# Anticipation of the winter wheat growth based on seasonal weather forecasts over France

Nicolas Canal, Olivier Deudon, Xavier Le Bris, Philippe Gate, Grégoire

Pigeon, Mathieu Regimbeau, Jean-Christophe Calvet

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Nicolas Canal, Olivier Deudon, Xavier Le Bris, Philippe Gate, Grégoire Pigeon, et al.. Anticipation of the winter wheat growth based on seasonal weather forecasts over France. Meteorological Applications, 2017, 24 (3), pp.432-443. 10.1002/met.1642. meteo-05339418

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METEOROLOGICAL APPLICATIONS

Meteorol. Appl. 24: 432–443 (2017)

Published online in Wiley Online Library

(wileyonlinelibrary.com) DOI: 10.1002/met.1642

RMetS

Royal Meteorological Society

# Anticipation of the winter wheat growth based on seasonal weather forecasts over France

Nicolas Canal,ª Olivier Deudon,ª Xavier Le Bris,ª Philippe Gate,ª Grégoire Pigeon,ᵇ* Mathieu Regimbeauᵇ and Jean-Christophe Calvetᶜ

**ABSTRACT:** The aim of this study was to examine the ability of a crop model forced by seasonal weather forecasts to anticipate correctly the agrometeorological variables affecting the growth of winter wheat in France. The method is based on wheat growth simulations over 199 sites in France for the period 1981–2005 driven by seasonal weather forecasts, consisting of 6 month long, 45 member hindcasts starting on 1 February. These simulations were challenging a climatologically based wheat growth forecasting system currently used by ARVALIS – Institut du végétal. Both simulations were evaluated against a reference dataset. The simulations using the seasonal weather forecast present good skills for the dates of occurrence of the main growth stages: the ‘leaf sheaths strongly erect’ stage (also referenced as Zadoks scale Z30), the ‘head visible’ stage (Zadoks scale Z50) and the ‘kernel hard’ stage (Zadoks scale Z91). The simulations forced by the seasonal weather forecasts have lower bias than those forced by the climatological data: respectively –0.2 against 1.4 days for the Z30 stage, 1.1 against 3.2 days for the Z50 stage, 1.3 against 4.2 days for the Z91 stage. It is shown that the seasonal forecasting system provides more hit rates than the climatologically based method does. Thus, for the different events forecasted here, better predictability is found using the seasonal forecasting system. Despite shortcomings highlighted in this study, it is shown that a method based on seasonal weather forecasts could be applied in an operational context over France, in order to improve the method currently used in the agricultural sector.

**KEY WORDS** weather seasonal forecast; crop model; winter wheat; France; ensemble prediction

Received 6 July 2015; Revised 12 October 2016; Accepted 28 October 2016

## 1. Introduction

Since the end of the 1950–1990 period, when a growing trend in cereal yields was observed in Europe (Calderini and Slafer, 1998), mainly explained by genetic and technical improvements, a stagnation in yields has been pointed out (Brisson et al., 2010). During the rainfed crop growing season, an important role is played by climate variability, which has consequences on the final crop production (Hammer et al., 1987) and can explain this stagnation. Thus, it appears essential to anticipate correctly the evolution of the weather on a seasonal scale and to offer some solutions for improving crop yields. The numerous studies dealing with weather forecasting linked to farming in many regions of the world (a review was proposed by Hammer et al., 2001) underline the major interest of this research field and were carried out in order to build strategies for improving both decision making and agricultural management. Furthermore, they address ways to limit the risk of crop disasters by promoting a policy of food production security (G20 Global Agricultural Monitoring, 2011). Initially, crop growth forecasts were obtained from climatological data using statistical methods (e.g. Chipanshi et al., 1997; Supit, 1997). It was then shown that seasonal forecasts could

furnish information useful in crop prediction (Jones et al., 2000; Meinke and Stone, 2005; Doblas-Reyes et al., 2006; Hansen and Sivakumar, 2006; Thornton, 2006; Challinor, 2009), with satisfactory results obtained in the Tropics (Challinor et al., 2005; Hansen, 2005; Coelho and Costa, 2010; Asseng et al., 2012; Malherbe et al., 2014). As the El Niño–Southern Oscillation (ENSO) (Neelin et al., 1998) variability explains the main climate predictability worldwide (Diaz et al., 2001), some methods taking the ENSO phases into account (e.g. using the Southern Oscillation Index) have been developed in order to predict the final crop yields in regions where the ENSO has an influence on the local climate, e.g. in Australia (Hansen et al., 2004), East Africa (Hansen and Indeje, 2004; Hansen et al., 2009), Argentina (Podestà et al., 2002), the Philippines (Koide et al., 2013) and the United States (Mjelde et al., 1997).

