Effect of climate variability and climate change on crop production and water resources in Cyprus.pdf

7/29/2019 Effect of climate variability and climate change on crop production and water resources in Cyprus.pdf

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Effect of climate variability and climate change

on crop production and water resources in Cyprus

Adriana Bruggeman1, Christos Zoumides

2, Stelios Pashiardis

3,

Panos Hadjinicolaou1, Manfred A. Lange

1and Theodoros Zachariadis

2

1

The Cyprus Institute,2

Cyprus University of Technology,3Cyprus Meteorological Service

Nicosia, Cyprus, June 2011


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ii

Contents . . . . . . . . . 1

Executive summary . . . . . . . . 3

Acknowledgements . . . . . . . . 4

1. Introduction . . . . . . . . . 52. Methods . . . . . . . . . 7

2.1 Description of the Green-Blue Water Model . . . . . 7

2.2 Data Sources . . . . . . . . . 10

2.2.1 Climate . . . . . . . . . 10

2.2.2 Land Use and Production . . . . . . . 12

2.2.3 Soils . . . . . . . . . 14

2.2.4 Agricultural Management . . . . . . . 14

2.3 Analysis of Climate Variability and Climate Change . . . . 17

3. Results and Discussion . . . . . . . 19

3.1 Climate Variability and Climate Change . . . . . 19

3.2 Effect of Climate Variability on Crop Production . . . . 23

3.3 Effect of Climate Change and Reduced Irrigation Water Supply on Crop Production 30

4. Conclusion . . . . . . . . . 32

References . . . . . . . . . 33

Appendix 1 Effect of Climate Variability on Crop Production

Appendix 2 Effect of Climate Change on Crop Production, Scenario 1

Appendix 3 Effect of Climate Change on Crop Production, Scenario 2


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1

H ,

. ,

, .

()

30 , ()

,

()

(2013/14-2019/20),

.

,

, Green-Blue

(FAO).

( ) ( ).

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.

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101,9103 , ,

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, 5,3103

13,3103 , 5,7103 12,0103

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1980/81-2008/09 , ,

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8% 5%

,

. ,

190106../ ' 1980/81-2009/10,

2% 2%

. 1989/90 (219106..),

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2007/08 368106.. 2003/04.

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3

Executive Summary

Crop production in Cyprus is constrained by a highly variable climate, limited precipitation and high

temperatures. In addition, global climate change and water management policies that support the

sustainable use of water resources are also reducing irrigation water supply. The main aims of thisstudy were (i) to assess trends in climate parameters during the past 30 years; (ii) to assess the effect

of climate variability on changes in agricultural land use, production and irrigation water demand;

and (iii) to assess the effect of possible climate change scenarios and reduced irrigation water supply

on crop production for the last seven seasons of this decade (2013/14-2019/20).

A daily soil water balance model, based on the FAO dual crop coefficient approach, referred to as

the Green-Blue Water Model, was developed to compute the crop soil water use, originating from

precipitation (green water) and from irrigation (blue water). Crop area and production data for 30

seasons (1979/80-2008/09), 87 different crops and 431 communities were obtained from the

Agricultural Statistics and Censuses. Crop areas registered by the Cyprus Agricultural Payment

Organisation (CAPO) in 2010 were used to simulate future scenarios. Daily climate data from 34

stations and precipitation data from 70 gauges were used for the water balance computations.

The monthly averages of the daily minimum temperatures were found to have statistically significant

upward trends, at the 5% significance level, for the summer months at all four stations that were

analyzed for trends (Larnaca, Kornos, Platania and Prodromos). The monthly averages of the daily

maximum temperatures were also found to have statistically significant positive trends at

Prodromos in the Troodos mountains (five months), Kornos in the eastern foot hills of the mountains

(seven months), and Larnaca at the coast (nine months). Precipitation was highly variable and the

only statistically significant trend was a downward trend for March at Kornos.

The total harvested area of temporary (annual) crops peaked at 101.9103

ha in 2005, after a

sequence of three wet seasons, and dwindled to 70.9103

ha during the 2008 drought year. The

harvested permanent crop area decreased by nearly 40%, from 62.2103

ha in 1980 to 38.4103

ha

in 2009. The main loss was for the vine growing area, which decreased from 34.3 to 8.3103

ha, and

for the areas planted with nut trees, which shrank from 13.3 to 5.3103

ha, while the olive area

increased from 5.7 to 12.0103

ha.

The 1980/81-2008/09 seasons were divided in seven dry years, fifteen average and seven wet years,based on their aridity ratio (precipitation over reference evapotranspiration). Average annual crop

production was 8% lower during the dry years and 5% higher during the wet years, relative to the

production during the fifteen average years. Model computations indicated that total blue water use

averaged 190106

m3/yr during the 1980/81-2008/09 seasons and was only 2% higher during the dry

years and 2% lower during the wet years. Blue water was computed to have peaked at 219106

m3

in 1989/90, while it fell to a record low (150106

m3) during the 2007/08 drought year. Total green

water use ranged between 135106

m3

in 2007/08 and 368106

m3

in 2003/04.

The irrigated areas occupied 23% of the cropland, but were responsible for 65% of the total national

crop production, while consuming 48% of the blue and green water used by crops. The rain-fed


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areas produced on average 273103

ton/yr, fueled by 277106

m3/yr green water. This water may

otherwise have returned back to the atmosphere without much local benefit.

Climate change projections for Cyprus from an ensemble of six Regional Climate Models, under the

medium A1B emission scenario of the UN Intergovernmental Panel on Climate Change (IPCC-SRES),

indicated an increase in temperatures and highly variable but slightly lower precipitation amounts

for the 2013/14-2019/20 seasons. Two climate scenarios were simulated: (1) a worst case scenario,

represented by the seven dry years from the 1980/81-2008/09 record; and (2) a medium scenario

made up of three dry years, two average years and two wet years, each with the highest

evapotranspiration rates within their class. For both scenarios, irrigation water demand was reduced

to 129106

m3/yr, as recommended by recent national water management policies, which was

achieved by reducing all irrigated crop areas of the 2010 CAPO crop areas by 25%. The computed

annual national crop production for 2013/14-2019/2020 was reduced by 41%, on average, under

scenario 1 and by 43% under scenario 2, relative to 1980/81-2008/09. The average loss of irrigated

production was 193103

ton/yr under scenario 1 and 216103

ton/yr under scenario 2, whereas theaverage rain-fed production loss was 13210

3ton/yr (scenario 1) and 12510

3ton/yr (scenario 2).

These results indicated that within the near future water management policies could be critical for

agriculture. Of course, irrigation water supply is likely to be reduced even further by climate change.

The modeling analysis also showed high variability in water use for the different crops, communities

and years, indicating that there are various options for climate change adaptation.

Acknowledgements

This research was conducted within the framework of a study on the economic impact of climate

change on Cypriot agriculture, funded by the Ministry of Agriculture, Natural Resources and

Environment, and led by the Agricultural Research Institute of Cyprus. The authors would like to

acknowledge the support of Marinos Markou of the Agricultural Research Institute, Christos

Hadjiantonis and George Nicolaou of the Department of Agriculture, Silas Michaelides, Anastasia

Onoufriou, Andri Georgiou and Elenitsa Ioannou of the Cyprus Meteorological Service, Zomenia

Zomeni from the Cyprus Geological Survey, Daniel Karageorgis, Haris Chimonides and Demetris

Papadopoulos from the Cyprus Agricultural Payment Organization, and Damianos Pitiris and Loukia

Alexandrou from the Cyprus Statistical Service. Theodoros Zachariadis acknowledges also the

financial contribution of the European Commission under Marie Curie Reintegration Grant No.

