Assessment of Climate Change Impacts on Olive Production in … · 2014. 11. 17. · Ahmed MOHAMED HARB RABIA Assem ABDELMONEM AHMED MOHAMED Emad FAWZY ABDELATY Fatma WASSAR Hanene

DIPLOMAzia Education program

Assessment of Climate Change Impacts on Olive Production in Oum Zessar Watershed

Medenine, Tunisia

Area della Ricerca CNR, Florence, 06 November 2014

Research Team Ahmed MOHAMED HARB RABIA

Assem ABDELMONEM AHMED MOHAMED Emad FAWZY ABDELATY

Fatma WASSAR Hanene MAIRECH

Maha LOTFY ELSAYED

Outline

1. Introduction

2. Presentation of the study area

3. Assessment of climate impacts on olives

4. Agro-economics analysis

5. Land use and land cover changes

6. NDVI analysis

7. Olive productivity modelling

8. Conclusion and perspectives

CNR, Florence, 06 November 2014




Introduction

Tunisia is the most important olive oil producer country of the Southern Mediterranean basin

The olive-growing sector occupies a strategic place in the Tunisian economy

Tunisia occupies the second world rank for the production of olive oil after the European Union

The international trade of the olive oil represents 50 % of the total agricultural exports, 5.5 % of total exports and constitutes the fifth source of currencies of the country.

Olive grove at Oum Zessar watershed of Tunisia represents the key crop in the area.

4

North Africa (Algeria, Morocco, Libya, and Tunisia) is extremely vulnerable to climate variations (IPCC, 2007) . The severity of climate change impacts on North African countries is related to the geographic and ecological particularity of the region. Historical data confirm that the annual rainfall in the region has declined since the early twentieth century while annual mean temperature has increased. The frequency and severity of floods and heat waves in addition to years of recurring drought combined with the expansion of the Sahara desert into farmlands, confirm that climate change has already begun to affect the region.

Climate variability and seasonality pronounced by climatic factors, particularly temperature and precipitation, characterize the Tunisian natural climate cycle. In this context, extreme weather situations are presenting hazards and risks to ecosystems and agriculture.

The agricultural production has to adapt to the newest climate conditions for sustainability. The more vulnerable cultivations in this case change much more slowly than annual systems (lobell et al., 2006). Olive tree is a perennial crop cultivated in rainfed conditions under the minimum viable rainfall and it is indubitably one of the more at risk crops.





Objectives

Land use and land cover change detection in the watershed in order to study the climate change and human activities impacts on the total area of olive plantation using high resolution satellite images of 2005 and 2013. Characterize and estimate NDVI and solar radiation and then to use those data to run the crop model C-Fix in order to estimate changes of productivity of olive trees in Oum Zessar watershed, Tunisia, under weather conditions of the last ten years (2003-2013).

Study of intra- and inter-annual climate variability impacts on olive production and identification of climate factors with high effects on it.

Evolution of the production and economic indicators of olive orchard for Tunisia in general and for the study area in particular. Analysis of the relationship between olive production and different climatic factors (temperature and precipitation)

Projecting of future olive production for 2030-2050 and 2080-2100 periods using climate change scenarios data.

Outline

1. Introduction





6. NDVI analysis





Location 1. Location

2. Watershed Context

3. Biophysical Characteristics

4. Data Base Collected



Study Area (Watershed)

Tunisia Map

Medenine Region

Watershed Context 1. Location

2. Watershed context





Oum Zessar has the following key biophysical and socio-economic characteristics:

Degraded dry-lands; Low rainfall; Water scarce; Accelerated expansion of rain-fed and irrigated agriculture for olive trees and cereals; High demand for irrigation; Mixed communal and private agrarian system; Rapid population growth and urbanization.

The watershed highly vulnerable to the impacts of climate change

The region has been the focus of many research and development interventions in the agriculture, natural resources management, rural development and economic sectors.

Biophysical Characteristics 1. Location






Oum Zessar Climate is Influenced by the Dahar, the Matmatas (continental arid) and the Mediterranean Sea (Gulf of Gabès, maritime arid).

The climate in the upper catchment is drier with temperate conditions in winter and less arid and with mild winters in the lower catchment area.

The coldest months are December, January and February .

