Simone Bregaglio
email: simoneugomaria.bregaglio@crea.gov.it

Laure Hossard
email: laure.hossard@inra.fr

Giovanni Cappelli

Rémi Resmond

Stefano Bocchi

Jean-Marc Marc Barbier

Francoise Ruget

Sylvestre Delmotte

Remi Resmond

Françoise Ruget

Identifying trends and associated uncertainties in potential rice production under climate change in Mediterranean areas

Keywords: Adaptation strategies, Camargue, Lomellina, rice yields, STICS, WARM

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The effects of climate change on the future rice (Oryza sativa L.) production is still under debate in the three top producing countries -China [START_REF] Tao | Climate change, high-temperature stress, rice productivity, and water use in eastern china: a new superensemble-based probabilistic projection[END_REF], India [START_REF] Aggarwal | Climate change and rice yields in diverse agro-environments of India. ii. effect of uncertainties in scenarios and crop models on impact assessment[END_REF] and Indonesia [START_REF] Naylor | Assessing risks of climate variability and climate change for Indonesian rice agriculture[END_REF] -due to the counterbalance between the beneficial effect of rising atmospheric CO 2 concentration and the exacerbation of abiotic stresses on crop growth, such as heat [START_REF] Baker | Effects of CO2 and temperature on growth and yield of rice[END_REF], drought [START_REF] Wassmann | Climate Change Affecting Rice Production: The Physiological and Agronomic Basis for Possible Adaptation Strategies[END_REF] and salinity [START_REF] Redman | Increased Fitness of Rice Plants to Abiotic Stress Via Habitat Adapted Symbiosis: A Strategy for Mitigating Impacts of Climate Change[END_REF].

The available estimates are discordant, and range between an increase of 10-15% in 2020 [START_REF] Tao | Climate change, high-temperature stress, rice productivity, and water use in eastern china: a new superensemble-based probabilistic projection[END_REF] to 7-10% yield losses per every 1 °C increase in air temperature [START_REF] Peng | Rice yields decline with higher night temperature from global warming[END_REF][START_REF] Krishnan | Impact of elevated CO 2 and temperature on rice yield and methods of adaptation as evaluated by crop simulation studies[END_REF]. Given the leading role of rice as a staple food for humans (Fitzgerald et al., 2008;[START_REF] Soora | An assessment of regional vulnerability of rice to climate change in India[END_REF] and the need of doubling crop production by 2050 to meet the projected demand of the global population [START_REF] Ray | Yield trends are insufficient to double global crop production by 2050[END_REF], this research question is in the spotlight, and efforts are still needed to deepen current knowledge. In the meanwhile, rice growers and stakeholders of the rice sector are already implementing adaptation strategies, ranging from individual autonomous local reactions [START_REF] Dharmarathna | Changing the planting date as a climate change adaptation strategy for rice production in Kurunegala district, Sri Lanka[END_REF] to planned policy interventions [START_REF] Huang | Farmers' adaptation to extreme weather events through farm management and its impacts on the mean and risk of rice yield in China[END_REF], in order to alleviate the negative impacts of climate change in the main producing environments [START_REF] Howden | Adapting agriculture to climate change[END_REF].

Nevertheless, less research has been devoted to analyse the consequences of climate change on rice production in Europe (EU), where rice is the 6 th most produced cereal, with almost 4 million tons on about 650,000 ha in 2013, for an average yield of 6 t ha -1 (FAOSTAT, 2014). Although not being a staple food crop in EU, rice plays a pivotal sociocultural [START_REF] Picazo-Tadeo | Farming efficiency and the survival of valuable agro-ecosystems: a case-study of rice farming in European Mediterranean wetlands[END_REF] and ecological [START_REF] Longoni | Rice fields and waterbirds in the mediterranean region and the middle east[END_REF] role in several Mediterranean countries, where the human consumption is steadily increasing [START_REF] Ferrero | Rice Cultivation in the E.U. Ecological Conditions and Agronomical Practices[END_REF][START_REF] Worldatlas | The countries producing the most rice in the world[END_REF]. Italy, Spain, Greece, Portugal and France are the five top European producing countries, and present a higher per capita annual rice consumption (6-18 kg year -1 ) than in non-producing northern countries (3.5-5.5 kg year -1 , [START_REF] Maclean | Rice Almanac[END_REF]. The typical European rice production system presents paddy cultivation under continuous flooding during most part of the crop cycle [START_REF] Hill | Direct-seeded rice in the temperate climates of Australia, Italy, and the United States[END_REF], and water drainages to allow rooting, top-dressing fertilization, herbicide spraying and harvesting (Fusi et al., 2014). Cold temperatures are the main environmental constraint to rice production in Mediterranean countries [START_REF] Jena | Advances in temperate rice research[END_REF]. Damages to rice crop due to low temperatures can occur at any growth stage, being particularly severe at sowing, when they can undermine the germination capacity and the establishment of rice plants [START_REF] Ferrero | Agronomical constraints in rice culture: are there any possible solutions from biotechnology?[END_REF], and during microsporogenesis stage [START_REF] Dingkuhn | Climatic determinants of irrigated rice performance in the Sahel, I. Photothermal and microclimatic responses of flowering[END_REF], when high diurnal temperature ranges can increase the risk of pollen sterility [START_REF] Russo | Rice production and research in Italy[END_REF]. With increased temperatures due to climate change, heat stress could be more frequent on temperate rice crop. Recent findings in rice physiology indicate that heat stress causing sterility in the exerted part of the panicle is likely to occur even in Mediterranean countries, especially in warm and humid years [START_REF] Julia | Predicting temperature induced sterility of rice spikelets requires simulation of crop-generated microclimate[END_REF].