In mid-latitudes, the strong climate variability limits the ability of general circulation models (GCMs) to perform high skill forecasts with a lead time of several months (Palmer and Anderson, 1994). Nevertheless, weather predictability on a long temporal scale exists in Europe (Fraedrich, 1994; Lloyd-Hughes and Saunders, 2002; Pozo-Vàzquez et al., 2005; Frías et al., 2010) and allows long-range weather forecasts to be used in agriculture (Calanca et al., 2011) even though the efficient use of seasonal forecasts requires great caution, such as the use of adapted temporal and spatial scales in the forecasting system (Ogallo et al., 2000).

Further European projects have contributed to the development of seasonal forecast products and have proved that the weather

<footnote>ª Direction Scientifique, de l'innovation méthodologique et du programme - ARVALIS - Institut du végétal, Boigneville, France</footnote>

<footnote>ᵇ Division Agrométéorologie, Département Etudes et Consultance, Direction des Services Météorologiques, Météo-France, Toulouse, France</footnote>

<footnote>ᶜ Centre National de Recherches Météorologiques - Groupe d'Etude de l'Atmosphère Météorologique, Météo-France and CNRS, Toulouse, France</footnote>

<footnote>* Correspondence: G. Pigeon, Division Agrométéorologie, Département Etudes et Consultance, Direction des Services Météorologiques, Météo-France, 42 avenue Coriolis, Toulouse, France. E-mail: gregoire.pigeon@meteo.fr</footnote>
seasonal forecasting systems could give useful information in
various sectors (e.g. diseases, water supply and agriculture). In
particular, the project ‘Development of a European Multimodel
Ensemble System for Seasonal to Interannual Climate Predic-
tion Project’ (DEMETER) (Palmer et al., 2004) has evaluated
the feasibility of using seasonal weather forecasts in agricul-
ture in Europe (Cantelaube and Terres, 2005; Marletto et al.,
2005, 2007; Semenov and Doblas-Reyes, 2007). Cantelaube and
Terres (2005) have shown that a coupled ocean–atmosphere
multi-model seasonal forecast, combined with the crop model
World Food Study (WOFOST) (Van Diepen et al., 1989), is able
to provide better results in crop yield prediction than the Joint
Research Centre (JRC) operational method based on historical
statistical data. In many regions of Europe, this new ensem-
ble forecasting system provides more accurate yield forecasts
early in the growing season. Van Diepen et al. (1989) also con-
clude that it is essential to deliver probabilistic information when
long-range weather forecasts are used, because of their large
uncertainties. However, Semenov and Doblas-Reyes (2007) have
shown that these results cannot easily be found at a local scale
unless all the different sources of uncertainties are taken into
account. The use of a seasonal forecast, especially in Europe
where the climatic variability is high, still remains a real chal-
lenge and needs further improvement. Following the DEMETER
project, a new dataset containing a series of 46 year (1960–2005)
hindcasts (or retrospective forecasts) was developed within the
European FP6 ENSEMBLES project (Weisheimer et al., 2009).
Five coupled ocean–atmosphere models at 2.5° horizontal reso-
lution contributed to these data. An overview of the comparison
between the ENSEMBLES and the DEMETER seasonal hind-
casts can be found in Alessandri et al. (2011).

France is the largest European producer of annual wheat crops,
with 28% of the total European harvest and around 35 Mt of win-
ter wheat produced every year (Agreste, agreste.agriculture.gouv
.fr/IMG/pdf/Gaf13p121-126.pdf, accessed September 2016).
The management of winter wheat crop growth in France is thus
of great interest.

The main goal of this study was to assess whether a system
using the seasonal weather forecast during 1981–2005 to predict
the winter wheat growing season over France provides better
results than those obtained with a current climatologically based
method used by ARVALIS – Institut du végétal.

The latter combines various models such as water budget, phe-
nology and diseases in order to simulate the evolution of crop
growth. In this way, it is possible to predict different variables,
such as the date of occurrence of the main stages linked to winter
wheat growing and cumulative variables which take into account
the quantity of water received by the crop between two stages
or around a stage. All these predictions provide essential infor-
mation for farmers. Indeed, comparing these predictions with the
theoretical values allows crop management to be improved (e.g.
nitrogen fertilizer inputs) and crop yields to be optimized.