June, July and August are the hottest months when temperatures reach 48°C

Low rainfall is highly variable in time and space and can fall over short periods at high intensities:

150 mm of rain in the downstream area and 240 mm in the upper part

The evapo-transpiration (ETP) is very high, reaching 1450 mm

10

Land use in Oum Zessar watershed Soil Classification in Oum Zessar watershed



1. Location




Biophysical Characteristics

Data Base Collected 1. Location






Soil and Land use Maps: Institute of Arid Regions (IRA)

Olive production data + Agro-economic data: Tunisian Ministry of Agriculture, the Food and Agriculture Organization (FAO), the Arab Organization for Agricultural Development (AOAD), and local peoples.

Climate data: The Tunisian National Institute of Meteorology (INM)

Outline

1. Introduction





6. NDVI analysis





Objectives

1. Objectives

2. Climate analysis

3. Assessment of climate factors acting on olive

4. Modelling olive production

5. Olive production under climate change scenarios

6. conclusion



Study of intra- and inter-annual climate variability impacts on olive production.

Identifying climate factors with high effects on phenological cycle of olives and evaluating their correlation with olive production.

Simulating olive production using climate factors as predictors.

Projecting future production for 2030-2050 and 2080-2100 periods using climate change scenarios.

Precipitation variability 1. Objectives

2. Climate analysis






0

50

100

150

200

250

300

350

400

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

20

12

Pre

cip

itat

ion

(mm

)

Years

-2

-1

0

1

2

3

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

20

02

20

04

20

06

20

08

20

10

20

12

Stan

dar

diz

ed

Pre

cip

itat

ion

In

dex

Years

• Coefficient of variation of 46% and a standard deviation of 76.5mm. Mean annual precipitation is of 167 mm.

• The minimum annual total was registered in 1981 with an amount of 49.5 mm. In the last ten years it was in 2008 with an amount of 59.6 mm. The maximum annual total was that of 2003 with a total amount of 361.8 mm.

It’s a little rainfall area characteristic of arid climate.

Temperature variability 1. Objectives

2. Climate analysis






0

5

10

15

20

25

30

35

40

45

Jan Feb Mar Apr Mai Jun Jul Aug Sep Oct Nov Dec

Tem

per

atu

re (°

C)

Months

• Medenine region is characterised by a mild to warm winter and hot to extremely hot summers. • Daily minimum temperature vary between -1°C (02 February 2012) and 32.1°C (2 July 2002). • Daily maximum temperature vary between 7.4°C, (8 January 1981), and 48.1°C (24 July 1997 and

19 July 2001). It belongs to a bioclimatic arid region.

Phenology of olives 1. Objectives

2. Climate analysis






Month Stages Jan Winter rest Feb Induction, initiation and floral differentiation

Mar Flowering, bud development and bloom Apr Full bloom

May Fertilization and fruit setting Jun Fruit development (cell division) Jul Fruit development (cell division)

Aug Fruit development (cell enlargement) Sep Growth phase: oil ripening and colour change

Oct Growth phase:maturation of fruit, oil accumulation and fortification

Nov Start fruit harvest Dec Winter rest

Phenology of olives and climate impacts

1. Objectives

2. Climate analysis






Month Stages climate effects

rain Temperature

Jan winter rest rain can increase olive production

Feb induction, initiation and floral differentiation

Temperature (0,-1°C) affect flowering

Mar Flowering bud development/Bloom Drought leads to incomplete flowering and poor fruit set

Hot dry wind leads to incomplete flowering and poor fuit set. Best conditions are 10 weeks ith temparature of 12-13°C Apr full bloom

May fertilization and fruit setting Rain can damage pollen Jun fruit development (cell division) High temperature (>40°C) stops

plant growth and dry olive fruits and influence the quality and the quantity of olive oil production.

Jul fruit development (cell division)

Aug fruit development (cell enlargement) Rain revive trees

Sep Growth phase: oil ripening and colour change High temperature damage foliage

and can cause fruit fall Oct

Growth phase:maturation of fruit, oil accumulation and fortification

Rain delay harvesting which influence olive oil quality

Nov Start fruit harvest

Dec winter rest Rain can increase olive production

Impacts of precipitation on olives

1. Objectives

2. Climate analysis






Olive

Production mai sep-march nov-march march-apr Sep-Oct Dec-Jan June-August

Olive Production 1,00 mai -0,36 1,00

sep-march 0,86 -0,20 1,00 nov-march 0,95 -0,26 0,96 1,00

march-apr 0,79 -0,52 0,78 0,77 1,00

Sep-Oct -0,23 0,03 -0,31 -0,31 -0,23 1,00

Dec-Jan 0,35 0,22 0,43 0,39 -0,12 -0,34 1,00 June-August -0,36 -0,35 -0,38 -0,37 -0,30 0,12 -0,20 1,00

Correlation between olive production and precipitation

Impacts of precipitation on olives 1. Objectives

2. Climate analysis






-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

20

03

-20

04

20

04

-20

05

20

05

-20

06

20

06

-20

07

20

07

-20

08

20

08

-20

09

20

09

-20

10

20

10

-20

11

20

11

-20

12

20

12

-20

13

OLI

VE

PR

OD

UC

TIO

N

PR

ECIP

ITA

TIO

N

YEARS

Precipitation Olive production

Inter-annual variability of olive production and precipitation of September-March season

with one year lag

Clear coherence between the amount of precipitation and the production of olives with a coefficient of correlation of 0,86. This indicate that olive production in this region rely on precipitation amount.


