Status ReportCROP CIS

Geoland2 Project Review

Ispra, 25th of January 2012

Institute of Geodesy and Cartography

ISPRA2012-01-25

Utility assessment of BioPAR products for

wheat yield forecasting in Europe.

Crop yield estimation. Detailed description of methods and

comparison of results on MARSOP and BioPar data

ISPRA2012-01-25

10400 - Utility Assessment – IGiK contribution

The objective of the work is to test the performance of MARS and

BioPar indicators for yield forecast on an European window. The

purpose is to show and assess their practical use in crop

monitoring/yield forecasting. The work is aimed at comparing the

differences in yield estimation accuracy, based on the two data

sets.

Objective

ISPRA2012-01-25

NDVI and FAPAR imagesfrom

- MARS OP - BioPar

databases

resolution 1km2

10-day periods

1998 – 2011

unsmoothed

Satellite indices

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minmax

min100NDVINDVI

NDVINDVIVCI

minmax

min100fAPARfAPAR

fAPARfAPARFCI

[VALUE]max - the highest (of all years) index value for a given pixel in a given decade

[VALUE]min - the lowest (of all years) index value for a given pixel in a given decade

Satellite indices

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Arable fraction image - from JRC

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- arable land fraction > 50 %

- clumps, which are contiguous groups of pixels in one thematic class (region) > 10 pixels

- number of arable pixels in one region (thematic class) > 100

geometric correction to NDVI images

Arable land mask – created in IGIK

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Indices’ profiles

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Indices’ profiles

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Eurostat,Regional Agriculture StatisticsDatabase

1998 - 2010

29 NUTS0109 NUTS1299 NUTS2

Wheat yield data

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Missing yield data for all

years: 76 NUTS2 regions

Wheat yield data

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Missing yield data for more than

two years: 92 NUTS2 regions

Wheat yield data

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Adding NUTS1 regions for

DE, DK and UK. Number of

added NUTS1 polygons: 25

Adding the last 3 years

(2008; 2009; 2010) of yield

data for Spanish regions from

Spanish National Statistical

Office

Wheat yield data

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NUTS 2 regions FR81, FR82 and RO21,

RO22, RO31,RO31,RO41, PT18 excluded

due to erroneous yield data (one order of

magnitude less than other)

Wheat yield data

ISPRA2012-01-25

Wheat yield data

These NUTS 2 regions which have less

than 100 pixels representing arable land

were excluded. Number of excluded

polygons: 17

AT13 FI20

ITC3 FI13

AT32 FR83

AT33 NL21

BE21 NL22

BE34 NL31

DEC PT15

PT17

UKI

UKL

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European agro-climatic zones

Iglesias, A., Garrote, L., Quiroga, S., Moneo, M.: Impacts of climate change in agriculture in Europe. PESETA-Agriculture study. EUR 24107 EN; DOI 10.2791/33218; EC 2009.

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Analized regions

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Growing seasons

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Another grouping of regions

mean ordinal

number of the decade

in which the annual maximum of NDVI

occurred

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Another grouping of regions

The starts and the ends of the growing seasons:

in each zone, the season starts two decades before the lowest - occurred in this zone - ordinal number of the decade with annual maximum NDVI;

in each zone the season ends two decades after the highest - occurred in this zone - ordinal number of the decade with annual maximum NDVI.

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Growing seasons

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Statistical model

Partial Least Squares RegressionPartial Least Squares Regression (PLSR)

- to choose a few components being linear combinations of explanatory variables X and to perform linear regression of response variable Y on these variables instead of performing regression with use of all X-variables

Y - response variable (yield value); Xn - explanatory variables (values of vegetation indices); n - sequential number of ten-day period taken into account; d_beg, d_end – number of ten-day period corresponding to the beginning and

the end of growing season, respectively (different for different agro-climatic zones); cNn - function f – coefficients generated by the PLS regression algorithm.