PERG03-GA-2008-230595Assessment of Economic Impacts of Climate Change in Cyprus in the

framework of the 7th European Community Framework Programme.


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1.IntroductionAgriculture in the Mediterranean region is constrained by the limited and highly variable rainfall and

high temperatures. The often shallow and stony soils and the low soil organic carbon contents (Lal,

2009) are additional factors that limit root zone soil water storage and affect agricultural production

in this region. A recent study found that in the Mediterranean European countries, the modeled,

non-water limited potential yields were lower, and the gaps between modeled yields and reported

average national yields were higher than in the European countries at higher latitudes (Supit et al.,

2010). For example, modeled potential barley yields during the period 1976-2005 reached 9.5

ton/ha in The Netherlands and 7.5 ton/ha in Greece, whereas the reported average national yields

were 5.5 and 2.4 ton/ha, respectively. In Cyprus, which was not included in this study, the reported

average national barley yields during this period were even lower (2.0 ton/ha). It may be good to put

the word potential, as used in yield gap modeling studies, in perspective. Unless the arid and semi-

arid regions of the Mediterranean receive water resources from outside the region, such as the Nile

River in Egypt, or mine their non-renewable groundwater resources, such as the famous man-made

river in Libya, they will only be able to achieve these non-water limiting potential yields on a very

small fraction of their agricultural land.

The Intergovernmental Panel on Climate Change identified the Mediterranean as one of the regions

that is likely to experience a decrease in water resources due to climate change (Kundzewicz, 2007).

Giorgi (2006) also showed that the Mediterranean region is a hotspot of climate change. Similar to

many Sahel countries (Kevane and Grey, 2008), Cyprus has already experienced a decrease in

precipitation in the recent past. A regional analysis of the changes in precipitation found a

statistically significant step change in the 1916-2000 annual precipitation time series between the

hydrologic years 1968/69 and 1971/72, with a 15-25% reduction in precipitation for the last 30 yearsof the 20th century (Rossel, 2001). Data from the Cyprus Meteorological Service (CMS, 2010; 2011)

give an average annual precipitation over the government controlled area of Cyprus of 541 mm for

1901/02-1969/70 and 466 mm for the 1970/71-2009/10 period. This decrease in precipitation has

resulted in an even greater decrease in the countrys water resources. Considering that the majority

of Cyprus dams and irrigation projects have been designed on the basis of precipitation data from a

wetter past, they have not always lived up to the expectations they raised.

A recent study by the Ministry of Agriculture, Natural Resources and Environment (Vakakis and

Associates, 2010), which addressed the future of agriculture in Cyprus, indicated that climate change

is likely to increase irrigation water demands, reduce yields and increase soil degradation. On theother hand, the Water Development Departments (WDD) review of the countrys water policy for

the implementation of articles 11, 13, and 15 of the European Water Framework Directive (WFD)

recommended a 15% reduction in irrigation water demand (Karavokyris et al., 2010). Considering

their estimated irrigation water demand of 152 Mm3/yr, this would bring the irrigation water supply

down to 129.2 Mm3/yr. But not only is there expected to be less water for agriculture, this water will

also come at a higher price. The proposed new water pricing policy of the WDD, which is supposed

to bring Cyprus in line with the cost recovery principles of the European WFD, and include financial,

environmental and economic costs, proposed an increase from 0.17 to 0.24 /m3

for government

supplied irrigation water and an associated increase in the fixed charge from 1.71 to 6.61 /decare

(WDD, 2010). Farmers are also expected to start paying for the water they pump from streams or


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aquifers at a rate of 0.11 /m3. The pricing framework did take the social importance and financial

viability of the agricultural sector into consideration and the cost recovery of irrigation water supply

will still be fairly low (54%).

Although there is a critical need for a more sustainable use of the water resources in Cyprus (e.g.,

Karavokyris et al., 2010), crop farmers, who have always struggled in this marginal environment,

seem now to be hit by both climate change and policy changes. This phenomenon where certain

sectors or groups are affected by changes related to both global warming and economic

globalization has been referred to as double exposure in the socio-climate literature (OBrien and

Leichenko, 2000).

The main goal of this study is to obtain a better understanding of the effect of climate variability and

climate change on crop production in Cyprus. The specific objectives of this study are:

to assess trends in climate parameters during the past 30 years (1980-2010)

to assess changes in agricultural land use during the past 29 crop seasons (1980/81-2008/09) to asses variability and changes in agricultural production during 1980/81-2008/09; to assess variability and changes in irrigation water demand in Cyprus during 1980/81-

2009/10;

to assess the effect of potential climate change scenarios and reduced irrigation watersupply on water use and agricultural production under the new CAP (2013/14-2019/20).

This research builds on the work of Zoumides and Bruggeman (2010), who developed a monthly

water balance model to assess blue and green water use of all crop areas in all communities in

Cyprus and analyzed the agro-meteorological seasons 1996/97-2007/08. The terms green and blue

water have been recently introduced in global water research to shift our thinking from a supply-

driven focus on water resources that can be pumped from streams and aquifers (blue water) to a

more holistic thinking about precipitation as the primary water source, with both green water (soil

moisture) and blue water (rivers and groundwater) providing society with the necessary resources

and ecosystems services (e.g., Falkenmark and Rockstrom, 2010).

The current study uses a 30-year time series of daily meteorological data (1980/81-2009/10) and

crop areas and production data of 29 crop seasons (1981-2009). Soil water holding capacities were

computed over each communitys agricultural area, instead of over the full community area, as in

the previous study of Zoumides and Bruggeman (2010). Soil water balances and irrigation

requirements are computed on a daily basis using a dual crop coefficient approach (Allen et al.,

1998).


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2.Methods2.1 Description of the Green-Blue Water Model

A Fortran program was written to compute the continuous soil water balances and water uses of allcrops grown in all communities in Cyprus, on a daily basis. The model uses the dual crop coefficient

approach and procedures presented in Allen et al. (1998) for computing crop evapotranspiration

(ETc) and scheduling irrigation, as summarized below. The model distinguishes the crops use of

precipitation (green) and irrigation water (blue) and is thus named the green-blue water model.

Crop evapotranspiration

The computation of crop evapotranspiration is a two-step process. First, the evapotranspiration of a

standard, hypothetical grass surface, named reference evapotranspiration (ETo), is computed from

the daily climate parameters and the reference surface characteristics. Secondly, the crop

evapotranspiration (ETc) of a specific crop is computed from the reference evapotranspiration (ET o)

by using coefficients for the specific crop and crop stage.

For the reference surface, Allen et al. (1998) selected a hypothetical grass crop with a height of 0.12

m, a surface resistance of 70 s/m and an albedo of 0.23. The resulting, so-called FAO Penman-

Monteith equation (Allen et al. 1998) is given as:

)0.34u(1

)e(eu273T

900G)(R0.408

ET2

as2n

o

(1)

where ETo is reference evapotranspiration (mm day-1

), is the slope of the vapour pressure curve

(kPa C-1

), Rn is the net radiation at the crop surface (MJ m-2

d-1

), G is the soil heat flux density (MJ m-2

day-1

), T is the daily mean air temperature (C), u2 is the wind speed at 2-m height (m s-1

), es is the

saturation vapour pressure (kPa), ea is the actual vapour pressure (kPa) and is the psychometric

constant (kPa C-1

).

