The topics. This chapter deals in a cross-disciplinary way with the

Transcription

1 B The topics

2 The topics B This chapter deals in a cross-disciplinary way with the effects of climate change on crops, from a viewpoint which is meant to be complementary to industrial or geographical ones. Six major agricultural and environmental challenges are covered. B1 - Timing Changes in cropping schedules in response (mainly) to increasing temperatures. B2 - Water Changes in crop water requirements and aquifer recharge in response to changes in precipitation and evapotranspiration. B3 - Irrigation The future of irrigation as related to changes in the water balance and the advancement of development. B4 - Organic matter Changes in the soil organic matter stock (hence carbon) as related particularly with temperature and moisture conditions. B5 - Health Pest and disease pressures on cultivated species, as related mainly to atmospheric humidity and temperature. B6 - Yield Trends in yield, by definition an integrative variable, as related to all the soil and weather conditions experienced by the crop during its growth. These sections, which allow important analyses about the mechanisms involved, offer the reader a better understanding of the interactions between climate change and the growth and development of plants. 64

3 Advancement of phenological stages and shortening of phases Philippe Gate, Nadine Brisson 1 BTiming A Plant development* Temperature drives development The rate of plant development depends on the temperature, over a range which varies with the species considered. The minimum temperature for development is called zero growth (it is 0ºC for wheat, 4.8ºC for sunflower, 6ºC for maize, 10ºC for vines, 12ºC for sugar cane and 14ºC for bananas). Above zero growth, the rate of development is proportional to the difference between the daily temperature and that for zero growth, up to a maximum threshold, often about 30ºC for numerous cultivated species, (the sum of degrees-days notion: Durand, 1967). For all species, the main driver of development is therefore the temperature: in the event of rising temperatures expected with climate change*, one can therefore expect an advancement in the phenological stages. This will have variable consequences on crop cycles, depending on whether the crop is annual or perennial. For certain species like maize, sorghum, and sunflower, the high zero growth temperature can at present hinder very early sowings in certain regions. This general role of temperature may be modified in certain circumstances or species with particular behaviour. A brake on the driving effect of temperature in certain species The action of temperature can be limited by photoperiod (the period from sunrise to sunset) for certain species like small grain cereals in general and wheat in particular, forage grasses and oilseed rape. Hence a rise in temperature at a time when daylength is limiting (autumn, winter, early spring) will have less effect on the advancement of developmental stages, especially when the variety is photosensitive (daylength demanding) and when sowing is early in autumn. For certain species the transition from the vegetative stage to the floral stage requires a period of low temperatures, with a retarding effect if temperatures are too low or too high. Thus, paradoxically, too high temperatures during this vegetative stage of growth, which allows the plant to pass into a so-called vernalised state, results in a prolongation of the vegetative period. This scenario can occur for species like wheat, forage grasses or rape, but also for vines, for which one talks about dormancy. Seasonal timing of the growth cycle We work with three main types of species: winter species, sown in autumn, whose growth cycle extends from the end of August (rape) or the very beginning of October (wheat) until summer (June to mid-august); spring species, sown (according to the region) between April and May, with harvests at the end of September till the end of October; and finally perennial species like vines, whose period from flowering till maturity is situated somewhere between those of the two previous types. As for perennial crops harvested for their biomass (grasslands, forests), the whole of the year is put to use and it is rather the growth* capacity of the plants which needs to be taken into account. Green Book The topics Timing Philippe Gate, Nadine Brisson 65

4 B 1 Timing Geographical situation The local temperature regime, which can be calculated as a statistic over several years, differs from one region to another. In fact there is a north-south gradient but also a west-east gradient due to the semi-continental nature of France s hexagonal climate*. According to the seasons therefore, the temperatures are near to, or far from the cardinal temperatures of the species for development (i.e. the zero growth and maximum threshold values). This local element is important, as the advancement due to climate change will depend on both the estimated warming and this initial state. Moreover, for photosensitive species, the latitude needs to be taken into account. B Expected behaviour and variables studied All of these factors will result in differences in behaviour between species, as shown in figure 1. In fact, due to the seasonal timing of their growth, the spring crops can expect both advancement of developmental stages and a shortening of the grain-filling phase (sunflower, maize, sorghum). This is because the growth of these crops straddles the annual temperature peak, thus accentuating the mean warming effect. In comparison, the winter species do not have the same problems, since, as the whole growth period lies within the period of increasing temperature, the advancement allows the phases to occur at similar temperatures. However these considerations apply to fixed sowing dates; one can expect modifications to this scheme by varying the combinations of sowing date and varietal earliness. Winter crops Baseline Future Spring crops Figure 1: scheme for the difference in the impact of global warming on winter and spring crops, at a fixed sowing date. To analyse this behaviour, we have used three key variables: flowering, harvest and the length of time between these two stages, which represents the duration of filling of the harvested organs for the annual crops and vines crops on which our analyses will be focussed. As well as the variability between species and sites, special attention will be paid to the uncertainty introduced, either from the agronomic models or by the method of climate downscaling*. 66 Green Book The topics Timing Philippe Gate, Nadine Brisson

5 B 1 Timing C The advancement of phenological stages: the main impact of climate change Whatever the site, the crop, the agronomic model or the downscaling method, the advancement of phenological stages is significant: this therefore is a major result of our study, which has important consequences both on the farmer s technical decisions, including his work schedule, and on the ecophysiological behaviour of crops which will experience a time-shift between their developmental phases and the environmental stresses. Peculiarities of crops and sites The advancement of the harvest date (for the same sowing date) varies greatly between crops (tab. 1) and their relative ranking agrees with the indications given previously. In fact it is the vernalised and photosensitive winter crops (wheat and rape) which exhibit the smallest advancement about 8 days for the near future (NF*) and 16 for the distant future (DF*). As to geographical variability, note the relatively stable behaviour of wheat compared with that of rape, for which the advancement seems to be greater in the south than in the north. Periods NF DF Sites Wheat Maize Rape Sunflower Sorghum Vines Wheat Maize Rape Sunflower Sorghum Vines Avignon Bordeaux Clermont Colmar Dijon Lusignan Mirecourt Mons Rennes St Étienne Toulouse Versailles All sites Table 1: changes in the harvest date of 5 crops (at a fixed sowing date) as currently grown (late maturing varieties at Toulouse, Bordeaux and Avignon and early ones elsewhere).the significance of the change compared with yearto-year variability is shown as follows: Bold (p < 0.01), Italics (p < 0.05).Wheat is simulated with CERES and the other crops with the STICS model; the downscaling method is WT. At the other end of the scale one finds the spring crops, with maize exhibiting the most advancement (about 25 days for NF and 41 days for DF), particularly in the north. Vines are also greatly affected despite their perennial character which allows them to bring forward budburst and hence the beginning of growth. In general, we see that the advancement will be less pronounced for sites which are already warm. For two sites with opposing behaviour, Toulouse and Versailles, figure 2 allows an analysis of the partition between advancement of flowering and reduction of the grain filling period. We find that the winter crops exhibit mainly an advancement of flowering, and that the reduction in the grain filling period rarely exceeds 5 days (it may even be prolonged in rape). On the other hand, for spring crops, we should expect a marked reduction in the grain-filling period, detrimental to yield, of about days for maize in the NF (the biggest reductions occurring in the north) and of about 8 days for sunflower (less sensitive to geographical variation). As regards vines, the advancement of flowering, greater than that of maize, results in a smaller reduction in the maturation period than that of maize. This result is explained by the fact that the temperatures in autumn and in winter cause an earlier budburst and thus a bigger shift of the growth cycle towards spring, which limits the reduction of the maturation period compared with maize. This is an interesting example which foreshadows how the maize cycle might be modified by bringing forward the sowing dates. Green Book The topics Timing Philippe Gate, Nadine Brisson 67

6 B 1 Timing Figure 2: advancement of flowering (left) and modification of the filling period (right) for five crops and two sites (varieties adapted to each of them ) with the WT downscaling method (wheat simulated with CERES and other crops by STICS) and the two periods NF and DF. The effect of varieties As regards the advancement of flowering, an analysis of the role of varieties shows that it is marginal for wheat (the differences in advancement are about a day between the varieties Soissons, Charger and Arminda), the differences being small even for varieties sown in spring. For maturity the results show clearer varietal differences. For example for sunflower, the choice of variety results in differences in advancement of about 10 days between Prodisol, the early variety, and Mélody, the late variety which is brought forward more. In general, for spring-sown species, the advancement is greater for the late varieties. This is also true for vines, although the effect is smaller: grenache, the latest variety, is advanced slightly more than chardonnay and merlot. On the other hand, for the winter crops, we see the opposite trend, only smaller: the advancement is slightly greater for the early varieties (i.e. early at stem elongation). This distinction in behaviour can be explained by the fact that a late variety, sown in spring, will in the future experience more days with high temperatures. In the case of wheat, the late varieties are held back because of the daylength and more pronounced vernalisation*: they therefore profit less from the benefits of high temperatures. 68 Green Book The topics Timing Philippe Gate, Nadine Brisson

7 B 1 Timing Uncertainties Whatever the species, models or geographical locations, the QQ downscaling method always gives a smaller degree of advancement (fig. 3). It is a significant source of uncertainty usually greater than the difference between models (fig. 4). Figure 3: median values of advancement of flowering in days (for all the varieties studied) for three crops simulated by three different models. The differences due to the phenological models used in the different agronomic models are small, with however systematic trends: for example for wheat, STICS is always earlier than CERES or PANORAMIX. For vines, the big difference between the STICS and BHV models is because the first calculates the harvest date from sugar content accumulated in the grape berry while the second does so just from accumulated degrees-days (base 10) from flowering. Figure 4: advancement of flowering and shortening of the grain-filling period for sunflower, vines and wheat at Toulouse. Uncertainty associated with the agronomic models (STICS, SUNFLO, BHV, PANORAMIX) and with the downscaling methods of the climatic models. Green Book The topics Timing Philippe Gate, Nadine Brisson 69

8 B 1 Timing D Warming: the driver of advancement In an attempt to better identify the source of uncertainty due to the downscaling methods, which well represent the range of climatic-type uncertainty (cf. UNCERTAINTY and VARIABILITY sections), we expressed the advancement of stages as a function of mean increase in annual temperature, calculated for each site and downscaling method. This has the advantage of linking directly the advancement of stages with annual warming, characteristic of the most used future climatic scenarios (especially that of the IPCC*). For example for the flowering stage (fig. 5), the STICS model predicts a mean advancement of 5.8 days per degree of annual warming for wheat. This value is 8.6 days for vines with BHV. Figure 5: relation between the advancement of the flowering stage (j) as a function of mean annual warming (in ºC) per period and per site (for all varieties). Right for wheat with the STICS model and left for vines with the BHV model. The good quality of the fits (with a high r 2 ) justifies using these data to estimate simply the approximate magnitude of the advancement from the mean annual warming. Moreover, introducing into the calculation all the downscaling methods and varieties lends a certain robustness to the trends established. A synthesis of the models, downscaling methods and varieties gives approximately the figures in table 2 for advancement of flowering and harvest. Crop D flowering in d/ C D harvest in d/ C Wheat 5 6 Maize 5 15 Sunflower 4 9 Vines 8 10 Table 2: approximate advancement of flowering and harvest of wheat from the annual warming experienced by the crops, obtained by averaging all the results of the project for wheat, maize, sunflower and vines. 70 Green Book The topics Timing Philippe Gate, Nadine Brisson

9 B 1 Timing E The physiological consequences of advancement The advancement of growth stages enables the crops to suffer less from the deterioration in water supply (cf. WATER section), as shown by the difference in behaviour between early and late varieties. In fact varietal earliness has a significant effect on the water sufficiency*. For example for wheat, the water sufficiency of the early variety Soissons is impaired less than that of the late variety Arminda (fig. 6): at the same harvest date, the early variety has suffered less water stress* than the late variety, even on a deep soil with a high AWR. Although at present the value of the varietal earliness is small, especially on deep soils, it will become more and more important with the passage of time, with differences in water sufficiency in the order of 0.12 in the NF and 0.15 in the DF, even on deep soil. Figure 6: comparison of changes in water sufficiency, represented by the ratio ETR/ETM during grain-filling, for an early variety (Soissons) and a late one (Aminda) for two soil types with the WT downscaling method. The variability of sites and climates is represented by the harvest date on the abscissa and the sites are identifiable from their department number. We find similar results for spring crops and vines, which, in view of the shorter grain-filling period for early varieties, does not necessarily assure a better yield. There is a second beneficial effect of advancement for small grain cereals which will be explained in the following paragraph. It is to do with the reduction of the risks of heat stress* at the end of growth, due to the shift in the timing of the grain-filling phase. Green Book The topics Timing Philippe Gate, Nadine Brisson 71

10 B 1 Timing F Adaptation by the shift in the growth cycle The preceding results lead us naturally to envisage a change in sowing dates to optimise the growth cycle for the new climate. We did this prospective study for wheat and sunflower by varying the sowing dates and analysing the resulting changes in the climatic risks. The study of the technical possibilities for planting and harvesting for a wide range of dates will in the end reveal the feasibility of these changes for a typical arable farm. Changes in climatic risks during sensitive phases or the phenoclimatic risk Whether they affect the integrity of plants or severely restrict the yield components, the climatic risks studied are always detrimental to yield as regards quantity or quality (cf. VINES section). Suboptimal or freezing temperatures For wheat, it is both the risk of winter frost (lethal temperatures) and of apical freezing (a minimum temperature below 4ºC after the 1cm ear stage) which falls. However, as regards apical freezing, we should note that the high-altitude station (Clermont-Theix) is an exception for which the risk during stem elongation remains high. For all the other sites one can conclude that this type of risk will tend to disappear in the DF, with a frequency of appearance often falling below two years in ten from the NF, including for very early sowings. For sunflower, a similar analysis was made for the occurrence of suboptimal temperatures at the sensitive phases: a minimal temperature of 8ºC during the vegetative phase or below 15ºC between the floral button and flowering stages. These falling trends, particularly large for the DF, seem on the other hand insufficient for the NF when this kind of risk increases with early sowing. These events therefore restrict the use of escape strategies via earlier sowing or varieties. Super-optimal or heat stress temperatures The frequency of super-optimal temperatures at the end of growth (around flowering and during grain-filling) increases very strongly despite the advancement of stages. Hence for wheat, the number of heat stress days (maximum temperature above 25ºC) increases by 15-30% between the RP and the NF and by 40-50% between the NF and DF. Earlier sowing only slightly reduces this type of risk. Figure 7 illustrates, for sunflower, the change in the number of heat stress days during grain filling, for different dates of beginning of grain-filling at Toulouse. One sees very clearly a big increase in this risk with climate change, the increases expected for sunflower appearing even more pronounced than those of wheat. 72 Green Book The topics Timing Philippe Gate, Nadine Brisson

11 B 1 Timing Figure 7: changes in the occurrence of excessive temperatures (maximum temperate above 32ºC) during sunflower grain-filling for three periods at the Toulouse site and with the WT downscaling method. The role of advancement of stages in reducing the risk of high temperature damage We propose to assess the exact contribution of the advancement of developmental stages to the limitation of the risk of grain shrivelling (due to heat stress) in wheat. Figure 8 shows how we estimate this contribution: for the various dates of ear emergence (related to the sowing dates) there is a corresponding number of heat stress days, which increases as ear emergence occurs later. For a given ear emergence date, global warming (the red curve) should lead to a very large increase in this risk; however the advancement of the developmental stages reduces this increase by bringing forward ear emergence (black arrow); one can clearly see therefore the contribution of socalled natural advancement (in green) and what is missing (in red) to return to something akin to the present risk. It is also easy to identify the ear emergence date which, in the future, would enable the same risk level to be achieved, and to estimate the corresponding advancement. Figure 8: relation between the ear emergence date and the occurrence of the median risk of heat stress (the number of days on which the maximum temperature is above 25ºC during grain-filling): RP (in black); DF 9 (in red). Mons station (80); WT downscaling method. An advancement of stage of 14 days would reduce the risk by 5.53, leaving 4.27 in order to revert to the present risk; i.e. an additional advancement of 18 days. The application of the method described above to all the sites and with both downscaling methods is summarised in figure 9. We see that, for the NF, the natural advancement would be sufficient with the QQ method, but not with the WT method, illustrating once more the importance of the climatic downscaling method on possible farming decisions. For the DF, the natural advancement of stages is not nearly enough, whichever downscaling method is used. In terms of geographical variability, we see that, for the sites in the western zone (31,33,86), the risk increases very strongly. Green Book The topics Timing Philippe Gate, Nadine Brisson 73