,...)2,1( CompCompfY

endd

begdnnNn XcCompN

_

_

,...2,1N

ISPRA2012-01-25

Statistical model

Partial Least Squares RegressionPartial Least Squares Regression (PLSR)

- generalization of multiple regression - many (correlated) predictor variables

- few observations

- to derive orthogonal components using the cross-covariance matrix between the response variable and the explanatory variables

- dimension reduction technique similar to Principal Component Regression (PCR)

PCR - the coefficients reflect the covariance structure between the

predictor variables X

PLSR – the coefficients reflect the covariance structure between the

predictor X and response Y variables

ISPRA2012-01-25

Statistical model

Partial Least Squares RegressionPartial Least Squares Regression (PLSR)

http://www.youtube.com/watch?v=AxmqUKYeD-U&feature=related

the PLSPLS PACKAGEPACKAGE

RR software environment

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Model evaluation

OOne-leave-out ne-leave-out cross-validation:

- for each year of data the PLS regression model was built with this

year excluded

- the yield prediction for excluded year was performed

- predicted and actual yield values were compared

ISPRA2012-01-25

Model evaluation

OOne-leave-out ne-leave-out cross-validation:

Performances were evaluated in terms of cross-validation mean errors:

Mean Percentage Error (MPEMPE)

Mean Absolute Percentage Error (MAPEMAPE)

Root Mean Square Error (RMSERMSE)

100_

__1

1

N

i i

ii

obsYield

predYieldobsYield

NMPE 100

_

__1

1

N

i i

ii

obsYield

predYieldobsYield

NMAPE

N

predYieldobsYieldRMSE

N

iii

1

2__ Yield_obsi – actual yield in year i,

Yield_predi –yield prediction made for year i,

N – number of observations (years) taken into account

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Agro-climatic zoneMean yield

(dt/ha)

Number of

regions

RMSE (dt/ha) MPE (%) MAPE (%)

BioPar MARSNull

modelBioPar MARS

Null model

BioPar MARSNull

model

Alpine 52.4 5 4.48 5.62 5.23 -0.71 -0.83 -0.86 7.20 9.36 8.04

Atlantic Central 75.1 48 7.59 7.49 6.25 -2.05 -2.46 -0.76 8.50 8.20 6.88

Atlantic North 86.6 3 6.82 6.62 5.18 -0.20 -0.43 -0.33 6.81 6.81 5.26

Atlantic South 45.6 7 6.98 6.95 4.68 -4.00 -4.73 -2.17 14.98 14.77 11.88

Boreal 36.8 4 5.82 5.21 7.65 -3.04 -3.03 -3.04 13.83 12.36 12.55

Continental North 44.3 30 4.21 4.36 5.72 -1.51 -1.68 -1.89 8.25 8.39 11.40

Continental South 35.2 9 5.68 5.61 6.81 -2.54 -2.97 -3.63 13.52 13.69 16.16

Mediterranean North 38.6 18 5.22 5.70 5.08 -1.85 -1.55 -2.87 12.06 12.73 13.14

Mediterranean South 22.0 6 3.95 3.91 5.05 -4.61 -4.24 -6.73 16.68 15.90 23.34

Cross-validation prediction errors

Agro-climatic zones

Small differences in errors (MPE, MAPE) of yield prognosis for both MARS and BioPar databases

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Results - cross validation for Agroc-limatic zonesB

i o P

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Cross-validation prediction errors

Agro-climatic zonesMean errors for indices

IndexRMSE (dt/ha) MPE (%) MAPE (%)

BioPar MARS BioPar MARS BioPar MARS

NDVI 6.09 6.15 -2.16 -2.35 10.29 9.57

Fapar 6.06 6.00 -2.15 -2.22 10.15 9.56

VCI 5.86 6.04 -2.02 -2.41 10.03 9.47

FCI 5.82 5.94 -2.03 -2.24 9.87 9.44

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Results - cross validation Agro-climatic zonesB

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Cross-validation prediction errors

maxNDVI decades

NDVImax decade

Mean yield

(dt/ha)

Number of

regions

RMSE (dt/ha) MPE (%) MAPE (%)