The complex effects of global warming and CO 2 increase on rice growth in Mediterranean countries comprise both detrimental and beneficial effects. The meta-analysis carried out by [START_REF] Wang | Response of rice production to elevated [CO 2 ] and its interaction with rising temperature or nitrogen supply: a meta-analysis[END_REF] reports a percentage yield increase of 24.5% at 660-699 ppm, which is mainly associated to an increase in the number of panicles and grains per plant. Likewise, available free air CO 2 enrichment experiments allowed to quantify the atmospheric fertilization benefits on temperate rice yields in the range 7-15% at 586-645 µmol mol -1 for japonica type varieties [START_REF] Kim | Effects of free-air CO 2 enrichment and nitrogen supply on yield of temperate paddy rice crops[END_REF], which are the most widespread in EU [START_REF] Confalonieri | Evaluation of CropSyst for simulating the yield of flooded rice in northern Italy[END_REF]. The increase in air temperature can lead to the decreased occurrence of cold sterility events, which currently are a major constraint to European rice yield. Potential positive effects of higher temperature are also related to the anticipation and the extension of the growing season length [START_REF] Peng | Climate Change and Rice[END_REF], which can favour the cultivation of longer cycle varieties, and to the improvement of plant photosynthetic rates. The latter will probably increase because of the thermal requirements of this tropical crop [START_REF] Borjigidai | Seasonal changes in temperature dependence of photosynthetic rate in rice under a free-air CO2 enrichment[END_REF] and due to the higher CO 2 concentration in the atmosphere. The temperature increase can also cause detrimental effects on future rice production, such as the shortening of the crop cycle, especially of the grain filling period [START_REF] Matthews | Modelling the impacts of climate change and methane emission reductions on rice production: a review[END_REF] and the increased risk of heat sterility during pollination [START_REF] Hirabayashi | qEMF3, a novel QTL for the early-morning flowering trait from wild rice, Oryza officinalis, to mitigate heat stress damage at flowering in rice, O. sativa[END_REF], and the loss of sink activity of the panicle [START_REF] Kim | Relationship between grain filling duration and leaf senescence of temperate rice under high temperature[END_REF]. As for other driving variables of the biophysical system [START_REF] Dieleman | Simple additive effects are rare: a quantitative review of plant biomass and soil process responses to combined manipulations of CO 2 and temperature[END_REF], the predominance of the complex interactions on the simple additive effects between temperature and CO 2 in affecting crop production, constitutes an additional source of variability in the future trends of rice yield [START_REF] Long | Food for thought: lower than expected crop yield stimulation with rising CO 2 concentrations[END_REF].

The complex array of uncertainties connected with the study of the impact of climate change on crop yield -including observed climate inputs, future CO 2 concentrations, climate model outputs and projected impacts (Challinor et al., 2009) -makes current projections inherently highly uncertain [START_REF] Godfray | Food security: the challenge of feeding 9 billion people[END_REF][START_REF] Asseng | Uncertainty in simulating wheat yields under climate change[END_REF]. An effective and standard methodological framework to quantify these multiple sources of uncertainty is still lacking (Burke et al., 2015), even if this became a topic by itself [START_REF] Wesselink | Equipped to deal with uncertainty in climate and impacts predictions: lessons from internal peer review[END_REF]. Moreover, the ranking of their importance is considered a required milestone to improve the understanding of the real impacts of climate change, and to enhance the credibility of climate change studies for policy makers (Ruiz-Ramos and Minquez, 2010).

To date, the impacts of climate change on rice yield are scarcely investigated in European production areas, where the impacts of temperature changes in the 21 st century are expected to be huge in response to even slight changes in large-scale climatic factors [START_REF] Gao | Increased aridity in the Mediterranean region under greenhouse gas forcing estimated from high resolution simulations with a regional climate model[END_REF], and to substantially vary at fine spatial scale [START_REF] Gao | Projected changes in mean and extreme precipitation over the Mediterranean region from a high resolution double nested RCM simulation[END_REF]. In most studies, the EU rice Mediterranean region is included in global scale simulations, but its results are never specifically discussed. For instance, [START_REF] Ray | Climate variation explains a third of global crop yield variability[END_REF] studied the relations between climate and yield variabilities in 1979-2008, showing that climate variability is responsible for 13-43% of rice yield variability in Mediterannean countries, with the average very close to the world one (0.29 and 0.31, respectively; data retrieved from Supplementary Material). A recent assessment of future rice yields as affected by climate change was performed in Italy using a single crop model [START_REF] Bocchiola | Impact of potential climate change on crop yield and water footprint of rice in the Po valley of Italy[END_REF].

Focusing on irrigation water use, it reports a general yield increase until mid-century and a decrease later on. Mediterranean regions are indeed considered as one of the most prominent climate change hotspots [START_REF] Giorgi | Climate change hot-spots[END_REF], because they lay in the transition zone between the arid northern African climate and the wet-temperate climate of central EU [START_REF] Mariotti | Long-term climate change in the Mediterranean region in the midst of decadal variability[END_REF].

This paper aims at exploring the main trends of future Mediterranean rice yields, and to quantify and rank the multiple sources of uncertainty related to climate and yield predictions. Two rice crop models were applied in two main European rice producing areas -the Italian Lomellina and the French Camargue -to simulate potential rice growth and development in current conditions and under a wide range of climate change scenarios, considering projections of four General Circulation Models (GCM) and two contrasting Representative CO 2 Concentration pathways (RCP; IPCC, 2014).

Materials and methods

Figure 1. Activities performed in this study, with associated synthetic input information. Stat: statistical analysis on the simulation outputs.

The workflow of the activities performed in this study is presented in Figure 1.

Step A led to the generation of the future weather scenarios (section 2.2) as input for the crop model simulations, starting from baseline weather data and using RCP-based GCM projections as input for a weather generator. In Step B, current weather data were used as input for the crop models, which were calibrated against measurements from field experiments performed in the two study areas (section

Development of adaptation strategies

Step A

Step B

Step C

Simulation of baseline and future potential yields Step D

Analysis of variance of simulated future yields

Step E Step C aimed at developing the adaptation strategies, which were implemented for crop model simulations (section 2.3.3). Input data were the baseline and future weather scenarios to derive (1) the relationships between farmers' sowing dates and air temperature, and (2) new crop model parameters, which were modified to reproduce alternative varieties to be tested in the future scenarios.

Model

Step D concerned the simulation of potential rice yields (section 2.3.3) in current and future weather scenarios, considering no adaptation, and the implementation of adaptation strategies. In Step E, an analysis of variance was performed to quantify the contribution of each source of uncertainty to yield variability (section 2.4).

Characterization of the study areas

The Italian Lomellina and the French Camargue were selected as two representative areas to evaluate the impact of climate change on European rice production (Figure 2). Rice crop was grown in 2015 on around 80,000 ha in Lomellina and on around 20,000 ha in Camargue, representing together 22% of the total EU rice harvested area (Ente Nazionale Risi, www.enterisi.it;FAOSTAT, 2014).

According to the updated Köppen-Geiger climate classification [START_REF] Peel | Updated world map of the Köppen-Geiger climate classification[END_REF] and to the environmental stratification of EU [START_REF] Metzger | A climatic stratification of the environment of Europe[END_REF] The typical agricultural management of rice fields presents direct seeding and continuous flooding until milky ripening stage, with two-three water drainages during the growing season to promote rooting and before the application of weed control products and top dress fertilizers (mainly urea, average dose 150 kg ha -1 ), which are usually split at sowing, tillering and panicle differentiation stage. The most widespread rice varieties mainly belong to the japonica ecotype, with main differences in the duration of the growing cycle.

