme in relation to the temperature distribution in the near or distant future (the case of summer 2003 for example).

FAISAAn output variable* of crop models* indicating whether the crop has reached the end of growth* at harvest maturity (Yes: FAISA = 1; No: FAISA = 0). In CLIMATOR, this variable allows one to decide whether the increase in heat availability* is enough to allow a crop to be grown where it was not previously possible (for example vines in the north of France).

FeasibilityProportion of the number of years (usually expressed out of 10) for which a crop reaches physi-ological maturity thanks to appropriate weather conditions, especially temperature. For a given species, it varies with the variety, the sowing date and the weather conditions (dependent on both the location and the year). The increase in temperatures due to climate change generally tends to lead to an increase in the feasibility of crops.

FLOJulian date of flowering of a crop (e.g. wheat, sunflower, vine). It is one of the output* variables of the crop* models used in CLIMATOR. It can describe the phenological characteristics of the plant.

Greenhouse effect gas (GHG)The greenhouse effect gases are the gaseous components of the atmosphere, natural and an-thropogenic, which absorb and emit radiation at specific wavelengths in the infra-red radiation spectrum emitted by the earth’s surface, the atmosphere and clouds. This property causes the greenhouse effect. Water vapour (H2O), carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4) and ozone (O3) are the main greenhouse effect gases in the earth’s atmosphere.

Green waterThe fraction of the rainfall used by crops and natural vegetation. In the temperate zone, it is about two thirds of the rainfall (in France, generally, 600mm/yr).

Grid (of a climate model)Horizontal resolution of a climate model*, i.e. the horizontal distance between the calculation points of the climate values.


1EGlossary

GrowthAll the irreversible quantitative changes which occur in the course of the life of a plant: elon-gation of internodes, cell multiplication, multiplication and growth of leaves. In agronomy, the growth is equated to the accumulation of biomass and depends on the potential fixed by the species and variety, and then on the biophysical conditions (radiation, temperature, water and nutrient supply, pests and diseases) present throughout the growing period. The temperature increase brought about by climate change* will in general tend to increase growth.

Growth cycleAll the life stages of a crop, which are usually marked by reference points, called stages, describ-ing the most important stages. For annual crops, the life-cycle stages go from sowing to harvest. For perennial crops, all the stages are normally passed every year. Climate change, by increasing temperatures, will cause a shortening of crop cycles (advancement*).

Heat availabilitySee thermal availability

Heat stressA notion encompassing all the phenomena having an adverse effect on grain filling when tem-peratures are high during this phase.

HumificationThe process of transformation of organic matter into humus under the influence of the soil micro-fauna and microflora. Humification follows the decomposition of organic residues and includes the elaboration of new compounds by microbial and physicochemical means. This process is taken into account in CLIMATOR in order to calculate the organic matter added to the soil by residues and manures.

ImpactA positive or negative effect that a change has, or could have, on a phenomenon. For example the variations in crop yield or soil mineralisation* are impacts of climate change.

InitialisationAn action or procedure, before using a model*, intended to establish or estimate the initial value of certain variables. In CLIMATOR, an initialisation procedure was used to estimate, for example, the organic matter content of soils in the first year of simulations*.

Input variable (of a model)A model is a set of equations which aims to reproduce the mechanisms of a real phenomenon. For this it uses (among other things) input variables whose value is not calculated by the model but fixed by the user or already obtained from another model. In CLIMATOR, climatic variables, obtained from climate simulators*, are input variables of the crop models*.

IPCC (Intergovernmental Panel on Climate Change)An organisation created within the UNO, whose mission is to evaluate the information necessary to better understand the risks due to anthropogenic climate change, to work out more precisely the possible consequences of this change and to consider possible strategies of adaptation and alleviation. Its evaluations are based mainly on scientific and technical publications whose scien-tific value is widely recognised. The work of the IPCC is available at : www.ipcc.ch

LeachingThe phenomenon of the downward transport of ions (in this case nitrate), in the soil solution by the drainage water into deeper soil layers or into the groundwater.

Leaf area index (LAI)Ratio of the leaf area of a plant stand to the area of land which it occupies. It equals 0 at sowing (bare soil) and can reach 5 or 6 when the leaf area reaches its maximum. The LAI influences the ETR* of a crop. Its change over the course of crop growth is heavily dependent on the tempera-ture and water supply conditions of the plants.

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Glossary

MineralisationA process of decomposition of the soil organic matter (humus, organic residues) into mineral compounds by soil micro-organisms, leading, inter alia, to the emission of carbon dioxide into the atmosphere and the liberation of nitrogen in mineral form (ammonium and nitrate ions) and assimilable by plants from the soil. In CLIMATOR, mineralisation is taken into account in the calcu-lations of organic matter balance and of the nitrogen available to plants.

Maximum evapotranspiration (ETM) Evapotranspiration of a particular plant canopy (wheat, sunflower etc.), at a given phenological stage, in the absence of any water stress. ETM depends on ET0. On a daily (or 10-day) scale, it is a fraction of ET0 which increases as the ground cover increases. On an annual scale, the timing of the phenology of the crop plays an important role.

Model parametersA model is a set of equations which aims to reproduce the mechanisms of a real phenomenon. The parameters of a model are the quantities which drive the equations. They are fixed for a particular phenomenon, but vary from one case to another. For example the soil depth* is a pa-rameter of the STICS model.