2. Climate analysis






-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

20

03

-20

04

20

04

-20

05

20

05

-20

06

20

06

-20

07

20

07

-20

08

20

08

-20

09

20

09

-20

10

20

10

-20

11

20

11

-20

12

20

12

-20

13

YEARS

OLI

VE

PR

OD

UC

TIO

N

PR

ECIP

ITA

TIO

N


Inter-annual variability of olive production and precipitation of November-March season with

one year lag

Strong consistency with 0.95 as coefficient of correlation. September-March seasons with high amount of precipitation lead to an increase in olive production for the following harvesting and vice versa. This increase depends strongly on the rainfall amounts stored in the soil during winter rest which boost tree’s productivity for the following year. It depends also on the total amount received during stage of flowering.


2. Climate analysis






-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

20

03

-20

04

20

04

-20

05

20

05

-20

06

20

06

-20

07

20

07

-20

08

20

08

-20

09

20

09

-20

10

20

10

-20

11

20

11

-20

12

20

12

-20

13

OLI

VE

PR

OD

UC

TIO

N

PR

ECIP

ITA

TIO

N

YEARS


Inter-annual variability of precipitation of March-April season and olive production

One of the hypothesis formulated is that drought during flowering period leads to an incomplete flowering and poor fruit set. Strong interaction with a coefficient of correlation of 0.79. Precipitation during March-April season help establish a prosperous condition for bud development and bloom leading to an increase of quantity of olives.

Phenology of olives and climate impacts

1. Objectives

2. Climate analysis



5. Olive production under climate change scenariors



Month Stages climate effects

rain Temperature

Jan winter rest rain can increase olive production

Feb induction, initiation and floral differentiation

Temperature (0,-1°C) affect flowering

Mar Flowering bud development/Bloom Drought leads to incomplete flowering and poor fruit set

Hot dry wind leads to incomplete flowering and poor fuit set. Best conditions are 10 weeks ith temparature of 12-13°C Apr full bloom

May fertilization and fruit setting Rain can damage pollen Jun fruit development (cell division) High temperature (>40°C) stops

plant growth and dry olive fruits and influence the quality and the quantity of olive oil production.

Jul fruit development (cell division)

Aug fruit development (cell enlargement) Rain revive trees

Sep Growth phase: oil ripening and colour change High temperature damage foliage

and can cause fruit fall Oct

Growth phase:maturation of fruit, oil accumulation and fortification

Rain delay harvesting which influence olive oil quality

Nov Start fruit harvest

Dec winter rest Rain can increase olive production

Impacts of temperature on olives 1. Objectives

2. Climate analysis






Olive Production Sep-Oct Mar-Apr nb Tmax>40 Jun-Aug

Olive Production 1,00

Sep-Oct 0,61 1,00

Mar-Apr -0,65 -0,21 1,00

nb Tmax>40 Jun-Aug -0,39 -0,52 0,00 1,00

Correlation between olive production and temperature


2. Climate analysis






-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

20

03

-20

04

20

04

-20

05

20

05

-20

06

20

06

-20

07

20

07

-20

08

20

08

-20

09

20

09

-20

10

20

10

-20

11

20

11

-20

12

20

12

-20

13

OLI

VE

PR

OD

UC

TIO

N

TEM

PER

ATU

RE

YEARS

mean Tmax Olive production

Inter-annual variability of olive production and mean maximum temperature of March-April

season

Coefficient of correlation of -0.65. Hypothesis for this case suppose that high temperature leads to an incomplete flowering and to a poor fruit set. Seasons with low mean maximum temperature result in an increase of olive production quantity due to a better condition for bloom and bud development.