Weed infestation and cold temperatures are among the main constraints to rice production during the crop establishment phase in both French and Italian rice areas, whereas spikelet sterility due to cold air irruptions around flowering (Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie, BBCH codes 61-69) often occurs leading to yield losses [START_REF] Mariani | Space and time behavior of climatic hazard of low temperature for single rice crop in the mid latitude[END_REF]. Camargue case studies, respectively (Figure 2). The available weather variables were daily maximum and minimum air temperature (°C), precipitation (mm d [START_REF] Danuso | CLIMAK: a stochastic model for weather data generation[END_REF], in order to generate the 20-years of future weather series to be used in the simulation experiment, following the procedure used by [START_REF] Confalonieri | Climate Change and Agriculture in Latin America (2020-2050[END_REF], [START_REF] Cappelli | Are advantages from the partial replacement of corn with second-generation energy crops undermined by climate change? A case study for giant reed in northern Italy[END_REF] and [START_REF] Paleari | District specific, in silico evaluation of rice ideotypes improved for resistance/tolerance traits to biotic and abiotic stressors under climate change scenarios[END_REF]. In this simulation experiment 34 weather series of 20-years were used, as the sum of the 2 baseline for Lomellina and Camargue and of 32 future weather scenarios, as the product of 2 RCP × 4 GCM × 2 future time frames × 2 study areas.

Simulation experiment design

Crop simulation models

The simulation of rice growth and development in baseline and future weather scenarios was performed at potential production level, i.e., defined by the genotype of the rice variety and by solar radiation, temperature and CO 2 concentration (De Wit and Penning de [START_REF] De Wit | La synthese et la simulations des systems des production primaire[END_REF][START_REF] Rabbinge | The ecological background of food production[END_REF]. We did not consider the limiting effects of water and nutrient availability on yield -the attainable production level -because rice-cropping systems are flooded and receive generally a high fertilization rate in both areas. We used the generic crop model STICS [START_REF] Brisson | STICS: a generic model for the simulation of crops and their water and nitrogen balance. I. Theory and parametrization applied to wheat and corn[END_REF] adapted for rice [START_REF] Ruget | Parametrization of a crop model using a regional agronomical database : rice in Camargue with STICS[END_REF] and the rice model WARM [START_REF] Confalonieri | An improved model to simulate rice yield[END_REF] to perform the simulation experiment, as these two models were developed under temperate conditions. Although they both simulate crop phenological development as a function of thermal time accumulation and share the main concept of radiation use efficiency (RUE, g MJ -1 , Warren Wilson, 1967) to reproduce biomass growth, they markedly differ in the formalization of the algorithms used to mimic crop growth and respond to weather input data (Table 1). Main differences between the two models are the time step to compute rice growth and developmenthourly for WARM and daily for STICS -and the formalization of the plant processes leading to the dynamic increase of leaf area index and to the partitioning of assimilates to leaves, stems and panicles. Hourly data of air temperature and global radiation as input for the WARM model were estimated according to [START_REF] Campbell | Soil physics with BASIC: transport models for soil-plant systems[END_REF] and [START_REF] Chen | Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications[END_REF] starting from daily data.

The WARM model daily updates the leaf area index (m 2 m -2 ) by multiplying the daily increase of leaves biomass (kg m -2 ) by the specific leaf area (m 2 kg -1 ), and computes leaves senescence as driven by a threshold of thermal time, whereas STICS calculates the leaf area index increase as

Models' calibration in current weather conditions

We calibrated the two crop models using phenological observations (dates of flowering and maturity) and dynamic field data of leaf area index (LAI, m 2 m -2 ) and aboveground biomass (AGB, t ha -1 ) collected in 10 experimental trials in Lomellina (years 1989, 1990, 1995, 1996) and in surveys in 10 farmer fields in Camargue (years 1987, 1988 and 2009). Full information on these datasets including the description of the site, the sowing date, the variety and synthetic data on crop development and growth are provided in Appendix A. The agronomic management of the fields aimed at growing rice crop at potential production level. Experiments were carried out under flood irrigation and non-limiting nitrogen conditions, in order to avoid water and nitrogen stresses during the whole crop cycle. Weeds were controlled in pre-sowing and post-emergence in both sites, and a chemical treatment against blast disease was applied around flowering in Lomellina experiments.

The field datasets refer only to japonica-type varieties, which were split in two groups according to the length of their crop cycle (japonica early and japonica medium).

AGB and LAI were measured several times during the crop cycle to characterize the crop growth dynamics, whereas yield was sampled at harvest. The AGB samples were oven-dried until constant weight, whereas LAI was sampled by destructive planimetric methods, according to [START_REF] Jonckheere | Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography[END_REF]. The BBCH scale [START_REF] Lancashire | An uniform decimal code for growth stages of crops and weeds[END_REF] was used to assess the main rice phenological stages: emergence (BBCH=10), flowering (BBCH=65) and physiological maturity (BBCH=89).

STICS and WARM were calibrated and evaluated separately for japonica early and medium varieties. For each group, 10 French and 10 Italian datasets were split into independent calibration and evaluation subsets to assure the applicability of the two models across study areas. The crop model calibration was performed independently for the two models via automatic optimization tools using the root mean square error between simulated data and measurements as the objective function, and tuning parameter values within their biophysical ranges. For both models, we started from default parameterization for rice crop. For WARM model, the parameters under calibration involved with phenological development were the thermal thresholds to reach flowering and maturity (°C day), whereas to reproduce organs growth the potential radiation use efficiency (g MJ - 1 ), the three cardinal temperatures for biomass accumulation (minimum, optimum, maximum temperature, °C), the partitioning of assimilated to leaves at emergence (0-1, dimensionless), and the specific leaf area at emergence and tillering (m 2 kg -1 ). In STICS, phenology was calibrated by adjusting the thermal time requirements (°C day -1 ) between (i) emergence and maximum leaf area increase, (i) beginning of stem elongation and maximum LAI, (iii) emergence and beginning of grain filling, (iv) flowering and maturity, and the leaf life duration (°C day -1 ). The calibrated parameters related to biomass accumulation were the maximum rate of gross leaf surface area production (m 2 plant degree day -1 ), the coefficient of decrease of maximum leaf area as function of plant density (dimensionless), the potential radiation use efficiency (g MJ -1 ), the speed of thermal increase of harvest index (g grain g plant -1 day -1 ), the maximum grain weight (g) and the maximum number of grains (grains m -2 ). The calibrated temperature response functions to simulate both phenological development and the limitation to radiation use efficiency are reported in Appendix C, as well as the model parameters and the results of model calibration and evaluation.

Simulations in current and future weather scenarios

The calibrated sets of model parameters characterizing current rice varieties were used to simulate potential rice yield in baseline weather scenario (1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010). Simulations were performed by setting May, 1 st as a fixed sowing date in the two study areas, according to available datasets, farmers' interviews and expert knowledge. The simulation of the future trends of rice yield in the two areas were performed adopting the same model parameters and sowing date than in baseline scenario, and by testing the implementation of three adaptation strategies: (1) the anticipation of the sowing date (sowing adaptation), ( 2) the adoption of varieties with a longer crop cycle (variety adaptation), or (3) the combination of sowing and variety adaptations (double adaptation).