Near future (NF)A 30-year period, from 2020 to 2049, considered as an imminent time horizon when the impacts* of climate change* will be tangible, and the signal of climate change perceptible compared with the year-to-year variability*. The climatic data produced for this period are all the result of simu-lations, coupling the ARPÈGE model with downscaling* methods. Results of other IPCC models are not available for this period. As for all the periods, the results for the NF are produced in the form of statistics (mean, median, standard deviation, deciles) indicating the year-to-year vari-ability within the period and allowing one to judge the impacts* of climate change*. “Average years” for the period were also calculated to represent typical yearly patterns (e.g. of growth*, irrigation). The results can also be analysed in the form of projections, i.e. the difference from the reference period, RP.

Output variable (of a model)A model is a set of equations which aims to reproduce the mechanisms of a real phenomenon. Expressed as a computer program, a model transforms input variables* into a set of output vari-ables. For example STICS models cropping systems*; using a computer it transforms climatic vari-ables into agronomic variables such as yield, evapotranspiration*, etc.

PERCOLAn output* variable representing aquifer recharge*: PERCOL = Drainage + Runoff – Irrigation. In CLIMATOR, it contributes to the study of the effect of agriculture on the water resource.

Phenological stagesSee phenology*.

PhenologyStudy of the appearance of periodic events (usually annual) in the living world, determined by seasonal variations in the climate; the study of periodic events, for example emergence, flower-ing, budburst, fruiting, grain filling.

PhotosynthesisBiochemical process allowing plants to fix atmospheric CO2 by means of light energy and leading to the elaboration of biomass.

Photothermic quotientThe ratio, calculated for a given period, of the radiation to the temperature. Calculated for key periods for the elaboration of yield of small-grain cereals (the end of the elaboration of grain number, grain-filling), it represents the possible growth per unit of plant time (i.e. the tempera-ture sum): when it is particularly high, it usually indicates favourable weather conditions for good yields. It is used in CLIMATOR to evaluate the combined effects of these two input* variables on plant growth.


1EGlossary

Potential evapotranspiration (ET0)Reference evapotranspiration corresponding to a well-watered lawn and nowadays estimated in a standardised way by the meteorological services by means of the Penman-Monteith formula.

Projection-PredictionProjections are based on hypotheses about the behaviour of the system studied. One describes future states of the system on the basis of these hypotheses. Predictions are projections in which one would have assigned degrees of probability to the states thus reached. For example to model the climate in France in 2020 constitutes a projection, whereas to ascribe a degree of probability to this information constitutes a prediction.

Quality of productsAll the characteristics of a harvested product, associated either with its organoleptic value or its value for processing. Usually critical for its profitability, the quality of products is therefore comple-mentary to the yield in the evaluation of a crop. It results from ecophysiological processes sensitive to weather* or microclimate. Coupled with climatic variables or output* variables from models, suitable quality indicators for each crop can quantify the predictable impact of climate change*.

Quality of residues and manuresChemical and biochemical composition (nutrients, cellulose, hemicellulose, lignin etc.) which determine their agronomic efficiency. Sometimes the quality is defined simply by the C/N ratio. In this case, good quality is usually associated with a low C/N. It is used in CLIMATOR to estimate the mineralisation * and humification* rates of these substrates.

Quantile(s)A family of descriptors of a sample. The “x % quantile” of a sample is a value which indicates that x % of the elements of this sample are below this value. The 50% quantile is the median.

QQ or Quantile-Quantile (a downscaling method)This is one of three climate downscaling* methods used in CLIMATOR. It consists of correcting the variables simulated by a climate model by a correction function which restores the quantiles of the data simulated by the model for the present time to the quantiles observed over the same period, for each variable, season and measuring point. It is a dynamic downscaling method.

Radiational warmingThe fraction of the warming caused by a mean positive radiation balance. The radiation balance is composed of infra-red and global radiation (entering/leaving the atmosphere and soil). An ef-fect taken into account by certain models.

Range (of a variable)A range of values which a given variable can take. An example from CLIMATOR: “Rainfall ranges from 800 to 1 000mm per year”.

Real evapotranspiration (ETR)Actual evapotranspiration of a particular plant canopy (wheat, sunflower etc.) for any phenologi-cal stage and for all water conditions. The ETR is a fraction of the ETM equal to 1 when the canopy is fully supplied with water and below 1 when a lack of water in the rooting zone forces the plant to partially close its leaf stomata.

Reanalysis (of climatic variables)Methods of analysis, statistical for example, of weather records already resulting from simulation, or the application of statistical models to observations. In CLIMATOR, reanalysis has allowed us, for example, from the outputs of global climate models, to proceed to downscaling in order to obtain climatic projections for the study sites.

RECHarvest date of crops predicted by the agronomic models. For grain crops, this date is later than physiological maturity, to allow for drying. For vines, the harvest date depends on the sugar con-tent of the grape berry, while for forage crops the cutting dates depend on the quality expected from the forage. Although there are differences between the models for the final phases of matu-ration of the agricultural product, the estimation of REC is mainly based on a sum of degree-days.

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Glossary

For technical reasons (e.g. the need to sow the following crop or the management of livestock feeding), the harvest date can be set.

Recent past (RP)A 30-year period, from 1970 to 1999, used as a reference against which to judge the impacts* of climate change*. This is the period for which are available data measured on the sites* allowing us to downscale the large-scale climatic results produced by the general circulation models and in particular by ARPÈGE. To harmonize this series with future weather patterns* (NF and DF), the various downscaling methods* were applied to it so that the RP of the WT method is perceptibly different from the RP produced by the QQ method, both of which differ from the measured data, corresponding to the method of Anomalies. As for all the periods, the results for the RP are pro-duced in the form of statistics (mean, median, standard deviation*, deciles) over the period, indi-cating the year-to-year variability within the period and allowing one to judge the significance of the impacts of climate change. “Average years” for the period were also calculated to represent typical yearly patterns (e.g. growth, irrigation).

