RT6 -Assessments of Impacts and Climate Change Seasonal predictions of wheat yield and irrigation needs in Northern Italy Fausto Tomei, Valentina Pavan, Giulia Villani, Vittorio Marletto Arpa Emilia-Romagna, Servizio Idro-Meteo-Clima, Bologna, Italy

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RT6 -Assessments of Impacts and Climate Change
Seasonal predictions of wheat yield and
irrigation needs in Northern ItalyFausto
Tomei, Valentina Pavan, Giulia Villani, Vittorio
MarlettoArpa Emilia-Romagna, Servizio
Idro-Meteo-Clima, Bologna, Italy
Results The wheat yield prediction method has
been run for the years 1987-1999 using observed
data until the last day of April and, after that
day, the daily predictions of both
meteo-rological variables and watertable depth
obtained calibrating the seasonal multi-model
forecasts for the May-June-July (MJJ) period.
Figure 5 shows the comparison between observed
and predicted yield distributions using data
collected in the experimental farm of Cadriano,
Bologna. Comparison between the mean of the
observational data and the median of ensemble
forecasts gives a determination coefficient of
0.48.

• Introduction
• This work involves the application of downscaled
  multi-model seasonal predictions to forecasting
  useful agricultural information for the Italian
  region of Emilia-Romagna up to three months in
  advance.
• In particular we concentrated on the prediction
  of wheat yield and irrigation needs for kiwifruit
  orchards.
• For both we were able to obtain from local
  researchers important field data for validating
  the models and checking forecasting results.

Fig. 1 - locations of the study
Fig 5 - Comparison between observed wheat yield
at Cadriano, Bologna (blue band, indicating the
whole range of yields from all the experimental
plots) and the predicted distribution (box and
whiskers) using multi-model downscaled seasonal
forecasts, for the MJJ period.

• Simulations were carried out by with the
  CRITERIA/WOFOST modelling software, produced
  and/or adapted by us in the past and driven using
  ENSEMBLES Stream 2 downscaled multi-model
  predictions for the AMJ (wheat) and JJA
  (kiwifruit) periods.
• To carry out this work the modelling suite was
  integrated by a new routine to assess water table
  level (essential for better wheat yield
  prediction) from rainfall data, and by a weather
  generator enabling to transform seasonal
  predictions from monthly statistical anomalies to
  daily data series.
• The results were very interesting for both
  applications, and encouraged us to perform a
  first operational seasonal forecast of wheat
  yield for the current 2009 season, using output
  from the operational Ecmwf ensemble predictions
  system.
• Materials and method
• Figure 2 shows the computational scheme developed
  at ARPA-SIMC for agronomical predictions core of
  the scheme is the coupled soil water balance and
  crop growth model, CRITERIA/WOFOST (C/W), which
  describes the dynamics of water in agricultural
  soils with or without crops. The C/W software
  environment (Marletto et al., 2007) requires the
  input of soil and crop parameters and daily
  meteorological data, namely extreme temperatures,
  total precipitation and, if present, hypodermic
  water table depth.
• In order to obtain a statistical prediction,
  observational meteorological data are used up to
  the day of prediction. Starting from the first
  day of prediction, meteorological daily data are
  synthetically produced with a weather generator
  (wg), based on Richardson scheme, using as input
  predicted and downscaled seasonal anomalies added
  to the local climatology.