12 B 1 Timing Figure 9: reduction in the risk of heat stress during grain filling by means of advancement (number of days); extra reduction needed to revert to present-day risk levels. Median values, A1B scenario (WT and QQ downscaling methods), variety Soissins sown October 20; sites identified by their department number. 74 Green Book The topics Timing Philippe Gate, Nadine Brisson

13 B 1 Timing Changes in dates of farming operations (sowing and harvest) The available days* for the operations of sowing and harvest were estimated by using the OTELO simulator for winter crops (wheat chosen) and spring crops (maize chosen) and by assuming an arable farm practising a maize-wheat-rape-wheat rotation. OTELO predicts the opportunities for farm-work from the trafficability of the soil (its ability to carry farm vehicles without compaction etc). For the simulations, we assumed that this farm possessed standard machinery for a 400ha. cereal farm with two Annual Work Units (2 tractors, 2 ploughs, one combine harvester etc.). OTELO works out the number of available days from decision rules about the amount of water in the available reserve in the plough layer (one should not attempt operations when the soil is too wet) and about the rainfall during the 2-3 days preceding the operation (no operations until the surface soil is dry enough). The program also calculates the work flow rate (taking account of restrictions in farmhands working hours), which make it possible to establish a work schedule. The results are then related to the operations on the farm, the seasonal rainfall distribution and the changes in soil moisture status; there is however no provision in the decision rules for the soil being too dry for soil tillage. To begin with we studied changes in the number of available days by assuming that the range of sowing and harvesting dates would remain unchanged in the future. We fix a time spread of 20 days on either side of the mean sowing date and calculate the number of available days in this period of time. Next we estimated by modelling the expected sowing and harvest dates and checked, still by calculating the number of available days in the 20 days straddling the corrected sowing (or harvest) date, whether the change altered the possibilities for intervention. To provide additional information about sowing possibilities, sowing date calculations were made with STICS. These calculations for optimising sowing dates take account of the germination physiology of the plantlet as regards the moisture content and temperature of the seed-bed (the temperature required for growth, absence of frost, seed-bed not too dry, etc.) together with technical constraints to avoid compaction (soil not too wet). Harvest For the wheat harvest, we checked that, in the RP, available days were not limiting: they were always more than 15 days (over a period of 20 days) at all sites. For the two future periods, this number approached 20 days for all the downscaling methods. For maize, in the RP, the number of days available for harvesting fell by about 7 days between a harvest at the beginning of September and one in early November, i.e. from 17 to 10 days. In the future this situation will be much improved, thanks in part to the increase in the number of available days at a given harvest date (on average 2-3 days are gained for the latest harvest, whichever method of climate downscaling is used) and in part to the earlier harvests. Sowing Obviously, earlier sowing constitutes a major advantage as part of adaptation* to climate change. Figure 10: mean number of days in the 20-day period straddling the date shown on the abscissa for wheat and maize sowing in the RP (measured weather data) for several sites of interest and a wide range of possible periods. Green Book The topics Timing Philippe Gate, Nadine Brisson 75

14 B 1 Timing Figure 10 provides a picture of the evolution of the number of days available for sowing maize and wheat in the RP for a wide range of sowing periods. Whereas deferring the sowing of maize tends to increase the number of days available to carry out the operation, the reverse is true for wheat. However there are big differences between regions, favouring the low-rainfall sites. Figure 11 shows that sowing maize earlier will not pose any problems in terms of days available for sowing in the DF, but could do so however in the NF for the earliest sowings at the sites in northern France. There is a slight difference between the downscaling methods, which is explained by different rainfall events. However both methods indicate a trend towards an increase in available days between NF and DF. Figure 11: change (compared with the recent past) in the mean number of days available for sowing maize in the NF and DF for the WT climatic downscaling method and several sites of interest and a wide range of possible periods. If now another model is used to calculate the optimal sowing date for another spring crop, sunflower (fig. 12), we again see that earlier sowing will become possible as long as it does not result in conditions which are too dry, as in the case of Toulouse in the DF, where bringing forward sowing is limited by winter drought. Figure 12: mean dates for sowing sunflower, calculated by STICS to optimise planting. Regarding wheat sowing, OTELO predicts an increase in the number of available days, for early sowing, of about 1-5 days, depending on the site, from the NF for the WT downscaling methods, whereas one has to wait until the DF benefit from this advantage for the QQ method. These results would tend to lead us, rather hastily, to conclude that there is no restriction on bringing forward wheat sowing dates. In reality, Fig. 13 shows that the dry soil conditions in the autumn will probably lead to sowing being delayed and that in any case the apparent trends are negligible compared with the year-to-year variability and the sources of climatic uncertainty. 76 Green Book The topics Timing Philippe Gate, Nadine Brisson

15 B 1 Timing Figure 13: mean sowing dates for wheat calculated by STICS to optimise planting at Toulouse (right) and Colmar (left) for four sources of climatic uncertainty (cf. CLIMATE and UNCERTAINTY and VARIABILITY sections) showing the year-to-year variability (standard deviation). Green Book The topics Timing Philippe Gate, Nadine Brisson 77

16 B 1 Timing What you need to remember 3 The advancement is significant for all species, to an extent that depends on where the crop growth cycle comes within the year. It is thus less pronounced for wheat and rape than for maize, sunflower and vines. For the latter, the advancement of flowering is added to a significant shortening of the grain-filling phase, which could be detrimental to yield. Pragmatically, it is possible to assign an order of magnitude to the advancement of stages from the predicted annual warming, which allows us to estimate the difference between all the scenarios available to us. Despite the advancement of stages, certain risks increase for all species. This is particularly so for water and heat stress after flowering (especially heat stress). The risk of frost falls for maize but remains a problem for sunflower, casting doubt on the success of avoidance strategies. Earlier sowing will be possible for spring crops, but will come up against problems of dry soil, which hinders planting, for winter crops. The possibilities for avoidance seem more promising by means of early varieties than from sowing dates, especially in regard to heat stress. What needs further study 3 The adaptation of phenology seems to be worth prioritising. Consequently, one could imagine complementary work to identify, for each site, combinations of sowing date and earliness which minimise the problems near the end of crop life (drought and high temperatures). The crop models used need more work in terms of development: genetic sensitivity to daylength, taking account of devernalisation phenomena, and the role of CO 2 concentration on development. Also, not knowing the exact values of the maximum temperature thresholds could result in estimation errors once the frequency of daily temperature exceeding these thresholds becomes significant. Concerning the expected impacts on yield, the effect of the interaction between the very high temperatures during grain filling and the increase in CO 2 concentration should be studied in detail. As to the number of available days, the effect of soil dryness is a decision factor to be introduced. We also expect a big improvement in the climate predictions, especially as regards daily temperatures and sequences of consecutive hot or rainy days etc. All things which, as we have seen, greatly influence these estimates. To find out more Brisson, N., Delécolle, R., Développement et modèles de simulation de cultures. Agronomie, 12 : Durand, R., Action de la température et du rayonnement sur la croissance. Ann. Physiol. Veg., 9,5-27. Gate, P., Le blé face au changement climatique. Perspectives Agricoles 336, Papy, F., and L. Servettaz Jours disponibles et organisation du travail. Bulletin technique agricole : Papy, F., C. Aubry, and J. Mousset Éléments pour le choix des équipements et chantiers d implantation des cultures en liaison avec l organisation du travail. Les Colloques de l INRA 53: Vocanson, A Évaluation ex ante d innovations variétales en pois d hiver (Pisum sativum L.) : approche par modélisation au niveau de la parcelle et de l exploitation agricole. Thèse Ina P-G, Paris, France. 294 p. 78 Green Book The topics Timing Philippe Gate, Nadine Brisson

17 Water stress and aquifer recharge Bernard Itier 2 BWater A The challenges Model predictions of precipitation Climate change* is often equated simply with the warming due to the greenhouse effect. One should not forget however that there will also be changes in rainfall (both in absolute terms and variability) due to the changes in temperature and cloud cover. Regarding water, there will be changes in the supply (rainfall) and the demand (reference evapotranspiration*). As to precipitation, the predictions of the climatic models are much less certain than for temperatures. In general the models predict an increase in precipitation at high altitudes and in the intertropical zone and a reduction in the Mediterranean zone. They disagree about the boundaries between the zones of increase and decrease of rainfall, which for some models pass over central/northern France and for others over the south. As part of CLIMATOR, we have used the variability* of the predictions from the downscaling* methods of the ARPÈGE* model of Météo France (cf. CLIMATE section), which covers the range of climatic uncertainties* (cf. UNCERTAINTY and VARIABILITY section). However, whichever method is used, there is a trend towards a reduction over the whole country. This reduction is more marked in the south for the near future (NF) period, and more marked in the west for the distant future (DF), the eastern part of the country thus benefiting from southerly moisture-laden winds. The south-west will be the region most affected, in view of the water shortages which already occur there occasionally. Water requirements of crops and availability of water for other uses: two important agro-environmental questions Following the example of the INRA group assessment "Drought and Agriculture" (Amigues et al., 2006), the CLIMATOR project is concerned with two relationships between agriculture and the water resource: Dependence on the water resource: this is about analysing the effect of the reduction in rainfall on the water sufficiency* of the plants, bearing in mind that the water requirements will also be affected by the other aspects of climate change, either through the evaporative demand itself (which will change with the cloud cover, the temperature and the atmospheric CO 2 concentration) or through the timing of the growth cycle (dependent on temperature) in relation to the annual variation in evaporative demand. Effect on the water resources: this is a case of analysing how the consumption by agriculture will change of a resource shared by other members of society. This estimation can only be made for complete cropping systems, including bare fallow periods, and not for individual crops. It is based on the notion of aquifer recharge* which, in the long term, corresponds to the difference between the precipitation and the water used by cropping systems (or natural environments). It applies to irrigated as well as rainfed systems. Green Book The topics Water Bernard Itier 79

18 B 2 Water B The variables studied, the concepts used and the mechanisms involved To understand water sufficiency and aquifer recharge, it is first necessary to define various agronomic and environmental variables. As regards the evapotranspiration of plant canopies, we will use: the reference evapotranspiration, ET0* (also called PET): the variation in ET0 from one year to another (and thus from one period to another) will depend on the antagonistic effects of changes in radiation, CO 2 and temperature (cf. influence of CO 2 on ET0 in the MODEL section); the maximum evapotranspiration, ETM*: on the annual scale the phenology* of the crop plays an important role. Global warming will therefore also affect the ETM via the shifts and shortenings of the phenological cycles. Depending on the models, the drastic reductions in leaf cover caused by water stress* during the vegetative phase could also reduce the ETM. Other differences in the calculation of ETM are mentioned in the MODELS section; the actual evapotranspiration, ETR*. The water sufficiency will be estimated from the ratio ETR/ETM during the production period (flowering-maturity for annual crops and vines, from 1 st May until 30 th October for deciduous trees and the whole year for conifers), while the water deficit, or potential irrigation need, will be estimated from the difference ETM ETR, calculated over the same period. These two values, water sufficiency and water deficit, will be compared with the agroclimatic index, P ET0, representing the difference between the water supply (P) and the evaporative demand (ET0) for the whole year. Aquifer recharge will be defined as the quantity of water from precipitation which is not used by the crop cover or natural vegetation. To get an idea of this, let's say that for the French mainland, out of an average annual total of 900mm of rainfall, 600mm are lost by evapotranspiration and 300mm are returned to the environment, i.e. to recharge aquifers and rivers. Of course irrigation water must be taken into account in this calculation, as it is part of the water cycle on the scale of the catchment basin if it is withdrawn from the groundwater or from rivers not supplied by water originating from outside the catchment concerned. Figure 1: diagram of the water balance of a cropped field. Figure 1 shows the different water flows affecting a cropped field. Between two dates, the variation in the water stored in the rooting zone (DS) results from the balance of the inputs-outputs to/from the soil. The input flows are: the rainfall, or precipitation (P), the irrigation for irrigated 80 Green Book The topics Water Bernard Itier

19 B 2 Water crop systems (Irr) and, if necessary if the drought is severe, capillary rise. The outputs are the actual evapotranspiration (ETR), the water flowing laterally as surface runoff (Ro) and the water percolating through the soil into the groundwater, often called drainage (D). The variation in the soil water store is therefore written thus (Cf Itier et al., 1997): S = P (+ Irr) ETR Ro D (1) Aquifer recharge from the cropping system (usually estimated for a year) will be called PERCOL*. PERCOL = D + Ro ( Irr) (2) Over a long period of time (for example 30 years of simulation of "periods" in the CLIMATOR project), one can say that PERCOL is the difference between the sum of precipitation and that of the water used by the cropping system. PERCOL ~ (P ETR) (3) Writing equation (3) for the reference period, or recent past (RP)*, and for the projected periods, the near future (NF)* and distant future (DF)*, leads to the relation: PERCOL ~ ( P ETR) (4) where is the difference between one of the projected periods and the RP. This means that any reduction in rainfall ( P), resulting from climate change will be shared between reduced consumption by crops and natural vegetation ( ETR) and a reduction in the aquifer recharge ( PER- COL) which, as shown in figure 2 and as we will see later, is bigger than ETR. Figure 2: distribution of the reduction in rainfall between green water (soil drying) and blue water (hydrological drying). Climate change, usually characterised in our latitudes by a reduction in annual rainfall, will therefore have two effects which will have to be studied for different cropping systems: a reduction in the availability of water for consumption by plants (soil dryness); a reduction in aquifer recharge (hydrological drought). Green Book The topics Water Bernard Itier 81

20 B 2 Water C Effect of climate change on the availability of water to crops Changes in water sufficiency Water sufficiency is showing a falling trend. Vines, a perennial crop, are experiencing a relatively small reduction in the southern areas where they are already "stressed" (fig. 3), whereas this reduction is greater for the northerly regions where at present the situation is easier. Water sufficiency for wheat, a winter crop, is changing little, undoubtedly because of a developmental shift in early spring (cf. TIMING section) combined with a slight shortening of the lifespan. For sunflower, a spring crop, the change is minor in the north but the reduction is serious in the south (Avignon and Toulouse). Standard deviation (%) < > Mons Rennes Bordeaux Lusignan Toulouse Versailles Mirecourt Dijon Clermont St Étienne Avignon Colmar ETR/ETM (%) Figure 3: changes in the ETR/ETM ratio for vines on a soil with a low available water reserve (73mm). Figure 4, which shows the changes in the ETR/ETM ratio for conifers and deciduous wood obtained from the BILJOU model, as a function of P ET0, shows a similar decrease. The range of values covered by the deciduous woods is lower than that of the conifers because the water sufficiency is not calculated over the same periods (cf. B). Figure 4: changes calculated by BILJOU of the ratio ETR/ETM for conifers (right) and deciduous forests (left) as a function of the agroclimatic index (P ET0). recent past, near future, distant future for twelve sites in France (abbreviated in the box on the right). 82 Green Book The topics Water Bernard Itier