The sowing adaptation corresponds to a shifting of the rice sowing date according to a mathematical rule, which was derived on baseline weather scenarios. Considering May 1 st as the average sowing date in current conditions, we computed the number of times when the weekly moving average of daily average air temperature was above 10°C [START_REF] Gao | An investigation of the growth season and climatic ecology of cultivated system of rice in China[END_REF] in the period March, 1 st -May, 1 st .

The application of this rule led to different values in the two study sites: in Camargue, i.e., 6.3 weekly averages were above 10°C in the specified two-months period, and 4.3 in Lomellina. Then, we applied the same rule and thresholds on the future 20-year weather series for Camargue and Lomellina, in order to derive the new sowing dates to be used in future weather scenarios (Table 2).

The variety adaptation was developed using a 1-year weather series, computed by daily averaging the 32 future weather scenarios resulting by all the available combinations of 4 GCM × 2 RCP × 2 sites. This choice allowed us to obtain average weather conditions from the GCM and RCP ensemble projections, leading to calibrate two model parameter sets, i.e., one for japonica medium and the other for japonica late variety.

The calibrated values of thermal time threshold to reach flowering and maturity dates in STICS and WARM were adjusted by running the two models on this synthetic year, using the average sowing date coming from sowing adaptation, and forcing the length of the crop cycle to match 140 days and 160 days, as representative of japonica medium and late varieties. Compared to calibrated sets of parameters, the values changes involved only the thermal thresholds, i.e. amount of the growing degree-days required to reach flowering and maturity stage in the two models. The ratio between the thermal time requirements in the periods emergence-flowering and flowering-maturity was kept equal to the model calibration in current weather conditions. team, 2015). The analysis of variance was performed using the 'aov' function and the RMSE was calculated using the 'rmse' function of package hydroGOF (Zambrano-Bigiarini, 2014). With no adaptation, the differences in simulated yield between the two sites were small in 2030, and the associated mean yields ranged between 6.3 t ha -1 (Lomellina) and 7 t ha -1 (Camargue), being lower than in baseline conditions by -4.5% ÷ -11.8% (average -8.2%) for Camargue and Lomellina, respectively. In 2070, a decrease of final yield was simulated in Lomellina (5.8 t ha -1 ) whereas in Camargue mean yield remained more stable (6.8 t ha -1 ), because of the beneficial effect of the temperature increase on rice growth rates, which counterbalanced the shortening in phenological development. This led, with respect to baseline simulated yields, to smaller yield loss in Camargue (-6.4%) than in Lomellina (-19.6%) in 2070. The implementation of double adaptation in Camargue led to mean yield in 2070 (9.6 t ha -1 ) even higher than in 2030 (9.4 t ha -1 ). On the contrary, mean simulated yield were higher in 2030 (9 t ha -1 ) than in 2070 (8.4 t ha -1 ) in Lomellina. For both time frames, yield increases, relatively to site specific baseline conditions, were lower in Lomellina than in Camargue, with a mean increase of +26% in 2030 and +16.9% in 2070 in Lomellina, while Camargue experienced average yield increases of +29.2% and +31.8% in 2030 and 2070, respectively. The cumulative distributions of simulated potential yields in baseline and scenarios in both sites and future time frames are displayed in Figure 4. Simulations with no adaptation led to the lowest yields in all conditions tested. In Camargue, values ranged from 4.5 t ha -1 to 8.6 t ha -1 in 2030 and from 1.6 t ha -1 to 8.5 t ha -1 in 2070. In Lomellina, simulated yields were even lower, ranging from 1.4 t ha -1 to 8.6 t ha -1 in 2030 and from 0 t ha -1 to 8.8 t ha -1 in 2070 (see Appendix D for details). The benefits due to the implementation of double adaptation were consistent, since it allowed simulated yields ranging between 6 t ha -1 and 11.8 t ha -1 in 2030, and between 3 t ha -1 and 12.2 t ha -1 in 2070 in Camargue. In Lomellina, simulated yields were in the range 2.5-12.2 t ha -1 in 2030 and 0-12.4 t ha -1 in 2070. The almost total yield loss (around 0 t ha -1 ) simulated in 2070 corresponded to the occurrence of massive heat stress due to extreme weather conditions, which were more frequent in Lomellina than in Camargue in almost all of simulations (90%) for each adaptation strategy (mean difference of 4 days for double adaptation, and of 3 days for no, sowing and variety adaptation; see Appendix E). We refer here to "heat stress" as the combined effects of panicle sterility simulated by WARM and of the reduction of grain number and weight simulated by STICS.

Results

Future trends of rice yield

Yield dynamics

Yield distribution

In baseline, the frequency of simulated yields leading to yield loss above 10% was lower in Camargue (8.75%) than in Lomellina (18.75%) (Figure 4). Similarly, a lower number of simulations resulted in yields gain above 10% in Camargue (1.25%) than in Lomellina (16.25%).

No adaptation and sowing adaptation led to much higher risks to get more than 10% of yield loss, as compared with the site-specific mean of the baseline scenario (more than 22% and 41% of simulated yields, respectively, see Figure 4). No adaptation and sowing adaptation led to more than 10% of yield increase in less than 10% and 5% of the cases in Camargue and Lomellina, respectively. The number of simulated future yields associated with more than 10% of yield loss with respect to the site-specific mean baseline was strongly reduced with variety and double adaptation (maximum of 5% and 20% in Camargue and Lomellina, respectively). Simulated yields for variety and double adaptation resulted in more than 10% of yield increase, as compared to the site-specific mean baseline, in most of the simulations (more than 87% and 69% of the simulated yields in Camargue and Lomellina, respectively). The risks of yields with more than 10% of yield loss were lower in 2030 than in 2070 for both sites, with larger differences between the two time frames in Lomellina (Figure 4). The fraction of simulated yields with more than 10% of yield increase was also larger in 2030 than in 2070, with larger differences in Camargue (e.g., 9% and 15% of the simulated yields with sowing adaptation in 2030 and 2070, respectively). The effect of the sowing adaptation on final yield was negligible in all conditions tested, as the distributions of simulations with earlier sowing dates were almost overlapped with the ones with current sowing date (May, 1 st ), whereas simulations with double adaptation were very similar than with variety adaptation. Figure 5 summarizes the effects of each factor by synthetizing its distribution (10, 25, 50, 75 and 90 th quantiles) for each factor modality (e.g., STICS and WARM modalities for the crop model factor). The ranking of the modalities' median values was stable for every factor whatever the adaptation strategy considered, whereas the associated yield variability differed according to the different modalities of each factor (Figure 5). Median yield values and yield variability simulated by WARM were higher than for STICS, this difference increasing with variety and double adaptation (Figure 5). The adoption of varieties with a longer cycle had an opposite impact on the yield variability simulated by the two models, with WARM increasing and STICS reducing the yield variability. The varieties with longer cycles displayed higher yields than early varieties, consistently between the four tested strategies. The yield variability associated to GCM was similar between the four modalities.