2. Climate analysis






-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

20

03

-20

04

20

04

-20

05

20

05

-20

06

20

06

-20

07

20

07

-20

08

20

08

-20

09

20

09

-20

10

20

10

-20

11

20

11

-20

12

20

12

-20

13

OLI

VE

PR

OD

UC

TIO

N

TEM

PER

ATU

RE

YEARS

mean Tmax Olive production

Inter-annual variability of olive production and mean maximum temperature of September-

October season

Coefficient of correlation of 0.61. Olive trees have preference to hot weather for a better olive grow and ripening. Temperature in September-October season can lead to a decrease in olive production in case of extremes events of heat waves that damage fruits and foliage with the high temperature.


2. Climate analysis






-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

20

03

-20

04

20

04

-20

05

20

05

-20

06

20

06

-20

07

20

07

-20

08

20

08

-20

09

20

09

-20

10

20

10

-20

11

20

11

-20

12

20

12

-20

13

OLI

VE

PR

OD

UC

TIO

N

TEM

PER

ATU

RE

YEARS

nb Tmax>40°C Olive production

Inter-annual variability of oil production and number of days with maximum temperature

above 40°C of June-August season

Moderate interaction with a correlation coefficient of -0.52. June-August season correspond to fruit development stage. Temperature above 40°C may cause fruit drop decreasing therefore olive production.

Multiple regression analysis 1. Objectives

2. Climate analysis






Call: lm(formula = Olive.Production ~ pcp_sep.march + pcp_nov.march + pcp_march.apr + Tmax_Sep.Oct + Tmax_Mar.Apr)

Selection of predictor variables 1. Objectives

2. Climate analysis






Variables Precipitation Mean maximum temperature

November-March March-April September-October March-April

VIF 4.19 2.81 2.67 1.75

Olive production under climate change scenarios

1. Objectives

2. Climate analysis






Observed precipitation and maximum temperature data of Mednine station.

Precipitation and maximum temperature of Regional climate model SMHI-ECHAM5 with a 25km resolution.

• 1979-1995: calibration period

• 1996-2012: validation period

2030-2050 and 2080-2100 periods of projection

RCM data correction 1. Objectives

2. Climate analysis






RCM data correction 1. Objectives

2. Climate analysis






Method 3:

• For precipitation: Quantile based mapping (Panofsky and Brier, 1968)

• For temperature: Linear Regression correction

RCM precipitation data correction 1. Objectives

2. Climate analysis






RCM precipitation data correction 1. Objectives

2. Climate analysis






RCM temperature data correction 1. Objectives

2. Climate analysis






RCM temperature data correction 1. Objectives

2. Climate analysis






Precipitation (mm) Temperature (°C)

Method 1 13.40 1.52

Method 2 0.44 0.002

Method 3 20.32 1.84

Results of RMSE using the three bias correction methods

Olive production 2030-2050 1. Objectives

2. Climate analysis






0

20000

40000

60000

80000

100000

120000

140000

160000

20

30

-20

31

20

31

-20

32

20

32

-20

33

20

33

-20

34

20

34

-20

35

20

35

-20

36

20

36

-20

37

20

37

-20

38

20

38

-20

39

20

39

-20

40

20

40

-20

41

20

41

-20

42

20

42

-20

43

20

43

-20

44

20

44

-20

45

20

45

-20

46

20

46

-20

47

20

47

-20

48

20

48

-20

49

20

49

-20

50

Oliv

e p

rod

uct

ion

years

-2

-1

0

1

2

3

20

30

20

32

20

34

20

36

20

38

20

40

20

42

20

44

20

46

20

48

20

50

SPI

Year

Reduction in precipitation of 6,14% compared to 1980-2012 period

0

5

10

15

20

25

30

35

40

45

1

14

27

40

53

66

79

92

10

5

11

8

13

1

14

4

15

7

17

0

18

3

19

6

20

9

22

2

23

5

24

8

Tmax

(°C

)

Months

Temperature projection 2030-2050

Increase in temperatue of 2,48% (0,7°C)compared to 1980-2012 period

Olive production 2080-2100 1. Objectives

2. Climate analysis






0

20000

40000

60000

80000

100000

120000

140000

20

80

-20

81

20

81

-20

82

20

82

-20

83

20

83

-20

84

20

84

-20

85

20

85

-20

86

20

86

-20

87

20

87

-20

88

20

88

-20

89

20

89

-20

90

20

90

-20

91

20

91

-20

92

20

92

-20

93

20

93

-20

94

20

94

-20

95

20

95

-20

96

20

96

-20

97

20

97

-20

98

20

98

-20

99

20

99

-21

00

Oliv

e p

rod

uct

ion

years

-2

-1

0

1

2

3

20

80

20

82

20

84

20

86

20

88

20

90

20

92

20

94

20

96

20

98

21

00

SPI

Year

Reduction in precipitation of 8,68% compared to 1980-2012 period

0

5

10

15

20

25

30

35

40

45

1

15

29

43

57

71

85

99

11

3

12

7

14

1

15

5

16

9

18

3

19

7

21

1

22

5

23

9

Tmax

(°C

)