In this study, the ENSEMBLES multi-model seasonal hind-
casts are used as an input to the growth crop model called
PANORAMIX (Gate, 1995) developed by ARVALIS – Insti-
tut du végétal. In order to be consistent with the spatial and
temporal resolution of the meteorological forcing needed by the
crop model (Meinke and Stone, 2005; Troccoli, 2010), statistical
downscaling was applied to each member of the GCM atmo-
spheric outputs. This approach using seasonal weather forecasts
does not aim to predict the final yield as various studies have
done (Cantelaube and Terres, 2005; Marletto et al., 2005, 2007)
but to predict the key variables and yield components of winter
wheat growth. Predicting variables such as the main stages of

crop growth with a well-adapted model developed by ARVALIS
– Institut du vegetal permits cumulative variables linked to the
sum of temperatures to be focused on instead of the final crop
yield, which is a more complex variable and depends on many
factors. Thus, it is suggested that in this configuration the pre-
dictability of these variables, especially those linked to the sum
of temperatures (Weisheimer and Palmer, 2014), could be bet-
ter than the crop yield. This study aimed to verify whether this
hypothesis is true and whether an anticipation of the main stages
of the winter wheat could be done early in the growing season.

The different simulations computed and the statistical down-
scaling method used are introduced in Section 2. The results are
detailed in Section 3 and discussed in Section 4; conclusions are
presented in Section 5.

## 2. Data and methodology

2.1. SAFRAN atmospheric reanalysis database

The SAFRAN (Système d'Analyse Fournissant des Renseigne-
ments Atmosphériques à la Neige, a reanalysis system that
provides information concerning snow) atmospheric reanalysis
database (Quintana-Seguí et al., 2008; Vidal et al., 2010), pro-
jected on a regular 8×8 km grid over France, was included
in the different simulations. Météo-France initially developed
SAFRAN as a dataset of different meteorological surface vari-
ables (air temperature and air humidity, snowfall and rainfall,
10 m wind speed, incoming solar radiation and incoming infrared
radiation) for the forecast of avalanches in the Alps (Durand
et al., 1993, 1999). Since then, the database has been extended to
the whole of metropolitan France (Le Moigne, 2002). SAFRAN
was built up with reanalysis from model forecasts and *in situ* data,
and takes the orography into account by using climatologically
homogeneous zones. Each SAFRAN grid cell has been provid-
ing hourly data since 1958 (Vidal et al., 2010). The database was
checked against independent *in situ* data (Quintana-Seguí et al.,
2008). Over France, the root mean square error is about 2 mm for
daily total precipitation and 0.6 °C for daily average temperature.
In this study, 199 of the 9892 SAFRAN grid cells were selected
and are presented in Figure 1. These sites broadly correspond to
the meteorological station database used by ARVALIS – Institut
du végétal for the calibration and evaluation of the PANORAMIX
model (see Section 2.4).

2.2. Seasonal hindcast dataset from the ENSEMBLES project

The FP6 ENSEMBLES project produced a multi-model hind-
cast dataset over the globe (Weisheimer et al., 2009). Like the
former DEMETER project, this database was built with the con-
tribution of five European meteorological centres: Météo-France
(MF, France), the European Centre for Medium-Range Weather
Forecasts (ECMWF), the Met Office (United Kingdom), the
Centro Euro-Mediterraneo per i Cambiamenti Climatici (Italy)
and the Leibniz-Institut für Meereswissenschaften (Germany).
Each centre provided a seasonal hindcast dataset from a coupled
atmosphere-ocean global model and projected it onto a common
2.5° horizontal resolution grid. Further details about the config-
urations of the different coupled models used, about the method
employed to build the initial conditions and about the differences
with the previous DEMETER project database can be found in
Weisheimer et al. (2009) and Alessandri et al. (2011).

Each model computed a nine member seasonal hindcast start-
ing on 1 February, 1 May, 1 August and 1 November. In order to
<figure><img src="image_3.png" /><figcaption>Figure 1. Location and altitude of the 199 crop sites in France from the SAFRAN grid cells. [Colour figure can be viewed at wileyonlinelibrary.com].</figcaption></figure>

carry out simulations of the winter wheat growth period, the seasonal hindcasts of the five models starting on 1 February each year of the 1981–2005 period were set up as a multi-model ensemble of 45 members with a 6 month range (i.e. until 31 July). As mentioned in the Introduction, the goal of using a starting date of 1 February and a lead time of 6 months was to verify if pertinent information about the following key growing stages is available early in the crop season. Even if the predictability of the seasonal weather forecast is limited, especially for the stages at the end of the growing season, it is of interest to check the predictability of this original approach.