21 B 2 Water Changes in the water deficit for various cropping systems The water deficit, ETM ETR, can be equated to a potential irrigation requirement. Here we will show how to calculate it and we will see in the IRRIGATION section the practical consequences in terms of changes in the supply of water to crops. This deficit depends on changes in ETM and in the ETR/ETM ratio mentioned earlier: ETM ETR = ETM x (1 ETR/ETM) The falling trend in the water sufficiency, ETR/ETM, leads to a slight increase in (1 ETR/ETM). Four factors will affect the changes in ETM during the flowering-maturity period (the period for calculating the water sufficiency for the majority of species): ET0, the crop coefficient (kc) which represents the influence of the foliage, the duration of the period from flowering till maturity and its seasonal timing in relation to the evaporative demand. The value of ET0, on a given calendar date, tends to rise because of the increase in temperature and radiation. This increase is, however, limited by the antagonistic effect of the CO 2, especially in C3 plants (wheat, sunflower, fescue, vines, conifers and deciduous trees), because of their ability to reduce their transpiration in response to the increase in the CO 2 concentration; in C4 plants (maize, sorghum), this limitation is barely active. The value of the crop coefficient can fall in extreme drought conditions in the vegetative phase, affecting the leaf area index*, which can happen in rainfed spring crops on shallow soils. Consequently it is the calendar timing of the period from flowering to maturity, by moving the whole life cycle, and the shortening of phases resulting from the higher temperatures, which will play the major role. for winter crops there will be an advancement* of the growing period, with a small reduction in the length of the phases. The main effect will come from ET0 which is lower in the early season as the radiation is lower: daily ET0 during the period from flowering to maturity will be lower; for spring crops there will be both an advancement of the growing cycle and a large reduction in the length of the grain-filling phase (cf. TIMING section). The advancement of the cycle will not greatly affect the ET0 as the phenological shift will move the grain-filling phase into a period of increasing temperature (unlike winter crops: cf. TIMING section); on the other hand, the shortening of the phases will result in a big fall: a similar daily ET0 but a shorter period from flowering to maturity. Thus, whatever the cropping system, ETM will tend to fall. In these conditions, ETM ETR, which represents the potential irrigation need, will be subject to two antagonistic effects which only simulation by crop models can separate. Green Book The topics Water Bernard Itier 83

22 B 2 Water The result obtained for the annual crops by the different models used leads to an increase in the water deficit between the RP and the NF, followed by a decrease between the NF and the DF despite the continued increase in the difference P ET0 on all the geographical sites for the index (cf. fig. 5 for wheat and sunflower). Figure 5 : changes in the difference ETM ETR for wheat (CERES) and sunflower (STICS) as a function of the agroclimatic index P ET0: recent past, near future, distant future. Hence figure 6 shows, for the different crops, a lowering of the overall regression of the water deficit, ETM ETR on P ET0. This slightly alleviates the water problem in a situation of declining rainfall, and this result agrees with the maps of irrigation quantities needed for growing maize at 80% of ETM, which indicate a lessening of irrigation in the DF (cf. IRRIGATION section). Once again, this underlines the close relationship between the effects of temperature and water on crop behaviour. Figure 6 : relations between the water deficit, ETM ETR, and the agroclimatic index, P ET0,obtained for wheat monocultures with CERES (left) and sunflower STICS (right) for all the sites and three periods of interest (RP, NF, DF). 84 Green Book The topics Water Bernard Itier

23 B 2 Water D Effect of climate change on the water resource: analysis of different cropping systems Water is a resource shared by different actors. The various systems of cropping and natural vegetation each behave differently as regards aquifer recharge. This is shown in figure 7 which compares the relationships (linear regressions) between PERCOL and P, the annual rainfall, obtained for the reference period (RP) over twelve sites in France for five cropping systems (monocultures of wheat, sunflower, maize irrigated to 80% of the ETM, vines and fescue) with the STICS model, and two forest systems (deciduous and coniferous) with the BILJOU model. In this figure, the higher a crop's regression line, the more water it returns to the environment. This figure therefore illustrates the environmental value of deciduous perennials (broad-leaved trees, vines) compared with evergreen species (fescue, conifers) which return less water to the environment. For annual crop systems, it shows clearly that a winter crop (wheat) returns more water to the environment than spring crops. Whilst this result is fairly intuitive for irrigated maize (because the irrigation water counts negatively in the balance), it is less so for rainfed sunflower. It is explained by the fact that, as for maize, there is a long period of bare soil in the monoculture sunflower system, with frequent showers when the water is lost by direct evaporation from the bare soil. Figure 7: linear regressions between aquifer recharge (PERCOL) and annual rainfall for different cropping systems or natural vegetation in the recent past from twelve sites. The regressions in figure 7, established from specific models and under the climatic conditions of the RP on twelve sites, raise various questions: For each system, do other models confirm these relations? (cf. section UNCERTAINTY and VARIABILITY). Will the geographical relationships established for different rainfall regimes apply to changes in rainfall due to climate change, i.e. occurring over time? Changes over time of aquifer recharge can be approached in two ways: by comparing the relationship between PERCOL and P for each system and both future periods; by deriving the relationship between PERCOL and P for each system, where represents the difference between one of the future periods and the RP (fig. 3). Green Book The topics Water Bernard Itier 85

24 B 2 Water Very generally, the relation between PERCOL and P does not vary greatly, except in the case of irrigated maize in the distant future. It is of the type PERCOL past = a x P past b, and changes to PERCOL future = a x P future b where a = a α and b =b β. This change can be illustrated by figure 8 for sunflower with SUNFLO and for vines with BHV. Thus, for a given value of P, one can estimate PERCOL = α P+β. The values obtained for α and β for the different cropping systems are sufficiently close to one another to allow us to define mean relationships: Between RP and NF: PERCOL = P ; i.e. + 16,5 to 500mm, + 5 to 600mm and 6.5 to 700mm. Between RP and DF: PERCOL = 0,.1825 P ; i.e to 500mm, + 12 to 600mm and 6 to 700mm. The highest value of PERCOL, for the same rainfall, represents the positive influence of accelerated development. However it is not just a matter of one component, since we know that the reduction in rainfall will reduce PERCOL (see below in figure 9, the association between the reduction in rainfall and the change in PERCOL at a given location). Figure 8: relation between aquifer recharge (PERCOL) and rainfall (P) for sunflower(cv Prodisol) with SUNFLO (left) and vines (cv merlot) with BHV (right), for three periods on the same soil, using the WT* downscaling method. Figure 9: changes in aquifer recharge (PERCOL) as affected by annual rainfall (P) at different locations, for wheat and sunflower with STICS ( recent past, near future, distant future). 86 Green Book The topics Water Bernard Itier

25 B 2 Water In the case of irrigated maize, one obtains with STICS, for the early variety Meribel, the relation: PERCOL = P + 41 between RP and DF, or 45mm for P = 500mm. A spring cereal, maize undergoes a considerable time-shift with a fall in the duration of growth phases (cf. TIMING section) which thus leads to a reduction in the ETM, which allows a smaller fall in aquifer recharge with falling rainfall. Although here we are concerned with the relation PERCOL vs P for a given system and location, one can see from figure 9 that the change over time is very similar to the change over space (a given colour representing the same site over the three periods). This result is confirmed by figure 10 which shows the change in PERCOL as a function of P for NF and DF for the same crops. Note that the fall in aquifer recharge is always more than about 50% of that of the rainfall; which will present problems for the recharge of aquifers, some of which are used directly for irrigation and all of which feed into rivers. The advantage of winter crops over rainfed summer crops, as regards aquifer recharge, diminishes in the future. We do not need to conclude from this that it will be worth replacing the former by the latter, as winter rainfed crops will continue to be feasible whereas the production of summer rainfed crops will be difficult to maintain (see SORGHUM and SUNFLOWER sections). Figure 10: decrease in aquifer recharge related to the difference in rainfall between the future (NF and DF) and the RP for wheat and sunflower for different sites with the STICS model. WT downscaling method and soil 1. E Effect of uncertainty (in downscaling methods, agronomic models) and variability (soils, varieties, practices) on the results Uncertainties (cf. section UNCERTAINTY and VARIABILITY) The climatic uncertainty, assessed with the WT and QQ downscaling methods, was analysed for PERCOL, and we found that the relationships obtained for the rainfed crops with the two downscaling methods were very much the same (differences less than 1/10 th of the rainfall), and identical for the irrigated crops (maize and vines). It is the same for the crop models* which give very similar values of PERCOL for a given cropping system*, since the same factors are well accounted for (drainage, runoff and irrigation). Green Book The topics Water Bernard Itier 87

26 B 2 Water For ETR/ETM, in view of the different calculation for ETM in the crop models, the comparison between models will be restricted to BILJOU and GRAECO for the conifers in figure 11, which shows the changes for identical times and places. Figure 11: comparison of the changes in the ratio ETR/ETM of conifers with BILJOU et GRAECO. The sources of variability* It is important to place the year-to-year variability, inherent in the climate, in relation to the other sources of variability. Estimating the variability attributable to several sources (the equivalent of a standard deviation) for the NF and the DF analysed separately, shows that year-to year variability is a major source of variability for PERCOL and that it represents more than 90mm/year. The geographical variability and the variability between systems are always more than the effects of climate change. The relative influence of the soils on PERCOL is small for the irrigated systems, both for evapotranspiration and for aquifer recharge. On the other hand they can be considerable for rainfed systems: a soil with a small AWR will always end up with bigger water deficits and more aquifer recharge. This inverse relationship with the water reserve is always found for wheat, sunflower, vines, grasslands and deciduous and coniferous woods, whichever agronomic model or downscaling method is used. As an illustration, we have shown in table 3 the amount of additional recharge on a soil with a low available water reserve* (AWR = 104mm for annual crops, 73mm for vines, 92mm for grasslands, 126mm for deciduous woods and 103mm for conifers) compared with a soil with a large available reserve* (AWR = 226mm), obtained on different cropping systems with three different models for three values of rainfall. 88 Green Book The topics Water Bernard Itier

27 B 2 Water Crop Model AWR Difference at 500 Difference at 600 Difference at 700 Wheat monoculture CERES Wheat monoculture STICS Sunflower monoculture STICS Sunflower monoculture SUNFLO Rainfed vines STICS Rainfed vines BHV Forage grasses PASIM Conifers GRAECO Conifers BILJOU Deciduous woods BILJOU Table 3: differences (in mm) in recharge between a soil with a small AWR and one with a large AWR (226mm), the difference in AWR (mm) being shown in column, for different rainfall values and different cropping systems. For ETR/ETM, the effect of the available water reserve is particularly marked for sunflower, a spring crop, and vines, whereas it is less clear for wheat, deciduous woods and conifers. The influence of cultural practices is illustrated by the change in density of vines grown on bare soil. With both STICS and BHV, PERCOL is higher at low plant density since the ETR is less than at high density: for P = 600mm, both models give differences of about 30mm between a density of and a density of plants/ha. The influence of the duration of rotation* of conifers, analysed by GRAECO, shows that the aquifer recharge decreases with the length of the rotation: for P = 600mm, the difference in PERCOL between a long rotation (80 years) and an average rotation (50 years) is about 15mm. The effect of varieties on water sufficiency is particularly convincing. For example figure 12 shows how the earliness of the Soissons variety allows, by escape, a better response to drought than that of the variety Arminda for both periods (the difference being up to 10%). For vines, this effect is also visible on soils with a small water reserve between the varieties Merlot and Grenache. For grasses, the values of ETR/ETM of fescue and ryegrass are similar for low values of P ET0, whilst the difference widens to the detriment of fescue for high values (0.8 for fescue and 0.9 for ryegrass at Avignon and Toulouse). Figure 12: changes in water sufficiency at 12 sites for two wheat varieties: Arminda (late) (left) and Soissons (early) (right). Green Book The topics Water Bernard Itier 89

28 B 2 Water F Dangers and opportunities: differences between regions In the future water will play an important role in the adaptation to climate change. Thus the adaptations to temperature increase will depend on the capacity of the water resource to cope with it. This applies to both rainfed and irrigated crops. For rainfed crops, the rainfall will need to be enough to allow the growth cycle to be completed in conditions compatible with profitable production. For irrigated crops, there will need to be enough water for irrigation. Without going into the question of the water supply capacity of the dams upstream of the cultivated zones, it is important to consider the question of the irrigation capacities of the zones which depend on their own aquifers. This is the case for almost the whole of the south-west of France. As this is the zone most affected by the decline in rainfall, it seems likely that the feasibility of growing irrigated monoculture crops in the future is highly questionable in the zones which are already somewhat marginal, at least in the region controlled by the Adour-Garonne agency (cf. REGIONS, IRRIGATION and MAIZE-SORGHUM sections). The north-eastern quarter of France will benefit from an increase in temperature without having to suffer a substantial reduction in rainfall. In this connection we might suggest the possibility of growing crops which at present are limited by low temperatures (cf. TIMING section). Perhaps we should raise the question of resiting certain industries? Will climate change affect water quality? One cannot give a simple answer to this question, as the climate changes expected will give rise to opposing effects. In fact the reduction in the amount of water percolating through the soil profile will tend to reduce the leaching of nitrate, while favouring an increase in its concentration in the layer of drainage water. At the same time, the increase in temperatures, favouring microbial activity, (cf. ORGANIC MATTER section), will increase the availability of mineral nitrogen to the plant but also for leaching. These opposing effects explain how the trends could reverse between the NF and DF periods. The analysis of different sources of variability shows that the cropping systems, site, soils, and particularly the year-to-year weather variability, have a much bigger effect on leaching than the climate change trend. This is illustrated by the differences between sites or between soils, (fig. 13). The large interactions which are typical of climate change mean that effects could exist, but only for certain combinations of sites, soils and systems. Figure 13: comparison of nitrogen leaching obtained with STICS at Rennes and Toulouse, with different cropping systems, for the three periods (A =RP, B =NF, C =DF) and the same soil. Among the cropping systems, grassland seems the most variable, as it provides large quantities of residues which mineralise, producing a lot of nitrogen which, if growing conditions become worse, (e.g. at Toulouse), will not be absorbed by the plants. 90 Green Book The topics Water Bernard Itier

29 B 2 Water What you need to remember 3 In France, Climate change will result in declining rainfall (the supply) coupled with an increase in the reference evapotranspiration (evaporative demand). This will be particularly marked in the west, affecting zones which are already vulnerable (the south-west). This imbalance between supply and demand, expressed by the index P ET0, will make itself felt : firstly on the water supply for rainfed crops. The ratio ETR/ETM will decrease for all the systems studied, while the virtual irrigation requirement (ETM ETR) will increase in the near future before decreasing in the distant future due to the shortening of the develop mental phases, secondly on the capacity to irrigate the irrigated crops (cf. IRRIGATION section). In fact the aquifer recharge (drainage + runoff irrigation) of all the systems (rainfed and irrigated) will decrease (roughly 2/3 of the reduction in rainfall will affect the "blue"* water), while the irrigation amounts needed by the crops will increase. This increase in irrigation rates will however be moderated by the fall in ETM resulting from the shortening of developmental phases. 3 These results are hardly affected by the downscaling method used, and they are in agreement from one model to another for all the cropping systems. The simulations can reveal, for the future just as for the present, the influence of the soils (on a poor soil, crop water needs less satisfied, but more aquifer recharge), varieties (early varieties suffer less water stress) and practices (denser canopies suffer more stress and return less water to aquifers). Note however that the year-to-year variation which remains predominant may mask these long-term changes. 3 All these things exacerbate the regional problem by way of the dangers (like the capacity to continue with irrigated monoculture in the south-west) and opportunities (like the possibility of growing maize in the north-east, so long as the high enough temperatures are not counter-balanced by low rainfall (see REGION section). Green Book The topics Water Bernard Itier 91

30 B 2 Water What needs further study 3 To make better predictions of the size of the water resource and the water supply to crops, it is important to make progress in the domains closely related to the CLIMATOR project. In particular we need better predictions of regional rainfall and better knowledge of the effect of the organisation of cropping systems on the water resource on the catchment scale (Itier et al., 2009). 3 As to CLIMATOR's own topics, the main point to explore in the future is the effect of optimising practices, mainly for spring crops which water shortage penalises in two ways: a serious yield loss for rainfed crops (whereas winter crops benefit naturally by escape when growth recommences); increased irrigation requirements for irrigated crops when the water resource is diminishing because of the fall in aquifer recharge from all the crops and natural vegetation, but also a diminishing supply due to the reduction in rainfall in the surrounding mountain regions. 3 Optimised sowing (in fact earlier) gives some hope of reducing these penalties (together with genetic improvement of crop water efficiency). Some preliminary simulations have not yet allowed us to draw conclusions. It will be necessary to systematize the approach in a sequel to CLIMATOR. To find out more Amigues, J.-P., Debaecke, P., Itier, B., Lemaire, G., Seguin, B., Tardieu, F. and Thomas, A. (eds.), Sécheresse et agriculture : réduire la vulnérabilité de l agriculture à un risque accru de manque d eau. Expertise scientifique collective, synthèse du rapport d expertise réalisé par l INRA à la demande du Ministère de l Agriculture et de la Pêche, INRA, Paris. 72 p. Itier B., Brisson N., Doussan C., Tournebize R Bilan hydrique en agrométéorologie. In: «Du couvert végétal à la région», Lagouarde J.-P., Cruiziat P. (eds.), École-Chercheurs INRA en Bioclimatologie, Le Croisic, 25-29/03/1996, INRA Département de Bioclimatologie, tome 2, Itier B., Le Bissonais Y., Merot P., Allain S., Brisson N., Caballero Y., Chanzy A., Gascuel C., Ferrand N., King C., Pauwels H., Sierra G., Walter C ARP/ANR ADAGE, Tâche 13 : «EAU et SOL» 92 Green Book The topics Water Bernard Itier