BioPar MARSNull

modelBioPar MARS

Null model

BioPar MARSNull

model

11 17.7 3 3.48 3.53 4.61 -4.07 -5.36 -6.96 19.09 18.27 23.41

12 28.5 8 4.56 4.65 9.71 -3.86 -3.76 -5.29 15.10 14.74 19.81

13 34.9 8 4.56 4.99 7.96 -2.62 -2.35 -2.61 11.11 11.47 12.32

14 46.8 8 7.69 7.20 10.39 -3.79 -4.31 -2.34 13.95 12.79 12.56

15 59.4 16 6.63 6.53 7.51 -1.98 -3.02 -2.42 10.15 9.97 12.28

16 57.7 37 5.52 5.50 5.56 -1.78 -1.98 -1.33 8.40 8.20 9.22

17 58.0 19 4.58 4.86 5.05 -1.06 -1.26 -1.21 7.12 7.74 8.52

18 68.3 11 5.11 5.22 5.66 -0.33 -0.57 -0.83 6.31 6.37 7.51

19 64.5 15 6.46 6.67 6.15 -1.30 -1.25 -1.16 8.28 8.80 8.04

20 44.0 5 6.12 5.74 5.82 -2.11 -2.59 -2.49 12.71 11.41 12.25

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Results - cross validation maxNDVI decadesB

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Cross-validation prediction errors

maxNDVI decadesMean errors for indices

IndexRMSE (dt/ha) MPE (%) MAPE (%)

BioPar MARS BioPar MARS BioPar MARS

NDVI 5.65 5.66 -1.94 -2.12 9.73 10.31

Fapar 5.56 5.66 -1.92 -2.13 9.44 10.08

VCI 5.58 5.61 -1.84 -2.17 9.64 10.10

FCI 5.54 5.59 -1.85 -2.12 9.47 9.92

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Results - cross validation maxNDVIB

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Cross-validation prediction errors - annual MPEs

Index 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

NDVI -1.44 -2.87 -7.02 -1.73 -10.49 6.47 2.28 0.51 -11.24 3.90 -2.10

fAPAR -2.07 -3.46 -7.45 -2.54 -10.09 6.26 2.78 0.49 -9.26 4.04 -0.46

VCI -1.69 -2.42 -6.37 -1.58 -11.65 6.04 2.21 0.10 -10.92 3.67 -1.61

FCI -2.32 -3.26 -7.06 -2.74 -10.93 6.19 2.91 0.48 -9.04 4.14 -0.21

Average -1.88 -3.00 -6.97 -2.15 -10.79 6.24 2.54 0.39 -10.12 3.94 -1.10

Index 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

NDVI 2.07 -0.91 -5.21 0.90 -10.70 6.00 0.71 -0.69 -14.37 2.22 -2.47

fAPAR 1.29 -1.62 -6.15 0.43 -9.56 6.35 1.05 -0.48 -13.53 2.17 -2.07

VCI 2.51 0.31 -4.08 1.41 -11.33 5.57 0.07 -1.50 -13.37 1.97 -2.73

FCI 1.55 0.09 -5.19 0.79 -10.16 5.90 0.48 -1.30 -12.67 1.76 -2.46

Average 1.86 -0.53 -5.16 0.88 -10.44 5.96 0.58 -0.99 -13.48 2.03 -2.43

MARS

BioPar

The largest errors: 2003 (drought in Europe) and 2007

ISPRA2012-01-25

Cross-validation prediction errors - annual MPEs

MARS

BioPar

Agroclim zone 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Number

of regions

Alpine -6.29 -0.32 4.08 1.69 -11.03 5.11 4.00 -1.24 -1.68 0.85 -4.79 5

Atlantic Central 1.94 0.02 -6.22 -0.10 -8.04 2.83 -0.09 -3.04 -19.56 4.33 6.64 48

Atlantic North 3.43 8.40 0.12 -9.39 -4.90 9.67 -2.42 5.55 -7.25 0.27 -11.90 3

Atlantic South 2.36 0.47 -27.88 17.69 -12.77 5.25 -9.93 4.19 -15.71 3.22 -30.28 7

Boreal -19.03 9.32 -4.19 -2.29 6.15 -3.43 -3.23 -1.03 3.12 -12.31 -7.55 4

Continental North -5.90 -14.85 -3.39 -3.84 -4.32 9.89 6.60 -3.68 -5.72 7.37 -0.70 30

Continental South -9.63 -3.39 3.20 -7.58 -26.78 15.32 13.35 11.95 -21.39 0.72 -8.43 9

Mediterranean North 1.39 2.92 -15.01 -3.69 -19.91 7.28 2.95 4.38 4.57 2.30 -4.30 18

Mediterranean South -5.87 -3.25 -12.23 -19.87 -25.45 5.31 2.38 13.54 2.41 16.20 6.02 6