Region (zone)Spatial entities created by regrouping French administrative regions in order to have a smaller number of areas, each having a degree of climatic, geographic and agricultural uniformity. In CLIMATOR, seven regions were defined, each including one to three climatic sites* (stations):Centre-North: Nord-Pas-de-Calais/Picardie/Champagne-Ardenne/Île-de-France/Haute-Norman-die/CentreWest: Basse-Normandie/Bretagne/Pays de la LoireNorth-East: Alsace/Lorraine/Franche-Comté/BourgogneCentre-East: Rhône-Alpes/Auvergne/LimousinSouth-East: Provence-Alpes-Côte d’Azur/Languedoc-RoussillonSouth-West: Midi-Pyrénées/Aquitaine/Poitou-CharentesWest Indies: Guadeloupe/MartiniqueThese regions are used in CLIMATOR to classify geographically the impacts* of climate change* on cultivated systems.

SAFRANHourly and daily interpolation, produced by Météo-France, incorporating weather records from observation stations and presented on a regular grid* of 8km horizontal resolution for the pe-riod 1958-2008. Seven atmospheric variables are available: temperature and relative humidity at 2m above ground, liquid precipitation (rain), solid precipitation (snow), incident global radiation, infra-red radiation, and wind speed 10m above ground. This observed database is used in CLIMA-TOR for the weather-type downscaling method.

SignificanceA difference between two results is judged to be significant if it is not due to simple random vari-ation. In the framework of CLIMATOR, it is the year-to-year variability of the weather which is the main source of random variation. To establish the significance of the effect of climate change* as predicted by the crop models, one compares the means over 30-year periods. Statistical tests give the probability that the difference in the means between periods can be attributed to chance. When this probability is below a certain fixed threshold, the difference is judged to be significant. The threshold most commonly used is 5%, but in CLIMATOR we also use thresholds of 1 and 10%.

Simulator (or model)A computer program used by researchers to study the behaviour of a complex system without doing a real experiment. A simulation is the numeric outcome of simulators. In CLIMATOR, the impacts of climate change* on cultivated systems are calculated by combining climate simula-tors with crop simulators.

Site (station)A point in France where there is a weather station, located in an agricultural environment, on a surface conforming with WMO standard conditions for meteorological measurements. In CLIMA-TOR, thirteen sites were chosen (twelve in metropolitan France and one in the West Indies) ac-cording to the following criteria: the required weather variables (max/min temperatures, rainfall, radiation, wind speed and air humidity) had to be available daily for at least 30 years (to be able


1EGlossary

to downscale the climate predictions) and be well representative of the surrounding weather. Twelve stations were located on plains; only the Clermont-Ferrand station at Theix is at 800m al-titude. All these sites provided a representation of the variety of French climates* in their various forms: northern, Mediterranean, continental, and oceanic. Twelve of the thirteen stations belong to the INRA AGROCLIM network; the Saint-Étienne station is part of the Météo-France network. Each of the six CLIMATOR zones (seven with Guadeloupe) is represented by 1-3 weather stations. All the soils and cropping systems* were studied at each of the sites.

Standard deviationA measure of the variability of a sample which is expressed in the units of the variable studied. By definition, the square of the standard deviation is the square of the mean of the deviations between the variable and its arithmetic mean. For a variable distributed according to a Gaussian law, 95% of the values are found in the interval [mean ± two standard deviations].

SRESSee Emission scenario*.

Stochastic weather generatorAn algorithm producing a time series of weather data statistically comparable with real series. In CLIMATOR, the WACS-gen generator was used as a downscaling* method.

Synoptic climatologyCharacterisation of climatic parameters from different sites which can be explained by the large-scale atmospheric circulation (for example the Azores High). In CLIMATOR, this concept is used in the “weather types” (WT) downscaling method.

Trap crop (for nitrate)A crop introduced between two crops in a rotation and intended to absorb (or trap) the nitrate arising from the mineralisation of the residues of the previous crop. The lengthening of the peri-ods between crops and the additional mineralisation* of the soil organic nitrogen due to climate change* will probably lead to more use of trap crops.

Thermal availability or thermal timeCumulative daily mean temperatures exceeding a threshold, called the base temperature, over a period of interest. This quantity is correlated with cumulative biological effects by the phenol-ogy* of the plants. Thermal availability strongly determines the feasibility* (of growing a crop). It increases with climate change*.

ToleranceDrought resistance. As opposed to escape* and avoidance* which are two ways of not suffering drought, tolerance is a characteristic which allows a plant to live and produce (and not merely survive) in conditions of restriction of its water exchange and even of its water status. Sorghum is more tolerant than maize because it accepts restrictions at flowering which maize does not (not to be confused with its better exploitation of soil water, which is a form of avoidance).

UncertaintyA term whose statistical meaning refers to the imprecision attached to every measurement proc-ess or simulation. It can usually be quantified by means of statistical methods based on the analy-sis of differences between replicates or errors compared with a reference.

VariabilityNotion used to describe the magnitude of the variations of a phenomenon around its mean.

VernalisationVernalisation corresponds to the quantitative cold requirements needed at the vegetative api-ces for their transition to reproductive development*. These requirements are counted as the number of days with mean temperatures below a threshold which depends on the species. Cli-mate change* will alter the number of days* available in winter to complete this vernalisation.