31 Changes in crop irrigation requirements Frédéric Levrault, Bernard Itier, Nadine Brisson 3 BIrrigation A Irrigation in France Historically practised in south-eastern France and to a lesser extent in the south-west, irrigation has developed strongly since the 1970s because of its profitability, (due to increased and stabilised yields), our ample water resources, the dry spells at the turn of the 1990s and the CAP, which introduced direct aid for irrigated schemes. From 1970 until 2000 therefore, the irrigated area tripled (from to ha, according to Agreste). Since then, due to increasing public misgivings and tighter regulations, it has fallen somewhat. In 2007, the irrigated area was ha (5.6% of the UAA) and the area equipped for irrigation was ha (10% of the UAA), putting France at about the average for the world. Irrigation has thus become a major feature of French agriculture in 40 years: of the professional farms in metropolitan France in 2007, (23%) were equipped for irrigation, and (20%) actually practised it. Irrigation is practised to a greater or lesser extent in all the regions and the threshold of 5% of the UAA is exceeded in 55 departments (fig. 1). Fraction of UAA less than 5% 5 to 10% 10 to 20% more than 20% Figure 1: fraction of the UAA equipped for irrigation in 2007 (source Agreste). Irrigation is used for a wide range of crops: maize, forage crops, trees, vegetable crops, cereals (other than maize), oil and protein crops, sugar beet, potatoes etc. However maize (including forage maize) accounts for half of the irrigated area (fig. 2 left), drawing attention and controversy to this cereal. Four regions alone represent three quarters of the area of irrigated maize: Aquitaine, Midi-Pyrénées, Centre and Poitou-Charentes (fig. 2 right). It is in these same regions that the imbalance between resources and abstraction is most problematic. Green Book The topics Irrigation Frédéric Levrault, Bernard Itier, Nadine Brisson 93

32 B 3 Irrigation Figure 2: distribution by crop of the irrigated areas in France (left) and the area of irrigated maize by region in 2007 (right) for a total of ha (source Agreste). Just as the influence of irrigation on the profitability of maize is considerable, so is its effect on the environment: out of 32.6 billion m 3 abstracted from the aquifers in France in 2006 (fig. 3), irrigation represents the third largest share (4.8 billion m 3 ie 11%) after the production of energy (19.1 billion m 3 ie 59%) and of drinking water (5.9 billion m 3 ie 18%). Expressed in relation to the irrigated area, this represents about 3 000m 3 per hectare, or 300mm. When one analyses the volumes of water not returned to the aquifers, (consumption) irrigation is the largest user. The management arrangements established after the 1990s, since revised, aim to establish a balance, year after year, between the availability of the resource and the requirements for irrigation (fig. 4). We see that the volumes of water used for irrigation vary markedly from one year to another according to the weather conditions: the national averages for maize are 1 600m 3 per hectare in 2002, 2 400m 3 per hectare in 2003 and 2 700m 3 per hectare in 2005 (source Agreste). 11% 18% Withdrawals 12% 59% Consumption 5% 3% 24% 68% Agriculture Energy Industry (without energy) Drinkable water Figure 3: fraction used by different sectors in the abstraction of water in France (from Leenhardt, 2007). These economic, geographical and environmental figures show that the impact of climate change* on irrigation is very important for French agriculture. If we begin to study these effects now it will assist our choice of adaptation to climate change* in terms of production methods. 94 Green Book The topics Irrigation Frédéric Levrault, Bernard Itier, Nadine Brisson

33 B 3 Irrigation State of decrees concerning limitation of uses: None No decree Planned measures Measures for limitation of use not in force but have been planned for the long term in case of necessity (framework decrees). 29 Measures already in force Level 1- restricted measures: all limitation measures for use up to one day/week or 15% of the volume on at least one catchment zone. Level 2 - strong measures: limitation measures for use at least one day/week on at least one catchment zone, but less than 7 days/week. Level 3 - total restriction: total restriction on at least one catchment zone. New departments concerned (restrictions updated weekly). Data source: Prefectures Mapping basis: IGN - BD GÉOFLA Figure 4: prefectorial decrees for limitation of water use at August 2009 (Source: MEEDM) B 2A B Climate change challenges for irrigation The practice of irrigation is widespread. Regional differences in climate change must be taken into account: we should not be thinking about "the effect" but about "the effects" of climate change. The irrigation of maize gives rise to many questions. However other crops are currently irrigated (wheat, sunflower, sorghum etc.) which must not be forgotten. One might even consider the value of irrigation for crops which are not currently irrigated (e.g. vines, grassland, rape) Climate change will affect the irrigation requirements of crops, and these can be studied as they are now. However the capacity to supply this water, which is related to the availability of the resource itself, remains a fundamental aspect of the problem. Finally, if questions arise about volumes of irrigation, so they do for its seasonal timing, which is dependent on the dynamics (recharge/release) of the aquifers. C Changes in the irrigation requirements of crops currently irrigated The question of irrigation requirements can be examined in different ways: one can choose to analyse "to the potential" which avoids any limitation of the water supply of the plants (ETR* = ETM*) (cf. WATER section fig. 6), or else accept the hydrological and agricultural reality of a varying degree of water shortage (ETR < ETM). We have chosen the second case, with irrigation able to supply 80% of the water needs of the crop in the case of maize, 70% for wheat and 50% for sorghum (irrigating when ETR reaches 0.8, 0.7 and 0.5 of ETM respectively). We distinguish crops for which irrigation is "central", typically maize, from those for which it is only given towards the end of life, typically wheat, and crops which may need occasional watering, such as sorghum or sunflower. Green Book The topics Irrigation Frédéric Levrault, Bernard Itier, Nadine Brisson 95

34 B 3 Irrigation Maize Figure 5 shows the changes in irrigation amounts needed to grow maize at 80% of ETM according to the difference between the rainfall and the evaporative demand represented by reference evapotranspiration ET0. Almost everywhere one can see an increase between the recent past and the near future, followed by a decline between the near future and distant future. This result, obtained with the WT downscaling method and in complete agreement with that shown in the WATER section for the difference ETM ETR, is explained by the correlations between the reduction in annual rainfall and the increase in temperature. In fact the diminishing amounts of irrigation required in the second part of the century are due to the shortening of the lifespan. Although this shortening has a beneficial effect on irrigation, we must not forget that it reduces the total amount of radiation intercepted by the crop and the duration of grain-filling, which, without varietal change, will also affect the yield (cf. MAIZE-SORGHUM section). Figure 5: changes in the amount of irrigation needed for growing a maize crop at 80% of ETM as a function of the water deficit (P ET0) at various places in France. Varieties used: DKC5783 in the sites* in the south and Méribel elsewhere. Soil with a high AWR* and WT downscaling method. This reduction in crop lifespan results in a different relationship between the amount of irrigation and the annual agroclimatic index (P ET0) as shown in figure 6. This different relationship explains, in particular, the change in the relationship between aquifer recharge and rainfall under irrigated maize for the end of the century (cf. WATER section), which will alleviate the pressure on the resource, given the declining rainfall (provided that longer-term varieties are not used!). Figure 6: relation between the irrigation requirement of maize and the water deficit (P ET0) for three periods (red: recent past, blue: near future, and green: distant future). 96 Green Book The topics Irrigation Frédéric Levrault, Bernard Itier, Nadine Brisson

35 B 3 Irrigation This being so, the demand for water for irrigating maize will increase in the near future as shown in figure 7, whatever the uncertainty represented by the two downscaling methods (WT* and QQ*). This extra requirement can be estimated at about 40mm. Figure 7: comparison of differences in irrigation requirements of maize between the NF and RP with two downscaling methods (soil 1 with a high AWR). However the influence of the year-to-year variability and the choice of soil remain predominant: for a deep soil they are estimated at about 60mm. Wheat On a deep silty soil (fig. 8 left), the irrigation requirements do not change much, remaining on average within a range of 40-60mm per year, and occurring mainly towards the end of growth. On the other hand changes do appear for the sites in the south (Toulouse and Avignon) and the east (St-Étienne, Colmar, Dijon) on soils with a small AWR (fig. 8 right) with irrigation needed before flowering. Apart from the Toulouse site, we do not see any significant fall by the end of the century, as for maize, as the advancement in developmental stages is not accompanied in wheat by a shortening of the phase subject to water deficit (cf. TIMING section). Moreover, we see that the increase in irrigation needs is greater with the WT downscaling method than with the QQ method. Figure 8: changes in the irrigation requirement for growing a wheat crop at 70% of the ETM as a function of the water deficit (P ET0) in various places in France on two arable soils, one with a high AWR (223mm) (left), and the other with a low AWR (104mm) (right). Green Book The topics Irrigation Frédéric Levrault, Bernard Itier, Nadine Brisson 97

36 B 3 Irrigation Figure 9: comparison of the differences in irrigation requirements of wheat between the NF and RP using two downscaling methods (soil 2, with a low AWR). Sorghum The supplementary irrigation of sorghum at Toulouse shows a similar trend to that for maize, with an increase in the NF of about 20%, followed by a fall in the distant future (fig. 10). As opposed to maize, this trend does not affect yield* (cf. MAIZE-SORGHUM section). There is, however, an increase in the variability of the irrigation, and if the mean values at the end of the century are about the same as in the RP, the 200mm of irrigation will be exceeded more often. Figure 10: changes in irrigation requirements of sorghum grown with supplementary irrigation (50% of requirements) at Toulouse. Soil1, WT climate downscaling method. 98 Green Book The topics Irrigation Frédéric Levrault, Bernard Itier, Nadine Brisson

37 B 3 Irrigation D New needs for irrigation Vines Irrigation of vines is very controversial, but as part of our prospective study we have analysed the changes in the amounts of irrigation needed for growing vines at 30% of the ETM on a soil with a low AWR (73mm) by drip irrigation, as is often found in vineyards. We see (fig. 11) that, apart from Avignon, where the requirements are rather stable and then decreasing in the DF, there is an increase in irrigation requirements for vines. However, as for maize, the increases are especially large between the RP and NF. Because the foliage remains on the vines throughout the season of high evaporative demand, this change in slope in the second part of the century is rather the result of the antitranspirant effect of CO 2, active in C3 plants (like vines) but barely so in C4 plants (like maize). Figure 11: changes in irrigation requirements for growing a crop of vines at 30% of the ETM as a function of the water deficit (P ET0) at various places in France on a soil with a low available water reserve (73mm). Grassland Except for a few isolated examples (the Crau grasslands for example, well represented by the Avignon site), grasslands are not currently irrigated. Figure 12 shows that effectively, in the NF, the rainfall in the traditional pasture areas, like Clermont-Theix, Mirecourt or Rennes, should be able to supply 80% of the needs of the crop. Figure 12: changes in average requirements of a fescue grassland to supply 80% of its water needs (AWR=92mm) simulated with the PASIM model and the WT downscaling method. Green Book The topics Irrigation Frédéric Levrault, Bernard Itier, Nadine Brisson 99

38 B 3 Irrigation However the increase in the water deficit in the future could generate significant irrigation requirements to continue to assure forage production. Thus, in the DF, the irrigation needed to supply 80% of the average water needs on a typical grassland soil will almost everywhere be more than 100mm. "Starter" irrigation Even for traditional rainfed crops like rape, sunflower or sorghum, a change in the planting conditions could necessitate the use of irrigation at the beginning of growth ("starter" irrigation) in order to start the crop off. Thus we see at Toulouse (fig. 13), from the 2030s, the seed-bed moisture conditions deteriorate, and also in the layer below, which could hinder the growth* of the young plants. Figure 13: changes in the gravimetric water content of an arable soil near Toulouse, on average, over the months of possible sowing of spring crops (March, April, May) with (left) the surface layer, and (right) the 10-30cm layer. The data have been smoothed (running means over 10 years), except for the month of April, in order to show the year-to-year variability. For rape (the example of Versailles in figure 14), the problem of establishing the crop will be particularly difficult in the near future (cf. OILSEED RAPE section) and, without the application of some additional water, there would be a risk of nitrogen deficiency. Figure 14: changes in the gravimetric water content of an arable soil near Versailles, on average, over the months of possible sowing of rape (August, September) with (left) the surface layer, and (right) the 10-30cm layer. The data have been smoothed (running means over 10 years),except for the month of August, in order to show the year-toyear variability. 100 Green Book The topics Irrigation Frédéric Levrault, Bernard Itier, Nadine Brisson

39 B 3 Irrigation E Year-to-year dynamics of water abstraction (example of maize) One difficulty confronting irrigation, when water is abstracted from the environment without any intermediate storage, is not so much the volumes abstracted annually as the timing (spring and summer) and the intensity of these abstractions, at the very time of year when the aquifers are vulnerable (so-called low-water levels). Figure 15: daily irrigation requirements (means over thirty years, smoothed over 7-day periods) of a maize crop irrigated to 0.8 ETM (for Lusignan and Versailles, early varieties; for Toulouse a late variety). Green Book The topics Irrigation Frédéric Levrault, Bernard Itier, Nadine Brisson 101

40 B 3 Irrigation In this context it is important to study how the advancement expected in the phenological cycles will affect the irrigation schedules, and hence the year-to-year dynamics of water abstraction. For this purpose will we examine here the case of irrigated maize in three geographical locations: Toulouse (late variety DKC5783), Lusignan and Versailles (early variety Méribel). The same criteria for triggering irrigation and the same soil (AWR = 226mm) are used for all these simulations. We see that the changes in irrigation patterns (mm/day on average per 30-year period) apply over the whole irrigation period (fig. 15). On the triggering date for irrigation This changes little at Toulouse, but is about ten days earlier at Lusignan and Versailles between the recent past and the distant future (this effect could only be accentuated by optimising the sowing dates, i.e. by sowing earlier: bear in mind that all the simulations were done with "fixed practices"). On the increase in requirements at the beginning of growth This increase is due largely to the increase in leaf area. It is shorter and more marked (with a steeper slope) for all three sites, starting in the near future. The increased temperatures are the cause of an earlier and more rapid increase in the leaf area of the crop. On the maximum irrigation requirements This is constant at Toulouse, slightly higher at Lusignan (+ 0,5mm/d), and distinctly higher at Versailles (+ 1mm/d). With the varieties used, the maximum leaf areas are reached in the recent past at Toulouse and Lusignan, but not at Versailles. On the decrease in irrigation requirements towards the end of growth This is slightly earlier and distinctly more rapid at Toulouse, earlier and more rapid at Lusignan, a little later and distinctly more rapid at Versailles. Generally, the irrigation requirements remain higher in the south than in the north of France, but relatively speaking the largest changes are seen at Versailles. For confirmation, these results should be complemented by an analysis of the water balances including the climate* and the soil, by taking into account the advancement of sowing dates, and by including a larger number of sites. The "local" character of these results is striking, due especially to the geographical variations in climate change and the diversity of agricultural practices/sowing dates and the choice of varieties in particular. These results should also be compared with the changes (as affected by climate change) in the variability of the water resource (cf. following ). In addition to studies on the value of increased water storage, comparisons of "irrigation/availability of the resource" will allow us to determine: at what point will this expected advancement in irrigation schedules compensate for the earlier and more severe low-water levels predicted by hydrologists because of climate change; what adaptations of irrigation practices should be introduced to boost the positive effects of developmental advancement (sowing dates, choice of earlier varieties) and hence to limit abstraction in the low-water level period. F On the scale of water management zones The study of the effect of climate change on the balance between availability from the environment and abstraction of water for irrigation (including for winter recharge of artificial reservoirs) will require the use of aquifer models (absent from CLIMATOR) coupled with climate models. Apart from changes in the amount of available water from rainfall in the upper regions of the catchment areas concerned, the simulations made in CLIMATOR have shown (cf. WATER section) that aquifer recharge by the different systems in the catchment area itself will fall by about two thirds of the reduction in rainfall. This is of major importance, not only in the catchments which can only be supplied from their own aquifers (e.g.: the Beauce plain, south west of Paris), but also for those with only small external sources (e.g.: Gascogne hills, on the left bank of the river 102 Green Book The topics Irrigation Frédéric Levrault, Bernard Itier, Nadine Brisson