For the second step, the computation of crop evapotranspiration, the dual crop coefficient approach

was used. The main equations can be expressed as follows:

ETc = (Kcb + Ke) ETo (2)

ETa = (Ks Kcb + Ke) ETo (3)

Ke = Kr (Kc max - Kcb) few Kc max (4)

where ETc is the crop evapotranspiration with no limits on water availability (mm/d), Kcb is the basal

crop coefficient, Ke is the soil evaporation coefficient, ETo is the reference evapotranspiration

(mm/d), ETa is the actual plant water use (mm/d), Ks is a stress coefficient (0-1), Kr is an evaporation

reduction coefficient (0-1), Kc max is the maximum possible evapotranspiration (1.05-1.3) and few is the

fraction of the soil that is both exposed to radiation and wetted.


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Crop coefficients for the mid and late season stage (as explained below) are adjusted for climate

effects, if exceeding 0.45, according to:

Kcb = Kcb + [(0.04 (u2 2) 0.004 (RHmin - 45)] (h/3)0.3

(5)

where RHmin is the daily minimum relative humidity, h is the mean plant height (m), and all otherparameters are as defined previously.

Irrigation is scheduled when soil water in the root zone falls below the readily available water level.

The readily available water (RAW) is computed as:

RAW = p TAW (6)

TAW = (FC WP ) z (7)

where p is the soil water depletion fraction, which is the threshold at which stress starts to occur

and depends on the crop, TAW is the total available water, FC is the soil water content at field

capacity (fraction), WP is the wilting point water content (fraction) and z is the depth of the root

zone (mm). When the soil water content falls below RAW and no irrigation is given, K s decreases

linearly towards its minimum value of 0.0 at WP. Considering the general limited soil depths in the

study area, the model did not compute the crop root zone development, but used the full root zone

and a maximum irrigation depth of 50 mm, based on local irrigation practices.

Similarly to the stress coefficient, the soil evaporation reduction coefficient (K r) is at its maximum

value of 1.0 until the readily evaporable soil water (REW) has evaporated, and then decreases

linearly to 0.0 as evaporation approaches the soils total evaporable water (TEW). The fraction of thesoil that is wetted is set based on the irrigation method used (e.g., drip systems or micro-sprinklers).

Thus, field irrigation application efficiencies (evaporation losses) are computed by the model.

The blue water is computed as the total irrigation water given to the crop during the season. The

green water is the total seasonal ETa minus surface runoff or drainage, and change in soil moisture

storage.

Crop development

The crop coefficient is a function of the crop growth stage. The growing period can be divided into

four distinct growth stages: initial, crop development, mid-season and late season (see Figure 1).These stages have been described by Allen et al. (1998), as presented below.

The initial stage runs from planting date to approximately 10% ground cover. For perennial crops

(fruit trees and vines), the planting date is the 'green-up' date, i.e., the time when the initiation of

new leaves occurs. For this study, the initial period and the Kc_ini were used to cover the winter

months, with low evapotranspiration rates.


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Figure 1. Crop development stages and crop coefficients (taken from Bos et al., 2009)

The crop development stage runs from 10% ground cover to effective full cover. Effective full cover

for many crops occurs at the initiation of flowering. For some crops, especially those taller than 0.5

m, the average fraction of the ground surface covered by vegetation (fc) at the start of effective full

cover is about 0.7-0.8. For dense grasses, effective full cover may occur at about 0.10-0.15 m height.

Densely planted forages such as alfalfa and clover reach effective full cover at about 0.3-0.4 m. The

crop or plant can still continue to grow after the time of effective full cover.

The mid-season stage runs from effective full cover to the start of maturity. The start of maturity is

often indicated by the beginning of the ageing, yellowing or senescence of leaves, leaf drop, or the

browning of fruit. The mid-season stage is the longest stage for perennials and for many annuals, but

it may be relatively short for vegetable crops that are harvested fresh for their green vegetation.

The late season stage runs from the start of maturity to harvest, full senescence, or leave drop. For

perennial vegetation that grows year round (in frost free climates) the date of termination may be

taken the same as the date of planting.

Yield reduction

Yield reduction fractions for rain-fed crops are computed using the equations of Allen et al. (1998):

(8)

where Ya is the actual yield (ton), Ym is the yield under no water-stress conditions (ton), Ky is a yield

response factor, ETa is the actual seasonal crop evapotranspiration (mm) and ETc is the seasonal crop

evapotranspiration for standard conditions without water stress (mm).


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For crops that are grown both under irrigated and rain-fed conditions (e.g., wheat, olives), it was

assumed that the irrigated crop area would achieve an optimal yield (Ym). Thus, within any given

year, the average irrigated and rain-fed yields of a selected crop could be computed as:

(9)

(10)

here YIR is the yield of the selected crop under irrigated conditions (ton/ha), YRF is the yield of the

crop under rain-fed conditions (ton/ha), ProdTOT is the total production of the selected crop (ton),

ARF, AIR, ATOT are the rain-fed, irrigated and total area of the selected crop (ha), and Y a/Ym is the yield

reduction fraction computed by the model. Note that YIR = Ym and Ya = YRF.

Similarly, water productivities for irrigated and rain-fed crops were computed as follows:

(11)

(12)

where WPIR is the water productivity of an irrigated crop (kg/m3), Wblue is the irrigation water

supplied to the crop (m3/ha), Wgreen is the precipitation used by the crop (m

3/ha) and WPRF is the

water productivity of a rain-fed crop (kg/m3).

Model input data

The input data for the model are: cropped areas for each community and season; crop parameters

for all crops (crop coefficients, duration of stages, soil water depletion fraction at which stress

occurs, yield response factor, irrigation method); the average total available soil water storage

capacity (TAW) for each community, and daily climate data, from the climate station and rain gauge

that has been allocated to each community. All input and output files are comma separated files

(csv), which can be easily generated from and used in standard spreadsheet software.

Model computations start after the dry summer months on September 1, which is the start of the

hydro-meteorological year. The model uses one spin-up year to provide expected initial values for

the soil moisture.

2.2 Data Sources

2.2.1 Climate

Daily data from 70 precipitation gauges and 42 climate stations, for the period January 1980 to

December 2010, were made available by the Cyprus Meteorological Service. The locations of the

stations are given in Figure 2. All 42 stations had records of daily maximum and minimum

temperature and relative humidity measured at 8 am. Eight of the climate stations had relativelyshort records (less than 15 years) or were located very close to other stations. These stations were


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only used to fill in missing data for the final set of 34 stations, and are referred to as extra in Figure

2. Relative humidity at 1 pm was measured at 24 of the 34 stations, wind speed was measured at 22

stations, and sun hours at 7 stations. The daily mean relative humidity was estimated as the average

between the measured 8-am and 1-pm values.

All climate data were checked for errors. To fill in missing data, linear regression equations were

developed for all parameters, between all stations. For each parameter and station, two or three

neighboring stations, within 50-km distance and an 800-m elevation range, were selected, based on

the coefficient of determination of the regression equations. Missing values were subsequently

computed as the average of the values computed with the linear regression equations of the

selected stations. The relative humidity at 1 pm for the stations without afternoon measurements

were obtained from the linear regression equation between the 8-am and 1-pm humidity from a

nearby station, which was selected based on the relation between the 8-am humidity of the two

stations, similar to above. Wind speed data were taken from a nearby station with similar

characteristics. Missing sun hour data were obtained from station 731 at Larnaca, using a coefficient

for each station, based on elevation and latitude, obtained from the 7 stations with sun hour data.