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Glossary

Water balanceAlgebraic sum of the water fluxes for an agricultural plot (not to be confused with the hydro-logical balance, the term used on the scale of a catchment basin). For a site on level ground, the positive contributors to the balance are: the rainfall, irrigation and capillary rise. The negative ones are: the real evapotranspiration, the runoff (resulting from the limited infiltration capacity) and the drainage (or percolation). The simplified balance ignores the capillary rise. The variation in the water store (daily, weekly, or for 10-day periods) within the available water reserve is gov-erned by this balance : ∆S = P + (I) – ETR – Runoff – D.

Water stressOne speaks of water stress when the supply of water from the soil fails to satisfy the maximal evapotranspiration* requirements of a plant canopy, thus reducing plant growth*. Water stress = 1 – water sufficiency. In CLIMATOR, the water stress is included in the simulations in relation to all the terms in the water balance*.

Water sufficiencyThis term expresses the capacity of the plant canopy to function at its full potential, i.e. without stomatal closure due to water stress. It is expressed in CLIMATOR by the ratio ETR/ETM* during the vegetative period (restricted to flowering-harvest in annuals, the foliage period of deciduous trees, the whole year for conifers and grassland).

Wet degree-days By analogy with the degree-day concept used as the driver of plant development*, this is an indicator proposed for the infection phase of crop pathogens and calculated as the product of the wetting duration* and the mean temperature over the wetted period and divided by 24 to be restored to days.

Wetting durationDuration (expressed in hours) of the presence of liquid water on the leaves of a crop canopy. It depends on the canopy architecture, the surface properties of the leaves and the microclimate within the vegetation. The phenomenon begins with a dew deposit or a fall of rain. Once these episodes are over, a drying phase begins, whose length depends on the amount of water depos-ited on the leaves and the evaporative conditions. The wetting duration is critical for the develop-ment of certain pathogenic fungi studied in CLIMATOR.

WT (weather type method)See Downscaling methods* and page 21 of the Green Book for more details.

YieldThe agricultural production obtained from a given cultivated area. It may be part of the plant (in general, the grain) or the whole plant. In CLIMATOR, the yield is always an output* variable of the crop models*. Unless otherwise stated, it is expressed in tonnes of dry matter per hectare, with a water content of 0%. Losses (e.g. tractor wheelings, field headlands, harvest losses) suffered in agricultural fields are not simulated in CLIMATOR, so that CLIMATOR yields are rather like those of experimental plots. If physiological maturity is not reached by the final harvest date allowed by the models, the yield is considered to be zero.

ZoneSee region*.

Nadine BRISSON is director of research at INRA. An agronomist, specialising in the modelling of crops, their ecophysiological be-haviour and agroclimatology, she directed the INRA agroclimatic service from 2005 to 2009. She developed the generic crop model STICS and as a result of this is collaborating with the agronomic research departments and the R & D services of trade organisa-tions. For eight years she has been heavily involved in studies of the impact of climate change on agriculture, especially for arable crops and vines. She devised and then coordinated the CLIMA-TOR project.

Nadine BRISSON AGROCLIM – INRA AvignonAGROPARC, 84914 AVIGNON cedex 9E-mail : [email protected]él : 04 32 72 23 83

Denis ALLARD is director of research at INRA. For five years he has directed the Unit of Biostatistics and Spatial Processes at Avignon. He carries out research into spatial and spatiotemporal statistics applied to the environment and to the climate. He is a member and treasurer of the Environmental Group of the French Statistical Society. In the CLIMATOR project he was responsible for the statistical anal-ysis of the uncertainty problem. In connection with the CLIMATOR project he also developed a stochastic method of downscaling of climatic variables.

Denis ALLARD Biostatistique et Processus SpatiauxINRA, Site Agroparc, 84914 AVIGNON cedex 9Tél : 04 32 72 21 71http://denis.biosp.org

Christel ANGER was recruited during the final year of the Clima-tor project as a trainee graduate in the Agronomy Research Unit of INRA at Grignon. Supervised by Jean Roger-Estrade, she carried out simulations with the OTELO model to evaluate the effect of cli-mate change on the organisation of farm work (available days and operation schedules). She also took part in the creation of the on-line course.

Christel ANGER Ingénieur d’Étude « PLANTACOMP »UE Génétique et Biomasse Forestiè[email protected], UE0995, F-45075 Orléans

2EAppendix

323

The women and the men of CLIMATOR

| Green Book | Appendix

E 2

| Green Book | Appendix324

Appendix

Vincent BADEAU is a research scientist in the Forest Ecology and Ecophysiology Research Unit (UMR INRA-UHP 1137). He is a special-ist in the characterisation of natural environments, the manage-ment of environmental databases and the statistical modelling of the climatic habitats of forest species. He is, among other things, the scientific head of the Amance Arboretum (INRA Nancy) and in charge of the Public Arboretum Network. In CLIMATOR, he contributed to discussions on geoclimatology, no-tably from his niche model ÉVOLFOR.

Vincent BADEAU Centre INRA de Nancy54280 CHAMPENOUxE-mail : [email protected]

Marie-Odile BANCAL is a teacher-researcher at AgroParisTech. She teaches the ecophysiology of crops under biotic and abiotic stress. As a member of the Environment and Arable Crops Research Unit at Grignon, her research work is centred on the analysis and model-ling of the effects of airborne leaf diseases on the elaboration of yield and quality in wheat. Since 2003 she has taken part in the modification of the CERES crop model to be able to take account of the effect of brown rust on wheat growth. Coupled with a simplified epidemiological model, this model is used in the CLIMATOR project to predict the changes in the im-pacts of the rust on wheat yields with climate change.