41 B 3 Irrigation Garonne). For a reduction in rainfall of about 100mm over most of the sites in western and central France, this represents a deficit in the recharge of about 60-70mm/yr. Hence a major problem confronting catchment area managers will be to reconcile the fall in the recharge with the increase in demand for spring crops (about 40mm for maize), which, if the present land use patterns remain the same, will lead to an overall deficit of about 100mm for these crops. What you need to remember 3 The irrigation requirements of crops currently irrigated will increase. For maize, we can expect an increase of about 40mm on average in the present production areas of irrigated maize (Toulouse, Lusignan) between the RP and NF, which corresponds to two irrigations with current cultural practices. The increase in irrigation of wheat will only be needed in particularly unfavourable situations of shallow soils, with water applications needed before flowering. As to supplementary irrigation (e.g. for sorghum), we can expect to have to apply about 20% more in the NF than in the RP. In the DF, the effects of shortening of the lifespan of spring crops will bring about a stabilisation, or even a fall, in the applications of water which will only be effective if the varieties remain unchanged, obliging farmers to accept substantial reductions in yield. Although there is a lot of uncertainty between downscaling methods, there is no doubt about the rising trend. It is subject to year-to-year variability which can hide it. 3 New requirements will appear here and there for vines, grassland (for a more regular supply of forage throughout the year) or for annual crops like rape or sunflower. In fact for the last two, "starter" irrigations will be needed to assure the establishment and crop of the crop stand. 3 The advancement of irrigation schedules due to warming, intensified by the choice of early varieties, appears to be an effective adaptation to the reduced water availability. The probable decline in winter rainfall, generalised for the distant future, will cause difficulties with aquifer recharge. Projects for increased storage capacity for water for irrigation should take this into account. Green Book The topics Irrigation Frédéric Levrault, Bernard Itier, Nadine Brisson 103

42 B 3 Irrigation What needs further study 3 The irrigation requirements of crops should be studied not at a fixed level of water sufficiency but for a constant yield, which would make it possible to calculate the economics of water use. 3 The effect of sowing dates and the choice of varietal earliness should be studied carefully because they seem to offer a worthwhile way of saving water, especially for maize. 3 Our work should be coupled with models of aquifers to identify the possibilities for recharge from building new reservoirs dedicated to irrigation. To find out more... Amigues J.-P., Debaeke P., Itier B., Lemaire G., Seguin B., Tardieu F., Thomas A., Sécheresse et agriculture : réduire la vulnérabilité de l agriculture à un risque accru de manque d eau. Expertise scientifique collective, Rapport, INRA (France), 380 pages + annexes. Cabelguenne M. et al., Simulation de l extraction d eau et de la réponse des cultures en présence de contraintes hydriques diversifiées : Application aux grandes cultures du Sud-Ouest (blé, maïs, tournesol, soja, sorgho) In Comptes-rendus de l Académie d agriculture de France, vol 84-6, pp Debaeke P., Amigues J.-P., Face à la sécheresse et à la pénurie d eau, quelles mesures pour ajuster la demande agricole à l offre de ressource en eau? La Houille blanche n 3. Ducharne A., Impact du changement climatique sur les ressources en eau et les extrêmes hydrologiques dans les bassins de la Seine et de la Somme. Présentation au Colloque de restitution du programme GICC Gleyses G., Rhieu T., L irrigation en France. État des lieux 2000 et évolution. 60p CEMAGREF. Gresillo, J.-M. et al., Changement climatique et évènements extrêmes : crues, inondations, sécheresses. Que peut-on dire aujourd hui? Revue française de géotechnie, , pp Leenhardt D., Usage agricole et autres usages de l eau : comment mieux partager l eau? Conférence de l association «Les eaux et les hommes» Vittel, Lorgeou J. et al., Conséquences de l évolution des conditions climatiques des vingt dernières années sur la production de maïs grain et stratégies d adaptation. In Colloque «Changement climatique : conséquences et enseignements pour les grandes cultures et l élevage herbivore», Paris Octobre 2009, Sauquet E., Climat et aménagements de la Garonne : quelles incertitudes sur la ressource en eau en 2030? Présentation du projet IMAGINE marseille19et20mars2009/presentationsdesprojets/session3/sauquetrdtmarseille.pdf/file_view 104 Green Book The topics Irrigation Frédéric Levrault, Bernard Itier, Nadine Brisson

43 Storage and release of carbon in soils Jorge Sierra 4 BOrganic matter A Concepts and definitions The organic matter (OM) contains between 50 and 60% of carbon (C). Among its functions, the OM helps to preserve the structure and porosity of the soil (thus influencing water storage, aeration, and the risk of erosion), to stimulate biological activity and preserve the soil biodiversity, to supply nutrients to the plant (nitrogen, phosphorus, sulphur etc.) and to retain certain micropollutants (thus affecting water quality). The organic C content of the soil is the result of a balance between inputs and outputs over a given period. This balance may be positive (storage), negative (release) or nil. Variations in the balance on a field or farm scale, due to a change in land use or farming practices, affect all the agro-environmental functions just mentioned, and consequently the physical, chemical and biological quality of the soil as a whole. On the planetary scale, the quantity of organic C in the soils represents about three times that stored in the vegetation and twice that present in the atmosphere. This implies that a variation in the content in soils, for example due to climate change*, could have considerable impacts on the atmospheric C, involving an increase (with release) or a mitigation (with storage) in global warming. The quality and quantity of the inputs to the balance are determined by the vegetation and the management of the cropping system*. For example, in agriculture, the main inputs are the crop residues, the roots, and organic manures (compost, FYM etc.) In grassland, the inputs are animal excreta (on pasture) and dead and recycled roots following grazing or cutting. These C inputs undergo processes of mineralisation* and humification*, whose rates depend on the quality of the residues and the manures. The outputs of the soil OM balance arise mainly from its mineralisation and, in certain cases, from the erosion and leaching of its soluble organic compounds. The mineralisation and humification are carried out by the soil microfauna (insects, earthworms, etc) and microflora (bacteria and fungi), and are affected by the physical and physico-chemical conditions of the soil (temperature, water and clay content, ph, etc.). What is the effect of climate change in terms of storage/release of C? What are the variables which contribute to the vulnerability of the soil? What factors or processes can reduce a trend towards release? Should we expect a geographical gradient in the impact of climate change? These are the questions which we will try to answer in this chapter, after having revisited the main mechanisms involved on the response of the soil OM to climate change. B Mechanisms involved Figure 1 shows, in a simplified way, the C balance of a cropping system affected by climate change. The balance, for a given period, corresponds to the difference between the final and initial store. All the climatic variables affect the C inputs and outputs to various degrees. Hence the level of inputs depends primarily on the production of plant biomass, which in turn depends on the type of crop (for example C3* plants, such as temperate forage grasses, wheat, sunflower, oilseed rape, bananas and vines, respond better to an increase in CO 2 concentration than C4* plants such as maize, sorghum or tropical forage grasses: cf. YIELD section), the crop lifespan (e.g. the increase in temperature shortens the lifespan, which can reduce the total quantity of biomass: cf TIMING section), the growth rate* (e.g. an increase in temperature and CO 2 increases the daily growth of winter crops during their vegetative phase), and practices (such as fertilisation, irrigation, management of residues, etc.). Finally, the amount of residues (the quantity of C effectively humified) depends directly on the yield of biomass and on the quality of the residues and manures (e.g. humification is greater for better quality residues). Green Book The topics Organic matter Jorge SierraJorge Sierra 105

44 B 4 Organic matter Regarding the outputs, they are heavily dependent on the factors which control the biological activity of the soil, including mineralisation, mainly temperature and water content. The temperature plays a major role in the process of mineralisation: in the absence of other limiting factors, an increase of 10 C can double its rate. The effect of temperature on mineralisation explains the lower OM content commonly found in tropical soils (because mineralisation continues throughout the year) compared with temperate soils (with slow mineralisation in winter). However the micro-organisms can progressively adapt to climate warming and increase their optimum temperature for metabolism, which would have the effect of reducing their response to increasing temperature. Also, seasonal changes in ground cover (e.g. by perennial or annual crops) and farming practices (e.g. irrigation) have an effect on mineralisation by changing the soil temperature and water content. In natural or unfertilised systems, the nitrogen resulting from mineralisation is one of the factors which limits production of plant biomass (the green dashed line in figure 1). On the other hand in more intensive cropping systems, where the nitrogen is supplied mainly in the form of fertiliser, the yield of biomass is less dependent on the mineralisation rate. In this case, the inputs and outputs of C of the system act as flows which are almost independent of one another. Figure 1: factors and processes involved in the soil C balance. The examples given as evidence of the impact of climate change on the C content and the vulnerability of the soil result from a whole complex of interactions (e.g. the climate/plant/soil or temperature/co 2 interactions). Hence a change in a given climatic factor (e.g. temperature increase) may have simultaneous positive effects (e.g. an increase in plant biomass) and negative effects (e.g. increased mineralisation) on the balance. 106 Green Book The topics Organic matter Jorge Sierra

45 C Changing trends : effect of the cropping system and site* Table 1 explains the notation used for each cropping system. Figure 2 shows the variation in the OM in the near future* (NF) and the distant future* (DF). The positive and negative values of variation indicate an increase or a fall in the OM content respectively. By considering the mean values of storage/release the cropping systems can be classified as: positive, mostly corresponding to rotations (SWSgW, ORG and MWRW) and to FG in the DF, moderately positive (WW), neutral (V and MI in mainland France), rather negative (SS), and negative (tropical cropping systems: MI and BA in Guadeloupe). In order to understand these forms of behaviour, one has to specify certain choices for the management of residues, which were made outside the simulations. Table 1 shows the residues assumed to be incorporated for each cropping system. Straw not incorporated is assumed to be exported for livestock. B 4 Organic matter Systems SS Sunflower monoculture MI Irrigated maize monoculture V Rainfed vines WW Wheat monoculture FG Rainfed fescue grassland Residue straw and roots stubble and roots (straw and roots for tropical MI) wood stubble and roots roots + senescent biomass Systems MWRW Maize-soft wheat-rape-hard wheat rotation ORG Soft wheatfescue-fescuepea rotation SWSgW Sunflowerwheat-sorghumdurum wheat rotation BA Bananas Residue stubble for soft wheat and maize straw for rape and hard wheat + roots stubble for soft wheat straw and roots straw for peas and roots (W, P, G) aerial biomass and roots Table 1: notation and residues incorporated in each cropping system. The gradation observed in the storage capacity of rotations (e.g., SWSgW > ORG > MWRW in figure 2) is largely explained by the different quantity of residues applied (e.g. highest for SWSgW, cf. table 1). In the case of the ORG system, the manure applied for wheat (cf. AGRICULTURE section) and the large root biomass of the forage grass help to increase C storage in comparison with MWRW. On the other hand for the annual crops grown in monoculture (WW, MI and SS), these differences in storage capacity have more to do with losses of C than with their inputs. Thus, whereas the returns are larger for the SS system than for the MI and WW systems, SS has the more negative behaviour. This is explained by the lower ground cover of the vegetation, which favours higher soil temperatures and mineralisation rates. Moreover, although the returns in MI are slightly higher than those of WW in the NF, the situation is reversed in the DF because of the lowering of yields and returns for MI (cf. MAIZE-SORGHUM section). Also, the lower ground cover of the MI system and the more favourable conditions for mineralisation in summer (high soil temperature, adequate water content because of irrigation) encourage losses of C. For perennial crops, the neutral behaviour of V agrees closely with that of a system where the small increase in yields and residues at most sites just compensates for the higher mineralisation due to warming and the low ground cover. The storage behaviour of FG, notably in the DF, is explained by the relatively low mineralisation in the soils under grass (due largely to the high ground cover) and the substantial recycling of roots and senescent aerial biomass (tab. 1). The latter tends to increase in the DF due to the effect of water stress. Green Book The topics Organic matter Jorge Sierra 107

46 B 4 Organic matter The most negative systems for storage are the tropical systems (fig. 2). In the case of MI, the release is due mainly to the drastic reduction in yields (cf. WEST INDIES section) and in the returns. For BA, the yield and the returns are rather stable over time, and the factor which drives the release is the mineralisation, favoured by the increases in temperature and the soil water content caused by the rain. Figure 2: variations in the OM content for different cropping systems and sites (cf. AGRICULTURE section for the cropping system notation and the components of rotations). As well as the differences between cropping systems in France and the tropics, figure 2 shows that there is no clear geographical trend. For the systems storing the most OM in the DF (SWSgW, ORG, MWRW and G), there is however a slight tendency to more storage as one goes from the south-west to the east (e.g. from Toulouse to Dijon in figure 2) which is partly associated with a smaller reduction in rainfall (cf. CLIMATE section). It seems therefore that the effect of climate change on the OM is localised and depends on the interactions between the cropping system and the site, figure 2 showing a very variable behaviour from one site to another for the same cropping system. The test for thermal* adaptation of soil micro-organisms has shown that this mechanism can play a significant role by reducing losses of C by mineralisation (fig. 3). On average, the magnitude of this reduction of losses is estimated at about 35%. Furthermore, for MI at Toulouse, taking account of the adaptation varies its behaviour, which passes from slight release in the NF to slight storage in the DF. It seems therefore that the adaptation of the micro-organisms could partially change the trends discussed above, by reducing the release and increasing the storage in the different cropping systems. 108 Green Book The topics Organic matter Jorge Sierra

47 B 4 Organic matter Figure 3: effects of thermal adaptation of the micro-organisms on the variation in OM content for the two systems and the two regions tested. ( ) without adaptation, (+) with adaptation. D Dynamics of the organic matter and yield Is variation in yield a good indicator of the variation in soil OM? In this section we will analyze that question. With figure 1 we have shown that the behaviour of the plant and that of the OM can be linked in two ways: firstly: plant biomass return input to the OM and mineralisation of the OM plant nutrition plant biomass. The second way can be ignored in this analysis, as all the cropping systems were fertilised and in consequence the plant nutrition depended little on the nitrogen released by mineralisation. Figure 4: relations between the yield variations (tons/ha) and OM (tons C/ha). Each short line connects the value for the NF (without a symbol) with the value for the DF (marked with a triangle, whose colour indicates the site concerned). Figure 4 shows the relation between the variation in yields (ΔYIELD) and that of the OM content for the WW and SS systems. A simple and direct relationship between yield and OM (i.e. a simultaneous increase or decrease over time) would be indicated by the presence of lines in quadrants 2 and 3 (numbered in red) only, and aligned in the direction indicated by the black dotted arrows. Figure 4 shows that the distribution of the points and the orientation of the lines do not, in most cases, follow these trends. Similar results were obtained with other cropping systems. These results imply that the variation in OM does not directly reflect the changes in yield, as other factors prevent a simple relationship between the two variables (in particular all the factors which affect the mineralisation rate, described in figure 1). Green Book The topics Organic matter Jorge Sierra 109