Months

Temperature projection 2080-2100

Increase in temperatue of 10% (2,7°C) compared to 1980-2012 period

Future variation of olive production

1. Objectives

2. Climate analysis






0

10000

20000

30000

40000

50000

60000

70000

1 2 3 4

Oliv

e p

rod

uct

ion

(to

nn

es)

Periods of 5 years

2030-2050 2080-2100 2003-2012

Periods (years) Percentage of variation (%)

2030-2050 2080-2100

01-05 24,25 -35,00

05-10 -60,13 -84,57

10-15 29,13 -17,92

15-20 1,55 -37,80

Total -1,29 -43,82

Outline

1. Introduction





6. NDVI analysis





content



Introduction (sectors of economic, agricultural, demographic

characteristics, and olives in Tunisia).

Methodology

Results

Land use in Tunisia 2011

Evolution of the production of olives

Evolution of annual producer prices of olives

Evolution of exports of olive oil

Evolution of some production indicators in the study area.

1. Research plan

2. Introduction

3. Olives in Tunisia

4. Methodology

5. Results and discussion

Introduction

1. Research plan

2. Introduction


4. Methodology




Tunisia is a small country of 164.000 km² with a

population of 10. 778 inhabitants in 2012.

Tunisia is an emerging economy.

The economic structure is dominated by services (59.4%

of GDP). The weight of agriculture is limited (9.4% of

GDP), in 2012.

The area of land used for agricultural purposes is

estimated at 10 million hectares.

The cultivated area was 5205.62 thousand hectare.

Cont.



per capita area from the total area was 1.54 hectare.

The agricultural sector employs 16% of the total labour

force.

The area equipped for irrigation covers 516 thousand

hectares, which represents 5% and 10% respectively of

farmlands and arable land.

The water mobilization efficiency has reached 93%.

1. Research plan

2. Introduction


4. Methodology


Olives in Tunisia



Tunisia has about 75 million olive trees.

Spread over one- third of Tunisia’s arable land.

The olive crop the main domestic source of edible oils.

Olive trees are cultivated in widely varied climatic

conditions, thus from north to south.

They are situated as follow:

15% in the North.

66% in Central Tunisia.

And 19% in the South.

1. Research plan

2. Introduction


4. Methodology


Methodology



The study follows the descriptive and quantitative manner

in the analyses of data, using regression and multi

regression analysis and general trend equation.

The study depends on many of the published and

unpublished data issued by Ministry of Agriculture, the

Food and Agriculture Organization (FAO), and the Arab

Organization for Agricultural Development (AOAD). Also

data has been obtained from other sources available on the

internet, and many of the studies and references that

tackled similar issues.

1. Research plan

2. Introduction


4. Methodology


Results and discussion



Production of top 5 producers in 2013

1. Research plan

2. Introduction


4. Methodology




Countries delivering the 5 highest yields



The evolution of the production of olives in Tunisia

0

200

400

600

800

1000

1200

1400

1600

Production thousand ton.

Production

The evolution of the harvested area in Tunisia

0

500

1000

1500

2000

2500

3000

Harvested area thous. hectare

Harvested area

The evolution of olives yield in Tunisia

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

Yield

Yield

The evolution of producers prices in Tunisia

0

100

200

300

400

500

600

700

800

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Producer prices

Producer prices

The evolution of export quantity in Tunisia

0

50,000

100,000

150,000

200,000

250,000

300,000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Export quantity thousand ton.

Export quantity

The evolution of export value in Tunisia

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

1,000,000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Export value

Export value

General trend equations of the olive production, area

harvested, yield, producer prices, export quantity and

export value for olive and olive oils

Items Equation R2 F T No.

Olive production

(thou.tonnes) Ŷi=601.9+ 32.1Xi 0.11 1.49 1.22 1

Area harvested

(thou. Ha) Ŷi=1585.7+ 29.02Xi 0.10 1.16 1.07 2

Yield

(Hg/ Ha) Ŷi=3721.8+117.9Xi 0.07 0.81 0.90 3

producer prices

(USD/ tonnes) Ŷi=273.9+ 22.8Xi 0.50 7.57 2.75 4

Exports quantity

(thousands tonnes) Ŷi=9546.3+5268.5Xi 0.07 0.8 0.89 5

Exports Value

(1000 US$) Ŷi=1878.5+286.1Xi 0.15 1.88 1.37 6



Where:

Ŷi = the estimated value for the

dependent variable in the year i.

