economic impacts of climate change on israeli agriculture

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Economic Impacts of Climate Change on Israeli Agriculture

David Haim, ††

Mordechai Shechter‡‡

and Pedro Berliner§§

Abstract

Climate changes, ensued by accumulation of greenhouse gases, are expected to have a

profound influence on agricultural sustainability in Israel, a semi-arid area

characterized by a cold-wet winter and a dry-worm summer. The intention of this

study is to explore economic aspects associated with agricultural production under

projected climate-change scenarios. To this end, we apply the methodology known as

the “production function” approach on two representative crops: wheat, as the major

crop grown in the dry southern region and cotton, stands for the more humid climate

at the north of the country. Adjusting outputs of the global climate model HadCM3 to

the specific research locations, we generated projections for 2070-2100 temperatures

and precipitations for two climate change scenarios. Results for wheat vary among

climate scenarios; net revenues become negative under the severe scenario but,

however, may increase under the moderate one, depending on nitrogen applied to the

crop. Distribution of rain events was found to play a major role in yield production.

On the other hand, under both scenarios there is a considerable decrease in cotton

yield, resulting in significant economic losses. Additional irrigation and nitrogen may

reduce farming losses as opposed to changes in seeding dates.

††Natural Resource & Environmental Research Center, University of Haifa, Mount Carmel, 31905,

Haifa, Israel. Phone: 972-4-6042296, Fax: 972-4-8249971, E-mail:[email protected];

Corresponding author.

‡‡ Natural Resource & Environmental Research Center, University of Haifa, Israel

§§ Jacob Blaustein Institute for Desert Research, Ben Gurion University, Israel.


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1. Introduction

Agricultural productivity in drylands is mainly determined by precipitation and

extremely is vulnerable to changes in precipitation patterns. This vulnerability

increases with a decrease in the total precipitation and is exacerbated in regions in

which the rainfall distribution is unimodal. The Eastern Mediterranean, with its cold

and wet winters and dry and warm summers, is an example of such a region and

Israel, situated in the eastern extreme of the region, is additionally characterized by

the presence of sharp precipitation gradients (north to south and west to east)

Expected climate changes in the region, rainfall in particular, would therefore affect

agricultural productivity and profitability. Yehoshua & Shechter (2003) explored the

effects of water shortage impacts on Israel's agriculture. The study assumed that all

shortage will be observed by agriculture and that agricultural output price levels will

remain constant. Three scenarios were tested; 1.'Naïve', assumes that cutbacks will be

taken in an arbitrary way (a proportionate cutback in water use by each crop group

relative to its present water consumption); 2.Partial adaptation, assuming that cropped

areas will be adjusted according to the crop water requirements and the water-use

efficiency of the different crops (cutbacks in water allocated to crops whose water-use

efficiency is relatively low, based on the marginal value product of water); and

3.Augmenting domestic freshwater water supplies with desalinated water at current

production costs (desalination of 80 million cm to overcome the assumed water

shortage). The results showed total damage of 208, 102 and 126 million U.S dollars

for scenarios 1, 2 and 3, respectively. Kadishi et al. (2005) explored the effects the

changes in annual rainfall patterns would have on the profitability of crop production

in Israel, in both dryland and irrigated crops. They simulated net-profit expectations

under a future projected scenario of precipitation patterns using the production


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function approach and rainfall distributions, which were described with Gamma-

distribution functions. Their results showed a decline in net profits by 2100 relative to

the 1990's. Other studies carried out in the region suggest the same pattern of impacts.

Yates & Strzepek (1998) assessed the integrated impacts of climate change on the

agricultural economy of Egypt in 2060 (with 2XCO2) with and without adaptation.

Using the outputs from three general circulation models (GFDL, UKMO, and GISS

A1) the researchers found a decrease in yields of (-5)-(-51) % for wheat, (-5)-(-27) %

for rice and (-2)-(-21) % for other cereals and fruit. The authors state that yield

reductions could be decreased by up to 50% if proper adaptation measures (changes in

crops, fertilizers, and planting and irrigation patterns) were implemented.

The third report of the IPCC (Intergovernmental Panel for Climate Change, 2001)

predict severe climatic changes in the Mediterranean region, such as: decrease in

precipitation of 3-35%, increase of between 3.2-5.5°C and 3-4°C in summer and

winter temperatures, respectively.

The main objective of this study was to assess the economic impacts in terms of net

incomes in Israel's agricultural sector, given expected changes in climatic variables.