Average -4.18 -0.08 -6.84 -3.04 -11.89 6.36 1.51 3.40 -6.80 2.55 -6.14  

Agroclim zone 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Number

of regions

Alpine -5.51 0.99 2.71 1.20 -7.16 4.54 -0.48 -1.32 -0.33 1.66 -4.79 5

Atlantic Central 3.33 0.54 -4.05 1.97 -7.25 3.36 -1.04 -2.98 -20.37 2.35 6.06 48

Atlantic North 0.35 8.18 6.15 -7.56 -6.20 12.06 -5.02 4.55 -7.78 2.03 -13.40 3

Atlantic South 4.12 -0.24 -26.36 16.54 -13.59 4.42 -8.68 6.37 -14.39 4.28 -23.90 7

Boreal -21.27 12.19 -8.26 3.84 5.47 4.88 -2.32 -3.71 5.52 -20.42 -10.16 4

Continental North 0.57 -10.77 0.02 1.35 -5.77 9.21 4.44 -5.94 -12.01 4.13 -1.93 30

Continental South -0.78 3.99 7.27 -2.83 -26.79 16.67 11.50 5.47 -32.25 2.85 -13.03 9

Mediterranean North 6.33 5.61 -14.20 -1.11 -17.95 3.80 -0.71 1.78 0.06 0.57 -7.33 18

Mediterranean South 2.88 4.56 -16.86 -14.87 -23.96 1.48 -3.62 11.55 -5.93 13.61 -3.20 6

Average -1.11 2.78 -5.95 -0.16 -11.47 6.71 -0.66 1.75 -9.72 1.23 -7.96  

ISPRA2012-01-25

Cross-validation prediction errors - annual MAPEs

MARS

BioPar

Index 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

NDVI 6.95 9.34 12.13 7.68 14.38 9.83 8.45 8.51 17.06 8.42 9.20

fAPAR 6.93 9.41 11.80 8.25 13.99 9.39 8.62 8.58 15.66 8.67 7.82

VCI 6.88 9.01 11.87 7.76 14.60 9.40 8.33 8.18 16.25 8.41 8.99

FCI 6.97 9.08 11.45 8.20 13.84 9.24 8.85 8.37 15.21 8.73 7.53

Average 6.93 9.21 11.81 7.97 14.20 9.47 8.56 8.41 16.05 8.56 8.38

Index 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

NDVI 7.59 9.52 11.36 7.84 13.69 10.04 8.37 7.63 18.58 7.59 9.43

fAPAR 7.33 9.43 11.37 7.69 13.46 9.92 8.40 7.67 18.13 7.31 9.21

VCI 7.72 9.45 10.69 8.11 13.65 9.87 8.22 7.56 16.95 7.46 9.06

FCI 7.44 9.35 10.84 7.92 13.25 9.74 7.95 7.56 16.57 7.35 8.94

Average 7.52 9.44 11.06 7.89 13.51 9.89 8.24 7.61 17.56 7.43 9.16

ISPRA2012-01-25

Cross-validation prediction errors - annual MAPEs

MARS

BioPar

Agroclim zone 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Number

of regions

Alpine 9.49 12.46 8.85 3.88 11.57 7.91 9.46 11.82 11.44 9.26 7.48 5

Atlantic Central 4.47 4.08 11.56 5.78 10.42 7.69 4.14 6.01 20.39 6.62 7.66 48

Atlantic North 7.62 8.40 5.32 9.39 4.90 9.67 5.90 5.55 7.25 1.16 11.90 3

Atlantic South 4.87 5.27 27.88 17.69 13.46 11.71 16.16 6.74 25.40 18.53 30.28 7

Boreal 21.71 12.45 10.88 5.89 7.57 12.16 9.38 15.09 5.49 21.85 14.79 4

Continental North 7.40 15.45 6.63 6.75 11.13 9.90 7.63 8.07 6.90 8.14 3.98 30

Continental South 9.70 8.24 6.69 11.22 33.49 15.32 13.35 13.43 21.39 7.69 10.02 9

Mediterranean North 6.69 9.27 19.15 7.64 20.03 8.94 13.07 9.82 20.94 11.71 15.23 18

Mediterranean South 13.42 20.93 12.95 20.51 25.62 11.07 15.54 13.54 10.14 16.20 6.02 6

