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Journal of Business, Economics and Finance -JBEF (2017), Vol.6(4), p.336-347 Dumrul, Kilicarslan
_________________________________________________________________________________________________
DOI: 10.17261/Pressacademia.2017.766 336
ECONOMIC IMPACTS OF CLIMATE CHANGE ON AGRICULTURE: EMPIRICAL EVIDENCE FROM ARDL
APPROACH FOR TURKEY
DOI: 10.17261/Pressacademia.2017.766
JBEF- V.6-ISS.4-2017(5)-p.336-347
Yasemin Dumrul1
,
Zerrin Kilicarslan2
Erciyes University, Develi Hüseyin Şahin Vocational School, Kayseri, Turkey. ydumrul@erciyes.edu.tr
Erciyes University, , Kayseri Vocational School, Kayseri, Turkey. zkaan@erciyes@edu.tr
To cite this document
Dumrul, Y., Kilicarslan, Z., (2017). Economic impacts of climate change on agriculture: empirical evidence from ARDL approach for Turkey.
Journal of Business, Economics and Finance (JBEF), V.6, Iss.4, p.336-347.
Permemant link to this document: http://doi.org/10.17261/Pressacademia.2017.766
Copyright: Published by PressAcademia and limited licenced re-use rights only.
ABSTRACT
Purpose- The agricultural sector is one of the sectors most sensitive to climate change. This sector is directly affected by temperature and
precipitation, which is an input in agricultural production. The main objective of this study is to evaluate the effects of climate change in
agricultural production in Turkey.
Methodology- The data cover the period 1961-2013. In this study, economic effects of climate change on agriculture were analyzed for
Turkey using a time series approach.
Findings- The increase in precipitation affects agricultural GDP positively, while the increase in temperature has a negative effect on
agricultural GDP.
Conclusion- In order to minimize the adverse effects of climate change in Turkey, which is one of the largest countries in the world in terms
of agricultural land, it is important to establish policies, strategies, plans and programs to combat climate change.
Keywords: Climate change, agriculture, economic impact, ARDL, Turkey
JEL Codes: Q54,, Q51, C22
1. INTRODUCTION
One of the most defining aspects of this century is climate change. Climate change can lead to the emergence of various
socio-economic problems such as poverty, economic growth and unsustainability of development, health and safety (Swart
et al., 2003). Climate change will have direct impacts on the size of state budgets (flood, conservation of forest areas,
control of pollution), terms of trade (change in agricultural yield and labor productivity), economic growth rates (change in
agricultural yield and labor productivity, depletion of natural resources) and social welfare (price increases, flood, pollution
and health effects) (Cuervo and Gandhi, 1998). United Nations Framework Convention on Climate Change (UNFCCC) defines
climate change as “a change of climate which is attributed directly or indirectly to human activity that alters the
composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time
periods (UN, 1992). Intergovernmental Panel on Climate IPCC (2007) defines climate change as “a change in the state of the
climate that can be identified by changes in the mean and/or variability of its properties that persists for an extended
period, typically decades or longer”. Thus, climate change refers to any change in climate over time, whether due to natural
variability or as a result of human activity. The effects of climate change arise as a result of the increase in CO2 and the rise
in temperature due to this increase and precipitation regime changes. While these effects are harmful to one region, they
can be positive in another region. For example, increases in temperature can have both positive and negative effects on
product yield (Adams et. al., 1998). Meadow and grassy areas may increase in cold regions due to warming and
temperature increase can contribute to the development of livestock in these regions (Demir and Cevger, 2007). However,
in general, it is suggested that the increase in temperature reduces the yield and quality of many crops. Increases in
precipitation (i.e. level, timing and variability) can also benefit soil semi-arid areas and other water resources by increasing
soil moisture. But while increases in precipitation may worsen problems in regions with excessive amounts of water, a
Year: 2017 Volume: 6 Issue: 4
Journal of Business, Economics and Finance (JBEF), ISSN: 2146 – 7943, http://www.pressacademia.org/journals/jbef
Journal of Business, Economics and Finance -JBEF (2017), Vol.6(4), p.336-347 Dumrul, Kilicarslan
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DOI: 10.17261/Pressacademia.2017.766 337
reduction in the amount of precipitation may have an adverse effect (Adams et. al, 1998). In other words, extremes in
climate elements lead to serious economic losses by increasing the frequency and severity of climate-induced natural
disasters such as drought, floods and storms (Başoğlu and Telatar, 2013).