The effect of the latter are evaluated using a combination of agronomic and economic

models that take into account the changes in the major climatic parameters that affect

the crops tested. This study focuses on two representative crops in Israel; wheat which

represents a non-irrigated winter crop in the southern region of Israel and depends

therefore on precipitation amounts and distribution through the growing season and

cotton which represents an irrigated summer crop in the more humid regions in the

north of Israel and would thus be affected mostly by changes in the temperature

regime. Both crops account for approximately 35% of the field crops grown in Israel

(Ministry of Agriculture and Rural Development, 1999).


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2. Scientific Background

2.1 Agronomic Background

2.1.1 Wheat. Among agricultural crops, wheat is one of the most studied crops in

terms of response to climate change. Lawlor & Mitchell (2000) predicted, using

output from many experiments that wheat productivity (biomass and grain yield) will

increase by some 7-11% per 100 µmol mol-1

increase in CO2, without other

environmental changes under well-fertilized and watered conditions. On the other

hand the combined effects of CO2 enrichment, temperature increase and water and

nutrient shortages, on wheat yield as observed on field trials are not consistent.

Hunsaker et al (2000), using the free-air field CO2 enrichment (FACE) experiment in

Arizona, U.S., found that under limited irrigation and adequate nutrient supply, the

stimulatory effects of CO2 enrichment on wheat tend to counteract the effects of water

stress on reducing yield while Schuts & Fangmeier (2001), using open-top chambers,

found that CO2 enrichment only partially compensated wheat yield reduction resulting

from water stress. Amthor (2001) compared more than 150 experiments of effects of

CO2 concentration on wheat yield. He divided the studies into five categories based

on the methods controlling the CO2: laboratory-chamber, greenhouse, closed-top field

chamber, open-top field chamber and FACE system. His results suggest that the large

variation in the effect of CO2 on yield, even with sufficient water and nutrients,

probably reflected interactions between CO2 and other factors. Moreover, the

combination of doubled CO2 and warming of 1.6-4˚C typically reduced yield. In

general, the results suggested that the predictions of the effects of CO2 increase on

wheat yield carry with them intrinsic uncertainty (Amthor, 2001). Therefore, we

included in this study only the two mostly important factors which effect wheat yield;

https://www.researchgate.net/publication/222688738_Effects_of_atmospheric_CO2_concentration_on_wheat_yield_review_of_results_from_experiments_using_various_approaches_to_control_CO2_concentration._Field_Crop_Res?el=1_x_8&enrichId=rgreq-afc67e03-709a-431d-9896-af2a7949e949&enrichSource=Y292ZXJQYWdlOzI2ODMyODYxNDtBUzoxNjYyNzMwODY0MDI1NjBAMTQxNjY1NDAwMTg0MQ==

https://www.researchgate.net/publication/222688738_Effects_of_atmospheric_CO2_concentration_on_wheat_yield_review_of_results_from_experiments_using_various_approaches_to_control_CO2_concentration._Field_Crop_Res?el=1_x_8&enrichId=rgreq-afc67e03-709a-431d-9896-af2a7949e949&enrichSource=Y292ZXJQYWdlOzI2ODMyODYxNDtBUzoxNjYyNzMwODY0MDI1NjBAMTQxNjY1NDAwMTg0MQ==


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water and nitrogen fertilizer and excluded the effect of CO2 enrichment effects on

wheat yield due to the uncertainties mentioned above.

2.1.2 Cotton. Measurements of cotton yield responses to CO2 enrichment from both

free air CO2 enrichment (FACE) technology (Mauney et al, 1994) and chambers

(Reddy et al, 1998; Reddy et al, 1995) show a significant increase in both biomass

and yield. However, results of simulations using one of the most widely used models

(GOSSYM), are not consistent. Reddy et al. (2000) used the outputs from a Regional

climate model as inputs to the GOSSYM model in order to assess changes in cotton

yields in the South-central region of U.S.A. They found an increase of 35 and 13% in

cotton yield for CO2 enrichment only and for CO2 enrichment and associated changes

in other climate variables (namely; max and min temperatures, precipitation, solar

radiation, wind), respectively. On the contrary, Reddy et al. (2001) found a decrease

of 3-37% in cotton yield resulting from expected climate change in cotton-belt

countries. Moreover, inconsistencies in the prediction of cotton yield changes due to

climate change (including CO2 enrichment) were reported by Doherty et al (2003). In

view of this lack of agreement on the expected impact of CO2 increase on cotton yield

we excluded the impacts of the former on the latter in the current study and

concentrated on the impacts of higher temperatures and irrigation regime.