The coefficients ranged between 0.77 for station 270 at Troodos square (1725 m asl) and 1.02 for

station 41 at Polis Chrysochous along the west coast of the island.

Figure 2. Location of precipitation gauges and climate stations used in the study, the stations

identified as climate (extra) were used to fill in data gaps only.

Time trends were analyzed for four stations with no more than three days missing during theJanuary 1980 December 2010 record. These stations were Prodromos (225), Platania (310), Kornos


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(660) and Larnaca (731). The monthly average daily minimum and maximum temperature, relative

humidity at 8 am, and monthly precipitation were analyzed using a set of non-parametric tests

(seasonal Kendall, Mann-Kendall for individual months, Sen slopes), as described by Gilbert (1987).

Significance was assessed at the alpha 0.05 level. Parameter values for the few missing days were

assumed to be equal to the monthly average value.

Climate change projections for the medium emission A1B scenario from six Regional Climate Models,

driven by three different General Circulation Models (CNRM-ARPEGE, HadCM3Q0, ECHAM5-r3),

from the European FP7 ENSEMBLES project, have been extracted and evaluated for Cyprus by

Hadjinicolaou et al. (2010). These authors assessed the simulated changes between 1976-2000 and

2026-2050 for Nicosia, Limassol, and Saitas. These results were reviewed to assess climate change

effects.

2.2.2 Land Use and Production

The agricultural censuses of the past three decades (Statistical Service, 1987; 1996; 2006) provide

spatial information on crop areas by the farmers community of residence. Because many owners of

a plot of agricultural land live in urban areas, these data do not precisely reflect the actual

distribution of the crop areas for rural communities. The agricultural census of 2003, however, gives

also data on the area of each crop group per community based on the actual location of the field

(Statistical Service, 2006). Twelve crop groups were used: (1) cereals; (2) forages and legumes; (3)

industrial crops; (4) potatoes, (5) vegetables, melons and strawberries; (6) flowers and ornamentals;

(7) greenhouse crops and flowers; (8) olives, (9) fruit trees, (10) citrus, (11) vines, (12) nuts and

carobs. It was assumed that the relative distribution of the crops within each crop group was the

same for all communities and that the relative distribution of the crops over the communitiesremained constant over the 1980-2009 study period. Thus, the area of each crop for each

community could be computed as follows:

A(crop,com,year) = A(crop,year) x A(cropgroup,com,2003) / A(cropgroup,2003) (13)

where A(crop,com,year) is the area of a selected crop, in a selected community and year,

A(crop,year) is the national area of the selected crop and year from the annual agricultural statistics

(Statistical Service, 1982-2010), A(cropgroup,com,2003) is the area of the crop group that includes

the selected crop for the selected community in the 2003 Census, and A(crop-group,2003) is the

total national area of this crop group in the 2003 Census.

Data on total area and production per crop were extracted from the annual agricultural statistics for

the years 1980 to 2008 (Statistical Service, 1982-2010). The agricultural statistics provide data of 60

crops. However, some of these crops include different management systems (11 greenhouse crops)

or subcategories (4 fodder crops, 2 main potato planting seasons, 3 leafy vegetable crops). Thus,

crop water use was computed for a total of 87 different agricultural production systems. Area and

production data from FAO Stat (http://faostat.fao.org/site/567/default.aspx#ancor) were used for

the year 2009 and to fill in a few missing data points and inconsistencies. This crop area dataset is

referred to as the Cystat dataset.
http://faostat.fao.org/site/567/default.aspx#ancorhttp://faostat.fao.org/site/567/default.aspx#ancorhttp://faostat.fao.org/site/567/default.aspx#ancorhttp://faostat.fao.org/site/567/default.aspx#ancor


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The Cyprus Agricultural Payment Organisation (CAPO) hosts a second source of spatially distributed

agricultural crop area data, covering the period 2005-2010. These data are derived from farmers'

applications for the Single Area Payment Scheme for land that is maintained in good agricultural

condition. The farmer applications are checked through on-the-spot field inspection visits and with

the help of satellite images, following EU regulations (CAPO, 2008). CAPO officials estimate these

areas to cover 95% of all planted areas in Cyprus. A drawback of the CAPO dataset is that the crop

categories lack a clear definition and categories have been added or removed over time. For the

current study 87 different crops were identified.

Figure 3 provides an overview of the area covered by temporary (annual) and permanent crops, as

present in the Cystat and CAPO datasets. In general, the CAPO values are higher than the Cystat

values. This could be attributed to the fact that the CAPO data are the claimed planted crop areas

whereas Cystat data are the harvested crop areas. However, if two crops are grown during one year

in the same field, this would not show up in the CAPO dataset, while in the Cystat dataset this area

would be counted twice. It should be noted that some of the changes in the CAPO areas during the

first few years (2005-2007) are due to an improvement in the quality of the dataset. However, even

after 2007, the data show a steady decrease of the permanent crop area. A similar decrease is also

observed in the CyStat dataset.

Figure 3. Areas of temporary and permanent crops in the Cystat (2005-2009) and CAPO dataset

(2005-2010).

The 2003 agricultural census (Statistical Service, 2006) reports crop-group areas for 426

communities, while the CAPO dataset contains agricultural plots in 428 communities. Five

communities from the census are not listed in the 2005-2010 CAPO dataset, while seven

communities in the CAPO data set are not listed in the census. Three of the five additional censuscommunities (Mandres Lefkosia, Variseia, Ammadies) have been occupied by Turkish forces and are

0

20

40

60

80

100

120

140

160

2005 2006 2007 2008 2009 2010

Croparea(103h

a)

Permanent crops (Cystat) Permanent crops (CAPO)

Temporary crops (Cystat) Temporary crops (CAPO)

Total crop area (Cystat) Total crop area (CAPO)


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14

abandoned. Therefore, these three communities were excluded from the dataset. The remaining

two communities: Anthopouli (code 1022) and Livadi (6370) were added to the CAPO dataset. The

community of Pano Amiantos (5353), which appears in the population census of 2001 (Statistical

Service, 2004) with 44 living quarters and a permanent population of 3 people, was added for

completeness, although it currently does not have agricultural land and was listed in neither the

2003 census nor the CAPO dataset. Thus, the final dataset used for this study contains 431

communities that have all or part of their land located in the Government controlled area of Cyprus.

It should be noted that some of the communities with agricultural land are not inhabited anymore.

The 2001 Census of Population (Statistical Service, 2004) has counted people in 388 communities.

2.2.3 Soils

Cyprus has never been covered by a full set of soil surveys. Therefore, soil depths and water holding

capacities were estimated based on the soil units of the 1:250,000 digital soil map of Cyprus

(Hadjiparaskevas, 2005), which was provided by the Department of Agriculture. Soil depths andwater holding capacities of the 19 dominant soils of this map were derived from the 0.5 degree

Harmonized World Soil database (FAO et al., 2009) and from soil physical information for similar soils

provided by the European Soil Bureau Network (2005). These soil water capacities were added to

the soil units in the digital soil map. To define the average soil water holding capacity of the

agricultural land in each community, the soil water capacity for the center point of each agricultural

plot in the CAPO dataset was extracted with the help of a Geographical Information System (GIS).

The average soil water holding capacity of each community was subsequently computed as the

average of all CAPO plot values within the community.