## 2.3. Statistical downscaling

A statistical method was applied to downscale the ENSEMBLES seasonal hindcast dataset. The aim of this post-processing was twofold: to reduce the bias of the GCM atmospheric outputs and to compute local-scale meteorological parameters from the original coarser spatial resolution of the GCM. Thus, a quantile mapping method was chosen (Déqué, 2007) using the SAFRAN atmospheric database as the climatological reference. For each weather forecast system and each month of prediction (from February to July), the statistical distribution of each atmospheric parameter was mapped with the SAFRAN statistical distribution calculated over the 1960–2005 period (see Déqué, 2007, for more details).

## 2.4. Crop growth modelling with PANORAMIX

The crop growth simulations were performed with the PANORAMIX model. Using atmospheric forcing inputs (i.e. minimum and maximum 2 m air temperatures, rainfall and snowfall, evapotranspiration and solar radiation), the model is able to forecast the different stages of winter wheat growth according to relations described by Gate (1995). The global concept of the crop growth modelling used in PANORAMIX is detailed in Meynard (1997) and Chatelin *et al.* (2005). PANORAMIX needs soil characteristics (i.e. soil type and soil water content)

and crop information (i.e. crop variety and sowing dates). This study focused on winter wheat and predicted the growth of a mid-early maturity variety called Soissons, widely grown in France since 1990. The sowing date was fixed at 20 October of each year (average date of sowing observed in France for this crop) and the soil available water capacity was set to 140 mm (corresponding to a high level of water capacity for the soil type selected) in order to avoid early water stress and to work with a variability mainly driven by atmospheric forcing. Finally, a mixed silt-clay soil type was parameterized. Four different agrometeorological variables were simulated, including three main growth stages identified by the Zadoks scale (more details can be found in Zadoks *et al.*, 1974): the dates of occurrence of the ‘leaf sheaths strongly erect’ (Z30) stage, the ‘head visible’ (Z50) stage and the ‘kernel hard’ (Z91) stage. Furthermore, as the water inputs are of high importance during ripening, the sum of total precipitation (i.e. rainfall plus snowfall, even though the model does not distinguish between solid and liquid water) between the ‘milky ripe’ (Zadoks scale Z75) stage and the ‘kernel hard’ (Z91) stage was also predicted.

## 2.5. Plan of simulations

In order to address the objective of this study, two sets of agro-nomic simulations with PANORAMIX were evaluated against a reference dataset over the period 1981–2005. For each year in this period, the agronomic simulations started on 1 August and extended to the harvest in July of the next year. The year of harvest was used as the year of the simulation. Until 31 January of the harvest year, both simulations were driven by the SAFRAN atmospheric reanalysis database of the current year. The two simulations to be evaluated differed from the others only by the atmospheric forcing data used from 1 February to 31 July.

* The first simulation had the atmospheric variables of the downscaled ENSEMBLES multi-model 6 month range atmospheric variables from 1 February until 31 July. This
simulation was a 45 member forecast and formed the seasonal forecasting system.

* The second simulation was forced by a climatic ensemble based on the last 20 years of the SAFRAN atmospheric meteorological data. As an example, the 20 years from 1980 to 1999 taken from the SAFRAN atmospheric reanalysis database were used for simulating the end of the simulation for the year 2000. Thus, a 20 member forecast was carried out in order to constitute the climatological forecasting system and to be compared with the seasonal forecasting one.

No dataset collecting the observed date of occurrence of the wheat growing stages is available over France for this historical period to evaluate these simulations. Given the performance of the SAFRAN reanalysis on the one hand and the PANORAMIX model on the other, the reference dataset was built with PANORAMIX simulations forced by the SAFRAN atmospheric dataset.

For all simulations, the soil water content was empty when the simulation started and then filled with the daily total precipitation until the soil water available capacity (140 mm) was reached.

A recapitulation of the three simulations carried out in this study is presented in Table 1.

## 2.6. Evaluation method

After a first global evaluation using deterministic scores obtained with the median of each simulation, hindcasts needed to be assessed according to probabilistic tools by distinguishing the below- and above-normal forecasts, i.e. the lower and upper tercile of the forecast distribution (corresponding to below the 33rd