2.2 Climate Change Modeling. The Special Report on Emission Scenarios (SRES)

by the working group III of the Intergovernmental Panel of Climate Change (IPCC,

2001) describes six different scenario groups drawn from a four different story lines

(families). Each story line represents different demographic, social, economic,

technological and environmental developments. In this study we apply climate change

projections from Hadley's center global circulate model, HadCM3, using the


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emissions scenarios reported in the Special Report on Emissions Scenarios (SRES,

2000) by the Intergovernmental Panel on climate Change (IPCC) for two sets of

emission scenarios families; A2 and B2, as the climate change forecast for the period

of 2070-2099 and a control run for the period of 1960-1990. The coarse spatial

resolution of the global model, 2.5° by 3.75° (latitude by longitude), led us to apply

the LARS-WG (Long Ashton Research Station – Weather Generator) weather

generator in order to downscale its outputs to fit the specific research sites. LARS-

WG (ftp.lars.bbsrc.ac.uk) generates synthetic daily weather data using statistic

characteristics of climatic parameters which are calculated from observations in the

specific site and several climatic parameters ratios calculated from HadCM3.

Precipitation is considered as the primary variable, its occurrence is based on

distributions of length of continues sequences of wet and dry days. The other three

variables, namely, maximum and minimum temperature and solar radiation, on a

given day are considered on whether the day is wet or dry. LARS-WG has been

validated across Europe and has been shown to perform well in the simulation of

different weather conditions (Semenov & Barrow, 1997). The adjusted climatic

scenarios appear to describe well the two research locations (t-Test of unpaired

samples (with equal variance) revealed no differences between averages of observed

and control run predictions of rainfall, maximum and minimum temperatures).

2.3 Economic Modeling. Two alternative economic models may be employed to

assess the impact of climate change on agricultural production: The Production

Function approach and the Ricardian approach. The Production function approach

(Adams et al. 1990, 1995, 1999; Iglesias et al, 2000) takes an underlying production

function and varies the relevant environmental input variable to estimate the impact of

these inputs on crop yield. The Ricardian approach (Mendelsohn et al. 1994, 1999;

https://www.researchgate.net/publication/248167289_Agricultural_Impacts_of_Climate_Change_in_Spain_Developing_Tools_for_a_Spatial_Analysis?el=1_x_8&enrichId=rgreq-afc67e03-709a-431d-9896-af2a7949e949&enrichSource=Y292ZXJQYWdlOzI2ODMyODYxNDtBUzoxNjYyNzMwODY0MDI1NjBAMTQxNjY1NDAwMTg0MQ==


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Sanghi et al. 1998), instead of looking at the yields of specific crops, examines how

actual climate in different places affects the net rent or value of farmland. The

production function approach has the advantages of being based directly on scientific

experiments so it can predict phenomena (such as carbon fertilization) that have not

yet occurred in nature. In addition the method explicitly includes climate, crop yield

and market equilibrium. However, the approach is somewhat mechanistic, adaptations

measures that can be implemented by farmers are difficult to model explicitly. Thus,

the production function approach may have an inherent bias in that it tends to

overestimate the damage from climate change by failing to incorporate economic

substitutions by farmers as environmental conditions change. The Ricardian approach,

by relying upon how farmers and ecosystems have actually adjusted to varying local

conditions, incorporates adaptation readily. However, the Ricardian approach does not

provide much information about the process of climate change or about conditions

which are not evident in today's environment, such as carbon fertilization. Each

method has its own strengths and weakness and the two approaches complement each

other (Mendelsohn et al, 1999). We decided to employ the production function

approach for two main reasons; one concerns the relative availability of the

agronomic models in the literature, and the other is associated with the difficulty of

using the Ricardian approach in countries like Israel, in which most of their lands is

state owned and land prices don't necessarily represent the market price of it. Two

major annual crops in Israel were examined: Wheat, which represents a non irrigated

winter crop and cotton, which represents an irrigated summer crop. Each crop has a

specific production function that takes into account major climatic variables that

affect it.