Marie-Odile BANCAL UMR AgroParisTechINRA Environnement et Grandes Cultures78850 THIVERVAL GRIGNONE-mail : [email protected]él : 01 30 81 55 55

Kamel BEZZOU was a trainee graduate in the ecophysiology of plants in the Environmental Stress Research Unit (LEPSE) of INRA at Montpellier during the course of the Climator project. Supervised by Lydie Guilioni, he translated the SUNFLO crop model into an R program and managed the database simulations with this model.

Kamel BEZZOU BiostatisticianE-mail : [email protected] rue de la Cavalerie 34000 Montpellier

| Green Book | Appendix 325

2EAppendix

Alexandre BOSC is a research scientist in forest ecophysiology in the ÉPHISE Research Unit. He is responsible for implementing research projects on the monitoring and characterisation of the fluxes of H2O and CO2 in forest ecosystems in the context of climate change. For ten years he has been the main developer of the GRAECO model. In the CLIMATOR project he carries out simulations of the response of pine forests.

Alexandre BOSC INRA, UR1263 ÉPHYSE69, route d’Arcachon33612 CESTASE-mail : [email protected]él : 05 57 12 28 49

Natalie BREDA is director of research in the Forest Ecology and Eco-physiology Research Unit. She is both a forester and an ecophysi-ologist specialised in forest disorders. She coordinates the DRYADE programme and leads the Vulnerability topic of the AFORCE mul-titechnological network “Forests facing climate change”. She is in-volved in the analysis of climatic and biotic forest decline by combin-ing retrospective (dendrochronology) and mechanistic approaches. She also leads research work on the role of carbon management in the different effects of drought- and defoliation-type stresses. In CLIMATOR she was responsible for the “Forest” part, and in par-ticular the simulations of the BILJOU model. Nathalie BREDA Centre INRA de Nancy54280 CHAMPENOUxE-mail : [email protected]

Julie CAUBEL was a trainee graduate at the Agroclim unit at INRA Avignon for a year. Supervised by Nadine Brisson, she was respon-sible, in collaboration with Dominique Ripoche, for carrying out simulations with several models used in the CLIMATOR project and the analysis of agronomic and environmental output variables of these models.

Julie CAUBEL DoctoranteAGROCLIME-mail : [email protected], F-84914 Avignon

E 2


Appendix

Damien CHAMBERT is a C.I.T programmer at INRA. He has worked since December 2007 at the AGROCLIM unit of INRA on studies on climate and climate change. He is responsible for the unit’s websites, carrying out simulations for the Agroclimatic Monitor (http://www.avignon.inra.fr/veille_agrocli-matique) and takes part in the vigilance systems for the acquisition of meteorological data. In the CLIMATOR project he has worked on the online IT facilities.

Damien Chambert Unité AGROCLIMINRA, domaine St Paul, Site Agroparc84914 AVIGNON cedex 9 E-mail : [email protected]

David DELANNOY is a C.I.T. programmer and software engineer at INRA. He works in the AGROCLIM unit at Avignon and is in charge of the creation of agroclimatic databases (weather and weather-related data). In the CLIMATOR project he has created and maintained the database which brings together all the simulated data to do with the project and he created the web interface which enables it to be used.

David DELANNOY Unité AGROCLIMINRA, domaine St Paul, Site Agroparc 84914 AVIGNON cedex 9 E-mail : [email protected] Tél : 04 32 72 24 13

Michel DEQUE is responsible at Météo-France for the development, dissemination and maintenance of the ARPEGE-Climat model of the atmosphere, which arose from the prediction model of Météo-France and the European Centre for Medium-term Meteorological Prediction. This model can be used for global simulations when coupled with an ocean model like that of the IPCC, or for regional simulations, thanks to its variable grid (from 60km in France to 300km in the South Pacific).In the CLIMATOR project he has produced three scenarios (A1B, A2 and B1) for the 21st century and developed a technique to calibrate the model outputs over the daily series of the twelve stations for seven meteorological variables.

Michel DEQUE – Météo-France CNRM/GMGEC/EAC42, avenue Coriolis31057 TOULOUSE cedex 1E-mail : [email protected]él : 05 61 07 93 82


2EAppendix

Jean-Louis DURAND is a research scientist in the multi-disciplinary Research Unit on grassland and forage plants. He works on water use by plant stands and its relationship with mineral nutrition. His current work is to do with the various sources of variation in the depth of extraction of water by plants on a given soil: genetic vari-ation, atmospheric demand and root system architecture. In the CLIMATOR project he was responsible for the coordination of work on grassland.

Jean-Louis Durand INRA URP3FDomaine du Chêne- B.P.6 - 86600 LUSIGNANE-mail : [email protected]él : 05 49 55 60 94

Cédric FLECHER is a PhD student in Agronomic Sciences at SUPAGRO Montpellier. His work is part of a cooperation between INRA Avignon and LSCE (Laboratory of Sciences of Climate and the Environment). A graduate agronomist specialised in biostatistics and modelling, he developed the WACS-Gen weather generator under the direction of Nadine Brisson and the joint supervision of Denis ALLARD and Philippe NAVEAU. He suggested the innovative statistical methods necessary for the refinement of WACS-Gen and for the spatial down-scaling of climatic variables, leading to a methodology for evaluat-ing the sensitivity of crops climatic variability and its changes.