Since temperature and precipitation are a direct input into agricultural production, it is thought that the greatest impact
will be on the agricultural sector (Deschenes and Greenstone, 2007; Barnwall and Kotani, 2013). Because the agricultural
sector among all sectors is the most sensitive and most vulnerable to climate change (Deressa et al., 2005). The agricultural
sector is a sector that creates employment, provides food security, supplies raw materials to the industry sector and
provides foreign exchange input to the country in foreign trade. Surveys show that the agriculture sector has a slow growth
rate over the years due to climate change. It can be said that this situation is more worrisome when the urbanization
phenomenon and population growth rate are considered (Amponsah et al., 2015). Agricultural products and livestock are
directly affected by changes in the climate factor, such as temperature, precipitation, the severity and frequency of
extraordinary events, the increase CO2 concentration in the atmosphere, climate variability and the increase in sea level
(Adams et. al., 1998). Depending on the physical characteristics of the region and the crops produced, both the positive and
negative effects of climate change on agriculture can emerge (Mishra and Sahu, 2014). The changes in agricultural
production are due to changes in crop yield and changes in crops (size of land, area of land). Changes in yields of crops are a
result of climate change and the intervention of producers in such a way as to increase agricultural productivity. “These
mitigating interventions of producers can be in the form of increasing fertilizer or water use or adopting a new crop species
(Adams et. al., 1998). The rapid increase in world population will increase demand for food and fuel. For this reason, an
increase in agricultural production will be needed. However, climate change puts pressure on agriculture, threatening
future food production and supply, makes adaptation measures and resilience very expensive (Maharjan and Joshi, 2012).
In addition, climate change will change the prices of agricultural commodities, the reallocation of resources in the
agricultural sector, the structure of many country economies and international trade patterns (Gbetibouo and Hassan,
2005). However, it also changes the comparative advantage of the country. In addition, changes in the amount of
agricultural production may also have adverse effects on inflation, unemployment, current account deficit and budget. The
decrease in agricultural production will have a negative effect on inflation by increasing agricultural product prices. The
decrease in the number of employees in the agricultural sector will have a negative effect on unemployment. The supply
deficit in agricultural products covered by imports has a negative effect on the current account deficit. Compensating for
some or all of the producer damages due to climate change by governments will affect the budget negatively (Bayraç and
Doğan, 2016). However, climate change has more harmful effects on agriculture. Besides, in different regions, the grade of
the specified effect is different (Maharjan and Joshi, 2012). The main objective of this study is to evaluate the effects of
climate change in agricultural production in Turkey. The rest of the paper is organized as follows. Section Literature Review
presents the “empirical background” Section “data and methodology” describes data sources and presents our empirical
strategy. Section “findings and discussions” describes the emprical results. Section “conclusion” concludes.
2.LITERATURE REVIEW
Different approaches have been adopted in different studies on economic effects of climate change in agriculture. These
are mentioned below.
i. The approach of functioning (also known as crop modeling or agronomic-economic approach)
ii. Ricardian Approach
iii. Advanced Ricardian (Panel Data) Approach
iv. The time series approach
Two of the most commonly used approaches to these approaches are “Production function approach”(known as product
modeling and agricultural models) and “Ricardian Approach” (Guiteras, 2005; Sarker et al.,2014; Barnwall and Kotani,
2013).
2.1.Production Function Approach
The production function approach is based on empirical or empirical analysis of the relationship between climate variables
(environmental factors) and yield (Deressa et al. 2005).This approach, which is used to predict the impact of climate
change, is based on empirical or experimental production functions to estimate environmental damage.This approach, also
known as product modeling, deals with a basic production function and estimates the effects of climate change by changing
one or more input variables such as temperature, precipitation and carbon dioxide levels (Mendelsohn et.al., 1994). This
approach based on controlled experiments simulates (several transient climate change scenarios) climate factors and
product yields in a laboratory-type environment. This approach does not take into account farmers’ attitudes towards
adaptation, although climate change is a useful basis for predicting the impact on farming (Mishra and Sahu, 2014). In other
Journal of Business, Economics and Finance -JBEF (2017), Vol.6(4), p.336-347 Dumrul, Kilicarslan
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DOI: 10.17261/Pressacademia.2017.766 338
words, in response to climate change, farmer adaptations such as farmers changing fertilizers, differentiating the
composition of crops, or using agricultural land for another activity (such as a housing complex) are totally ignored in the
approach of production function (Deschenes and Greenstone, 2007). This leads to an overestimate of the negative effects
and an underestimation of the positive effects (Sarker et al., 2014; Mendelsohn et.al., 1994). In other words, there is a
tendency to predict too much prejudice and damage in studies based on this approach.This prejudice is sometimes called to
as a “dumb farmer scenario” to express that farmers neglect the various adaptations that they give in response to changing
economic and environmental conditions (Mendelsohn et.al., 1994). Table 1 presents the literature summary of the
production function approach.