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3. Agronomic models

3.1 Wheat

The production function we use in this research, which was developed for dryland

wheat in South-Africa by Korentajer et al. (1988), and describes the combined effects

of moisture stress and nitrogen fertilizer application on grain yield [Eq.1]:

(1) 2

12210 NSNSNSY ⋅+⋅+++= βαααα

Where, Y, N and S stand for yield [kg ha-1

], nitrogen application [kg ha-1

], and

moisture stress levels, respectively; 1210 ,, ααα , and β are regression coefficients. The

value of S varies from 0 to 1, so that S=0 corresponds to the situation of maximum

stress, and S=1 describes the situation of absence of stress, basis and summarize for

each phonological stage of wheat growing season [Eq 2]. In the computation of stress

index it is assumed that the impact of each phonological stage on yield reduction is

similar and therefore equally weighted.

(2) 25.0

)/( ii

PETETS ∏=

Where the index i (i=1,…,4) refers to the various growth stages, ET and PET are the

sum of actual and potential evapotranspiration for each phonological stage,

respectively. The values of PET ware calculated on a daily basis from Penman's

equation (Doorenbos & Pruitt, 1977). The inputs required to calculate PET are max

and min temperatures, rainfall and mean values of relative humidity, wind speed and

solar radiation. Actual evapotranspiration is computed on a daily basis using a simple

water balance, based on the assumption that the ratio ET/PET (actual to potential

evapotranspiration) is a function of total water content in the soil profile. The above

ratio reaches to maximum at field capacity (FC) and decreases between FC and

https://www.researchgate.net/publication/250102632_Effects_of_Moisture_Stress_on_Nitrogen_Fertilizer_Response_in_Dryland_Wheat?el=1_x_8&enrichId=rgreq-afc67e03-709a-431d-9896-af2a7949e949&enrichSource=Y292ZXJQYWdlOzI2ODMyODYxNDtBUzoxNjYyNzMwODY0MDI1NjBAMTQxNjY1NDAwMTg0MQ==


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wilting point and between FC and saturation, due to decrease in water availability to

the crop and reduction in transpiration due to anoxia, respectively (Proffitt et al,

1985). Water looses and gains are computed on daily basis. The total amount of water

in the soil profile (VW) was updated daily by subtracting the computed

evapotranspiration (ET) and adding daily rainfall (R). The value of VW can't exceed

saturation value (Sat) [Eq 3].

(3) ),( 1 SatRETVWMinimumVW JJJJ +−= −

Where J and J-1 indicate present and previous day, respectively.

We assume that rainfall is absorbed by the profile until it is saturated in its entirety.

Excess rain is considered as lost as run-off or as deep drainage below maximum

rooting depth. ET was computed using the ET/PET ratio which corresponded to the

total amount of water present in the soil profile the previous evening.

3.2 Cotton model

The model we used is Cotton2K. This is a process-level model that simulates the

processes occurring in the soil, plant, the near microenvironment, and the interactions

between these processes and the various inputs. This model, developed by A. Marani

(2000), is a derivative of the GOSSYM model. The main purpose in developing the

new model was to make it more useful for conditions of cotton production under

irrigation in the arid regions of western US. The model has been validated using data

sets from California, Arizona and Israel. A detailed account of the model may be

found in Marani et al, (1992a); Marani et al, (1992b); and Marani et al, (1992c) and in

the simulation manual guide (http://departments.agri.huji.ac.il/fieldcrops/cotton/)


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3.3 Meteorological data

Climatic data for this study was obtained from the Israeli Metrological Service and

from Gilat Experimental Center. The crop data (inputs and yields) was supplied by the

Israeli Bureau of Statistics, the Gilat Experimental Farm (Volcani Center, ARO)

located in the southern part of Israel, farmers in the specific research sites and the

Ministry of Agriculture and Rural Development.

3.4 Changes in predicted yield and net revenues

Yield differences (statistically significant) between control run and each one of the

future scenarios were assessed with a t-Test for unpaired samples (with equal

variance) both in wheat and cotton models. Crop price levels were obtained from the

Israeli Agricultural Research Organization (ARO) reports. An underlying assumption

is that relative (real) price levels for agricultural inputs and outputs remain constant.

3.5 Validation of the agronomic models

Although both of the proposed models preformed well in semi-arid and

Mediterranean regions (Korentajer & Berliner, 1988; Korentajer et al, 1989; Berliner

& Dijkhuis, Unpublished; Marani et al, 1993) we had to validate them to the specific

research sites.