2.2.4 Agricultural Management

Irrigation

The fraction of the crop area that was irrigated was obtained from the Cystat dataset (Statistical

Service, 1982-2010; Table 7). These tables define 10 crop groups, nearly identical to the crop groups

of the census defined above, i.e. (1) cereals; (2) forages; (3) legumes; (4) industrial crops; (5)

vegetables, melons, strawberries and potatoes; (6) olive and carob, (7) fruit trees, (8) citrus, (9)

vines, and (10) nuts. It should be noted that these tables refer to the areas irrigable, which is defined

in the 2003 census as land that has the potential to be irrigated, but does not include areas in which

mobile irrigation equipment was used. Irrigated land, on the other hand, is land that has beenirrigated at least once during the reference season (Statistical Service, 2006). The 2003 census

reported 35,928 ha irrigated land and 45,449 ha irrigable land (Statistical Service, 2006). However,

the 2003 agricultural statistics list 32,700 ha irrigable land (Statistical Service, 2007). Thus, it was

assumed that the numbers for irrigable land in the annual statistics refer in fact to irrigated land.

All areas planted with industrial crops, vegetables and melons and fresh fruit (trees) were irrigated,

with a few exceptions during 1980-1986 for industrial crops and fruit trees, which were ignored. The

fraction of the crop area irrigated for the other groups is presented in Figure 4. The data exhibited

inconsistent jumps, especially before and after the census years. Therefore, all irrigation fractions

were kept constant for this study. It should be noted that the fractions are also affected by the areal

fraction of the individual crops within the group, and within the legume group also the inconsistent


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15

inclusion of fresh green beans, cow peas and broad beans either under the legume or vegetable

group.

The irrigated area fractions of the individual crops, which make up these groups, were derived from

the data of the 1985, 1994 and 2003 census (Statistical Service, 1988; 1997; 2006), the cereals

statistics for 2006-2009 (Statistical Service, 2007-2010) and the vine statistics for 2006-2009

(Statistical Service, 2007-2009) and CAPO data from 2010 (Table 1). It was assumed that all carob

trees were grown under rain-fed conditions. On the other hand, all alfalfa and maize crops are fully

irrigated and the areas occupied by these crops are not included in the fodder crop groups listed in

Table 1. Given that no complete information exists regarding the location of the irrigated areas, this

same set of irrigation fractions was used for all 431 communities.

The commonly used irrigation method for each crop was taken from Markou and Mavrogenis (2002,

cited in Karavokyris et al, 2010). The fraction of the surface area wetted by irrigation, for the

different irrigation methods, was based on the guidelines given by Allen et al. (1998). This fractionwas 0.35 for drip systems, 0.70 for micro-sprinklers, and 1.0 low-pressure sprinklers.

Figure 4. Fraction of crop area irrigated (Statistical Service, 1982-2010).

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

1980 1985 1990 1995 2000 2005 2010

Fractionirrigate

d

Cereals Legumes Fodder crops Vines Nuts Olives and carobs


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Table 1. Fraction of crop area irrigated, for all crops which are grown under both irrigated and

rain-fed conditions, and fraction of crop area in greenhouses.

Crop name Fraction irrigated Crop name Fraction greenhouse

Wheat 0.09 Tomatoes 0.30

Barley 0.02 Zucchini (marrows) 0.05

Oats 0.03 Cucumbers 0.50

Broad beans 0.40 Eggplant 0.30

Chickpeas 0.60 Peppers 0.20

Lentils 0.05 Green beans 0.10

Louvana 0.10 Strawberries 0.40

Vetches (vicos, rovi, favetta) 0.05 Water melons 0.10

Cereal fodder 0.05 Sweet melons 0.10

Legume fodder 0.05 Flowers 0.50

Wine Grapes 0.05 Nursery products 0.50

Table Grapes 0.90Almonds 0.10

Walnuts 0.90

Hazelnuts 0.45

Pistachios 0.55

Olives 0.40

Greenhouses

Fractions of the area of tomatoes, cucumbers, zucchini, eggplant, pepper, green beans, melons

strawberries, flowers and nursery products that are grown in greenhouses or low plastic tunnels

were again based on the 1985, 1994 and 2003 census and on data of 2005 from Markou and

Papadavid (2008) and on data for the 2006/07-2009/10 seasons provided by the Agriculture

Department. Because of the lack of specific production statistics for these systems, greenhouses and

plastic tunnels were grouped together, and hereinafter referred to as greenhouses. Most

greenhouse area fractions were surprisingly constant over the 1985-2010 period. Therefore, the

average areal greenhouse fraction over the time period was taken for each crop (see Table 1); and it

was assumed that these fractions remained constant over the study period.

Water use of greenhouse crops was computed separately by the green-blue water model. Thesenumbers were subsequently added to the water use of each same crop grown in the open field, such

that the water productivity of these crops could be computed from the total water use and reported

total production values.

Crop coefficients

Crop coefficients were taken from Allen et al. (1998) and Allen and Pereira (2009). It was assumed

that most trees were planted with high or medium density. Crop coefficients for greenhouse crops

were taken from the studies of Orgaz et al. (2005) and Bonachela et al. (2006) in Spain. These

authors also found reference evapotranspiration inside greenhouses to be slightly higher than half

the reference evapotranspiration in the open air (Fernandez et al., 2010). A factor 0.6 was used to

convert the computed reference evapotranspiration to a value for greenhouses and plastic tunnels.


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Crop heights, depletion fractions (p) and yield response factors (Ky) were also obtained from Allen et

al. (1998). The yield response factor for beans was taken from Lorite et al. (2004).

Planting dates and crop development

From the coastal plains to the top of the Troodos mountains and down again to the Mesaoria plain

in the rain shadow of the mountains, Cyprus has a large range of climates. Thus, optimal planting

and harvesting dates and phenological development of both annual and permanent crops is affected

by the crops location on the island. Information on planting and harvesting dates and crop

development periods for the different agricultural districts in Cyprus have been provided by the

Department of Agriculture through surveys. Additional information was obtained from studies of the

Agricultural Research Institute (e.g., Eliades et al., 1995; Chimonidou, 2001; Metochis, 1999, 2006a,

2006b; Josephides and Kyratzis, 2007; Kari, 2007). Critical stages of most crops grown in Cyprus can

also be found in the agricultural labour input calendars of the Norm Input/Output report of Markou

and Papadavid (2008). For the current study, representative, average dates that would cover the

crops growing environments were selected. It should also be noted that some of the differences incrop development are also evened out because of the specific locations where the crops are

generally grown, e.g., fruit trees in the mountains and vegetables in the coastal plains. Furthermore,

the mild winter climate in the plains allows cultivation of irrigated vegetables nearly year round.

In the Mediterranean region, potatoes are grown in different seasons. Potatoes are either planted in

January (spring potatoes), August (fall potatoes), or October (winter potatoes). The total potato area

listed in the annual agricultural statistics was assumed to cover 25% winter potatoes and 75% spring

potatoes, as was indicated by data from the 1985 and 1994 census (Statistical Service, 1988; 1997).

The much smaller area of potatoes planted in August was not computed separately.

2.3 Analysis of Climate Variability and Climate Change

To assess the effect of climate variability on crop production and irrigation water use in Cyprus, crop

water requirements for the spatially distributed Cystat crop areas from the 1980/81 to 2008/09

seasons were computed for all 431 communities with the green-blue water model, with the daily

climate data of the 34 climate stations and the 70 rain gauges. Annual yield reduction fractions of

each crop were computed by the model according to equation 8. Yield and water productivities for

rain-fed and irrigated crops were subsequently computed from the national agricultural statistics,using equations 9-12. The results of the 29 crop seasons were divided into three classes: seven dry

years, fifteen average years, and seven wet years, based on the seasons average aridity ratio

(P/ETo), computed as the average of the 34 climate station. Thus, these classes do not only represent

the variability in precipitation but also changes in temperatures.