Quantifying yield uncertainty due to climate changes projections

Overview of the magnitude of each factor

Median yields for RCP 2.6 were higher than for RCP 8.5 for the four strategies, with lower differences for the variety and double adaptations. Yields simulated with RCP 2.6 were less variable than with RCP 8.5. Lomellina median yields showed a wider distribution, and lower values, than

Camargue yields for all adaptation strategies. The median yield difference between the two sites was lower with variety and double adaptation, as compared to no and sowing adaptation (Figure 5).

The median simulated yield variability was higher in 2070 than in 2030, while their ranking differed between no and sowing adaptations compared to variety and double adaptation. For variety and double adaptation, most of the simulated yields were above the median, and even the first quartile of baseline yield distribution (Figure 5). Thus, while the ranking of factors and modalities remained stable for the different adaptation strategies, there was a systematic yield increase when adapting the variety (variety and double adaptation), as compared to no adaptation and to sowing adaptation. The results of the analysis of variance performed for each adaptation strategy are presented in Table 3. The site was the most explanatory factor of simulated yields in future weather scenarios, either considering or not adaptation strategies (Table 3). On average, simulated yields in Camargue were higher than in Lomellina, with an increase of 0.8-0.9 t ha -1 (Figure 6), except for the simulations considering only the variety adaptation strategy (0.6 t ha -1 , see Appendix G). This site effect could be explained by the highest occurrence in Lomellina, as compared to Camargue, of extremely high temperature during the summer (Appendix F), inducing more days with heat stress during ripening (Appendix E). For instance, Lomellina highlighted, for the July-August period, 58% of years where maximum temperatures exceeded 36°C (i.e., sterility threshold), while it was only 36% of the years in Camargue (Appendix F). The ranking of the importance of the different factors was consistent when considering simulations with no adaptation, variety and double adaptation (Table 3). In these situations a decreasing impact of RCP, GCM, variety group and time frame on simulated yields was observed, whereas with sowing adaptation the effect of variety was higher than GCM, RCP and time frame.

Ranking the components of uncertainty

The crop model factor had a significant impact (p<0.05) on yields only with variety adaptation, with STICS leading to higher yields than WARM (0.3 t ha -1 on average, Appendix G). The variability explained with no and with double adaptation was higher (R 2 =0.33 and 0.34, respectively; Table F) and led to lower errors of prediction (~0.95 t ha -1 ) as compared to the ones with sowing and variety adaptation (~1.4 t ha -1 ) (Table F). The ranking of the factors' modalities was stable when considering no adaptation and double adaptation strategies (Figure 6). For these strategies, the highest difference between simulated yields was due to the GCM modality, with GISS leading to higher yields than HADGEM of about 1.1 t ha -1 . GISS and MIROC provided the closest yield estimation (0.2-0.3 t ha -1 ), although their difference was significant (p<0.001). RCP 2.6 always provided higher yields than RCP 8.5 of about 0.5 t ha -1 (Figure 6). The ranking of variety groups was stable for no and double adaptation, with yields of longer-cycle varieties higher than shorter-cycle varieties of about 0.4 t ha -1 .

Discussion

Lower yields in the future, except with adapted varieties

Our main objective was to assess the future rice yield trends in Mediterranean producing countries in the context of climate change, in order to provide information to be used by stakeholders in the EU rice sector. The results indicated that, without the implementation of adaptation strategies as anticipated sowing dates and adoption of varieties with longer crop cycle, average potential rice yield in the study areas would decrease by 8% in 2030 and 12% in 2070 time frame with respect to current conditions. Such yield reduction under climate change was already highlighted on other crops in France, e.g., maize and wheat [START_REF] Delecolle | Possible effects of climate change on wheat and maize crops in France[END_REF]. Potential rice yields in the future would be lower than average current yield in 69% and 67% of 2030 and 2070 simulations in Camargue, and in 83% and in 84% of 2030 and 2070 simulations in Lomellina. The main factors associated with these trends are the shortening of the crop phenological phases due to temperature increase and the rising occurrence of heat stress during flowering and ripening due to temperature extremes, especially in Lomellina. The latter represents a shift of paradigm with respect to cold stress, considered as one of the major yield-limiting factor in current conditions. In our simulations, the two models consistently simulated a low average number of days with heat stress in the baseline weather (0.52 days), and an increasing trend in 2030 (1.48 days in Camargue, 3.1 days in Lomellina) and 2070 (3.4 days in Camargue, 5.9 days in Lomellina), considering together no adaptation and adaptation strategies (Appendix D, tables D1-D4).

However, our results strictly depend on the two modelling approaches used to simulate this process, which do not represent the whole range of available models [START_REF] Krishnan | Impact of elevated CO 2 and temperature on rice yield and methods of adaptation as evaluated by crop simulation studies[END_REF][START_REF] Van Oort | A simple model for simulating heat induced sterility in rice as a function of flowering time and transpirational cooling[END_REF]. Moreover, the only model in this study simulating the percentage of heat sterility (WARM) did not implement a heat balance at canopy level, therefore it used hourly air temperature as the driving variable for heat sterility damage. This could potentially lead to its overestimation because of the lack of consideration of transpirational cooling at canopy level, which can smooth the effects of extreme hot temperatures during flowering [START_REF] Hasegawa | Spikelet sterility of rice observed in the record hot summer of 2007 and the factors associated with its variation[END_REF][START_REF] Julia | Predicting temperature induced sterility of rice spikelets requires simulation of crop-generated microclimate[END_REF][START_REF] Matsui | Lower-than-expected floret sterility of rice under extremely hot conditions in a flood-irrigated field in New South Wales, Australia[END_REF]. The emerging risk of heat stress in Lomellina is however proved by local rice growers and magazines [START_REF] Risoitaliano | Il grande caldo danneggerà il raccolto?[END_REF], reporting yield losses due to spikelet sterility in 2015.

Moreover, both the length of the crop cycle and the number of days with heat stress were largely correlated with simulated yields (e.g., R 2 = 0.472 and R 2 = 0.327 for simulations with no adaptation and with double adaptation respectively). We can then infer that these factors will be major determinants of rice yield variability in the future, in accordance with findings of Muller et al. (2015) for wheat and maize -for the length of the growing season -and of [START_REF] Deryng | Global crop yield response to extreme heat stress under multiple climate change futures[END_REF] for maize, wheat and soybean -for the impact of heat stress.