Cédric FLECHERE-mail : [email protected] ; [email protected]él : 06 78 34 42 78

Natalie GAGNAIRE is C.I.T. Programmer (ingénieur) at INRA in the Environment and Arable Crops (EGC) Research Unit. She partici-pates in the development of biophysical models for the evaluation of the environmental impacts of arable crops (CERES-EGC) and in making environmental balances by Life Cycle Analysis (LCA) in the agro-energy context (biofuels, heat). In the CLIMATOR project she has contributed to the simulation of crops and of microclimatic var-iables (wetting duration) according to different weather scenarios.

Nathalie GAGNAIRE INRAUMR INRA/AgroParisTech Environnement et Grandes Cultures78850 THIVERVAL-GRIGNON http://www.versailles-grignon.inra.fr/egcE-mail : [email protected] : 01 30 81 55 51

E 2


Appendix

Lydie GUILONI is a teacher-researcher at the SUPAGRO school of. She teaches ecophysiology and agrometeorology. She is a special-ist in the sunflower crop and has helped to develop several models of crop behaviour. In the CLIMATOR project she was responsible for the simulations of the sunflower crop, in particular with the SUNFLO model.

Lydie GUILIONI Montpellier SupAgro UMR 759 LEPSE2, place Viala, 34060 MONTPELLIER cedex 1 E-mail : [email protected]él : 04 99 61 29 57

Philippe GATE has spent much of his carrier at ITCF, which became ARVALIS-Institut du Végétal (Plant Institute) in 2002. Throughout his career he has tried to better understand the behaviour of ce-reals in their environment. His were the first predictive models for the developmental stages, and then for yield and grain quality. His interest in remote sensing led to the creation of Farmstar, a deci-sion support tool based on this discipline. He has also done work on the exploitation of ecophysiology in the biotechnology domain. For several years Philippe GATE has been interested in the physiol-ogy of the diseased and nutrient-deficient plant in order to pro-pose simpler and more efficient cultural practices. More recently his work has been concerned with the impacts of climate change. Since 2009 he has been the scientific director of ARVALIS.

Philippe GATE Arvalis – Institut du Végétal3, rue Joseph et Marie Hackin, 75116 PARISE-mail : [email protected] Tél : 01 44 31 10 00

David GOUACHE is a graduate agronomist of AgroPariTech. In 2006 he joined the ecophysiology team of ARVALIS-Institut du Végétal to take charge of studies on the physiology of cereals in their environ-ment. He has also developed a specific activity on understanding and predicting epidemics affecting cereals. From this he developed a model and a service for predicting leaf spot on soft wheat. He contributes to numerous national projects on this disease. In 2009 he became leader of the ecophysiology team at ARVALIS, which also works on the management of crops by remote sensing and on water management. Since 2007 he has been involved in different projects connected with the impacts of climate change on crops.

David GOUACHE ARVALIS – Institut du VégétalStation de La Minière78280 GUYANCOURTTél : 01 30 12 96 22


2EAppendix

Anne-Isabelle GRAUx is a PhD student in the grassland ecosystem Research Unit (UREP) of INRA. She models the impacts of climate change on grassland ecosystems and possible adaptations for live-stock systems. She has improved the simulation of the model of climatic impacts and adaptations, notably by developing animal and plant sub-models. The CLIMATOR project has served as a basis for localised projections for expected impacts as part of her PhD. She is also interested in possible adaptations for livestock systems via the use of an optimisation algorithm of agricultural practices, driven by the climate.

Anne-Isabelle GRAUX UR874 Unité de recherche sur l’écosystème prairialINRA, 234, avenue du Brézet, 63100 CLERMONT-FERRANDE-mail : [email protected]él : 04 73 62 45 72

Frédéric HUARD is a research graduate at INRA in the Agroclim Service Unit. He is a specialist in climatic and agroclimatic analysis and for several years has been interested mostly in climate change scenarios in collaboration with Météo-France. In the CLIMATOR project he was responsible for the technical com-ponents of the project with particular attention to making avail-able different scenarios, and the on-line course.

Frédéric Huard Unité AGROCLIMINRA, domaine Saint-Paul, Site Agroparc84914 AVIGNON cedex 9 E-mail : [email protected] Tél : 04 32 72 24 08

Laurent HUBER, was director of research at INRA. He worked on the role of the microclimate of arable crops on airborne fungal diseases at the “Environment and Arable Crops” Research Unit at Grignon. For five years he led the Biosphere-Atmosphere team and in partic-ular developed research on the dispersal of bio-aerosols and more generally on the biophysical mechanisms involved in the epidemi-ology of leaf fungal diseases in this Research Unit. In the CLIMATOR project, he was co-leader of the “diseases” group. At the moment Laurent Huber has scientific responsibility at ACTA, head of a network of agricultural technical institutes, whose main activity is concerned with plant health and the development of in-tegrated protection.

Laurent HUBERACTA-Réseau des Instituts des filières animales et végétales149 rue de Bercy75595 PARIS cedex 12E-mail : [email protected] : 01 40 04 49 05

E 2


Appendix

Bernard ITIER is director of research at the “Environment and Ar-able Crops” Research Unit (EGC) at Grignon. He works on the rela-tionship between agriculture and water: the dependence on the resource and the effect on the resource. He was chief of the “Bio-climatology” research department of INRA from 1992 to 1997 and “Environment and Agronomy” department from 1998 to 2001, then president of the INRA centre at Montpellier from 2003 to 2007. He led the expert group “Agriculture and drought” appointed by The French Ministry of Agriculture in 2005-6. In the CLIMATOR project, he was in charge of the “Water” topic.