Figure 6 shows the comparison between actual
irrigation data for kiwifruit measured at
Brisighella, in the hills of Emilia-Romagna, and
the hindcasts computed using downscaled STREAM2
dataset for the months JJA, in the years
1996-2005. The multi-model runs use five models,
nine members, and five weather generator
replicates (225 member replicates).
Fig. 6 - Comparison between actual irrigation
data (red line) for kiwifruit measured in
Brisighella, Italy, and the assessment of
irrigation water needs computed using downscaled
STREAM2 dataset for the JJA period (box and
whiskers). Blue stars represent the irrigation
water need computed with CRITERIA model using
actual weather data.
Figure 7 shows the comparison between hindcasts
of irrigation water needs for kiwifruit at
Brisighella computed using downscaled multi-model
STREAM2 dataset for the JJA period and the values
computed with CRITERIA model using actual weather
data (tier-2 verification), for the years
1987-2005. Table 1 shows two performance indices
(the determination coefficient R2 and the
modelling efficiency index EF) for the 20 years
period of tier-2 verification. EF is computed as
follows
The climate anomalies used for predictions are
obtained by calibrating and downscaling the
multi-model seasonal ensemble forecasts extracted
from the ENSEMBLES Stream 2 data-set and from the
EUROSIP ECMWF special project framework. The
calibration and downscaling method is based on
the MOS version of a statistical multi-linear
regression scheme developed in recent years at
ARPA-SIMC (Pavan et al, 2005). The high
resolution anmalies forecasts needed as input for
the wg consist of monthly cumulated
precipitation, wet day frequency, averaged
minimum and maximum temperature and the mean
difference of temperature between dry and wet
days. The observational data used to define the
local climate predictands are obtained starting
from the daily analysis produced operationally by
UCEA covering the whole Italian territory from
1987 to present, with an approximate resolution
of 35 km.
Fig. 7 - Comparison between irrigation water
needs for kiwifruit computed with CRITERIA model
(grey diamonds) using actual weather data of
Brisighella, and the assessments of irrigation
water needs (box and whiskers), computed using
downscaled multi-model STREAM2 dataset for the
JJA period.
where n represents the number of data pairs, i is
the pair index and AvgObserved is the average of
the observed data. EF provides a simple index of
model performance on a relative scale, where EF1
indicates a perfect fit, EF0 suggests that the
model predictions are no better than a simple
average, and a negative value would indicate an
eventually poor model performance. All STREAM2
model predictions perform better than a simple
average of observed data (EFgt0). The multi-model
R2 is the highest, while its efficiency is
comparable to the one of the best performing
models.
The synthetic precipitation data produced by wg
are used as input in an empirical equation
developed at ARPA-SIMC (Tomei et al., 2009) to
predict hypodermic groundwater level, that is
essential for a correct analysis of water
dynamics that influence crop growth. The starting
hypothesis of the formula is that trend of
groundwater depth during a year can be
approximated with a sinusoidal curve and that
observed variations related to this curve are
well correlated to precipitation anomalies
previous to the data to estimate. Figure 3 shows
the validation of formula using the data of a
well located in the Ferrara plain (R2 0.71).
Finally the WOFOST routines, when activated in
C/W model, compute dry biomass accumulation, so
it is possible to predict a statistical
distribution of wheat yield. Figure 4 shows an
example of wheat yield prediction for the year
2007 at Cadriano, Bologna.
Model R2 EF
MULTI-MODEL 0.51 0.32
LFPW 0.27 0.21
INGV 0.42 0.36
IFMK 0.41 0.32
EGRR 0.22 0.17
ECMF 0.22 0.15
Table 1 - Tier-2 verification of the 1987-2005
hindcasts of irrigation water needs for kiwifruit
at Brisighella, Italy, using STREAM2 dataset, JJA
period.
Fig. 3 - Comparison between observed groundwater
depth in Ferrarese plain (blue points) and
forecast values (red line, R2 0.71).
References Marletto V., Ventura F., Fontana G.,
Tomei F. (2007). Wheat growth simulation and
yield prediction with seasonal forecasts and a
numerical model. Agricultural and forest
meteorology 147, pp.71-79. Pavan V. et al.
(2005). Downscaling of DEMETER winter seasonal
hindcasts over Northern Italy. Tellus,
57A424-434. Tomei F. et al. (2008). Seasonal
weather predictions and crop modelling for wheat
yield forecasting in Northern Italy, European
Society for Agronomy Congress Proceedings,
Bologna, 15-19 September 2008. Tomei F. et al.
(2009). Sviluppo di unequazione empirica per la
stima e la previsione del livello piezometrico
utilizzando dati pregressi e anomalie nelle
precipitazioni. AIAM Congress Proceedings,
Sassari, 15-17 June 2009 (in Italian). Tomei F.,
Villani G., Pavan V., Pratizzoli W. And Marletto
V. (2009). Report on the quality of seasonal
predictions of wheat yield and irrigation needs
in Northern Italy.. Ensembles Project, 6th EU RD
Framework Programme, Research Theme 6,
Assessments of Impacts and Climate Change,
available as Deliverable 6.22 from
www.ensembles-eu.org.
Fig. 4 - Example of wheat yield prediction, year
2007. Left observed cumulated rainfall for the
first 6 months of 2007 at Cadriano (black solid
line) and 10 runs of WG using as input the
ensemble mean of seasonal predictions (grey thin
lines). Right wheat yield simulation from
observed daily data (black solid line) and wheat
yield simulations obtained using WG data (grey
thin lines).
ENSEMBLES Final Symposium - A Changing Climate
in Europe 17-19 November 2009, Exeter, UK