Another source of uncertainty in crop model applications in climate change scenarios is due to the model formalization for simulating the CO 2 response, as CO 2 atmospheric fertilization effect could deeply affect yield predictions [START_REF] Li | Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions[END_REF]. The CO 2 response functions implemented in the crop models used in this study (see section 2.3.1) act on the same process (i.e., RUE) and produce very similar outputs in the considered CO 2 range (380-660 ppm), starting to diverge at extreme high or low concentrations. This represents a limit of this study, given the simplistic and empirical representation of the CO 2 effect on yield as compared to more process-based Farquhar-type photosynthesis models, implemented in some rice crop simulators (e.g., [START_REF] Li | Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions[END_REF][START_REF] Confalonieri | A taxonomybased approach to shed light on the babel of mathematical models for rice simulations[END_REF]. Despite these limitations, a major finding of our study is that climate change, rather than being a threat, represents an opportunity for European rice growers, as the implementation of adaptation strategies could overturn the situation, leading to an average increase of 28% in 2030 and 25% in 2070 with respect to baseline mean yield levels. With the adoption of longer-cycle varieties, yields would be higher than baseline mean in the majority of cases (>76%), with however a larger frequency of lower yields in 2070, as compared to 2030, and in Lomellina, as compared to Camargue. The large differences in the yield frequency patterns in the two study areas can be explained by their climatic heterogeneity even in current conditions [START_REF] Metzger | A climatic stratification of the environment of Europe[END_REF], with Lomellina presenting more temperature extremes leading to cold (baseline) and heat (future) stress than in Camargue. Such site effect, even for close locations, was already highlighted in future yield studies on rice crop (e.g., in India, [START_REF] Krishnan | Impact of elevated CO 2 and temperature on rice yield and methods of adaptation as evaluated by crop simulation studies[END_REF]in China, Xiong et al., 2009), and on other crops grown in EU, e.g. winter wheat (in Denmark and Spain, [START_REF] Olesen | Uncertainties in projected impacts of climate change on European agriculture and terrestrial ecosystems based on scenarios from regional climate models[END_REF].

Factors of uncertainty for future yield predictions

In this study, the sources of uncertainty were ranked according to their impact on simulated yields, aiming at evaluating their importance on models predictions. This is considered an essential step to increase the reliability of the results of climate change studies for stakeholders and policy makers (Ruiz-Ramos and Minquez, 2010).

The site yielded as the most important source of variability in our analysis, even if the two Mediterranean rice growing areas considered here are very close in terms of distance, and similar in average climatic pattern. However, the field of validity of our results is limited to the narrow range of the explored climatic conditions, therefore the inclusion of additional sites would certainly affect our specific results and possibly change the ranking of the sources of uncertainty. The same considerations could be done for all the other factors, even if including the two extremes of RCPs and four different GCMs strengthen our findings. A limit of this study is represented by the application of only two crop growth simulators as impact models, as it is demonstrated that they can explore a limited part of the possible variation in yield prediction [START_REF] Li | Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions[END_REF].

Besides the expected predominant effect of study site, RCP and GCM were found to be high sources of uncertainty for yields simulated with the different strategies. The strong effect of RCP was expected, given the use of the two IPCC extremes scenarios, whereas the importance of GCM in explaining yield variability supports the need to adopt a multi-GCM approach to manage their difference to produce input data for crop modelling studies. The effect of these two factors on potential rice yield was much higher than the one of time frame and crop model in almost all cases.

While the site effect was also found by [START_REF] Olesen | Uncertainties in projected impacts of climate change on European agriculture and terrestrial ecosystems based on scenarios from regional climate models[END_REF] as one of the main factors explaining winter wheat yields, other studies demonstrated a high effect of the crop model on future rice yield quantification (e.g. Aggarwall and Mall, 2002;[START_REF] Matthews | Modelling the impacts of climate change and methane emission reductions on rice production: a review[END_REF], which was however not found in our study. [START_REF] Olesen | Uncertainties in projected impacts of climate change on European agriculture and terrestrial ecosystems based on scenarios from regional climate models[END_REF] also highlighted the magnitude of the effect of the method for scenario application, i.e., the adjustment or not of future climate series to past observed climate series. However, all these studies used different factors to simulate future yields with climate change (e.g., GCM, RCP, crop model, etc.), together with different modalities for these factors, making between-studies comparison not straightforward.

Effects of adaptation strategies

The consideration of adaptation strategies in climate change impact studies is mandatory, as it is proved that societies, organizations and individuals will autonomously react to in response to climatic changes in the same way they have adjusted their behavior in the past [START_REF] Adger | Successful adaptation to climate change across scales[END_REF].

This study reports that without adaptation, future yields are expected to be more variable, mainly due to the higher frequency of extreme weather events [START_REF] Olesen | Consequences of climate change for European agricultural productivity, land use and policy[END_REF][START_REF] Xiong | Potential impacts of climate change and climate variability on China's rice yield and production[END_REF].

In our study, crop-model simulations with adaptation strategies led to higher yields and yield variability, as compared to no adaptation and to baseline yields. A large increase in rice yields subsequent to variety adaptation was already shown in previous studies, e.g., in [START_REF] Matthews | Simulating the impact of climate change on rice production in Asia and evaluating options for adaptation[END_REF] with variety change towards no spikelet sterility; [START_REF] Krishnan | Impact of elevated CO 2 and temperature on rice yield and methods of adaptation as evaluated by crop simulation studies[END_REF] with variety displaying higher spikelet temperature tolerance. This last study also highlighted higher yield variability according to GCM with variety adaptation as compared to no adaptation. In our study, we showed that the variety adaptation was more efficient than the sowing one. Such results are in line with previous studies on rice and maize (e.g., [START_REF] Krishnan | Impact of elevated CO 2 and temperature on rice yield and methods of adaptation as evaluated by crop simulation studies[END_REF]Tingem et al., 209;[START_REF] Rotter | Use of crop simulation modeling to aid ideotype design of future cereal cultivars[END_REF]. There is a wide consensus on the need to evaluate the potential benefits of the implementation of adaptation strategies even in current climate, in order not to overestimate their impact in future conditions (Lobell, 2014). We performed this test by simulating the performances of the new varieties in the period 1991-2010 while keeping the original sowing date (May, 1 st ). We discovered that using adapted varieties in current climate lead to a major problem, which is the high number of cropping seasons in which physiological maturity is not reached. The application of the new parameter sets with the thermal requirements for adapted japonica medium varieties leads to the completion of the crop cycle in only 47.5% and 57.5% of the years in current conditions in Camargue and Lomellina, respectively (average of the two models). This situation is even worse with the new japonica late varieties, as the number of years in which physiological maturity is reached is even less (2 years out of 20) in both study areas. We can thus conclude that the adoption of longer cycle varieties is beneficial in light of the rising temperature associated to climate change projections. Regarding the sowing date anticipation per se, thus adaptation was not tested in current conditions. Indeed, we derived future sowing dates basing on current climate, considering the number of times when the weekly average of average temperature is above 10°C from March 1 st to May 1 st . Therefore, applying this rule in current climate leads to the sowing date already used to perform baseline simulations.