<table><caption>Table 1. Recapitulation of the atmospheric forcing used in the different simulations.</caption><tr><td>Period simulations</td><td>Atmospheric forcing</td><td></td></tr><tr><td></td><td>1 August before the harvest year until 31 January</td><td>1 February until 31 July</td></tr><tr><td>Reference</td><td>SAFRAN atmospheric reanalysis database</td><td></td></tr><tr><td>Seasonal forecasting system</td><td>SAFRAN atmospheric reanalysis database</td><td>Downscaled (quantile mapping) ENSEMBLES 6 month seasonal hindcasts</td></tr><tr><td>Climatological forecasting system</td><td></td><td>Climatology based method over the last 20 years using SAFRAN atmospheric reanalysis database</td></tr></table>

percentile and above the 67th percentile, respectively). The reliability diagram (Hamill, 1997) and the receiver (or relative) operating characteristic (ROC) curves (Mason, 1982) were used as probabilistic scores. On the one hand, the reliability diagram serves to check whether the observed frequency and the probability forecasts are consistent. If this is the case, the curve of the graph should be close to the diagonal line, i.e. low probabilities are associated with low observed frequencies, and conversely. The frequency histograms are also represented on the graph in

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /><figcaption>Figure 2. Median values obtained with the reference simulation for the date of the Z30 (a), the Z50 (b) and the Z91 stages (c) and for the sum of precipitation (in mm) between the Z75 and the Z91 stages (d). [Colour figure can be viewed at wileyonlinelibrary.com].</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Figure 3. Median forecasts obtained with the seasonal forecasting system (on the y-axis) versus the reference (on the x-axis) for the date (day of year, doy) of the Z30 stage (a), the Z50 stage (b) and the Z91 stage (c), and for the sum of precipitation (in mm) between the Z75 and the Z91 stages (d). The following scores are also indicated: bias, standard deviation of the differences (SDD), Pearson correlation (r) and the number of forecasts (N). The linear regression curve is the full line. [Colour figure can be viewed at wileyonlinelibrary.com].</figcaption></figure>

order to show the frequency of the computed probability forecasts. On the other hand, the ROC curves are obtained by relating the hit rates and the false alarm rates of the hindcasts using a contingency table. The cases when, for each probability of forecast, the event is forecasted in the upper (lower) tercile while the event is observed in the same (a different) tercile are counted. The area under the ROC curve (AUC) is also presented as an added indicator of the forecast quality (Marzban, 2004). The ideal AUC value is 1.0, and an AUC value less than or equal to 0.5 means that the hindcasts provide less information than would be available from a random based forecasting system.

## 3. Results

Figure 2 shows the median values obtained with the reference simulation for the four agrometeorological variables presented in Section 2.4. Most of the time, the Z30 stage (top left) occurs in March, except for sites located in the south of France (i.e. south of 46 ° 48' N) where it may occur up to 1 month earlier. An east-west gradient is observed, with later dates of occurrence in the eastern part of the country. Later dates of occurrence can also be seen for sites at altitude (pointed out in Figure 1). The Z50 stage (top right) presents the same spatial pattern, which globally occurs in May or April around the Mediterranean Sea. The Z91 stage (bottom left) has median dates in June over the southern part of France and in July in the northern part or for sites at altitude. As the 2 m air temperature plays a major role in the

occurrence of these growth stages, the observations are consistent with the fact that, the farther south the site location is, the higher the 2 m air temperatures are. This is also consistent with the observed dates found by Gate (1995). For the sum of total precipitation between the Z75 and the Z91 stages (bottom right), a more spatially heterogeneous pattern is visible. The median values range from 0 to 63 mm. Various wet zones are represented, especially in the northeast and the extreme southwest of France. Lastly, the period between the Z75 and the Z91 stages ranged from 14 to 25 days, with an average of 19 days (not shown here).

A first reanalysis of the forecasting system skills was achieved with an overall comparison between the median forecasts from the seasonal and climatological forecasting systems versus the reference, presented in Figures 3 and 4 respectively. For the three growth stages, the range of median values is similar to the reference one. Good consistency is observed between the seasonal forecasting system and the reference. The deterministic scores noted on the graph show the high quality of the seasonal forecasting system, especially for the Z30 stage. With regard to the climatological forecasting system, a similar pattern is observed. However, the bias is higher and rises simultaneously with the growth stages (from 1.1 days for the Z30 stage to 4.1 days for the Z91 stage). The climatological forecasting system tends to overestimate the occurrence dates of these stages but, at the same time, has a slightly lower standard deviation of differences (SDD) than the seasonal forecasting system. This can be explained by a lower variability of the median climatology based forecasts. In
<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /><figcaption>Figure 4. The same as Figure 3 but with median forecasts obtained with the climatological forecasting system. [Colour figure can be viewed at wileyonlinelibrary.com].</figcaption></figure>

