rs based crop forecasting in morocco
DESCRIPTION
RS based crop forecasting in Morocco. Riad BALAGHI National Institute for Agricultural Research – Morocco www.inra.org.ma. Key features of agriculture in Morocco. - PowerPoint PPT PresentationTRANSCRIPT
E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium)
RS based crop forecasting in Morocco
Riad BALAGHINational Institute for Agricultural Research – Morocco
www.inra.org.ma
E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium)
Moroccan agriculture is strongly dependent on rainfall (Avg. 340mm), as rainfed areas represent 85% of agricultural lands (7.9 millions hectares) ;
Most of lands are arid to semi-arid from which 75% are rangelands, 13% forests and 8% are cultivated ;
Rural population represents 45% of the total population.
Key features of agriculture in Morocco
E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium) 3
Morocco is a semi arid country with limited agricultural areas
Morocco is located in the northwest corner of Africa, bordered by the Mediterranean Sea and the Atlantic Ocean on the north and west, by Algeria on the east, and by Mauritania on the south. Its total land area is 710850 km2 and includes several zones, among which are agricultural plains and river valleys, plateaus, and mountain chains. Most of lands are arid to semi-arid from which 75% are rangelands, 13% forests and 8% are cultivated. Morocco has a Mediterranean climate characterized by a dry and hot summer (4 to 6 months) and a short and cold winter in elevations.
Key features of agriculture in Morocco
E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium)
0,0
10,0
20,0
30,0
40,0
50,0
60,0
0,0 1,0 2,0 3,0 4,0 5,0 6,0
Coe
f. Va
riatio
n (%
)
Average yield (Ton/ha)
Syria
Tunisia
Italy
Cyprus
FranceGreece
Spain
Morocco
Algeria
EgyptGermanyUSA
Russian Federation
Instability of crop production resulting from low and fluctuating rainfall and limited irrigation capacities
Increasing risk
Food security in Morocco
E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium)
0
1
2
3
4
5
6
7
1950 1960 1970 1980 1990 2000 2010
Yiel
d (T
on/h
a)
Egypt Italy Spain Morocco Algeria
Food security in Morocco
Data source : FAOSTAT
E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium)
0
5
10
15
20
25
1950 1960 1970 1980 1990 2000 2010
Ren
dem
ent (
Q/h
a)
1968
19861988 1991 2006
2
Technological progress for humid seasons
Average yield in dry seasons
Yiel
d in
qui
ntal
s per
hec
tare
Cereals: Technological trend
Food security in Morocco
Data source : DPAE
E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium)
Wheat yield vs. rainfall
Data source: DMN & DPAE
E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium)
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3
September October november December January February March April May
R2
Soft wheat Durum wheat Barley
Correlation between rainfall and cereal yields in Morocco at national level (Balaghi & Jlibene, 2009)
Statistical approach using weather predictors
E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium) 9
20041989
1990
1991
19921993
1994
1995
1996
1998
1999
2001
2002
2003
1988
2005
2006
2007
2008
Yield = 1.3925 ln(Rainfall) - 6.4243R² = 0.82
0,0
0,5
1,0
1,5
2,0
2,5
3,0
0 100 200 300 400 500 600 700 800
Yiel
d (T
/ha)
Cumulated rainfall from September to March (mm)
19881989
1990
1991
19921993
1994
1995
1996
1998
1999
2001
2002
2003
2004
2005
2006
2007
2008
Yield = 1.343 ln(Rainfall) - 6.2732R² = 0.77
0,0
0,5
1,0
1,5
2,0
2,5
3,0
0 100 200 300 400 500 600 700 800
Yiel
d (T
/ha)
Cumulated rainfall from September to March (mm)
20041989
1990
1991
1992
1993
1994
1995
1996
19982008 2001
2002
2003
1988
2005
2006
2007
1999
Yield = 1.0968 ln(Rainfall) - 5.2191R² = 0.73
0,0
0,5
1,0
1,5
2,0
2,5
3,0
0 100 200 300 400 500 600 700 800
Yiel
d (T
/ha)
Cumulated rainfall from September to March (mm)
Cereals: Yield vs. Cumulated Rainfall
(data from 1988 to 2008)
Balaghi et al. 2010
Data source : DMN
Soft wheat Durum wheat
Barley
E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium)
1988
1989 1990
1991
19921993
1994
1995
19961998
1999
20012005
20032004
2002
2006
2007
2008
y = 17,465ln(x) - 99,983R² = 0,61
0
5
10
15
20
25
30
0 200 400 600 800 1000 1200 1400 1600
Yiel
d (q
x/ha
)
Cumulated rainfall September to March (mm)
Cereals: Yield vs. Cumulated Rainfall
19881989 1990
1991
19951992
2003
2007
1996
19981999
2001
2002
19942004
2005
2006
1993
2008
y = 11,96ln(x) - 54,507R² = 0,79
0
5
10
15
20
25
30
0 100 200 300 400 500 600 700 800
Yiel
d (q
x/ha
)
Cumulated rainfall Septembre to March (mm)
1988
1989
1990
1991
1999
19941996
20072001
1992
2006
1995
20022003
2004
1993
2005
1998
2008
y = 7,3919ln(x) - 31,25R² = 0,54
0
5
10
15
20
25
30
0 100 200 300 400 500 600 700 800
Yiel
d (q
x/ha
)
Cumulated rainfall September to March (mm)
Balaghi et al. 2010
Data source : DMN
E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium)
NDVI based yield forecasts
What do we need ? Good and long time crop statistics ; Good and long time NDVI series (NOAA, SPOT, MODIS,
etc.); Accurate crop mask (GLC2000, CLC, Globcover, etc.) ; Good local expertise ; Good RS expertise (ΣNDVI, Median NDVI, Slope of
NDVI, etc.).