Methodological choices

The reported ranges of variation between future and current yields strongly vary in literature and are affected by many sources of uncertainty, including the choice of the crop models, the climate change scenario, the study period and the study region [START_REF] Hawkins | The potential to narrow uncertainty in regional climate predictions[END_REF]. In their review on the impacts of climate change on rice production, Matthews and Wassman (2003) reported that, according to the results of the IRRI/EPA project, future yield levels in Asia would range from -12.8% to +6.5% depending on the considered combination of the rice model and of the GCM.

These findings support the adoption of a multi-crop model and multi-scenario approach to quantify the uncertainty associated to the assessment of the climate change impacts on crop production. In this context, our methodological choices focused on the adoption of two crop models, which were already used to reproduce rice growth and development in the study areas, and on the use of the two extreme IPCC-RCP scenarios (IPCC's Fifth Assessment Report), as projected by four GCM in the short and long terms, using the methodology developed by [START_REF] Cappelli | Are advantages from the partial replacement of corn with second-generation energy crops undermined by climate change? A case study for giant reed in northern Italy[END_REF].

The choice of using four GCMs out of the 39 available in the study area [START_REF] Zubler | Climate change signals of CMIP5 general circulation models over the Alps -impact of model selection[END_REF] could represent a limit of this study, because we do not explore the whole range of variability in climate predictions. However, the extensive review performed by [START_REF] Burke | Incorporating climate uncertainty into estimates of climate change impacts[END_REF], including nearly 200 published papers in peer-reviewed and working paper series, reports three as the median number of GCMs applied to take into account the uncertainty in climate predictions on agricultural impact studies. Even if the GCMs' selection in our study was based on a random criterion, [START_REF] Zubler | Climate change signals of CMIP5 general circulation models over the Alps -impact of model selection[END_REF] demonstrated via hierarchical cluster analysis that the four GCMs we chose fall in separate clusters based on their temperature and precipitation outputs (corresponding to our study area), with GISS and NorESM presenting the largest dissimilarity. Moreover, [START_REF] Burke | Incorporating climate uncertainty into estimates of climate change impacts[END_REF] showed that the GCMs developed by the Hadley Centre (HADgem) is the most used in available literature (more than 50% of the papers), and we included it in our analysis. Therefore, our sampling of available GCMs to only four well-separated GCMs can be considered a reasonable choice, although we do not explore the whole range of available variability.

The biophysical adaptation strategies developed here -the anticipation of the sowing dates and the adoption of varieties with a longer cycle -are commonly tested in crop simulation studies aiming at assessing climate change impacts on crop production (e.g., [START_REF] Deryng | Simulating the effects of climate and agricultural management practices on global crop yield[END_REF][START_REF] Wang | Increased yield potential of wheat-maize cropping system in the North China Plain by climate change adaptation[END_REF].

They can be considered autonomous adaptations [START_REF] Olesen | Climate change as a driver for European agriculture[END_REF], as they represent short-term adjustments that are commonly implemented by farmers. We based our adaptation strategies on specific rules derived from the future climate projections used in our study, rather than applying a fixed number of days to shift sowing dates (e.g., [START_REF] Moradi | Adaptation strategies for maize cultivation under climate change in Iran: irrigation and planting date management[END_REF] or a fixed increase of thermal time requirements to develop adapted varieties (e.g., [START_REF] Tingem | Adaptation for crop agriculture to climate change in Cameroon: Turning on the heat[END_REF][START_REF] Tao | Adaptation of maize production to climate change in North China Plain: quantify the relative contributions of adaptation options[END_REF]. Our rationale was conservative, because we defined the future sowing dates basing on the relationships between the sowing period adopted by rice growers in the study areas and the current weather conditions. This led to a biophysical meaningful and literature derived rule, which was then applied to determine the new sowing dates to initialize crop model simulations in the future climate. The same logic was followed to plausibly determine the possible adapted varieties to be included in the simulation experiment. We started from expert knowledge of rice cropping systems in the two study areas to derive a standard duration of the crop cycle for medium and late varieties (140 and 160 days, respectively). The modification of model parameters to simulate adapted varieties involved only the thermal requirements to reach flowering and maturity stage, which were set to 1180 and 453 growing degree days (GDD, °C d -1 ) for WARM, and to 1170 and 610 GDD for STICS. The coherence of these new values with available genetic material is proved by the very similar values calibrated by [START_REF] Confalonieri | An improved model to simulate rice yield[END_REF] for Italian indica varieties with the WOFOST model (1170 °C d -1 from emergence to anthesis and 370 °C d -1 from anthesis to maturity). Similar thermal time requirements were also set by [START_REF] Li | Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions[END_REF] for the crop models SAMARA [START_REF] Dingkuhn | A cereal crop model to study G x E x M interaction and phenotypic plasticity, and explore ideotypes[END_REF] and STICS, which were calibrated using reference data collected on indica varieties in four sentinel datasets in Asian countries. The ratio between the duration of the emergence-flowering and flowering-maturity periods in our new varieties was kept as in current varieties, and it was on average 38% for WARM and 34% for STICS. This is also consistent with the above-cited studies, in which it ranged between 34% and 46%. Then, the varieties designed in this study toward the use in the near-and medium future could already be available for such adaptation, making it possible to improve future rice yields in the Mediterranean areas.

Conclusions

The variability of global rice production in a changing climate is a hot and controversial topic, and it still needs further investigation because of its prominence as a staple food in Asian top producing countries. Despite the huge number of crop modelling studies dealing with the impact of global warming on rice in tropical climates, the future trends of rice productions in Mediterranean areas is scarcely investigated and even more uncertain. Our findings indicate that the European rice sector has the potential to enhance current production levels, taking advantage of the increase of ambient CO 2 concentration and air temperature during the growing season associated with climate change conditions. The anticipation of rice sowing dates and even more the adoption of varieties with a longer crop cycle then represent effective adaptation strategies to increase rice yields and in turn farmers' income in the considered European rice areas in the short and long term.

Figure 2 .

 2 Figure 2. Location of the two study areas (A : Lomellina, B : Camargue). Black dots represent the locations of the meteorological stations

  mediated by air temperature. The partitioning pattern in STICS is driven by a dynamic harvest index, which is upper limited by the product of grain numbers by maximum grain weight, whereas in WARM it depends solely on crop development stage, with the weight of reproductive organs starting to grow after panicle initiation stage. The maximum value of radiation use efficiency is modulated in both models by temperature, radiation, development stage and CO 2 concentration, according to different modelling approaches. The two models differ in the simulation of the impacts of cold and heat stress on rice yield. The WARM model simulates a reduction of spikelet fertility due to cold and heat stresses acting in a critical time window around flowering, whereas STICS stops the grain filling process in post-anthesis while decreasing the number of grains during the ripening period as a response to low and high temperatures. The beneficial effect of increasing atmospheric CO 2 in future climate scenarios is simulated by the two models with empirical functions (Appendix B).

Figure 3 .