Xi = reflect time variable in the year

i.

i = 1, 2, 3……13

R2 = Coefficient of determination



Study area

General trend equations of the cultivated area, olive

production and production of olive oils in Medenine

Items Equation R2 F T No.

Cultivated area

(Ha) Ŷi =180908.2+ 1007.7Xi 0.96 217.8 14.75 1

Olive

production

(Tonnes)

Ŷi =82154.5- 4619.3Xi 0. 10 1.07 1.03 2

Yield

(Hg/ Ha) Ŷi =18271.6- 1122.5Xi 0.07 0.81 0.90 3

Where:

Ŷi = the estimated value for the dependent variable in the year i.

Xi = reflect time variable in the year i.

i = 1, 2, 3……11




The relationship between olive production and different

climatic factors in Mednine

Items Equation R2 F T

Olive

production

(Tonnes)

Ŷi =344801.4+ 640.89X1+ 92.15 X2- 7310.6X3-5445.3X4 0.90 22.16 4.48

Where:

Ŷi = the estimated value for the dependent variable in the year i.

X1= Total precipitation November- March.

X2= Total precipitation March - April.

X3 = Average of maximum temperature September- October.

X4 = Average of maximum temperature March- April.

i = 1, 2, 3……10




Economic effect of precipitation on olive production

Profit or deficit in the production value/ Us

Increasing or deficit in

production Ton

Price Us/ Ton

Critical period

313449 +641 489 Nov- March

Outline

1. Introduction





6. NDVI analysis





Change Detection for Olive Land Cover using High Resolution Imagery



Study area selection

Total Area: 30000 Ha

1. Introduction

2. Methodology

3. Results

4. Analysis

High Resolution Satellite Images



2005 2013

8 Years Spatial Resolution: Multispectral Images 2.4 m Panchromatic Images 0.6 m

1. Introduction

2. Methodology

3. Results

4. Analysis

What is GEOBIA



GEographic-Object-Based Image Analysis

is a newly developed area of Geographic Information Science and remote sensing in which automatic segmentation of images into objects of similar spectral, temporal and spatial characteristics of these objects is undertaken

Classification Progress

1. Introduction

2. Methodology

3. Results

4. Analysis

Processing Software “eCognition”



1

4

2

3

1. Introduction

2. Methodology

3. Results

4. Analysis

Vegetation Indices



1. Introduction

2. Methodology

3. Results

4. Analysis

Vegetation Indices



DVI

1. Introduction

2. Methodology

3. Results

4. Analysis

Vegetation Indices



EVI

1. Introduction

2. Methodology

3. Results

4. Analysis

Vegetation Indices



GDVI

1. Introduction

2. Methodology

3. Results

4. Analysis

Vegetation Indices



GNDVI

1. Introduction

2. Methodology

3. Results

4. Analysis

Vegetation Indices



NDVI

1. Introduction

2. Methodology

3. Results

4. Analysis

Vegetation Indices



NG

1. Introduction

2. Methodology

3. Results

4. Analysis

Vegetation Indices



NNIR

1. Introduction

2. Methodology

3. Results

4. Analysis

Vegetation Indices



NR

1. Introduction

2. Methodology

3. Results

4. Analysis

Vegetation Indices



RVI

1. Introduction

2. Methodology

3. Results

4. Analysis

Classification Progress



1. Introduction

2. Methodology

3. Results

4. Analysis

Parameters used for classification



During the classification procedure, only three vegetation indices showed high relevance to be used for the recognition of the different objects and classes.

These indices are

Normalized Difference Vegetation Index (NDVI)

Normalized Near Infrared (NNIR)

Ratio Vegetation Index (RVI).

1. Introduction

2. Methodology

3. Results

4. Analysis

Parameters used for classification



•The multi-resolution segmentation algorithm was selected as the main segmentation algorithm.