48 B 4 Organic matter E Sources of uncertainty and variability (including geographical) In this subsection we complete the analysis of the variables which affect changes in the OM, by referring to figure 5, which is based on an analysis of variance described in the section UNCER- TAINTY AND VARIABILITY. This analysis is able to rank different sources of variability (systems, sites, soils, years) for two periods of climate change (RP-NF or RP-DF). Figure 5: sources of variability and uncertainty for ΔOM. The values represent the percentage of the total variability. The interactions As a whole, interactions are responsible for nearly 60% of the total variability in ΔOM, which is much more than their effect on the plant variables (cf. YIELD section) and on other soil variables (cf. WATER section). Moreover, for the four variables analysed (fig. 5) the effect of the interactions is always more than the main effects. The large effect of interactions confirms what we discussed in relation to figure 1 and implies that the effect of a given variable (for example the site effect) cannot be ascertained completely without knowing the status of other variables (e.g. the site effect for a particular cropping system). The cropping systems Their effect was discussed in C. This is the main source of variation in ΔOM which is responsible for nearly a third of the total variability (i.e. the sum of main effects and interactions). The sites The site effect was dealt with partially in C. It is the second effect in order of importance (fig. 5) It can be visualised by considering the mean of the ΔOM of each site (fig. 2): the sites storing the most are Dijon, Toulouse and Mons in the NF, and Dijon, Lusignan and Mons in the DF. Similarly, the sites losing the most OM were Bordeaux, Avignon and Saint-Étienne in the NF and Avignon, Colmar and Bordeaux in the DF. We can see that for the two situations of storage and release, two sites always appear among the most striking in the NF and in the DF. This reflects a certain stability in the mean response of sites to climate change, which is the cause of this effect. To describe the behaviour of each particular site would be beyond the objectives of this chapter, considering that the interactions are numerous and larger than the main effects (fig. 5). Climate change (the period) This effect reflects the variation in the influence of climate change over the course of time. The change in the behaviour of FG between the NF and DF (fig. 2) is a good example of the period effect. Similarly, for the rotations, the Bordeaux site has varied its rather neutral behaviour in the NF to one of storage in the DF. 110 Green Book The topics Organic matter Jorge Sierra

49 B 4 Organic matter The soil This factor has not been discussed until now, as the results shown in C and D are for simulations made with a single soil. In this study, the soil effect concerns mostly differences in depth (between 90 and 215cm) and the initial OM content (between 1.4 and 2.3%), with a positive relationship between depth and OM content (for example the deepest soil is also the one with the most OM). In a climatic scenario characterised by a reduction in rainfall, the soil depth plays a significant role in the water supply due to differences in the amount of water stored in the profile, and hence on the yield of biomass and the volume of residues. However the main effect of the soil concerns its OM content via the effect of mineralisation. Thus the soil which is richest tends to release more in release situations and to store less in storage situations, which explains why the interactions are much bigger than the main effect of the soil (fig. 5). This phenomenon is associated with the larger quantity of substrate for mineralization in the soils with the most OM. The year The variation in OM is less sensitive to this factor than other variables such as yield (cf. YIELD section) and percolation (cf. WATER section). These variables are very dependent on the weather conditions in a particular year and retain little memory of previous years. The OM represents the opposite situation: it is more conservative and depends heavily on the longer-term soil memory. What you need to remember 3 Nearly 60% of the impact of climate change on the dynamics of OM is due to cropping system/site/period/soil interactions. This supports the idea that the changes in soil OM should be analysed by taking account of all the variables in the agrosystem. 3 Among these variables, the cropping system is the one with the biggest effect on the OM. It is involved in the returns via the management of residues (for example, ploughed in or carted off) and in mineralisation via the ground cover and farming practices (irrigated or rainfed cropping). These factors explain the differences between rotations and grassland (which store carbon) and annual crops which may or may not, and between the annual crops (WW, C store; MI in France, neutral; SS, C release; and MI tropical, C release). 3 For the fertilised systems analysed, the variation in yields is not a good indicator of the variation in OM. 3 Some of the sites tested were quite stable over time in their storage behaviour. (Dijon, Lusignan, Mons) or negative (releasing C) (Avignon, Guadeloupe), but without any particular geographical trend in France. 3 The soil effect is largely due to its OM content MO: the soils with the most OM store less and release more, according to the cropping system. 3 The dynamics of OM are weakly affected by year-to-year weather variation and they reflect rather the effects of the components of the system in the medium-long term. The low short-term reactivity of the OM can hide a process of soil degradation; hence it is important to monitor their quality with more detailed indicators, such as microbial biomass, biological activity, and labile fractions. Green Book The topics Organic matter Jorge Sierra 111

50 B 4 Organic matter What needs further study 3 The response of forage grasses to climate change merits special attention in view of their effect, in monoculture and rotation, on the soil s C storage capacity. It is largely a question of the effect of climate change (water stress, CO 2 ) on the senescence of the aerial biomass. 3 Through its role in reducing the impact of climate change, the change in the biological activity of the soil, in particular mineralisation, should be given priority from the point of view of thermal adaptation and the effects of soil wetting/drying sequences. In this respect, recent advances have been made experimentally and could be progressively incorporated into the models. 3 Finally it is worth recalling that the variation in the quantity of OM involves changes in other soil properties, such as the water storage capacity, the structure, and even the quality of the OM. These properties should be increasingly included in models in order to predict the changes in the soil agro-environmental functions under the impact of climate change. To find out more FAO Nouvelle base de données mondiale sur les sols. Maron P.-A., Dimimos : Lien entre la diversité microbienne et le turnover des matières organiques dans les sols agricoles. Projet ANR (Systerra). Persillet V., Le changement climatique : les enjeux pour le secteur agricole. Notes de service INRA-SAE2 : Mieux comprendre l actualité. Stengel P., Gelin S., Sol : interface fragile. INRA Ed., Paris. 222 p. UE., Directive du Parlement européen et du Conseil définissant un cadre pour la protection des sols. UE., Climsoil. Rapport final Green Book The topics Organic matter Jorge Sierra

51 Evolution of some pathosystems on wheat and vines David Gouache, Romain Roche, Philippe Pieri et Marie Odile Bancal 5 BHealth A Problem and methodology Plant pests (pathogenic organisms, weeds) are known to have variable effects on crops, depending on variations in year-to-year weather conditions: this has led to numerous studies being based on the relationships between pests and weather, with a view to tactically adjusting pest control practices during the year. It is likely therefore that climate change* will greatly affect the behaviour of pathogens, and here we intend to study the effects, both theoretical (yield* loss) and practical (fungicidal treatments and preventative measures) for various pathosystems. The study presented here is focussed on certain phytopathogenic fungi of wheat and vines. The models used have some notable peculiarities compared with the crop models used to treat the other topics. Here, the models vary greatly in their degree of complexity and in the way they take account of weather/host/pathogen relationships (the so-called disease triangle ). A first approach to the study of the relationships of the disease triangle is to almost completely ignore the host and only study the effect of weather conditions on the efficiency of infection by the fungus. Classically two weather variables are used: the temperature and the leaf wetness duration* (the presence of free water on the leaves) (L Homme and Jimenez, 1992). The former is readily available; it remains to calculate the latter: this is the variable HUMEC, calculated from a micrometeorological model assuming a wheat-type leaf. This variable has the advantage of being able to be expressed as a number of infectious days for a very wide range of pathogens. However the number of infectious days per se is not necessarily adequate to assess the severity of an epidemic. Other phases of the epidemic may also be limiting, or else there may be infectious days which are not harmful to the plant. To analyse these interactions, it is possible to couple a model of the behaviour of the pathogen with a crop model. This coupling can be made in a more or less integrated way between the biology of the pathogen and that of the plant. The models presented below offer different degrees of integration between disease and plant models. Finally, these models can be accompanied by decision rules for applying treatments. With these models we will therefore study a variable called severity, which is more or less closely linked to yield losses due to the disease, and trends in the frequency of fungicidal treatments. Table 1 describes the pathogens and models studied. Green Book The topics Health David Gouache, Romain Roche, Philippe Pieri et Marie Odile Bancal 113

52 B 5 Health Degree of plantpathogen integration + Phenomenon studied Leaf wetness duration Vine Botrytis (cv merlot) Wheat septoria tritici blotch Wheat brown rust Present importance Variable influencing nearly all fungi Very important (quantity and quality) Most important disease of soft wheat 2 nd most important disease of soft wheat Known impacts of climate* and manner in which it is accounted for in the model Constitution overwinter inoculum buildup Epidemiological progression* Epidemec progress Expression Yield loss T and hourly rainfall: taken into account depends on the previous year s epidemic (and hence its weather) and winter conditions: not taken into account depends on weather conditions (rain, humidity, radiation and T ) with a retarding effect on the appearance of symptoms: taken into account depends on the frequency of attack of the bunches and the proportion of berries attacked within the bunch depends on the rate of multiplication of the fungus in the field. This phase is currently limited by the winter cold, but can also be by the lack of rain and/ or humidity: taken into account for septoria ; not taken into account for rust. depends on the coincidence of infectious conditions (rain and T for septoria; duration of wetness and T for rust) and emergence of last leaves of the plant, then for septoria:temperature for the appearance of symptoms: taken into account; for rust: temperature and physiological status of leaves for the production of spores, and lastly rainfall and wind conditions for spore dispersal: taken into account. depends on the amount of contamination suffered in the last stages, but also on the size and the lifespan of the uninfected foliage for septoria: 2 nd aspect not taken into account; for rust: both aspects accounted for Calculation of the key variable HUMEC FREQ: % of bunches attacked and SEV: % of berries infected within bunches NUISI: % of foliage affected by the disease during the 1 st phase of grain-filling by the area below the curve technique. This indicator is known to be well related to the severity, but does not take into account differences in canopy growth, due to abiotic factors NUISI: Yield loss directly calculated Calcuation of fungicide treatments no yes yes no Table 1: summary of pathosystems studied and models used. 114 Green Book The topics Health David Gouache, Romain Roche, Philippe Pieri et Marie Odile Bancal

53 B 5 Health B Trends found and explanatory factors In this part we will deal with the results for the key variables (wetness duration and severity). Where it is relevant to do so, we will break down the different phases of the epidemic to bring to light certain contradictory trends. We will try to relate the trends we find to those of easilycalculated indicators. We also offer some ideas about the uncertainty associated with the work. Wetness duration In this analysis we have focussed on the spring and summer, as it is during these seasons that the largest differentiations are observed in relation to climate change and these are the most significant for the pathogens studied. Standard deviation (h) <0,4 0,4-0,5 0,5-0,6 >0,6 Mons Standard deviation (h) <0,4 0,4-0,5 0,5-0,6 >0,6 Mons Rennes Versailles Mirecourt Colmar Rennes Versailles Mirecourt Colmar Dijon Dijon Bordeaux Lusignan Clermont St Étienne Bordeaux Lusignan Clermont St Étienne Toulouse Avignon Toulouse Avignon Spring wetness duration (h) Summer wetness duration (h) Figure 1: wetness durations (in hours) in spring (left) and summer (right) using the WT* downscaling method. In the maps in figure 1, where the mean wetness durations are expressed in hours, one sees very clearly a north-south divide, with wetness durations about 1.5 times higher in the north than in the south. The future trend is towards a slight fall in the spring, which is much more pronounced in summer. The most northerly sites* (Mons and Mirecourt) and those to the west (Rennes) seem to be exceptions. While the fall in the mean values is small, we see in places a fall in the year-to-year variability (standard deviation) (e.g. Mirecourt, Versailles, Lusignan in spring). Finally, although the values fall in the centre-east, they do not, even for the distant future, (DF*), reach values as low as those of the most southerly sites for the recent past (RP*). Climate change will thus reduce the number of days favourable to infection for the two key seasons of spring and summer. However the disease pressure will remain stronger in the future in the north than it currently is in the south. For most pathogens the wetness duration, effective for infection, is considered in relation to the ambient temperature: for many, the higher the temperature, the shorter is the wetness duration needed for infection (strictly speaking, this is only applicable to diseases with a high optimum temperature such as wheat rust). Yet temperature is one of the main factors likely to be altered by climate change. We have therefore chosen, in the following maps, to show the trend in the number of wet degrees-days* for the different sites for spring and summer. Green Book The topics Health David Gouache, Romain Roche, Philippe Pieri et Marie Odile Bancal 115

54 B 5 Health Standard deviation (h) <0,20 0,20-0,25 0,25-0,30 >0,3 Mons Standard deviation (h) <0,30 0,30-0,35 0,35-0,40 >0,40 Mons Rennes Versailles Mirecourt Colmar Rennes Versailles Mirecourt Colmar Dijon Dijon Lusignan Bordeaux Clermont St-Étienne Lusignan Bordeaux Clermont St-Étienne Toulouse Avignon Toulouse Avignon Wet degree-days Wet degree-days Figure 2: degrees-days of wetness in spring (left) and in summer (right) using the WT downscaling method. With this approach (fig. 2), because of the temperature increases, the trend is reversed and now shows a slight increase to be expected in the future at certain sites. We do however still observe, although perhaps not so clearly, a certain north-south divide: in spring, only the three sites of Clermont, Saint-Étienne and Avignon stand out, with significantly shorter wetness periods than the others, be it for the recent past (RP*), the near future (NF*) or the distant future (DF*). For the summer only, the four most north-westerly sites stand out, with particularly high values for all the periods. This means that the infection conditions will be, in general, slightly more favourable in spring for diseases with a high optimum temperature. On the other hand in summer they will remain constant. These trends are generally confirmed by figure 3, showing, for spring at Colmar, the distributions of leaf wetness durations in intervals of 3 hours on the left, and of 1 day-degree on the right. We see a general shift in the distribution of the wetness durations towards lower values in the future, and conversely, a shift in the number of wet degrees-days towards higher values. This result is due to the dominating effect of warming on shortening the durations expressed in hours. However, before drawing conclusions about the effect of climate change on these diseases, one must also take account of the advancement of phenological* stages of host crops as affected by this same temperature factor, which could partially compensate for this effect of warming by repositioning key phases in cooler periods, in which case the reduction in wetness duration could still be just as large. Finally, the reasoning should include the full infectious cycle of the disease and not just the infection phase on which we have focussed in our interpretation: this is the objective of the following parts, which treat several important diseases in more detail. Figure 3: distribution of wetness durations (in 3-hour intervals) (left) and degrees-days of wetness (right) for Colmar, using the WT downscaling method. 116 Green Book The topics Health David Gouache, Romain Roche, Philippe Pieri et Marie Odile Bancal

55 B 5 Health Vine Botrytis The simulation results (only applicable to the merlot variety) expressed as numbers of risk days for increase in the frequency (FREQ: % of berries attacked) and severity (SEV: % of berries infected within bunches) of Botrytis attacks, are shown in figure 4 for the main wine-growing sites (Avignon, Bordeaux, Toulouse, Dijon) and some prospective sites (Lusignan, Versailles, Colmar). They show a clear trend towards a lowering of the infection risk for the traditional winegrowing sites, with a steady trend between RP, NF and DF. These results are barely affected by the downscaling method. For the sites where vines, although not currently grown, could be (success of the crop better than 80%, cf. section ADVANTAGES AND VULNERABILITIES) in the NF or DF, the associated infection risk is very high and very variable: we will return to this point later on. Figure 4: changes in the severity of Botrytis (vine cv. merlot) and its variability (extremes, median, 2 nd and 8 th deciles) expressed as number of risk days for frequency FREQ (left) and severity SEV (right) increase (cf. tab.1) Figure 5: relation between the severity of Botrytis on vines (cv. merlot) expressed by the variable FREQ (% of bunches attacked) and rainfall for three downscaling methods. The predicted variations in the epidemic risk are closely related to variation in rainfall during the period from veraison to maturity (fig. 5). These relationships are almost linear, with a dispersion which depends on the downscaling method (but a large proportion of the observed differences arise from differences in crop feasibility*), with a slope of about 0.2 risk days per mm of rain. Considered individually, each site follows this relation well: the amount of rainfall during the period from veraison to maturity thus constitutes a very good indicator to evaluate the impact of climate change on the change in infection risk. This indicator appears all the more useful as even the possible future sites fit this relationship, which appears to be of general value. Green Book The topics Health David Gouache, Romain Roche, Philippe Pieri et Marie Odile Bancal 117