3.5.1 Wheat model

As a first step we estimated the production function coefficients of [Eq 1] using

output from an experiment carried out at the Gilat Experimental Center, in the Negev

region, during the winter of 1971-72 (Shimshi and Kafkafi. (1978)).The model [Eq 4]

was found statistically significant (α=0.05) [AdjR2=0.9278, F=851.7, P<0.001].

(4) 294.0512.133.4764.104 NSNSNSY ⋅−⋅+−+=


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To validate the proposed production function [Eq 4] we used data from an experiment

that carried out at the same place during the years of 1996-2003 (Amir et al. (1991).

The experiment included four plots of "Nirit" spring wheat (Zeraim Seed Co., Israel)

sown at the beginning of January and harvested at the beginning of May. Four

applications of nitrogen fertilization were tested: 0, 50, 100 and 150 kg ha-1

(applied

prior to planting). The long term yearly rainfall average is 237 mm. 1999 was

declared as a drought year with only 72 mm of rainfall. No yields were harvested for

all plots in all treatments. The years of 1997 and 2000 were considered as dry years

and precipitation for the other five years was close to the long term yearly average.

We found a strong and significant correlation between expected and observed yield

[Eq 5] [R2=0.57, F=157.6, P<0.001].

(5) (exp))( 45.05.48 YY obs +−=

3.5.1 Cotton

The validation of the Cotton2K simulation model carried out at two sites in Yizrael

Valley. The input for the simulation included sowing, defoliation & harvest dates,

amounts and dates of water and nitrogen fertilizers application to the crop, soil

characteristics and daily climatic parameters (precipitation, maximum and minimum

temperature and global radiation) for the growing seasons in the years of 2001-2003.

T-Test for unpaired samples (with equal variance) revealed no difference between

averages of observed and expected lint yield [T(7)=1.894, P>0.05].

4. Analyzing economic impacts

4.1 Wheat

Average annual precipitation amount was found out to be not statistically different

between control run (220mm) and observation (237mm) in the research area. This


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value declines in both climatic scenarios to 120 & 193mm in A2 & B2 respectively.

The threshold of wheat production is 110 mm of water applied to the crop during

growing season (Turner, 1997). According to A2 scenario in 11 out of 29 predicted

years the amount of rainfall is below that threshold. Therefore, continues of wheat

production in that region is conditioned to additional irrigation of the crop. The

average yield amount in control run is 312.5 Kg/Dunam, very close to yield averages

in commercial fields in the Negev region. The results show a significant difference

(P<0.001) between present and future yield averages in both scenarios tested. The two

climatic scenarios revealed different tendencies in both yield and net revenue changes

(Figure 1). Additional nitrogen fertilizer minimizes the farmer losses up to an

additional gain (in the highest nitrogen application level) in B2 scenario. While,

higher presence of nitrogen causes more damage in A2 scenario. Under that scenario,

continues of wheat growing in the Negev region is not worthwhile, economically

speaking, since farmer's net revenue turns negative.

//Figure 1//

4.2 Cotton

Monthly increase of average temperature during growing season (April-October) is

around 5.3 & 3.6°C in A2 & B2 scenarios, respectively. Average yield amount

according to control run is 208 Kg of lint/dunam. Water and nitrogen application to

receive this amount of lint is 364 mm and 12 Kg, respectively, optimally applied

during the growing season by the simulation. The results (Table 1) show a significant

difference (P<0.001) between present and future yield averages in both scenarios

tested. The two climatic scenarios predict a considerable decrease in yield production

which leads to negative net revenues. For that reason, continues growing of cotton in

Yizrael Valley is not worthwhile without adaptation measures and/or subsidy.


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//Table 1//

5. Adaptation measures

5.1 Wheat

Both scenarios tested do not predict a shift in winter season, relatively to current state.

Further more, both scenarios predict significant decreases in precipitation at the first

period of it. In that case, there isn't enough water for the spore to sprout or, even

worse, sprout drying up just after germination. Therefore, earlier sowing is not a

successful adaptation measurement under the above climatic conditions. On the

contrary, adaptation by irrigation water turned out to be beneficial under B2 scenario

but not in A2 development (Figure 3). In B2 scenario, implementation of 60 mm of

water to the crop leads to yield increase in all levels of nitrogen application, relatively

to current state (Figure 2).