•Different Parameters have been used during the classification process such as:

o Values of NNIR – NDVI & RVI

o Area of the polygons

o Length of the polygons

o Existence of neighboring classes

1. Introduction

2. Methodology

3. Results

4. Analysis

Processing tree 1



1. Introduction

2. Methodology

3. Results

4. Analysis

Processing tree 2



1. Introduction

2. Methodology

3. Results

4. Analysis

Processing tree 3



1. Introduction

2. Methodology

3. Results

4. Analysis

Final Classification Overview



1. Introduction

2. Methodology

3. Results

4. Analysis

Final Classification 2005



1. Introduction

2. Methodology

3. Results

4. Analysis

Final Classification 2013



1. Introduction

2. Methodology

3. Results

4. Analysis

Change Detection Analysis



Olive Trees Bare Soil

1. Introduction

2. Methodology

3. Results

4. Analysis




Change in numbers and Size of trees

Olive Trees Bare Soil

1. Introduction

2. Methodology

3. Results

4. Analysis




2005 2013 Change

Tot Area (ha) 2672.144 2672.144 0

Olives (ha) 39.08916 62.31496 + 23.2258 ha + 59.42 %

Number Of Trees objects

9836 12069 + 2233 objects

+22.7 %

1. Introduction

2. Methodology

3. Results

4. Analysis




0

1000

2000

3000

4000

5000

6000

7000

8000

Tree size < 4 m2 Tree size 4 - 9 m2

Tree size 9 - 16 m2

Tree size 16 -25 m2

Total trees number

283 780

2112 1905

7085

47 568

2269 2295

7192 2005

2013

Change in Trees number based on the tree size in m2

Ob

ject

s N

um

be

r 1. Introduction

2. Methodology

3. Results

4. Analysis

Outline

1. Introduction





6. NDVI analysis







The Normalized Difference Vegetation Index (NDVI)

analysis



The goals from NDVI analysis:

1- Estimate the change in olive trees cultivation for the watershed from 2003 to 2013 using NDVI.

2- Extraction of NDVI values for olive classes for each

year to use it to Estimate Olive Production.



Materials and

Methods



https://mrtweb.cr.usgs.gov/

1- Download of MODIS Images





Materials and

Methods 2- Open MODIS Images in Arc-GIS



Materials and

Methods

3- Extract NDVI imagery just within

study area.



Materials and

Methods

3- Extract NDVI imagery just within

study area.



4- Extraction of NDVI values for olive

classes for each year. Materials and

Methods



4- Extraction of NDVI values for olive

classes for each year. Materials and

Methods



The average NDVI values for the study area from 2003 to 2013

The average NDVI values for the study area (2003)



Results




Results




Results




Results




Results




Results




Results

NDVI-2009 (mean)




Results




Results

NDVI-2011 (mean)




Results




Results



The average NDVI values for the study area from 2003 to 2013

Results



Outline

1. Introduction





6. NDVI analysis





C-Fix Model

1. Methodology

2. Input data

3. Results



C-Fix is a Monteith type parametric model. Modified C-Fix can predict the Gross Primary Production (GPP) of ecosystems for the day i (g C/m2/day) as:

Where:

ε is the maximum radiation use efficiency (g C/MJ APAR),

CO2fert is the normalized CO2 fertilization factor of the current year,

Tcori is the MODIS temperature correction factor,

Cwsi is the water stress index,

fAPARi is the fraction of Absorbed Photosynthetically Active Radiation, and

Radi is the solar incident PAR (MJ/m2/day), all referred to day i.

GPPi = ε·CO2fert · Tcori Cwsi fAPARi Radi

C-Fix Model




Maximum radiation use efficiency (ε) was currently set to 1.2 g C/MJ APAR

(Maselli et al., 2010). CO2fert was computed following Veroustraete et al. (2004), considering a CO2 increase of about 2 ppm/year (Le Treut et al., 2007).

Tcor was calculated as a function of minimum daily temperature (Heinsch et al., 2003).

Cws was obtained from a simplified site water budget, and more precisely from actual and potential evapotranspiration (AET and PET, respectively) estimated

over a two-month period (Maselli et al., 2009):

where:

PET was computed from the available meteorological data by means of the

empirical method of Jensen and Haise (1963), and

AET was assumed to equal precipitation up to PET.

Cws = 0.5 + 0.5 AET/PET

1. Methodology

2. Input data

3. Results

C-Fix Model




fAPAR (fraction of Absorbed Photosynthetically Active Radiation) was obtained from the top of canopy NDVI according to the linear

equation proposed by Myneni and Williams (1994):

and finally

Rad was computed as a constant fraction of incident solar radiation (0.464).

fAPAR = 1.1638 NDVI − 0.1426

1. Methodology

2. Input data

3. Results

Model Inputs



Input data required by the model are: • NDVI values (MODIS images from the website:

https://mrtweb.cr.usgs.gov/ and VGT images from the website: http://www.vito-eodata.be),

• Max. & min. temperature, • Precipitation, • Solar Radiation (generated using a climate model

called Mt-CLIM 4.3 model. The model was downloaded from the website:

http://www.ntsg.umt.edu/project/mtclim).