56 B 5 Health Wheat Septoria tritici blotch The key trend simulated is to diminishing severity of the disease, of approximately 20% in the DF as compared with the RP (fig. 6 right). Figure 6: changes in the inoculum during winter as a function of the number of days of frost (left) and the severity of septoria as a function of the number of days of rain in winter and in spring (right). The arrows indicate the trend with climate change. WT downscaling method However, this general finding seems rather variable depending on both the geographical sector and especially the downscaling method* chosen. In fact the trend appears rather gradual with the QQ* method, whereas with the WT* method we sometimes see a stagnation or even a slight increase in severity in the NF. The changes are not so clear with the Anomalies downscaling method. This general trend is confirmed when observing the earliness of epidemic onset, which is characterised by the date of the first fungicide spray recommended by the model (see on treatments): in relation to the plant developmental stages, the epidemic is delayed by 5-10 days. In seeking to explain this result we examined two phases that exhibit contradictory behaviour. The first, that of the formation of overwinter inoculum, is in fact favoured by climate change: the quantities of inoculum present in the fields at the end of winter are increased. The number of days of frost, a climatic variable mentioned in several studies of septoria (Beest et al., 2009), explains this trend well (fig. 6 right). The winter cold thus becomes much less limiting, but this is counterbalanced by the fact that the following phase, of the progress of the epidemic, becomes increasingly unfavourable to the disease. In fact it seems that the importance of overwinter inoculum as an explanatory factor in the final severity of the disease will diminish. The number of days of rain during winter and spring constitutes a simple explanatory variable for the changes in severity found (Shaw, 1993; Cohan et al., 2006). Departures from this trend could be explained by temperature: the sites whose climate* is milder by virtue of the oceanic influence are situated above the trend line. 118 Green Book The topics Health David Gouache, Romain Roche, Philippe Pieri et Marie Odile Bancal

57 B 5 Health Wheat brown rust The following maps (fig. 7) show the changes in absolute yield losses in tons per hectare for an early variety (Soissons) and a late variety (Arminda) of soft winter wheat, simulated for a sowing date of 10 October. Soissons Arminda Standard deviation 10-2 t ha -1 : <1,5 1,5-2,5 2,5-3,5 >3,5 Mons Standard deviation 10-2 t ha -1 : <1,5 1,5-2,5 2,5-3,5 >3,5 Mons Rennes Versailles Mirecourt Dijon Colmar Rennes Versailles Mirecourt Dijon Colmar Lusignan Bordeaux Clermont St-Étienne Lusignan Bordeaux Clermont St-Étienne Toulouse Avignon Toulouse Avignon Yield losses t ha Yield losses t ha Figure 7: changes in yield losses due to brown rust for Soissons (left) and Arminda (right), calculated with the A1B scenario and the WT downscaling method for soil 1. We essentially find a stagnation or even a reduction in yield losses due to brown rust, the latter trend being more marked for the late variety than for the early one. This varietal difference corresponds to yield differences between early and late varieties; in the same way, losses are less severe for the low-yielding sites and for the late variety. Also, in general, the number of infectious days and the maximum severity are slightly higher for the early variety, and fall in the NF and DF. Nevertheless we see an increase in losses for Soissons at Clermont-Theix and Avignon. For the mountainous site, the mean temperature increase can explain this trend to an increase in the maximum severity of the rust. The year-to-year variability in losses increases slightly, especially for the early variety. These results agree in general with those obtained for the QQ and ANO* downscaling methods, with however some divergences for certain sites: for example the ANO downscaling method predicts an increase in yield losses in the future for Versailles and Rennes (but only for the early variety). The results are thus very sensitive to future climatic uncertainty (cf. CLIMATE section and on uncertainty). In searching for relationships between climatic indicators and crop behaviour (fig. 8), we find that the simulated yield losses decline with the reduction in rainfall and increase in temperature predicted for the NF and DF. However the correlations with these general climatic variables are weak, because certain sites depart strongly from the trend, in particular Avignon, Clermont-Theix and Colmar, for one or other of the indicators. These site effects disappear when one considers water stress* as an explanatory variable for yield losses; on average it explains 75% of these losses. Lastly, it is also notable that the variation in yield losses is partly related to that of disease-free yields. However this last result is based more on differences between sites than between periods. In fact the changes between periods are often orthogonal to the trend, showing that the yield projections for healthy crops are not sufficient to diagnose those for the severity of brown rust. Green Book The topics Health David Gouache, Romain Roche, Philippe Pieri et Marie Odile Bancal 119

58 B 5 Health Figure 8: relation between simulated yield loss (t/ha) and direct or indirect indicators of climate change: P (mm), T ( C),water sufficiency (ETR/ETM)and the yield of disease-free crops (t/ha). C The uncertainties found A peculiarity of the models used here is their often non-linear character, the interaction between temperature and rainfall, and the importance of occurrences (rainfall, infectious episodes) rather than their accumulated values. This suggests that weather variability is particularly important. However this is not estimated in the same way between downscaling methods. When one looks at the effect of these downscaling methods over all the series of weather records used for wetness duration (e.g. Toulouse in spring: fig. 9), one notices two very different forms of behaviour: one group, formed by the scenarios downscaled with the QQ and ANO methods, which show a very large year-to-year variability, and another, containing the scenarios downscaled by the WT method and with a distinctly smaller year-to-year variability. The trend of decline of leaf wetness duration due to climate change is in the same direction for both groups, but much stronger for the first (the predicted trends are on average twice as high). 120 Green Book The topics Health David Gouache, Romain Roche, Philippe Pieri et Marie Odile Bancal

59 B 5 Health Figure 9: variability of wetting at Toulouse in spring as a function of climatic uncertainty represented by either the SRES* scenarios, the climate models, or the downscaling methods (cf. CLIMATE and UNCERTAINTY AND VARIABILITY). The five values for the boxes represent the minimum and maximum values, the 2nd and 8th deciles, and the median in the centre. For septoria also, it is the WT downscaling method which behaves differently from the others, with a stagnation phase or even a slight increase in the disease in the NF, whereas the other methods suggest a more gradual decline in the disease. However for the relation between severity and number of days of rain, it is the ANO method which gives the least favourable results. The case of Botrytis is rather different. For this pathosystem, studied on the variety merlot, the uncertainty in the results is largely due to the uncertainty as to the feasibility of growing this variety. The diagnostic relationships described above based on agroclimatic indicators help to account for some of this climatic uncertainty. D Practical consequences Fungicidal treatments Vine Botrytis The numbers of fungicidal treatments simulated by the model are close to current practice (0-2) but they vary widely (fig. 10). For the thirty-year means, the predictable trend is consistent with that of the risk, i.e. a fall in the number of treatments for the sites where merlot is already grown, especially the fairly humid ones, such as Bordeaux. For sites that could be converted to merlot, planting on these new lands will take place at the cost of using a large quantity of fungicide, casting doubt on its feasibility (cf. ADVANTAGES and VULNERABILITIES and VINES). Green Book The topics Health David Gouache, Romain Roche, Philippe Pieri et Marie Odile Bancal 121

60 B 5 Health Standard deviation <0,2 0,2-0,4 0,4-0,6 >0,6 (feas. <0.8) Mons Rennes Versailles Mirecourt Colmar Dijon RP NF DF Bordeaux Lusignan Clermont St Étienne Toulouse Avignon Nbr of treatments 0,4 0,6 0,8 0,2 Figure 10: number of treatments required against vine Botrytis (cv. merlot, QQ downscaling method). Wheat Septoria Together with the reduction in severity, we see a trend towards a reduction in anti-septoria fungicide treatments (fig. 11). The variations observed between sites and weather patterns are fully in line with those observed for the severity variable. Standard deviation (%) < >20 Mons Standard deviation (%) < >20 Mons Rennes Versailles Mirecourt Dijon Colmar Rennes Versailles Mirecourt Dijon Colmar RP NF DF Bordeaux Lusignan Clermont St Étienne RP NF DF Bordeaux Lusignan Clermont St Étienne Toulouse Avignon Toulouse Avignon Sprays/3 (%) % = 3 sprayings WT Sprays/3 (%) % = 3 sprayings WT Figure 11: probability of carrying out the 1 st anti-septoria treatment on wheat(cv. Soissons), WT downscaling method on left and QQ on right. 122 Green Book The topics Health David Gouache, Romain Roche, Philippe Pieri et Marie Odile Bancal

61 B 5 Health In terms of implementation, the dates of the first treatment are significantly earlier, by about days, between the present and future periods, with quite a steady trend. This apparent advancement should however be compared with the advancement of phenological stages: in fact, the advancement of crop stages is greater than that of the disease. This makes sense for two reasons. Firstly, the rate of development* of the plant depends only on the temperature, whereas that of the disease is limited by both temperature and leaf wetness. Secondly, Lovell et al. (2004), by studying the latency time of septoria, showed that this disease possessed a speed advantage when temperatures are cooler. Furthermore, we see, across both sites and time periods, that the earlier the growth stages, the later is the disease onset in relation to them. Compared with these two effects, the effect of varietal earliness is negligible. We also tried, using very simple indicators such as rain events around recommended spraying dates, to assess whether sprayings in favourable application conditions would be likely to be compromised: no trend appeared in this respect, but a systematic and multicriteria study is needed here. Effect of prophylactic practices The simulations for septoria and brown rust were made for a wide range of sowing dates. This enabled us to evaluate the impact of climate change on a non-chemical method of control, i.e. deferred sowing. Late sowings are known to limit the overwinter inoculum pressure for both these diseases, which thus have less time to multiply during this phase. For both diseases, we see reduced severity with late sowing. In the case of brown rust, this decrease is more marked for the early variety and for the RP. The variability in response between sites is large: we can distinguish sites where the yield loss declines slightly and continuously with the sowing date (Avignon and Colmar) and with little variation due to climate change, and other sites (Bordeaux, Lusignan, Toulouse three sites currently among the most subject to the disease at present and Mons), where climate change leads to a much clearer reduction in losses, especially for the late variety. This approach must be combined with the maintenance of a viable economic yield, bearing in mind that the yield also falls with later sowing, but that the increase in ambient CO 2 allows a higher yield potential to be reached (cf. TIMING and WHEAT sections). Green Book The topics Health David Gouache, Romain Roche, Philippe Pieri et Marie Odile Bancal 123

62 B 5 Health What you need to remember 3 In general, for all the simulated pathosystems, the impression which emerges is that of a lowering of the pressure from fungal diseases. However this assertion needs to be qualified a little. 3 Wetness duration, a common variable to many pathosystems (far beyond the three studied here), falls. However the variable wet degrees-days, theoretically more appropriate for diseases with a high optimum temperature such as brown rust of wheat, increases slightly. For vines, the pressure from Botrytis falls at the sites in the south where the modelled variety is already feasible now, but at the sites where this variety will become feasible in future, the Botrytis pressure will be very high. Finally, for septoria, there is uncertainty about the variation in the near future. 3 Note that the results are subject to climatic uncertainty, mainly associated with the downscaling method. Lastly, one must remember that for each pathosystem, we have been able to identify synthetic variables, simpler to calculate, which seem to be well related to the projected trends. However, these relationships are so far specific to each pathosystem. 124 Green Book The topics Health David Gouache, Romain Roche, Philippe Pieri et Marie Odile Bancal

63 B 5 Health What needs further study 3 Several aspects of the work done here would be worth further study. A limitation for the Botrytis and brown rust pathosystems is the failure to take account of the impact of climate change on inoculum overwintering. Yet it is likely that these effects may be strong. For vines, it would have been interesting to study several varieties, especially those adapted to the most northerly wine-growing areas. 3 More general limitations can be mentioned. Note, for example, that the effects of CO 2 on the pathogens are not considered, although several studies suggest that it may reduce the severity of diseases. Also, there is the fact that these studies were carried out on one pathogen at a time, yet climate change might, in a given place, modify the relative importance of pathogens, or even cause new ones to appear. Finally, this work does not take account of possible adaptations* of populations of pathogens to climate change: for example we have recently seen the establishment, in Australia, of a new race of yellow rust, better adapted to hot conditions (Millus et al., 2009). Green Book The topics Health David Gouache, Romain Roche, Philippe Pieri et Marie Odile Bancal 125

64 B 5 Health To find out more Beest D.-E., Shaw M.-W., Pietravalle S., Van den Bosch F., A predictive model for early-warning of Septoria leaf blotch on winter wheat. European Journal of Plant Pathology /s Chakraborty S., Luck J., Hollaway G., Freeman A., Norton R., Garrett K.-A., Percy K., Hopkins A., Davis C., Karnosky D.-F., Impacts of Global Change on Diseases of Agricultural Crops and Forest Trees. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources , No. 054, p Cohan J.-P., Georges S., Piraux F., and Couleaud G., Lutte contre la Septoriose (S. Tritici) du blé tendre d hiver en Haute-Normandie : modélisation statistique de la nuisibilité de la maladie à travers la pluviométrie et la cartographie à l échelle nationale. In AFPP - VIII e CIMA. Tours. Garrett K.-A., Dendy S.-P., Frank E.-E., Rouse M.-N., Travers S.-E., Climate Change Effects on Plant Disease: Genomes to Ecosystems. Annual Review of Phytopathology, 44: Lhomme J.-P., Jimenez F., Estimating dew duration on banana and plantain leaves from standard meteorological observations. Agricultural and Forest Meteorology 62, Lovell D.-J., Hunter T., Powers S.-J., Parker S.-R., Van den Bosch F., Effect of temperature on latent period of septoria leaf blotch on winter wheat under outdoor conditions. Plant Pathology 53 (2): Milus E.-A., Kristensen K., and Hovmøller M.-S., Evidence for increased aggressiveness in a recent widespread strain of Puccinia striiformis f. sp. tritici causing stripe rust of wheat. Phytopathology 99: Shaw M.-W., Factors determining the severity of epidemics of Mycosphaerella graminicola (Septoria tritici) on winter wheat in the UK. Plant Pathology 42 (6): Green Book The topics Health David Gouache, Romain Roche, Philippe Pieri et Marie Odile Bancal

65 Complexity of the yield trends of crops Romain Roche 6 BYield A Climate and yield Main mechanisms contributing to yield elaboration Ecophysiological modelling underlines the distinction between the phenology* of the plant, which is directly and mainly dependent on temperature, (but also on photoperiod for winter crops), and the growth* of biomass, related primarily to photosynthesis* processes and dependent directly on the radiation absorbed by the crop and the concentration of CO 2 in the atmosphere. The availability of nutrients and water is also very important. In fact while the plant exchanges CO 2 and O 2 in the processes of photosynthesis and respiration, it also loses water which evaporates from the stomata. If insufficient water is available, the resulting stomatal closure also affects photosynthesis by preventing gaseous exchange. Growth is also limited when other factors are lacking, such as mineral elements, mainly nitrogen, the main constituent element of proteins. In this study, by means of adequate nitrogen fertilisation, (based on current practices) or by not taking account of possible limitations (such as other mineral elements or nitrogen in certain models; cf. MODELS section), we have tried to choose a context in which factors not directly linked to the climate* are non-limiting. However there are close relationships between water supply and the absorption of mineral elements, and water shortage often causes nitrogen deficiency due to lack of absorption. Moreover it cannot be ruled out that nitrogen fertilisation, calculated on the basis of current practices, could have been insufficient in the situations with a high yield potential. For most crops the harvested product (or yield*) is formed, after flowering, during the final period of growth (or nearly so): this is so for crops whose harvested organ is the result of sexual reproduction. When the harvested organ is vegetative (such as grassland or forests), the harvested product is formed during the whole period of growth. However even in the first case, the number of harvested organs depends on the pre-flowering physiology of the crop, so that the yield is the result of all the processes which take place throughout growth. Because of this its determination is particularly complex, as it is subject to numerous interactions which we will try to illustrate. What are the predictable impacts of climate change? Whatever the scenario, it is expected that atmospheric CO 2 concentrations will continue to increase (cf. CLIMATE section for a deeper analysis of the climatic variables sensu stricto), which should favour the process of photosynthesis, particularly for C3* plants (wheat, sunflower, oilseed rape, vines, fescue, beech etc.) rather than C4* plants (maize, sorghum). Furthermore, for the same evaporative demand, the water requirements of the C3 crops should diminish, since the increase in CO2 causes, in their case, a reduction in stomatal opening (the antitranspirant effect). Combined probably with the reduction in rainfall (cf. CLIMATE section), we see a slight change in the increase in radiation (especially clear in summer), one of the other major factors in the process of photosynthesis. For the plant, this change has to be considered together with the changes in the seasonal timing of the crop cycles, which depend on the temperature (cf. TIMING section). Temperature is the most important and most publicised aspect of climate change, as there is agreement among experts and climate models to suggest the likelihood of warming. This means that crop growth cycles will accelerate, which will have consequences in terms of earliness of growth stages and/or the duration of phases (cf. TIMING section). This could allow better adaptation of certain crops to the conditions of the north of France, but on the other hand it could be harmful to crops which are sensitive to high temperatures towards the end of their growth. The shortening of the growth phases also means less time to accumulate radiation and fix CO 2, and hence less growth. Green Book The topics Yield Romain Roche 127