both ensemble forecasting systems, the Pearson correlation (r) is high except for the sum of total precipitation between the Z75 and the Z91 stages (bottom), where low but positive values are obtained that are higher for the seasonal forecasting system (0.26 against 0.20). For this agrometeorological variable, the pattern is clearly different from that of the growth stages, with a range of forecasts lower than the reference range (e.g. median values vary less than the range of the value obtained for each year). This expresses an under-variability for both forecasting systems, explained by a dry median forecast during the period between the two growth stages because of the climatological precipitations which have a gamma or exponential distribution (Wilks, 2011). Lastly, many forecasts are randomly distributed. The consequences are a high SDD of about 28 mm for both forecasting systems. Even if forecasts are not very good for this variable, the seasonal forecasting system gives more accurate information than the climatological forecasting system does (SDD and r scores are better for the seasonal system even if this variable occurs after the theoretical limit of predictability of the seasonal weather forecast). Except for the hydric-linked variable, these results are not surprising. Indeed, good results obtained with both forecasting systems can be explained by the fact that growing stages are mainly piloted by a function of cumulated temperatures. Because of the good skills of the seasonal weather forecasts concerning temperature predictions, the seasonal forecasting system is slightly better than the climatological one.

The deterministic scores presented are very useful for a first assessment of a forecasting system but are insufficient to draw

a conclusion on which is the best. Such an assessment needs probabilistic scores (see Section 2.6).

Figures 5–8 show the reliability diagrams and the associated frequency histograms for each variable and for both above- and below-normal forecasts. For the dates of occurrence of the growth stages, the reliability level is high (with curves close to the diagonal line), except for the high probability forecasts (above 70%) where a lack of forecasts is generally observed in the frequency histograms. Forecasts with low probability are often used by the two different forecasting systems and a similar pattern is observed between the above- and below-normal forecasts. For the sum of total precipitation between the Z75 and the Z91 stages, the reliability is only high for a range of forecast probability from 10 to 60% (Figure 8(a)). For this range, the curves of the seasonal forecasting system are close to the diagonal line, whereas the climatological forecasting system curves are more or less horizontal. In this case, the seasonal forecasting system is more reliable than the climatological one. Out of this range of forecast probability, neither forecasting system is able to compute forecasts because of a very low number of samples (Figure 8).

Figure 9 displays the ROC curves in order to check the ability of the two forecasting systems to predict the occurrence of above-normal or below-normal events. For the date of occurrence of the growth stages, curves are close to the top left hand corner and the AUC scores range between 0.73 and 0.84, showing high skills (the hit rates are higher than the false alarm rates). For the dates of occurrence of the Z30 and the Z50 stages, the
<figure><img src="image_11.png" /><figcaption>Figure 5. Reliability diagrams and their frequency histogram for the date of occurrence of the Z30 stage. (a) The above-normal (dashed lines) and below-normal (crosses) forecasts made with the seasonal forecasting system (black lines) and the above-normal (empty triangles) and below-normal (full triangles) climatological forecasts (grey lines) are represented. The diagonal line corresponds to the skill limit. Frequency histograms for the seasonal forecasting system (b), (c) and for the climatological forecasting system (d), (e) are presented. Parts (b) and (d) are for above-normal events (upper tercile) and parts (c) and (e) are for below-normal events (lower tercile).</figcaption></figure>

seasonal forecasting system has higher skill than the climatological forecasting system, for both above- and below-normal forecasts. After the Z50 stage, the below-normal forecasts of the seasonal forecasting system have a skill level lower than the climatological forecasting system. At the same time, the above-normal forecasts of the seasonal forecasting system have a skill level equivalent to the climatological forecasting system. Thus, the seasonal forecasting system provides valuable information until the moment where predictability is lost, identified as being between the head visible and the hard kernel stages, i.e. 4 months after the starting date of the period for which the seasonal forecasts are used. The AUC scores were calculated over two different areas of France, with 199 sites evenly distributed between the northern area (100 sites) and the southern area (99 sites). Table 2 shows that the AUC scores obtained by the seasonal forecasting system are globally poorer in the southern area. Thus, results obtained in the south can explain the lower AUC scores obtained for the Z50 stage over all sites, because of

the orography and the influence of the Mediterranean in this part of France. GCMs are not able to compute sufficiently good forecasts in this region. Globally, the AUC scores are better for the upper tercile compared to the lower tercile. Indeed, the frequency of observed events is higher for this category and improves the case of good predictions and thus the hit rates.