1. Methodology

2. Input data

3. Results










http://www.vito-eodata.be/










http://www.ntsg.umt.edu/project/mtclim










Model Inputs



1. Methodology

2. Input data

3. Results

0

0.05

0.1

0.15

0.2

0.25

0.3

1

17

33

49

65

81

97

11

3

12

9

14

5

16

1

17

7

19

3

20

9

22

5

24

1

25

7

27

3

28

9

30

5

32

1

33

7

NDVI - VGT Images

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012



y = 0.1056x + 0.108R² = 0.708

0

0.05

0.1

0.15

0.2

0 0.2 0.4 0.6 0.8

Water stress index vs. NDVI - VGT Images

Series1

Linear (Series1)

y = 0.068x + 0.1227R² = 0.4824

0

0.05

0.1

0.15

0.2

0 0.2 0.4 0.6 0.8

Water stress index vs. NDVI - MODIS Images

Series1

Linear (Series1)

1. Methodology

2. Input data

3. Results Water stress index vs. NDVI



y = 0.6043x - 0.0624R² = 0.6388

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0 0.2 0.4 0.6 0.8

Water stress indx vs. GPP C-FixVGT

Series1

Linear (Series1)

y = 0.4028x + 0.0224R² = 0.4141

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 0.2 0.4 0.6 0.8

Water stress index vs. GPP C-FixMODIS

Series1

Linear (Series1)

1. Methodology

2. Input data

3. Results Water stress index vs. GPP



0

10

20

30

40

50

60

70

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Years

Fresh Fruit Yield (g/m2)MODIS

C-Fix Output Real Data

-20

-10

0

10

20

30

40

50

60

70

2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Years

Fresh Fruit Yield (g/m2)VGT

C-Fix Output Real Data

1. Methodology

2. Input data

3. Results Yield of Olive Fresh Fruits



y = 1.0571x - 0.932R² = 0.9902

-10

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60

Fresh Fruit Yield (g/m2)MODIS

Series1

Linear (Series1)

y = 0.9129x + 5.1449R² = 0.9705

-10

0

10

20

30

40

50

60

70

-20 0 20 40 60 80

Fresh Fruit Yield (g/m2)VGT

Fresh Fruit Yield

Linear (Fresh Fruit Yield)

1. Methodology

2. Input data

3. Results Yield of Olive Fresh Fruits

(real vs. estimated)

Outline

1. Introduction





6. NDVI analysis







Conclusions and perspectives

The total area of olive trees increased by almost 60% from 2005 to 2013. The increase in total olive area was due to the plantation of new trees and the increase in

size of the old ones.

Studying the sensitivity of olive trees phenological stages to climate variability allowed us to determine the most influencing climate factors and their effects on olive production.

Future projections of olive production using climate change scenarios (RCM SMHI-ECHAM5) showed a tendency to a decrease of total production up to -43,82% in 2080-2100.

Correlation between yield obtained from C-Fix model and the real data was positively very high, which shows the high performance of the model in estimation of olives yield in the study area. Model is ready to be used for climate change studies.



Conclusions and perspectives

The results reported here would enable farmers and the regional government to have better knowledge of factors influencing olive fruit production and also to be able to better plan olive oil marketing strategies and distribution within the framework of the importing countries agricultural policy.

This study will improve our understanding of olive production variability and will lead to a formulation of a reliable mitigation strategies in order to adapt to climate changes impacts.



Acknowledgment

One of the joys of completion is to look over the journey past and remember all the persons, who have helped and supported us along this short but fulfilling road. To only some of whom it is possible to give particular mention here. Foremost, we would like to express our sincere gratitude to:

Prof. Andrea de Vecchia Prof. Antonio Raschi Dr. Vieri Tarchiani Dr. Fabrizio Ungaro Dr. Fabio Maselli and Dr. Marta Chiesi Dr. Massimiliano Pasqui Dr. Maurizio Bacci Dr. Edoardo Fiorillo Mr. Giacomo tagliaferri Ms. Francesca, Ms. Angela, Ms. Monica, Mr. Leonardo and all IBIMET staff



Thank You for Your Attention

Documents

Assessment of Climate Change Impacts on Olive Production in … · 2014. 11. 17. · Ahmed MOHAMED HARB RABIA Assem ABDELMONEM AHMED MOHAMED Emad FAWZY ABDELATY Fatma WASSAR Hanene