Data on the area of each crop at the community level from the Cyprus Agricultural Payment

Organisation (CAPO) for the year 2010 were used to assess the effect of climate change and reduced

irrigation water supply for a possible 2013/14-2019/20 climate future. For this future scenario, it was

also assumed that the Water Development Department would enforce sustainable water use limits,

according to the water management policy proposals for the implementation of the European Water

Framework Directive (Karavokyris et al., 2010). These authors estimated a current irrigation water


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18

demand of 152106

m3/yr and recommended a gradual reduction of 15%. This would bring irrigation

water supply down to 129.2106

m3/yr.

Neither Global Circulation Models nor Regional Climate Models reproduce the past and present

spatial and temporal distribution of daily precipitation very well (e.g., Dai, 2006; Boberg et al., 2009),

especially in complex topographic environments such as Cyprus (Hadjinicolaou et al., 2010).

Therefore, for this study we used the observed daily data of the past to represent the future. Two

different scenarios were developed.

The future scenarios make use of the three classes defined for the climate variability analysis of the

past 29 seasons (dry, average, wet). Scenario 1 represents a possible worst case climate future for

2013/14-2019/20. This scenario is made up of the seven driest years from the past, based on their

aridity ratio. Scenario 2 represents a perhaps more realistic scenario, made up of three dry years,

two average years and two wet years. Within these three aridity classes, the years with the highest

reference evapotranspiration were selected to represent a warmer future. For both scenarios,

irrigation water supply was brought down to 129106

m3/yr by reducing the area of each crop with

the same fraction.

First, the green-blue water model was used to compute the irrigation water demand for the CAPO

crop areas for the seven selected years. Secondly, all irrigated areas were reduced with an equal

percentage for all crops, to reduce the total irrigation water demand to 129106

m3/yr. Then the

model was run again to compute the total green and blue water use and the relative yield reductions

due to water stress for the rain-fed areas. Finally, the production data was estimated based on the

relative yield reductions and the historic yields of the selected years, under irrigated conditions.

Thus, while water stress is explicitly accounted for, other factors that affect crop yields, such asfrosts, high temperatures, wind, pests, diseases, salinity and management practices, are included in

the yield data.


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3.Results and Discussion3.1 Climate Variability and Climate Change

Average observed precipitation and the computed reference evapotranspiration (ETo) for the1980/81-2009/10 September-August hydrometeorological seasons are presented in Figure 5. These

values are the averages of the individual values of the 34 climate stations and the 70 rain gauges

used in this study, not spatially averaged values over the country. The 2007/08 season, which was

the driest in the 30 year record with 302 mm, also experienced the highest ET o (1395) of the 30-year

period. The wettest season (1991/92) received an average precipitation of 691 mm and had the

second lowest ETo (1257 mm). The ratio between precipitation and ETo (aridity ratio) is far from

constant; it ranged between 0.22 and 0.55 during the 30-year record.

As described in the previous section, for the climate variability modeling analysis, the 1981/82-

2008/09 seasons were divided into three periods. The seven seasons with the lowest aridity ratio

(1989/90, 1990/91, 1995/96, 1997/98, 1999/2000, 2005/06, 2007/08) were also the seasons with

the lowest precipitation, indicating the over-ruling influence of precipitation in this semi-arid

environment. Similarly, the seasons with the highest aridity ratio were also the wettest seasons

(1980/81, 1986/87, 1987/88, 1991/92, 2001/02, 2002/03, 2003/04). The spatially averaged

precipitation over the government controlled area of Cyprus for the seven driest, fifteen average

and seven wettest years was 344, 460 and 581 mm, respectively (CMS, 2010; 2011).

Figure 5. Average precipitation, reference evapotranspiration and aridity ratio for the September-

August hydro-meteorological years for the 70 rain gauges and 34 climate stations used in this

study.

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0

200

400

600

800

1000

1200

1400

1600

Ari

dityratio

Precipitationorevapotranpiration

(mm

/yr)

Precipitation Reference evapotranspiration Aridity ratio


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20

Figure 6 presents the monthly average precipitation and ETo for the 19802010 period at

Astromeritis (415) in the Mesaoria plain, which was the driest of the 34 stations; and Troodos

Square, which was the wettest and also the highest of the 34 stations, with an elevation of 1725 m

asl. With its aridity ratio of 0.21, Astromeritis would be considered semi-arid (less than 0.20 is arid),

according to the widely used classification of Middleton and Thomas (1997). The Troodos square had

an aridity ratio of 1.0 and classifies as humid. The aridity index was originally computed with the

potential evapotranpiration method of Thorntwaithe (Middleton and Thomas, 1997), thus,

classifications may differ slightly. The monthly pattern of Astromeritis shows a moisture deficit in 10

out of 12 months. On average, only in December and January the soils will receive some recharge. At

the Troodos square the moisture deficit starts in April and lasts until October. Precipitation in winter

is high but in most of the mountain areas the soils are too shallow to store all this water for the dry

season.

Figure 6. Average monthly precipitation and reference evapotranspiration (1980-2010) at

Astromeritis (station 415) and Troodos square (station 270).

The results of the Man-Kendall trend analysis of the monthly precipitation for the four stations with

complete records (see Section 2.2.1) underlined the variable nature of the precipitation. At a

probability level (alpha) of 0.05, the only statistically significant trend was found for March at the

foothill station of Kornos, with a Sen slope of -1.9 mm per year.

Statistically significant trends of increasing monthly average daily minimum temperatures were

found for the June-August summer months at Prodromos and Platania in the mountains and for June

and July at Kornos. At Larnaca both minimum and maximum temperatures showed significant

positive trends for almost all calendar months. Significant positive trends of maximum temperature

were also found at Prodromos (five months) and Kornos (seven months). The sun hours at Larnaca

exhibited statistically significant positive trends for the months May, June and July.

Average monthly relative humidity at 8 am had statistically significant negative trends for the

months March and September at Prodromos; January, February, September at Platania; November

at Kornos; and November-April at Larnaca. Thus, it seems that non-significant increases in

0

50

100

150

200

250

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

PrecipitationorETo(mm/mo)

415PrecipitationEvapotranspiration

0

50

100

150

200

250

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

PrecipitationorETo(mm/mo)

270


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21

temperature and non-significant decreases in precipitation combine into significant decreases in

humidities during the rainy season. These trends should be looked at in more detail.

Climate projections for Nicosia, provided by the six RCMs analyzed by Hadjinicolaou et al. (2010), are

presented in Figures 7 and 8. These figures also show the ensemble mean of the six models (ENS)

and the observed values for 1976-2000 (OBS). Most models represent the highly variable nature of

the precipitation quite well, but this variability is less well represented by the ensemble mean. It

does look like Nicosia could be expected to become somewhat drier in the future, but this trend is

masked by the high inter-annual variability, which continues into the future. Hadjinicolaou et al.

(2010) found a 2-8% decrease in precipitation for 2026-2050, relative to 1976-2000, for the three

analyzed locations in Cyprus (Nicosia, Limassol, Saitas).

Figure 7. Projections of annual precipitation for Nicosia from six Regional Climate Models, the

average of the six models (ENS) and the observed values for 1976-2000 (OBS).

All six RCM projections of the annual average daily maximum and minimum temperatures show

positive trends (Figure 8). These trends seem to have started already in the late 1990s, which was

also indicated by the significant trends found in the above presented non-parametric trend analysis.

The individual models seem to capture the inter-annual variability of the observations quite well,

whereas the ensemble mean seems again to smooth.