E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium)
NDVI in Morocco
Cumulated NDVI (February – March)
E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium)
NDVI for agricultural areas
NDVI in March 2009 Average NDVI in March
Data source : Agri4cast
E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium)
NDVI vs. Rainfall in Morocco
0
1
2
3
4
5
6
7
0 200 400 600 800 1000 1200 1400
∑N
DVI fé
vrie
r-avr
il
Pluviométrie (mm)
(Jlibene & Balaghi, 2007; not published)
ΣNDVIFeb-Apr is the accumulation of the NDVI values for the period of February until April and ΣRAINSept-May is the sum of the rains over the cropping season (from September until May). Data from 1999 to 2006, for 25 stations.
E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium)
Balaghi 2010
Data source : VITO
Cereals: Yield vs. Cumulated NDVI
E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium) 16
19971992
1999
2003
2005
2007
20081990
19962004
2002
2006
1995
2001
Yield = 6.2292 NDVI - 1.7936R² = 0.80
0,0
0,5
1,0
1,5
2,0
2,5
3,0
0,00 0,10 0,20 0,30 0,40 0,50 0,60 0,70
Yiel
d (T
/ha)
Average dekadal NDVI from February to March
1997
19921999
2004
2005
2007
2001
2008
2003
1996
2002
2006
1995
1990
Yield = 6.0278 NDVI - 1.8086R² = 0.79
0,0
0,5
1,0
1,5
2,0
2,5
3,0
0,00 0,10 0,20 0,30 0,40 0,50 0,60 0,70
Yiel
d (T
/ha)
Average dekadal NDVI from February to March
2005
1992
2002
1996
1999
20072001
1990
20042003
2008
2006
1995
1997
Yield = 4.0206 NDVI - 1.1558R² = 0.60
0,0
0,5
1,0
1,5
2,0
2,5
3,0
0,00 0,10 0,20 0,30 0,40 0,50 0,60 0,70
Yiel
d (T
/ha)
Average dekadal NDVI from February to March
Balaghi et al. 2010
(data from 1988 to 2008)
Data source : VITO
Cereals: Yield vs. Cumulated NDVI
Soft wheat Durum wheat
Barley
E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium)
0
10
20
30
40
50
60
Fin février
1ère décade mars
2ème décade mars
3ème décade mars
1ère décade
avril
2ème décade
avril
3ème décade
avril
Erre
ur in
pré
dict
ion
%
Balaghi et al. 2010
Cereals: NDVI based forecasting
Data source : VITO
Data from 1999 to 2004
E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium)
Espèce 1996+PT*
Pluviométrie
NDVI Moyenne
Blé tendre
24.5 21.8 19.9 22.1
Blé dur 22.8 20.3 18.5 20.5Orge 15.7 15.6 12.8 14.7
Espèce 1996+PT*
Pluviométrie
NDVI Moyenne
Blé tendre
49.0 43.6 39.8 44.1
Blé dur 20.5 18.3 16.7 18.5Orge 34.5 34.3 28.2 32.3Total 104.2 96.2 84.6 95.0
Rendements
Productions
Crop forecasting : combination of methods2008-09 cropping season
E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium)
CONCULUSION
NDVI based cereal forecasting is relatively easy in Morocco, at national level, because : Morocco is a semi arid country ; Most of the agricultural areas are rainfed ; Cereals are the dominating crops ; Quite good crop statistics for the main crops.
Cereals forecasting could be improved : Through an improved crop mask ?
Cereal forecasting at sub national level needs : The use of cumulated rainfall at explanatory variable ; The use of weather - crop modelling
E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium)
Many ways to forecast agricultural yields Combination of methods is the appropriate way to
reduce errors Long term good quality crop and weather statistics are
essentials Good local expertise is needed Good agronomic and statistical skills Computer, GIS and remote sensing should be available
CONCULUSION
E-AGRI Kick-off meeting, 24-25 March 2011, VITO (Mol, Belgium)
Thank you