 3 Figure 3. Dynamic of simulated panicle biomass growth from panicle initiation to physiological maturity in baseline (black line) and in future weather scenarios, considering no adaptation (cyan) and the implementation of double adaptation (considering together sowing and variety adaptations, orange). Plotted values are the mean of daily data simulated by the two crop models in the baseline (solid black line), and in the future (i.e., considering all crop model × variety × GCM × RCP combinations for no adaptation -cyan dashed line, varieties japonica early and medium -and with sowing and variety adaptation -orange dashed line, varieties japonica medium and late) with associated standard deviation (extreme coloured lines, for future conditions only). Data are presented divided by site (Camargue, A and B; Lomellina, C and D) and time frame (2030, A and C; 2070, B and D). For each combination site × time frame, the average duration of rice growth stages is reported in number of days (Veg-vegetative period, from germination to panicle inititation; Rep-reproductive period, from panicle initiation to flowering; Rip-ripening, from flowering to maturity; Tot-total cycle length, from germination to maturity).

Figure 4 .

 4 Figure 4. Cumulative frequency distribution of simulated yields in site-specific baseline and in future weather scenarios with no adaptation and with the implementation of the three adaptation strategies, as compared to the simulations in baseline conditions for the two sites (Camargue, A and B; Lomellina, C and D) and the two time frames (2030, A and C; 2070, B and D). Red line indicates the site-specific baseline; Dotted black line indicates no adaptation; Dotted grey line indicates Sowing adaptation; Full black line indicates the Variety adaptation; and Full grey line indicates the double adaptation; Magenta lower and upper lines correspond to -10% and +10% of site-specific mean baseline yield, respectively. Numbers correspond to the frequency of simulated yields above or below the ±10% mean baseline yield, respectively.

Figure 5 .

 5 Figure 5. Boxplots of final yields simulated according to each modality (x-axis) of the six factors considered here (crop model, group of varieties, GCM, RCP, site and time frame). Plotted values refer to simulations with no adaptation (A), according to the implementation of sowing (B) and variety adaptation (C) and to double adaptation (D). Extreme values correspond to quantiles 10 and 90. STI: Stics; WAR: Warm; EAR: early variety; MED: medium variety; LAT: late variety; GIS: Giss; HAD: Hadgem; MIR: Miroc; NOR: Noresm; 2.6: RCP 2.6; 8.5: RCP 8.5; LOM: Lomellina; CAM: Camargue; 2030: Time frame 2030; 2070: Time frame 2070. In red are indicated median baseline yield (bold line), and 25 th and 75 th quantiles considering together the simulated yields of the two sites, the two varieties and the two crop models.

Figure 6 .

 6 Figure 6. Effects of the different factors on simulated yields in future weather scenarios. Results are presented for simulations with no adaptation (in black) and with double adaptation (i.e., sowing dates and variety, in white). The bars represent the yield difference between the two modalities, in the order presented in the Modality column. The significance of the effects are indicated on the right size (***: p<0.001; **: p<0.01; *: p<0.05; NS: not significant, i.e., p>0.05)

Table 1

 1

	Process	Sub process	WARM	STICS
		Emergence	Thermal time sum	Sowing depth and soil humidity
		Flowering and	Thermal time sum	Thermal time sum
		maturity dates
		Thermal time	Nonlinear function between	Trapezoidal linear function
	Phenology	accumulation	minimum, optimum and maximum temperature	between minimum, two optimum and maximum
				temperature
		Input	Air temperature	Crop temperature
		temperature
		Time step	Hourly	Daily
		Leaf area index	Driven by specific leaf area and	Driven by temperature
	Leaf area	increase	leaves biomass
	dynamics	Leaf senescence Thermal time, leaf life duration Thermal time, leaf life duration
			Net photosynthesis, a single	Net photosynthesis, three values
	Biomass accumulation	Photosynthesis	value of maximum radiation use efficiency	of maximum radiation use efficiency according to the
				phenological phase.

. Modelling approaches used by the WARM and STICS models to simulate plant processes, as employed in this study.

Table 2 .

 2 Simulation settings adopted for the simulation of potential rice production in current and future weather conditions, considering the implementation of sowing (shift of the sowing date) and Camargue significance of each factor was then evaluated using F-tests. Finally, the goodness-of-fit of the model was assessed by the adjusted R 2 and the size of the errors in the residuals (Root Mean Square Error, RMSE). All analyses were performed with the R software version 3.2.3 (R Development core

	Site	Time	Setting	Adaptation strategy
		horizon		No	Sowing	Variety	Double

variety (adoption of varieties with longer crop cycle) adaptation strategies. JE = japonica early, JM = japonica medium, JL = japonica late

Table 3 .

 3 Analysis of variance of mean future yields with and without adaptation strategies, considering together the two time frames (2030 and 2070), the two RCP (2.6 and 8.5), the four GCM (Giss, Hadgem, Miroc and Noresm), the two sites (Camargue and Lomellina), the two crop models (Stics and Warm), and the two groups of variety.

		No adaptation Sowing	Variety	Double
	Factor	d.f. MS p-value MS p-value	MS p-value	MS p-value
	Time frame 1 79 <0.001	30 <0.001	97 <0.001	35 <0.001
	RCP	1 137 <0.001	109 <0.001	166 <0.001	175 <0.001
	GCM	3 136 <0.001	130 <0.001	158 <0.001	157 <0.001
	Site	1 455 <0.001	564 <0.001	217 <0.001	410 <0.001

Sowing May, 1 st April, 21 st May, 1 st April, 21 st Variety JE, JM JE, JM JM, JL JM, JL 2070 Sowing May, 1 st April, 9 th May, 1 st April, 9 th Variety JE, JM JE, JM JM, JL JM, JL Lomellina 2030 Sowing May, 1 st April, 26 th May, 1 st April, 26 th Variety JE, JM JE, JM JM, JL JM, JL 2070 Sowing May, 1 st April, 20 th May, 1 st April, 20 th Variety JE, JM JE, JM JM, JL JM, JL2.4 Statistical analysisUncertainties in yield predictions attributable to the GCM, the RCP, and the crop model were evaluated with an analysis of variance together with the effects of the time frame, the site and the variety group. The contribution of each source of uncertainty was assessed by the Mean Squared Error (MS), calculated as the sum of squares divided by the associated number of degrees of freedom (df). MS was used to compare the contributions of the different factors to the total variability in simulated rice yields (the highest the MS of a factor, the highest its contribution). The

Acknowledgements

This project was jointly supported by Agropolis Fondation (through the "Investissements d'avenir" programme (ANR-10-LABX-0001-01)) and Fondazione Cariplo under the reference ID SCENARICE 1201-008. This research was also partly funded by FranceAgriMer (SIVAL 2015-0689) and the French Environment & Energy Management Agency -ADEME (Climatac project n° 1260C0044). The authors thank Jean-Claude Mouret and Roy Hammond from INRA UMR Innovation for field data acquisition.