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22

Figure 8. Projections of average annual daily maximum (Tmax) and minimum temperature (Tmin)

for Nicosia from six Regional Climate Models, the average of the six models (ENS) and the

observed values for 1976-2000 (OBS).


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3.2 Effect of Climate Variability on Crop ProductionCrop areas

The trends in the total harvested areas of temporary and permanent crops over time are presented

in Figure 9. The highest crop land utilisation was found during the 1985 season and the lowest in

2008. The area of temporary crops peaked after a sequence of three wet seasons in 2005 (101.9103

ha) and decreased sharply during the 2008 drought year (70.9103

ha). The harvested permanent

crop area decreased by almost 40% from 62.2103

ha in 2008 to 38.4103

ha in 2009, but was at an

all-time low during the 2008 drought season (31.8103

ha).

On average, 50% of the temporary area is used for the production of cereals and 25% for fodder

crops (Figure 10). It is interesting to note that both groups are inversely correlated over time, e.g.

when cereals increased, fodder crops decreased. This fluctuation is attributed to the fact that most

crops included in these groups are rain-fed. During wet years, more area is harvested for cereals andless for fodder crops and vice versa. The remaining crops under the temporary area are mainly

irrigated crops and have steadily decreased over time. Potatoes, for example, decreased from 9.9

103

ha in 1995 to 5.4103

ha in 2009.

Figure 9. Areas with permanent and temporary crops and total crop areas for Cystat dataset (1980-2009)

0

25

50

75

100

125

150

175

1980 1985 1990 1995 2000 2005 2010

Croparea(103h

a)

Permanent crops (Cystat) Temporary crops (Cystat) Total crop area (Cystat)


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Figure 10. Areas of temporary crop groups (1980-2009).

Apart from the olive area, which increased from 5.7 to 12.0103

ha, most permanent crop areas

decreased during the period 1980-2009 (Figure 11). Most notably, grapes decreased from 34.3 to

8.3103

ha and nut trees from 13.3 to 5.3103

ha. This decline of the permanent crop area is likely

due to a combination of factors such as increased costs of labour, insufficient irrigation water supply,

and an overall reduction in profitability.

Figure 11. Areas of permanent crop groups (1980-2009).

0

10

20

30

40

50

60

70

80

1980 1985 1990 1995 2000 2005 2010

Temporarycroparea(103h

a)

Cereals Fodder crops Potatoes

Vegetables & melons Legumes Industrial crops & flowers

0

5

10

15

20

25

30

35

40

1980 1985 1990 1995 2000 2005 2010

Permanentcroparea(103h

a)

Grapes Citrus Fresh fruit Nuts Olives


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25

Crop production

The total annual production of temporary (seasonal) and permanent crops in Cyprus for the period

1980-2008, as reported in the annual agricultural statistics, are presented in Figure 12; for

comparative purposes, the mean annual precipitation is added to the graph. Temporary crop

production fluctuates over time, following a somewhat similar trend as the precipitation, i.e., in dry

years the production decreases and vice versa. Similar to the permanent crop area, the production

of permanent crops decreased by half over the past 29 years. Thus, the statistics do not indicate that

the more efficient farms with higher yields (production per area) remained while marginal farms

with low yields went out production.

Figure 12. Crop production and precipitation during 1980/81-2009/10.

The green-blue water model computations indicated that the irrigated crop areas, which cover 23%

of the crop area of the country, are, on average, responsible for 65% of the total production. During

wet years, the relative contribution of the irrigated crops decreased to 62%, while it was 71% during

dry years. The average annual crop production of all rain-fed and irrigated, seasonal and permanent

crops for the seven wettest, fifteen average and seven driest seasons are presented in Figure 13.

Even though crop areas, management practices, climate events (frosts, heat spells), pests and

diseases vary during the 29 seasons, the irrigated crop production remained nearly steady for all

three periods. Average annual production for irrigated permanent crops ranged between 209 103

ton (average years) and 215103

ton (wet years). For irrigated temporary crops, the production

ranged between 301103

ton (wet years) and 306103

ton (dry and average years). It is possible that

some of the wet years had lower production because they were too wet, but these small differences

could also be due to any of the other reasons mentioned above.

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0

200

400

600

800

1,000

1980 1985 1990 1995 2000 2005 2010

Precipitation(mm)

Croppoduction(103t

ons)

Precipitation Permanent crops Temporary crops Total production


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Figure 13. Average production of rain-fed and irrigated crops for the seven wettest, fifteen average and

seven driest seasons for the period 1980/81-2008/09.

Obviously, the rain-fed crops are the ones that are affected most by the differences in climate. The

average annual production for seasonal, rain-fed crops ranged between 128103

ton (dry years) and

197103

ton (wet years). For permanent, rain-fed crops production output was on average 83103

ton during dry years and 122103

ton during wet years. The lowest absolute production of rain-fed

crops was in 2008, with 14103

ton for seasonal crops and 39103

ton for permanent crops.

Average yields for the rain-fed and irrigated crops are presented in Figure 14. Clearly yields in the

rain-fed systems are much lower than in the irrigated areas. In the rain-fed systems yields are higher

for permanent crops (olives, wine grapes, carob) than for seasonal crops (e.g., barley), while in the

irrigated areas yields are higher for the seasonal crops (e.g., potatoes, tomatoes) than for the

permanent crops (fruit trees). The relatively small differences between the average yields of rain-fed

crops in dry, average and wet years is remarkable. One reason is that during wet years, part of the

precipitation cannot be stored in the soil profile and, thus, not all rain will contribute to the crop

production. A further explanation lies in the fact that, in general, during dry years, the more

marginal lands will not be harvested and reported. Finally, it could also be expected that during dry

years, some farmers may provide their rain-fed crops with supplementary irrigation.

0

100

200

300

400

500

600

Dry Average Wet Dry Average Wet

Cropproduction(1

03t

on/yr)

Temporary, rain-fed Permanent, rain-fed

Temporary, irrigated Permanent, irrigated


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Figure 14. Average yields of rain-fed and irrigated crops for the seven wettest, fifteen average and seven

driest seasons for the period 1980/81-2008/09.

Water use

The computed green water use and blue water demand of both irrigated and rain-fed crops duringthe 1980/81-2008/09 crop seasons is presented in Figure 15. Blue water demand was, on average,

190106

m3/yr for the 29 year period. The highest blue water demand was in 1989/90 with 21910

6

m3

and the lowest in 2008/09 with 150106

m3. However, average blue water demand for the seven

dry years was 193106

m3/yr, while it was 18610

6m

3/yr for the seven wet years. Even though the

water demand was low in the 2008 drought year, when very little water was available from the

Government Irrigation Projects and much fewer seasonal crops were planted, still not all blue water

demands may always be met. On the other hand, some farmers may also apply more water than

needed by the crop. Furthermore, in areas where groundwater has been affected by salt water

intrusion, additional water is used to leach salts from the root zone. Therefore, the computed blue

water demand is generally referred to as blue water use.

On average, 65106

m3

of green water was utilized by 33103

ha irrigated crops and 277106

m3

green water was used by 107103

ha rain-fed crops. For irrigated crops, the highest green water use

occurred in 1988 with 82106

m3

and the lowest in 2008 with 34106

m3. For rain-fed crops, the

highest green water use was in 2003 with 368106 m3 and the lowest in 2008 with 135106 m3. On

average, irrigated crops used 48% of the total annual blue and green water use in Cyprus.