66 B 6 Yield At present, with high-input cropping systems as generally practised in France, the water supply is the main factor influencing yield, whether it varies from one region to another (assuming the crop can be grown) or from one year to another. The climate projections forecast a reduction in rainfall (particularly in summer: cf. CLIMATE section) which would tend to intensify water stress (cf. WATER section), which in turn could lead to nitrogen stress by blocking mineralisation of the soil organic matter (cf. ORGANIC MATTER section) or reducing the availability of nitrogen fertilisers in the soil etc. B Can one estimate the trends from a crop classification? We propose in this part to investigate the changes in yields of various French crops by means of simple classifications. We find for example the contrast between C3 and C4 crops, which is very significant in terms of the utilisation of atmospheric CO 2, but also between winter and summer growth, thought to exploit temperature and water in different ways. But before going into these classifications, table 1 gives a summarised view of changes in yields for the whole of France and indicates the global impact brought about by climate change. Wheat Irrigated maize Rapeseed Sunflower Sorghum Vine Pine Festuca FP-PR FL-PR Table 1: yield differences between future periods (NF* and DF*) and the RP*, averaged over all sites*, in t/ha. All the values (except Festuca in the DF) are significant at the 1% threshold. All the crops except maize are rainfed. Among the annual crops, we see big differences between crops which are ecophysiologically far apart, like maize and rape, but also between crops which are similar (maize and sorghum). One also sees that climate change can have effects which are always harmful (as for pines). 128 Green Book The topics Yield Romain Roche

67 B 6 Yield C3/C4 The comparison of two summer crops, one C3 (sunflower) and the other C4 (irrigated maize), shows that the change is very clearly to the advantage of the former, especially in the distant future. This is in agreement with what we know about their physiology, C3 plants tending to better exploit increases in CO 2 concentration. For sorghum, another C4 crop, the large increase in yield results from several advantages compared with maize: a better exploitation of the higher temperatures, drought resistance, and expansion due to its zone of cultivation spreading northwards (cf. MAIZE-SORGHUM section). Hence the C3-C4 classification is not enough to predict the reaction of plants faced with climate change. Winter/summer The comparison of two oilseed crops such as rape and sunflower seems to show a clearly more favourable trend for winter crops (rape) compared with summer crops. Thus the yield increase in the DF is more than twice as large. In this case the determinant process seems to be one of avoidance*: for the winter crops, climatic warming* results in advancement of growth stages, allowing growth to be completed before the appearance of serious stress, while the summer crops suffer the full effects of the increased water and heat stress*, particularly severe at this time of year. Moreover the summer crops suffer a clear shortening of their grain-filling phase, detrimental to yield (cf. TIMING section). Annual/perennial The comparison of two C3 grasses, one annual (wheat) and the other perennial (fescue), shows a slight advantage to the former. To explain this result, one can, as a simplification, consider the grassland as an association of a winter crop and a summer crop, for which the moisture conditions are clearly less favourable. Moreover the grassland may suffer more than other crops from soil drying, due to its rather shallow rooting system. Also, the memory effect of the grassland by way of its winter reserves could amplify the drought problem if it becomes recurrent. Woody/herbaceous plants The trend for pine is the only example of a clearly negative prediction, and the advantage therefore seems clearly to be with the herbs (grassland). However the case of the pine, like that of the beech (cf. FOREST section) is particular, since, although they are C3 plants, these species do not benefit from the antitranspirant effect of the increase in atmospheric CO 2 ; it is therefore subject to more severe water stresses than the forage grass. Moreover because their buds are formed in the summer, the woody species are very exposed to the accumulated effects of water and heat stress. This leads to delayed effects on leaf development in the following years, and hence to a limitation of production even if growing conditions revert to being more favourable. The comparison with the vine is difficult, as the positive trend for the latter is mainly due to its spread to new growing regions made possible by the increase in temperature (cf. ADVANTAGES AND VULNERABILITIES section). Adapted or not to cold Wheat and rape are both C3 winter crops, but wheat foliage is more resistant to winter frosts than that of rape, so that the increase in temperatures is very favourable to growing rape, whose yield increase (in particular in the centre-north and north-eastern zones) is directly linked to the reduction in the risk of frost (cf. OILSEED RAPE section). Adapted or not to drought We have already cited the case of sorghum compared with maize, but the geographical expansion of sorghum can mask the effect of the single character of drought resistance. On the other hand, if one compares the results for fescue and ryegrass, the latter being less well adapted to drought, one obtains, for the same conditions (of model and climate) a 6% increase for fescue and only 4% for ryegrass (cf. GRASSLAND section). Green Book The topics Yield Romain Roche 129

68 B 6 Yield There is therefore a set of characters which can explain the response of plants to climate change: C3/C4, annual/perennial, winter/summer, adapted or not to cold and/or drought, and sensitivity to the antitranspirant effect of atmospheric CO 2. This complexity shows why it is necessary to make use of crop models to work out the effects of this set of interactions. C Some aspects of the analysis of the impact of the main climatic factors Although they cannot be separated, here is a rapid analysis of the impact of the three main yield factors likely to be altered by climate change: CO 2, water stress and temperature. Scale of the CO 2 effect For the present production zones of each crop, the increase in CO 2 concentrations is the main beneficial factor associated with climate change. We have seen in the part above that it would profit mainly C3 crops and that its benefit would be expressed more for winter crops, less subject to rainfall shortages. Without this large increase (a doubling by 2100 in the most studied scenarios), the yields of most crops would fall, even in the most favourable cases. Hence for winter wheat, in the hypothetical case of climate change of the predicted magnitude, but without the increase in CO 2, the yield of wheat would show an average fall over all sites in the distant future of more than 25%. Impact of moisture conditions on yield With climate change, the water sufficiency* of the crops will tend to deteriorate if practices are not altered (cf. WATER section), at least in the NF. This is illustrated in figure 1 for four rainfed crops representative of water behaviour : two perennial crops (grassland and pine forest) and two spring crops including one C3 (sunflower) and one C4 (sorghum). Figure 1, which relates water sufficiency and yield, and in which a low ETR/ETM* ratio indicates severe water stress, enables one to follow the dynamics of each site and to present regressions over all the sites for the period of interest. If we just look at the regressions for each period, we see that, for a given level of water stress, the yield shows an increasing trend, which if we are not careful could lead us to undue optimism. First we see that although the shift of the curves between the three periods is considerable for sunflower and grassland, it is much less for pines and almost non-existent for sorghum between the RP and NF. If we now look at what happens site by site, we find that the water stress increases, so that the trend is not always to the benefit of yield. For the perennial crops, after a phase of decreasing yield in the NF, fescue shows a slight increase in the DF; which is not the case for pines. For the annuals, we can distinguish between an increase in water stress in the NF followed by a decline in the DF. This decline, accompanied by an increase in feasibility (of growing the crop, due to more favourable temperatures in the north), causes an upward movement of the sorghum curve (a C4 plant, but not very sensitive to CO 2 ) in the DF and accentuates that of sunflower further. Thus we see the avoidance effect from which perennial plants are unable to benefit. 130 Green Book The topics Yield Romain Roche

69 B 6 Yield Figure 1: yield as a function of ETR/ETM for sunflower, simulated with SUNFLO (top left), fescue simulated with PASIM (top right), sorghum simulated with STICS (bottom left) and pines simulated with GRAECO (bottom right). The soil conditions (1) and climate (WT*) are the same for all, and each point represents the mean of 30 years for a site. Effects of temperature on yield As we have already mentioned in the previous part, temperature has a complex effect on yield, and its increase may have conflicting effects on different crops. In general, the temperature effect is rather positive. For certain crops, the higher temperatures will be more suited to the needs of the crop, or will reduce problems associated with the cold. This is the case for example for sunflower, sorghum and vines, whose expansion towards the north would be greatly favoured (cf. ADVANTAGES AND VULNERABILITIES section). Thanks to this temperature rise, winter crops (e.g. wheat and rape) are also less subject to frost. However, for these last two, the predominant temperature effect is that of advancement of phenological stages, which allows the crop to avoid, to a large extent, the increased stress in summer (cf. TIMING section), and thus to limit the yield losses. However we should not forget that this advancement can only take place without harm because the occurrences of late frost diminish. For perennial crops, the increase in temperature lengthens the growing periods: an earlier start in spring and a later cessation in winter. Hence the direct temperature effect is rather beneficial to yield, even though global warming may have harmful effects through interactions with other climatic factors, notably to do with water. There are however some negative effects. For annual spring crops, the increase in temperatures brings forward the growth stages, but also shortens the grain-filling phase (due to its seasonal timing explained in the TIMING section), which would tend to reduce the yield. Also, the avoidance resulting from the advancement of stages does not completely remove the harmful effects of high temperatures on wheat and sunflower (cf. TIMING section). Green Book The topics Yield Romain Roche 131

70 B 6 Yield D Sources of uncertainty The climate The UNCERTAINTY AND VARIABILITY section gives a clear picture of the effect of the uncertainty attributable to the different scenarios, climatic models and downscaling* methods on wheat yield. In particular it shows that this variability increases with the future period considered, both as a trend of the medians and for the year-to-year scatter. One can also see that the downscaling methods generate uncertainties* of about the same size as the SRES scenarios or the choice of climatic models. However these uncertainties as a whole are substantially smaller than the year-toyear variability: for example for wheat, the latter is about five times greater than the uncertainty due to the climatic scenarios, whichever agronomic model is used. Apart from these changes, one may also wonder what effect climate change will have on the stability of agricultural production. For this we have shown in figure 2 the coefficients of variation (standard deviation/mean) of yields over all the sites for crops representative of these sites. This way we see a slight tendency towards a decline in the standard deviation for most crops (except sorghum and pines) between the RP and NF, but this trend can be reversed between NF and DF, (as for wheat) and may differ with the agronomic models used (cf. following ). As a first approximation, it is therefore preferable to keep in mind the importance of this year-to-year variability, which remains the main climatic signal, even for crops more regulated by irrigation, such as maize, or by their perennial character, such as grassland. Figure 2: changes in the coefficients of variation (standard deviation/mean) of yields, for different crops (only maize being irrigated) and sites, calculated by the models and on the appropriate soils. WT downscaling method. 132 Green Book The topics Yield Romain Roche

71 B 6 Yield The agronomic models The agronomic models are at best simplified representations of reality (cf. MODELS section). Although their formalism is able to represent very precisely the processes at work (the most mechanistic models come close to them), there remain the uncertainties caused by errors in estimating the parameters* of the model equations (which become more numerous as the model becomes more mechanistic). These errors themselves arise from uncertainties and imprecision in the measurements of data used to calibrate the models and thus constitute part of the incompressible uncertainty (Passioura et al., 1996). To try to evaluate this uncertainty, we have, wherever possible, tried to use several models in parallel. Thus for sunflower and grassland, two models were used (STICS and SUNFLO, and STICS and PASIM respectively), whereas for soft wheat three models could be used (STICS, CERES, PANO- RAMIX). Our results show that the variability between models for a given situation can be very large, even for projections (the difference between NF or DF and RP), and is often at least one order of magnitude greater than the climate change signal. One can attribute this uncertainty to different reactivity of the models to variability in soil and weather, which generates up to twofold year-toyear variability and different behaviour depending on the soils. Thus we are able to identify three very distinct kinds of behaviour (fig. 3 and 4). Figure 3: boxplots* (with extreme values, medians and 2 nd and 8 th deciles) allowing one to compare the wheat models (left) and the sunflower models (right) at Toulouse for the deep soil (1) and the shallow soil (2). WT downscaling method. The periods are identified by letters (A=RP, B=NF, C=DF). The models show the same evolutionary trend with climate change: this is the case of STICS and CERES or of STICS and PASIM or STICS and SUNFLO for soil 1 at Toulouse. One of the models is rather insensitive to climate change: note the difference between PA- NORAMIX and the other wheat models or between STICS and SUNFLO for soil 2 at Toulouse. These results are due to compensation between the negative effects of stresses at the end of growth and the positive effect of CO 2. There is a strong interaction between the uncertainty between the models and the soil type: e.g. for STICS and PASIM, which seem to agree for soil 2 but not for soil 1 (the same for SUN- FLO and STICS). One finds similar behaviour for the grassland species, i.e. a better agreement between STICS and PASIM for ryegrass than for fescue. Each of these models is one point of view about the reality, and it is not our aim in this study to pronounce on their quality, especially when it comes to their projections for the future, which it is impossible for us to validate. However this uncertainty leads us to use the models independently of one another and to discuss their divergences in the sections as required. Although this uncertainty means that we have to be cautious about the figures shown, we have not found a case where they contradict the main thrust of the messages. Green Book The topics Yield Romain Roche 133

72 B 6 Yield Figure 4: comparison of STICS and PASIM for fescue and soils 1 (common arable soil) and 2 (shallow grassland soil). Each point represents the mean value per period (WT downscaling method). Importance of taking account of both positive and negative effects of climate change In order to understand the mode of action of the positive and negative effects of climate change, we tested, using the CERES wheat model, different options for the effects of CO 2 and warming*. Thus three options were combined: inclusion or not of grain shrivelling caused by high temperatures; inclusion or not of the CO 2 effect; and the method of calculating this CO 2 effect either by the mechanistic approach of Farquhar, explicitly simulating the processes of carboxylation, respiration, electron transfer etc. and their laws of action as a function of temperature, or the more empirical approach of Monteith, based on the notion of conversion efficiency of solar radiation into biomass (Radiation Use Efficiency). Our results (fig. 5) show that taking account of the increase in CO 2 concentrations in the atmosphere is the most important effect as one moves into the future, and that the way it is calculated (Farquhar or Monteith) has little effect. Heat stress has little effect on the median value, but always causes a lowering of the maximum attainable yield. Figure 5: comparison of the results of CERES wheat (mean of the Toulouse and Colmar sites) with or without three simulation options (CO 2 or not), heat stress or not, calculation of the CO 2 effect with Farquhar or Monteith for the two extreme periods (RP and DF). 134 Green Book The topics Yield Romain Roche

73 B 6 Yield E Sources of variability The systems In the framework of this project, most of the yields were simulated for monocultures with fixed practices so as to bring out the main effects of the expected trends in the climatic factors on crop yields. However their behaviour in different crop rotations was also tested with the STICS model, and for each system, several specific forms of variability were explored (cf. description in the AGRI- CULTURE section and analyses in the crop sections). These were mainly varietal earliness and soil variability. In the context of the grassland system, two different species were compared; fescue and ryegrass. For vines, varieties, planting densities and the use of irrigation were tested, whilst for pines it was the length of rotation which was considered. However we should emphasise that, except for a few specific cases (maize, wheat), adaptation of practices was not considered. The sites Clearly the largest source of variability is associated with the geography, represented in our study by the various sites scattered over the whole of France (the tropical crops being treated separately: see WEST INDIES section). Table 2, in which are shown the yield trends expected for each site between the RP and the DF, shows how it would be wrong to produce a generalised synthesis for the whole country and all the crops. The crops which seem to suffer most are the pines, which show a downward trend at all the sites, and irrigated maize, whose trend is downward in most of the present growing areas. The south-west region appears particularly affected as regards its main crops. On the other hand the winter crops like rape and wheat seem destined for productivity gains because of their capacity to benefit from the expected increase in CO 2 due to their C3 physiology, and to avoid the stresses which accumulate during the summer. Similarly, the regions in the north of France and the mountainous regions (represented in this study by Clermont-Theix) seem to be the most suited to benefit from climate change because of the possible expansion of the present production zones of crops requiring a warm climate. Green Book The topics Yield Romain Roche 135

74 B 6 Yield Wheat Maize Rape Sunflower Sorghum Vines Pines Fescue Avignon Bordeaux Clermont Colmar Dijon Lusignan Mirecourt Mons Rennes Saint-Étienne Toulouse Versailles Table 2: trend in yield between RP and DF, for each site and crop (soil 1, WT downscaling method, CERES for wheat, GRAECO for pines, PASIM for fescue and STICS for other crops). Bold: p < 0.01; Italics: p < 0.05; Normal: p < 0.10; Struck through: non significant. 136 Green Book The topics Yield Romain Roche