Bernard ITIERUMR1091 Environnement et Grandes CulturesINRA-EGC, 78850 THIVERVAL-GRIGNONE-mail : [email protected]

Romain LARDY is a research scientist at the Research Unit on Grass-land Ecosystems (UREP) at INRA Clermont-Ferrand. He works on the development and checking of C and N cycles in a biogeochemical simulation model of grassland ecosystems, PASIM, as part of the European project NitroEurope. In the CLIMATOR project, he is responsible for data engineering for PASIM. He also contributed to the development of the model.

Romain LARDY UR874 Unité de recherche sur l’écosystème prairialINRA, 234, avenue du Brézet63100 CLERMONT-FERRANDE-mail : [email protected]él : 04 73 62 49 07

Eric LEBON is a research scientist at the Ecophysiology of plants under Environmental Stress Research Unit (LEPSE). He is an eco-physiologist, specialising in the study of the response of the vine to environmental stresses (water deficit in the soil and the air). He participated in the development of different biophysical models adapted to the study of the behaviour of vineyards. Among those, the water balance model of the vine (BHV), used in the CLIMATOR project, can be used to study the impact of climate change on the changes in the water supply conditions to vineyards.

Eric LEBONINRA Montpellier SupAgroUMR 759 LEPSE2, place Pierre Viala34060 MONTPELLIERE-mail : [email protected]él : 04 99 61 29 54


2EAppendix

Frédéric LEVRAULT is in charge of the “Research and Innovation” programme at the Regional Chamber of Agriculture at Poitou-Charentes. There he organises the dialogue between agronomic research (INRA, CIRAD, CEMAGREF etc.) and the agricultural com-munity, whether it be farmers themselves or their agricultural advi-sors. For five years he has led a group of irrigation advisors, using a version of STICS adapted to the problems of maize irrigation. Cur-rently he is involved in questions of climate change, renewable en-ergy, and monitoring and evaluation of farming activities. In the CLIMATOR project, he was responsible for the Green Book.

Frédéric LEVRAULT Chambre régionale d’agriculture de Poitou-CharentesAgropôle, B.P. 5000286550 MIGNALOUx-BEAUVOIRE-mail : [email protected]él : 05 49 44 74 50

Manuel MARTIN is a research scientist in the INFOSOL Service Unit at INRA Orléans. His work involves mainly the study and modelling of changes in the organic carbon reserves of soils. For this he uses models of the dynamics of soil organic carbon, such as RothC or CENTURY. It is mainly in this capacity that he became involved in CLIMATOR. He also took part in the choice and parameterisation of the soils which served to support the different simulations carried out in the project.

Manuel MARTIN INRA – INFOSOL Centre de recherche d’Orléans 2163, avenue de la Pomme de Pin, 40001 ARDON 45075 ORLEANS cedex 2E-mail : [email protected] Tél : 02 38 41 48 21

Albert OLIOSO works at Avignon in the “Mediterranean Environ-ment and Modelling of Agro-Hydrosystems”Research Unit (EM-MAH). His research is concerned with the modelling of the energy and mass exchanges (H2O, CO2) between the land surface and the atmosphere and the use of remote sensing to map these ex-changes, particularly evapotranspiration. The present objective of this work is the analysis of the impacts of climate change on the relations between water resources and agricultural production on the scale of aquifers or small production regions. In the CLIMATOR project his main contribution has been to analyse the impact of the atmospheric CO2 content on the reference evapotranspiration.

Albert OLIOSO AGROCLIM – INRA AvignonAGROPARC 84914 AVIGNON cedex 9E-mail : [email protected]

E 2


Appendix

Christian PAGE has been a research scientist for two years at the European Centre for Advanced Research and Training in Scientific Computation (CERFACS) at Toulouse. He is a specialist in the field of methods of downscaling climatic scenarios. His work enables him to provide data from climatic scenarios for research projects and climatic impact studies, together with expertise on these data. He therefore acts as a link between the community of climate and im-pact scientists and has to collaborate with several groups who work on different aspects of climate impacts. In the CLIMATOR project he was responsible for providing the downscaled weather scenarios and the downscaling methodology.

Christian PAGÉ Centre Européen de Recherche et Formation Avancées en Calcul Scientifique (CERFACS)Global Change and Modelling Team42, avenue Coriolis, 31057 TOULOUSE cedex 1E-mail : [email protected]él : 05 61 19 30 11

Philippe PIERI is a researcher in the “Ecophysiology and Functional Genomics of the Vine” (EGFV) Research Unit of INRA which belongs to the Institute of Sciences of the Vine and Wine (ISVV) at Bordeaux. He has led various studies on the ecophysiology of the vine (radia-tion interception, energy balance, water balance, photosynthesis, development, microclimate and epidemiology, etc.) and produced models describing these aspects as a function of the weather, the soil, the management system and the variety. Currently he is in-volved in problems of climate change and relations between the microclimate of grape berries and their maturation. In the CLIMATOR project, he was responsible for simulations involv-ing the vine and the epidemiology of Botrytis on the grape.