Average 9.48 10.73 12.21 9.86 15.35 10.49 10.51 10.01 14.37 11.24 11.93  

Agroclim zone 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009Number

of regions

Alpine 11.66 8.35 6.33 4.96 7.68 6.76 6.68 8.98 5.97 4.90 7.86 5

Atlantic Central 5.73 5.24 10.75 6.74 10.77 8.12 4.49 5.14 20.88 5.57 8.03 48

Atlantic North 3.17 8.18 6.15 7.56 6.38 12.06 6.71 4.55 7.78 2.03 13.40 3

Atlantic South 6.73 5.01 26.36 16.54 14.24 14.93 16.62 7.78 25.34 18.11 23.90 7

Boreal 23.52 16.74 9.42 9.06 5.81 12.58 5.85 15.23 5.61 27.72 21.45 4

Continental North 6.66 13.42 5.29 6.20 11.20 9.32 6.74 8.20 12.07 6.16 5.26 30

Continental South 5.93 11.74 8.81 8.28 27.97 16.67 11.50 7.53 32.25 5.01 13.03 9

Mediterranean North 9.33 10.13 17.17 6.58 18.23 9.64 12.03 10.08 17.11 12.98 12.35 18

Mediterranean South 14.80 20.68 17.15 20.61 23.96 11.36 19.74 12.27 12.32 13.61 3.20 6

Average 9.72 11.05 11.94 9.61 14.03 11.27 10.04 8.86 15.48 10.68 12.05  

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Cross validation annual prediction errorsB

i o P

a r

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2009 forecastB

i o P

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Differences between prediction errors and errors of Null Model

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2009 forecast

Differences between prediction errors and errors of Null Model

endd

begdnnpredYieldobsYield

obsYieldLDecMAPE

_

_

___

1

L - number of 10-day periods within growing season;

Yield_obs – actual yield in year 2009;

Yield_predn – yield prediction made with knowledge of decadal indices from d_beg to n.