The AUC scores obtained with the climatological forecasting system are lower for the sum of total precipitation between the Z75 and the Z91 stages (range between 0.45 and 0.57) and even lower than 0.5 (with ROC curves under the diagonal line). The fact that this variable occurs during the 5th or 6th month after the starting date (not shown here) can explain the low scores. The short time between the two stages where the total precipitations were summed explains the high temporal variability and the lower skills obtained. Nevertheless, regarding AUC scores higher than 0.5, some predictability is found for this variable using the seasonal forecasting system, which is the only one able to discriminate correctly whether this event occurs or not.
<figure><img src="image_12.png" /><figcaption>Figure 6. The same as Figure 5 but for the Z50 stage.</figcaption></figure>

The quality of the seasonal forecasting system highlighted in the ROC curves presented in this section was somewhat artificially improved. The ensemble was composed of 45 members instead of only 20 as in the climatological forecasting system. The size of an ensemble forecast has an impact on the skill scores (Buizza and Palmer, 1998) and needs to be checked here. For this purpose, another simulation was run with an ensemble of only 18 members (using only the two downscaled outputs MF and ECMWF GCMs). As expected, the AUC scores obtained with the 18 member ensemble were substantially lower than those obtained with a 45 member ensemble (results not shown here). Only the scores obtained for the sum of total precipitation between the Z75 and the Z91 stages were impacted, with values close to 0.5 for the 18 member ensemble (instead of 0.57 for the 45 member ensemble).

## 4. Discussion

The high skill levels found here demonstrate the ability of a seasonal forecast based system to predict the growth stages better compared to the hydrology-linked variables, and underline the fact that the predictability differs according to the agrometeorological variable studied. Even if coupled

ocean-atmosphere global models are used, the predictability of the seasonal forecast is globally lost after the dates of occurrence of the Z50 stage, generally observed in May (Figure 2), i.e. about 4 months after the starting date of 1 February.

This study shows that good results can be found by applying seasonal forecasts to the agricultural sector in mid-latitude regions (up to 25° latitude), comparable with those obtained in Malherbe *et al.* (2014). An original method consisting of forecasting key stages during the crop growth period was used and showed good skills with seasonal forecasts as atmospheric input. Although this study does not deal with the crop yield as achieved by Cantelaube and Terres (2005) or Marletto *et al.* (2005, 2007), the results presented are of the same quality as those obtained in the previous work. The fact that the focus is on specific variables of plant growth permits a slightly higher predictability (of about 4 months, especially for temperature-linked variables). In particular, it is advised by those authors that a multi-model approach be used, as was done in this study, in order to optimize the use of seasonal weather forecasts and to take into account the GCM uncertainties, because of the shortcomings present in individual GCMs. Nevertheless, this is not the case concerning the uncertainties of the crop growth model. Thus, a future study could assess the crop growth model uncertainties by using another
<figure><img src="image_13.png" /><figcaption>Figure 7. The same as Figure 5 but for the Z91 stage.</figcaption></figure>

kind of model, such as the STICS (Simulateur Multidisciplinaire pour les Cultures Standard) multidisciplinary simulator for standard crops (Brisson *et al.*, 1998, 2003, 2009), the CERES (Crop Estimation through Resource and Environment Synthesis) wheat model (Ritchie and Otter, 1985) or the WOFOST model (Van Diepen *et al.*, 1989).

A single downscaling method was chosen for this study (quantile mapping), and it obviously had an impact on the final scores. A more complex downscaling method based on dynamical methods, for example based on weather predictors (Terray *et al.*, 2004), could be used and assessed in future work. The assessment of the forecast of the main growth stages could then be made on a more accurate scale than the national scale used in this study. A first analysis has been considered in the present study in defining north and south areas, highlighting the fact that different skills could be obtained in various parts of the country. An assessment on a regional scale is required in order to characterize the spatial variability of predictions processed with the seasonal forecasting system. In view of the resolution of the GCMs used for this study and even if a statistical downscaling is applied, it is not useful to lead these investigations using these data.

Specific information obtained with seasonal forecasting could be delivered to interested parties in order to help improve crop

management and to increase the final crop yield. To improve the results presented in this paper, monthly operational forecasts from real-time systems could be used in combination with the method developed here. Thus, other starting dates could be assessed and used to improve the accuracy of the forecasts during the growing period. Moreover, the ENSEMBLES project does not take account of the recent improvements made to seasonal weather forecasting systems, e.g. the horizontal and vertical resolutions within the GCMs, the representation of the atmospheric physics or the use of stochastic methods, by new projects such as SPECS (Seasonal-to-Decadal Climate Prediction for the Improvement of European Climate Services), started in 2012 (http:// specs-fp7.eu). These enhancements could permit further advances in this forecasting system for agriculture and push back the limits identified previously.