Philippe PIERI UMR EGFV – ISVV – INRA210, chemin de Leysotte, CS-50008, 33882 VILLENAVE D’ORNONE-mail : [email protected]

Dominique RIPOCHE is a C.I.T. programmer and software engineer at the Agroclim Service Unit. She has worked since 1994 on the STICS crop simulation model, used alone or coupled with other models, and whose originality lies in the way it can be adapted to very different species. Since 2006 she has been responsible for de-veloping and maintaining the crop model software and its inter-faces, and utilities specific to the models. In the CLIMATOR project she carried out simulations with the STICS model.

Dominique RIPOCHE Unité AGROCLIMINRA, domaine Saint-Paul, Site Agroparc, 84914 AVIGNON cedex 9 E-mail : [email protected]él : 04 32 72 23 84


2Appendix

Romain ROCHE is a researcher at the “Environment and Arable Crops” Research Unit (EGC). He works on the modelling of crop behaviour, par-ticularly the improvement or introduction of new modules of biotic (e.g. diseases) or abiotic (e.g. ozone) stresses. In CLIMATOR, apart from major participation in the Plant Health topic (the calculation of wetting durations and coupling the CERES model with a brown rust module) he took charge of the synthesis of the Yield aspects and for this purpose developed specific approaches to take account of CO2 according to the models.

Romain ROCHE UMR1091 Environnement et Grandes CulturesINRA-EGC, 78850 THIVERVAL-GRIGNONE-mail : [email protected]él : 01 30 81 55 06

Jean ROGER-ESTRADE is Professor of Agronomy at AgroParisTech. His speciality is the effect of cropping systems on soils (physical, chemical and especially biological components), a research programme which he de-veloped at the interdisciplinary agronomy Research Unit at Grignon. In the Climator project, he used the OTELO model, for evaluating the effect of climate change on available days and work schedule in arable farming.

Jean-Roger ESTRADE AgroParisTechCentre de Grignon, BP 01, 78850 THIVERVAL-GRIGNONE-mail : [email protected]él : 01 30 81 54 12

Jorge SIERRA is a Director of research at the Tropical Agrosystems Research Unit of French West Indies in Guadeloupe, and a teacher at the University of the West Indies-Guiana. He works on the behaviour of tropical soils, notably their carbon and nitrogen cycles, and on the recycling of organic residues. He has led several projects on soil man-agement under tropical crops (agroforestry, bananas, yams, maize) in collaboration with Caribbean partners and the local agricultural industry. Currently he is interested in the impact of climate change on tropical soils and crops. In CLIMATOR, he was co-leader of the Organic Matter and Nitrogen groups.

Jorge SIERRA UR1231 Agrosystèmes TropicauxINRA Antilles-Guyane,, 97170 PETIT-BOURGE-mail : [email protected]él : 05 90 25 59 49

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AppendixE 2

| Green Book | Appendix

Laurent TERRAY is director of research. He is the director of the Euro-pean Centre for Advanced Research and Training in Scientific Compu-tation (CERFACS) at Toulouse. He has worked for twenty years on the questions of variability in climate change. He contributed to the latest (2007) report of the IPCC. For several years he has been interested in the questions of climatic downscaling and in downscaling methods applied to climatic scenarios. In the CLIMATOR project he was responsible for the CLIMATE topic.

Laurent TERRAY CERFACS42, avenue Coriolis, 31057 TOULOUSE cedex 01E-mail : [email protected]él : 05 61 19 31 31

Printed by Maugein press

Maugein press is a member of the “Imprim’vert” (green press) and uses plant-based inks.

This document is printed on 100% PEFC-sourced paper, originating from sustainably managed forests.

PEFC/10-31-1508PROVENANT DE

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Copyright registered : June 2010Printed in France

ROClimate change, agriculture and forests in France:

simulations of the impacts on the main species

the green bookof the

project

Nadine Brisson and Frédéric Levrault

2007-2010R

O

What impacts might climate change have on French agriculture and particularly on its cropping systems in the course of the 21st century? To answer this question, 17 teams from 8 agricultural research and development organisations, coordinated by the National Agronomic Research Institute (INRA), collaborated for four years in the CLIMATOR research project (2007-2010), fi-nanced by the National Research Agency (ANR) as part of the programme “Vulnerability, Envi-ronments and Climate” (VMC).

To make a success of this study, these scientists combined climatic models with agronomic and forestry models to simulate the behaviour of cultivated plant stands under the effect of a cli-mate change. This approach, indispensable for exploring the future, brings together disciplines as varied as climatology, agronomy, ecophysiology, bioclimatology, soil science and statistics.

The future agricultural and forest impacts of climate change were analysed in terms of yield, quality of farm products, crop schedules, water requirements and crop health, without forget-ting possible displacements of crops. Thirteen sites, representative of the climatic and agricul-tural diversity of France, were studied in metropolitan France and overseas, while the systems selected represent a wide range of its agricultural diversity: annual crops grown in monoculture or rotation (wheat, sunflower, maize, sorghum, oilseed rape etc.), perennial crops (grapevines, grassland, forests, bananas, sugar cane), and with differing levels of inputs (rainfed or irrigated, conventional or organic).

This Green Book, published by Agency for the Environment and Energy Management (ADEME) brings together the main results of the CLIMATOR research project. Half-way between a scien-tific publication and a book for the general public, it is aimed at all those who, although not specialists in climate change, are concerned for the future of French agriculture: agricultural and forest managers, scientists and technicians, agronomists, government or company agents, teachers and students of agronomy or agriculture, representatives of associations.

The results presented are organised in four parts (methodology, topics, crops, regions) thus allowing everybody to choose what to read according to his or her main interests.

The Green Book published by

The partners of the CLIMATOR project

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Documents