Blue water use of permanent crops was more than double the blue water demand of the seasonal

crops (Figure 16). However, with the reduction of the orchard and vineyard areas over time the

demand of the permanent plantations has also reduced.

0

5

10

15

20

25

30

35


Yie

ld(ton

/ha

)

Temporary, rain-fed Permanent, rain-fed Temporary, irrigated Permanent, irrigated


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Figure 15. Computed blue and green water use of irrigated and rain-fed crops for the period 1981-

2009.

Figure 16. Blue water use of temporary and permanent crops during the 1980/81-2008/09

seasons.

The water productivities (production per unit water) of the rain-fed and irrigated, temporary andpermanent crops are presented in Figure 17. Thus, while rain-fed crops produce much less than

0

100

200

300

400

500

600

700

1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Cropwateruse(Mm

3)

Blue water use (irrigated) Green water use (irrigated) Green water use (rain-fed)

0

50

100

150

200

250

1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Buewateruse(Mm3)

Temporary Permanent Total Irrigation


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29

irrigated crops per unit of land (Figure 14), the difference between rain-fed and irrigated crops

becomes smaller, when considering the production per unit of water (Figure 17). It is also important

to point out that for irrigated crops the denominator of the water productivity equation constitutes

precipitation as well as irrigation water. On the other hand, the denominator for the rain-fed crops is

just precipitation. And while the precipitation fell from the sky for free, the irrigation water was

pumped from aquifers or captured and stored in dams and reservoirs and pumped through irrigation

networks. Thus, rain-fed crops are indeed rather effective users of the scarce water resources of

Cyprus.

The presented numbers are, of course, affected by the crops that make up these groups. The

detailed data on water use per crop, per year, and per community, computed by the green-blue

water model, indicated that there is an enormous scope for climate adaptation in Cyprus.

Figure 17. Water productivity of rainfed and irrigated crops for the seven wettest, fifteen average

and seven driest seasons for the period 1980/81-2008/09.

0.00

1.00

2.00

3.00

4.00

5.00

6.00


Waterpro

ductivity

(kg

/m3

)



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3.3 Effect of Climate Change and Reduced Irrigation Water Supply on CropProduction

The effects of the two future scenarios on crop production are presented in Figure 18 and 19. Total

irrigation water use was 129.1106

m3

for scenario 1 (seven dry years) and 128.0106

m3

for scenario2 (three dry, two average and two wet years), as compared to 189.710

6m

3during the 1980/81-

2008/09 reference period. Scenario 1 included the years 1989/90, 1990/91, 1995/96, 1997/98,

1999/2000, 2005/06, 2007/08, while scenario 2 included 1993/94, 1999/2000, 2002/03, 2003/04,

2005/06, 2007/08, 2008/09. The overwhelming contribution of recent years to scenario 2 is due to

the selection of years with the highest ETo within each aridity group. For both scenarios, the 2010

CAPO crop area dataset was used with all irrigated crop areas set to 75% of their 2010 areas. The use

of historic crop yield data for estimating the production of the selected scenario years ensured,

beside the modeled water stress effect, the inclusion of both abiotic and biotic stresses as well as

management effects.

Average annual crop production, which amounted to 790103

ton/yr during the reference period,

was reduced by 41% in scenario 1 (464103

ton/yr) and by 43% in scenario 2 (449103

ton/yr). The

loss in irrigated production was, on average, 193103


ton/yr

under scenario 2, relative to the 1980/81-2008/09 reference period. The loss in rain-fed production

was 132103


ton/yr under scenario 2. The loss in irrigated

production is mainly due to the reduction in irrigation water supply, whereas the loss in rain-fed

production is both due to climate change and an overall decrease in agricultural land use. During

1980/81-2008/09 the average harvested area of rain-fed crops and trees was 107103

ha, but based

on the CAPO data it was assumed to be 92

10

3

ha during the 2013-2020 future period. As could beconcluded from the nearly identical production reductions for the two scenarios, these computed

reductions could be considered a fairly robust estimate of future crop production in Cyprus. And

while the reduction in irrigation water supply, as a result of water management policies, had more

effect on the national crop production than the change in climate, it is likely that future irrigation

water supplies will be reduced further by climate change.

The reduction in the production of annual crops, relative to the reference period, was slightly higher

under scenario 1 (35%) than under scenario 2 (32%). However, for permanent crops (fruit and nut

trees) the production reduction was 50% under scenario 1 and 60% under scenario 2. The higher

decrease in production under scenario 2, as compared to scenario 1, were partly due to the loweryields of citrus trees during the two wet years that were part of scenario 2.

Under the two future scenarios, the irrigated areas covered 18% of the total crop area but

contributed 70% (scenario 1) and 67% (scenario 2) to the total crop production of the country.

Under the 1980/81-2008/09 reference conditions, as presented previously, the irrigated areas

covered 23% of the crop land and contributed 65% of the total production. These numbers indicate

that, as expected, irrigation becomes even more important under climate change.


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Figure 18. Average annual crop production of rain-fed and irrigated crops for 1980/81-2008/09 and

for 2013/14-2019/20 under scenario 1 (seven dry years and 129106 m3 irrigation water supply).

Figure 19. Average annual crop production of rain-fed and irrigated crops for 1980/81-2008/09 and

for 2013/14-2019/20 under scenario 2 (three dry, two average and two wet years and 128106 m3irrigation water supply).

0

100

200

300

400

500

600

Average 1981-2009 Average 2014-2020 Average 1981-2009 Average 2014-2020

Cropproduction(103ton/yr)


0

100

200

300

400

500

600

Average 1981-2009 Average 2014-2020 Average 1981-2009 Average 2014-2020

Cropproduction(103ton/yr)



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4.ConclusionsAnalysis of the daily climate and precipitation data for the past 30 seasons (1980/81-2009/10)

confirmed the highly variable nature of the climate in Cyprus, both in space and in time. Statistically

significant increasing trends were found for minimum and maximum temperature. Analysis of the

crop areas of the past 29 crop seasons (1980/81-2008/09) in Cyprus showed a 39% reduction in the

harvested areas of vines and fruit trees. The area planted with seasonal crops fluctuated between

dry and wet years, but overall remained fairly stable.

Average national crop production for the seven dry, fifteen average and seven wet years averaged

730103, 79610

3, and 83510

3ton/yr, respectively. This amounts to an average 8% yield reduction

during dry years, and 5% increase during wet years, relative to the 15 average years. Average

irrigation water use for these dry, average and wet years was 193106, 19010

6and 18610

6m

3/yr.

Irrigation has an important effect on reducing the variability in total annual production. Overall, the

irrigated area covered 23% of the land, used 48% of the total green and blue water and was

responsible for 65% of the total annual crop production.

Climate change projections extracted from six RCMs under the medium emission A1B scenario

showed a clear increase in temperature, while trends in precipitation are masked by the highly

variable nature of the precipitation. However, a reduction of 2-8% has been projected for 2026-

2050, relative to 1976-2000 (Hadjinicolaou et al., 2010). Analysis of two possible climate change

scenarios, represented by more dry years, higher evaporative demand, and less irrigation water

supply, which resulted in a reduction of the 2010 irrigated area by 25%, projected a possible

reduction of 41 to 43% in total national crop production for 2013/14-2019/2020, relative to

1980/81-2008/09.

The modeling results indicated that rain-fed crops are very effective users of water. Thus, under

future climate change, it may be wise to allocate some irrigation water to rain-fed crops in the drier

parts of the island, to ensure their yields during drought periods. The modeling analysis also showed

high variability in water use for the different crops, communities and years, indicating that there are

various options for climate change adaptation.


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