//Figure 2//

//Figure 3//

5.2 Cotton

We rerun Cotton2K simulation to examine two weeks earlier sowing (mid of March

instead of the beginning of April). The results suggest a smaller yield decrease rates

than without early sowing in both scenarios. However, net revenues of the farmer in

both scenarios remain negative (Figure 4) which means that early sowing as the only

adaptive measurement is not a good adaptation strategy in that region.

//Figure 4//

Adaptation by additional irrigation turned out to be beneficial in both scenarios.

Additional irrigation of 80 and 100 Cu.m brings the net revenues and the yield back to

current state in B2 and A2 scenarios, respectively (Figure 4).


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6. Discussion

Agriculture is one of the most vulnerable sectors to be affected by global climate

changes, mostly through the increase in average temperature, decrease in precipitation

amounts and changes in its distribution and the fertilization affect of CO2 in the

atmosphere. Agriculture in Israel is especially vulnerable to changes in climatic

parameters due to; closeness to the aridity line, relatively small cultivated area and the

steep gradient from north to south and from west to east of the country. The results of

this study varies among the two crop tested. Changes in wheat yields, and therefore in

net revenues, varies between the two climatic scenarios. Moreover, there is a different

trend in yield changes, among the scenarios, according to the amount of nitrogen

applied to the crop. During a relatively rainy season, a higher amount of nitrogen

fertilization is beneficial for the crop (smaller decreases in yield and even slight

increase) while in a dry year the opposite is true. Furthermore, the farmer usually

gives the nitrogen fertilization during the sowing, before he has any knowledge of the

precipitation amounts in the following season. Hence, the issue of weather prediction

becomes crucial for the farmer. The moderate increase in wheat yield in the B2

scenario, in spite of 14% decrease in average precipitation amounts per season, can be

explained by an increase of 17 and 10% in precipitation amounts in Jan and Mar,

respectively, in that scenario. Hence, we may conclude that the precipitation

distribution in the growing season as a considerable effect on the predicted yield in

the moderate scenario.

In addition, these results suggest that the farmer can compensate water looses by

nitrogen fertilization, as long as he faces a moderate climatic change. On the contrary,

the results for cotton yield suggest a considerable decrease in both scenarios

examined. This is suitable with Reddy's et al. (2000) findings of a 6-10% decrease in


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cotton yield under an increase of 1˚C during the growing season and to the results of

Rosenzweig & Tubiello (1996) which indicated a consistent yield decreases due to

daily temperature increase, without the fertilization effect of CO2.

The above yield decreases leads to a dramatic reduce in net revenues of the farmers.

Net revenues turn negative both from wheat production under the severe scenario and

cotton production under both scenarios. Therefore, farmers will have to take

adaptation measures in order to maintain growing of field crops in these regions,

under the examined climatic scenarios. Changes in sowing dates didn't appear to be

beneficial adaptive measurement, economically speaking, in both crop tested. For

cotton yield it did cause a decrease in the absolute yield changes but the net revenues

of the farmer remains negative. This is in consistent with the results of Reddy et al.

(2001) for the cotton belt in U.S.A. Adaptation by adding irrigation water found out to

be economically worthy except for the wheat production under the severe scenario.

Adding water to the crop minimizes net revenue losses and in some cases even

increases it, respectively to current state. This means that the benefit from the extra

irrigation exceeds its cost. Reddy et al. (2001) concluded similar results.

These results have crucial implications regards to determination of national policy to

climate change and the rule of agricultural production in it. Our results suggest a

considerable increase of 25% in water consumption of cotton. In addition, there is a

good possibility that rainfed crops, like wheat in the southern part of Israel, will

become irrigated in the future. On the contrary, water supply in the region is supposed

to decrease significantly due to less precipitation and an increase in population

growth.

The effect of CO2 fertilization on yield is excluded from this study due to the

uncertainty of it when combined with other environmental parameters. Rosenzweig &


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Tubiello (1996) found inconsistency in wheat yield changes under doubling of CO2

and an increase in daily average temperature. Other studies showed that this affect can

cause a considerable yield increases (Hunsaker et al, 2000; Reyenga et al, 2001;

Reddy et al, 1995), however, the interaction between this affect and changes in other

climatic parameters is still not completely well understood; Hunsaker et al. (2000)

found that CO2 fertilization effect tends to eliminate the effect of water stress on yield

decrease. On the contrary, Schuts & Fangmeier (2001) concluded that this effect only

partially reduces the yield decreases from water stress.