## 5. Conclusions

This study investigated the possibility of using seasonal weather forecasts in order to anticipate various agrometeorological variables connected with winter wheat growth between 1981 and 2005 in France. The objective was to assess the skill
<figure><img src="image_14.png" /><figcaption>Figure 8. The same as Figure 5 but for the sum of total precipitation (in mm) between the Z75 and the Z91 stages.</figcaption></figure>

of agronomic simulations (PANORAMIX model) forced by seasonal weather forecasts (ENSEMBLES seasonal hindcasts on which a dowscaling method had been applied) and to determine if they were able to provide more useful information than agronomic simulations forced by the climatological dataset currently in use. As expected, the bias of forecasts computed with the seasonal forecasting system was close to zero, and in some cases presented a lower standard deviation of the differences than the climatological method did. Because of its high natural variability, the sum of total precipitation between the Z75 and the Z91 stages proved to be hard to simulate, whatever the forecasting system used. Nevertheless, regarding the deterministic scores obtained, the seasonal forecasting system provided a relative gain of skill, even though the use of the median values of the ensemble was not sufficiently representative. In order to analyse all the information provided by the forecasting system, a probabilistic verification was introduced. Strong reliability was found for both forecasting systems, except for higher values. The frequency histograms showed that the systems were not able to compute the high probability events. For both systems, high skill was demonstrated by the receiver (or relative) operating

characteristic (ROC) curves. For the Z30 and Z50 stages, simulations were better performed by the seasonal forecasting system, with area under the ROC curve (AUC) scores close to 0.8. For the hard kernel stage, lower AUC scores were found and an explanation concerning the impact of the downscaled seasonal forecasts could be proposed. More investigations are needed at the regional scale in order to evaluate the impact of the spatial variability of the seasonal forecasts. As shown by Cantelaube and Terres (2005), the high skill provided by seasonal forecasting, and highlighted in this paper over a longer period of 25 years (against a period of 4 years in the earlier study), is sufficient to encourage the application of this method for the agricultural sector over France in an operational context, keeping in mind its strengths and weaknesses. In a first step, the focus could be on forecasting growth stages only (especially those occurring in spring) because further investigation is still needed for the hydrology based variables. The EUROSIP forecasting system (http://www.ecmwf.int/en/forecasts/documentation-and-support/long-range/seasonal-forecast-documentation/eurosip-user-guide/multi-model, accessed September 2016) could be used as an operational database in future work.
<figure><img src="image_15.png" /><figcaption>Figure 9. Receiver operating characteristic curves and the associated area under the curve scores for the date of the Z30 stage (a), the Z50 stage (b) and the Z91 stage (c), and for the sum of precipitation (in mm) between the Z75 and the Z91 stages (d). The above-normal (dashed lines) and below-normal (crosses) forecasts obtained with the seasonal forecasting system (black lines) and the above-normal (empty triangles) and below-normal (full triangles) forecasts obtained with the climatological forecasting system (grey lines) are represented.</figcaption></figure>

<table><caption>Table 2. Recapitulation of AUC scores calculated over the full 199 sites (first value), over the 100 northern sites (second value) and over the 99 southern sites (third value) of France.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Seasonal forecasting system</th><th colspan="2">Climatological forecasting system</th></tr><tr><th>Lower tercile</th><th>Upper tercile</th><th>Lower tercile</th><th>Upper tercile</th></tr></thead><tbody><tr><td>Z30 stage</td><td>0.82<br>0.80<br>0.84</td><td>0.84<br>0.84<br>0.85</td><td>0.75<br>0.70<br>0.79</td><td>0.80<br>0.80<br>0.81</td></tr><tr><td>Z50 stage</td><td>0.76<br>0.78<br>0.73</td><td>0.80<br>0.83<br>0.76</td><td>0.73<br>0.72<br>0.72</td><td>0.77<br>0.74<br>0.79</td></tr><tr><td>Z91 stage</td><td>0.74<br>0.78<br>0.70</td><td>0.84<br>0.85<br>0.83</td><td>0.79<br>0.78<br>0.79</td><td>0.84<br>0.80<br>0.87</td></tr><tr><td>Sum of total precipitation between the Z75 and the Z91 stages</td><td>0.57<br>0.57<br>0.56</td><td>0.57<br>0.58<br>0.55</td><td>0.45<br>0.47<br>0.44</td><td>0.45<br>0.49<br>0.42</td></tr></tbody></table>

## Acknowledgements

The doctoral scholarship of N. Canal was funded by the CIFRE program (contract 2012/0455) of the Association Nationale de la Recherche Technique through a partnership between Météo-France and ARVALIS – Institut du végétal.

The authors wish to thank the anonymous reviewers for their comments and advice on this paper.

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