ISPRA2012-01-25

2009 forecast – MARS data

Percentage of regions with forecast better than Null ModelNDVI

prognosis

decadeAustria Belgium Denmark Finland Germany Hungary Ireland

The

NederlandsPoland Portugal Romania Slovakia Spain Sweden

1 20 88 100 33 42 29 0 67 100 50 67 50 50 75

2 20 88 100 33 42 14 0 67 100 50 33 50 50 75

3 20 88 100 67 33 43 0 67 100 50 33 50 38 75

4 20 88 100 100 33 14 0 67 100 50 0 50 38 75

5 20 88 100 67 42 29 0 56 100 50 0 50 63 75

6 20 88 100 67 42 29 0 56 100 50 0 50 63 75

7 0 63 100 67 42 29 0 56 100 50 0 50 63 75

8 0 63 100 67 42 29 0 56 100 50 0 50 63 75

9 40 63 0 67 42 14 0 56 100 50 0 50 63 75

10 20 63 0 67 42 14 0 67 100 0 0 50 63 75

11 20 75 0 67 42 29 50 67 100 0 0 25 63 75

12 20 88 0 100 50 43 0 67 100 0 0 25 63 75

mean 18 78 67 67 41 26 4 62 100 38 11 46 56 75

fAPAR

prognosis

decadeAustria Belgium Denmark Finland Germany Hungary Ireland

The

NederlandsPoland Portugal Romania Slovakia Spain Sweden

1 25 67 0 33 0 79 56 67 0 0 67 50 50 75

2 25 70 0 0 25 86 44 67 0 0 67 50 38 75

3 50 67 0 0 50 86 22 50 100 0 33 50 38 75

4 50 59 0 33 25 82 22 50 100 0 0 50 38 75

5 25 67 0 67 25 82 22 50 100 0 0 50 50 75

6 25 67 0 33 25 82 11 67 100 50 0 50 50 75

7 25 67 0 0 50 82 11 67 100 50 0 50 50 75

8 50 70 0 0 25 79 11 67 100 100 0 50 63 75

9 25 52 0 0 25 79 11 67 100 100 0 75 63 75

10 25 56 0 0 25 79 11 67 100 50 0 75 63 75

11 25 56 0 33 25 79 11 67 100 50 0 75 63 75

12 25 59 0 33 75 79 11 67 100 50 0 50 50 75

mean 31 63 0 19 31 81 20 63 83 38 14 56 51 75

Number of

regions5 8 1 3 12 7 2 9 16 2 3 4 8 4

ISPRA2012-01-25

2009 forecast – BioPar data

Percentage of regions with forecast better than Null ModelNDVI

prognosis

decadeAustria Belgium Denmark Finland Germany Hungary Ireland

The

Nederland

s

Poland Portugal Romania Slovakia Spain Sweden

1 20 100 100 0 58 29 0 67 100 50 33 50 50 50

2 40 100 100 33 67 57 0 56 100 50 33 50 50 50

3 60 100 0 67 58 29 0 56 94 50 33 50 50 50

4 60 88 0 33 42 29 0 56 94 100 0 50 50 25

5 40 75 100 33 42 29 0 56 94 50 0 50 50 50

6 20 75 100 33 50 29 0 67 94 50 0 50 63 50

7 40 75 100 33 50 14 0 67 94 50 0 50 63 50

8 20 75 100 33 50 14 0 67 94 0 0 50 63 50

9 20 75 100 33 58 14 0 56 94 0 0 50 63 25

10 20 75 100 33 58 14 0 78 94 0 0 50 63 25

11 20 75 100 33 58 14 50 78 94 0 0 50 63 25

12 20 75 100 33 58 14 50 78 94 0 0 50 63 25

mean 32 82 83 33 54 24 8 65 95 33 8 50 57 40

fAPAR

prognosis

decadeAustria Belgium Denmark Finland Germany Hungary Ireland

The

Nederland

s

Poland Portugal Romania Slovakia Spain Sweden

1 20 100 100 0 58 43 0 44 100 50 67 50 50 50

2 40 100 100 33 67 43 0 56 100 0 33 50 50 50

3 60 100 100 67 67 29 0 56 100 0 0 50 50 25

4 60 88 0 33 58 29 0 56 100 50 0 50 50 25

5 40 88 100 33 50 29 0 67 100 100 0 50 50 50

6 40 75 100 33 50 14 0 78 100 50 0 50 63 50

7 40 75 100 67 50 14 0 78 100 0 0 50 63 50

8 40 75 100 33 50 14 0 89 100 0 0 50 63 50

9 20 63 100 33 50 14 0 67 100 0 0 50 63 0

10 20 75 100 33 50 14 0 67 100 0 0 50 63 0

11 20 75 100 33 50 14 0 67 100 50 0 50 63 0

12 20 88 100 100 50 14 0 67 100 50 0 50 63 0

mean 35 83 92 42 54 23 0 66 100 29 8 50 57 29

Number of

regions5 8 1 3 12 7 2 9 16 2 3 4 8 4

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In 2009 forecast – percentage of regions with lower error (MAPE) than error (MAPE) of Null Model

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Conclusions

The investigations did not reveal the substantial differences between MARS and BioPar databases, although the results from comparison are very close, and the differences are minimal in favour of BioPar dataset .

Observing the spatial distribution of the prediction errors, it can be noticed that the largest errors occurred in the countries in the periphery of Europe, while in the central, geographically close countries, the performance of the model is better for both datasets.

For two methods of regions grouping the better results were obtained for division of regions into zones according to maxNDVI decades (more than half of zones with better performance than for Null model) than for classical division into Agro-climatic zones. Again, the results are similar for both databases.

In the Annual predictions the averages of MPEs and MAPEs are lower for BioPar data.

ISPRA2012-01-25

Conclusions

In the yield forecast for the year 2009 the spatial stratification of the results can be observed. The best results were obtained in northern part of Central Europe (Poland, North-eastern Germany, Denmark) and in the large regions of Spain. The worst results were obtained for the countries of the northern part of Europe and located in the periphery of the continent (Sweden, Ireland, Portugal) and in southern part of Central Europe (southern Germany, Romania, Hungary).

The overall performance of the statistical model for both databases is not good enough. It can be justified by too short time series of data (11 years) and the large gaps in the yield data. Gathering more data over the years and complementing yield data for European NUTS regions are expected to improve the performance of the statistical model. The investigations of the methods of regions grouping (affecting the period of conducting the forecast) different from the classical one (agro-climatic zones) should also be done.

The effort should be done to get the yield statistic data for 2010 to do the yield prognosis for another year than 2009