The current study didn't include the effect of technological and breeding

improvements and agro technical changes (such as no tillage and rotation systems – 1

crop in 2 years) on the yield of the crop tested. However, we can extract the desirable

water-use efficiency value for breeder developers in each one of the climatic scenarios

(Table 2). For example, according to B2 scenario the water use efficiency value of

wheat species should be 16.2 Kg /ha/mm (instead of 13.2 today) in order to keep

current yield level. Excluding this element from the current study may lead to

overestimation of the predicted damage to the agricultural sector as consequences of

climate change.

//Table 2//

Following studies can include further agricultural branches (such as orchards or

flowers) or other climatic regions in Israel (like Upper Galilee-Golan and central

region) in order to get an accurate picture of climate change impacts on the Israeli

agricultural sector. In addition, adaptation strategies in the macro scale could be

evaluated. Such adaptations include technological developments, insurance programs

which are the responsibility of the government and the private industry.


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7. Acknowledgments

This research is part of the GLOWA - Jordan River Project funded by the

German Ministry of Science and Education (BMBF), in collaboration with the

Israeli Ministry of Science and Technology (MOST). Gratitude goes to Dr. Bonfil for

his professional assistance and for providing experimental data for this research.

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1. Adams, R.M., Rosenzweig, C., Peart, R.M., Ritchie, J.T., McCarl, B.A., Glyer,

D.J., Curry, B.R., Jones, J.W., Boote, K.J. and Allen, J.H. Jr.: 1990, “Global

Climate Change and U.S. Agriculture”, Nature. 345(6272), 219-224.

2. Adams, R.M., Fleming, R.A., Chang, C., McCarl, B. and Rosenzwieg, C.: 1995,

"A Reassessment of the Economic Effects of Global Climate Cchange on U.S.

Agriculture", Climatic Change. 30, 147-167.

3. Adams, R.M., McCarl, B.A., Segerson, K., Rosenzwieg, C., Bryant, K.J., Dixon,

B.L., Conner, R., Evenson, R.E. and Ojima. D.: 1999,”Economic Effects of

Climate Changes on U.S. Agriculture”, In Mendelson. R. and Neuman. J.E. (eds.),

The Impact of Climate Change on the United States Economy, Cambridge

University Press. Cambridge, pp. 18-55.

4. Amir, J., Krikun, J., Orion, D., Putter, J. and Klitman, S.: 1991, "Wheat

Production in an Arid Environment. 1. Water Use Efficiency, as Affected by

Management practices", Field Crops Research. 27(4), 351-364.

5. Amthor, J.S.: 2001, "Effects of Atmospheric CO2 Concentration on Wheat Yield:

Review of Results from Experiments Using Various Approaches to Control CO2

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Figure 1

-300

-250

-200

-150

-100

-50

0

50

0 5 10 15

Nitrogen Application (Kg/Dunam)

Ch

an

ge (

%)

Yield_A2 Yield_B2 Net Revenue_A2 Net Revenue_B2

Figure 1: Yield & net revenue changes (%) in wheat production according to A2 & B2 scenarios.

Figure 2

-60-50-40-30-20-10

01020Y

ield

Ch

an

ge

(%)

0 5 10 15 0 5 10 15

A2 B2

No Adaptation Early Seeding 60 MK/Dunam

Figure 2: Wheat yield changes (%) w/o adaptation according to A2 & B2 scenarios.


23

Figure 3

-300

-250

-200

-150

-100

-50

0

50NR

Ch

an

ge

s (%

)

0 5 10 15 0 5 10 15

A2 B2

No Adaptation Early Seeding 60 MK/Dunam

Figure 3: Net revenues changes (%) from wheat production w/o adaptation according to A2 & B2

scenarios.

Figure 4

-250

-200-150-100

-50

050

100150

Ch

an

ge

(%)

No

Adap

Early

Seed

60 mm 80 mm 100

mm

Yield A2 Yield B2 NR A2 NR B2

Figure 4: Yield & Net Revenues changes (%) in cotton production according to A2 & B2

scenarios.

Table 1


24

Scenario Yield Net revenue

A2 -52 -240

B2 -38 -173

Table 1: Yield & net revenue changes (%) in cotton production according to A2 & B2 scenario

Table 2

Crop Current value A2 scenario B2 scenario

Wheat 13.2 26 16.2

Cotton 5.7 8.5 6.8

Table 2: Predicted water use efficiency (Kg/ha/mm) in order to keep current yield

economic impacts of climate change on israeli agriculture

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