Table 1: Studies on The Impact of Climate Change on Agriculture: Production Function Approach
Author
Period/Country
Variables
Results
Aggarwal et. al.
(2010)
11 districts of the
Upper Ganga
Basin, India
1969-1990
Dependent Variable: Growth
and yield of rice and wheat crops
Independent Variable: Solar
radiation, temperatures, rainfall,
wind speed and vapour
pressure.
In the simulation analysis using infoCropWheat and
InfoCrop-Rice models found that rice and wheat crops
will be affected by climate change.
Mathauda et al.
(2000)
Punjab, India
1970-1990
Dependent Variable: Rice yield
Independent Variable:
Temperature change (extreme
warm, greater warm, moderate
warm, slight warm and normal
weather)
In the study, the effect of temperature on rice yield was
analyzed on 5 different weather scenarios. CERES RICE
simulation model was used in the study. The results of
the research show that the increase in temperature
reduces the rice yield in five scenarios. As the
temperature increases, the decrease in rice yield also
increases.
Southworth et
al. (2000)
Midwestern Great
Lakes Region
1987-1990
Dependent Variable: Maize
yields
Independent Variable:
Temperatures, rainfall
CERES maize model was created for the period 2050-
2059. It was found that high temperatures during the
tasseling of maize lead to significant decreases in
productivity.
Olesen et al.
(2000)
Denmark
1971-1997
Dependent Variable: Winter
wheat
Independent Variable: Carbon
dioxide emission (CO2) ,
temperatures, Rainfall,
Evapotranspiration
The CLIMCROP (crop simulation model) simulation
model was used in the study, assuming that water does
not limit growth. High temperatures reduce crop
duration of certain species. For wheat, a temperature
increase of 1°C during grain filling is estimated to reduce
the length of this phase by 5%.
Alexandrov and
Hoogenboom
(2000)
Bulgaria
1961–1990
Dependent Variable: Maize yield
and winter wheat grain yield
Independent Variable: Rainfall,
Temperature and solar radiation
At the current CO2 level, the transitory GCM scenarios
predicted a decrease in maize and winter wheat yields,
especially in the 2020s, 2050s and 2080s.
Lal, M. et al.
(1999)
Madhya Pradesh,
India
- Raipur (1971±97)
- Gwalior (1965±88)
- Indore (1985±95)
- Jabalpur (1969±97)
Dependent Variable: Soybean
Independent Variable: CO2
Based on simulations carried out the doubled CO2 level,
the effects of future climate change on soybean yields in
Central India were examined using the CROPGRO model.
Results suggest higher yields (50% increase) for soybean
crop for a doubling of CO2.
Kaiser et. al.
(1993)
Southern
Minnesota
1980-2070
Dependent Variable: Crop yields,
crop mix, and farm revenue
Independent Variable:
Temperature
A farm-level analysis was conducted to examine the
effects of climate change on farm operations and
profitability using the Monte Carlo simulation. Climate
warming scenarios used. The results indicate that grain
farmers in the southern region of Minnesota can
effectively adapt to the gradually.
2.2. Ricardian (Hedonic) Approach
It is an empirical approach based on cross-sectional data used to examine the sensitivity of agricultural production to
climate change.This approach is referred to as the “Ricardian Approach” in Ricardo (1817), under the conditions of perfect
competition, because of the work that promotes the net efficiency of the agricultural land of the land rent.This method is
described by Mendelsohn et al. in 1994 (Gbetibouo and Hassan, 2005; Deressa et al., 2005).This approach, also called the
hedonic approach, assesses the performance of the farms in climate regions.Land value or rent is considered a function of
climate, demographic, economic and physical conditions (Gumel et. al., 2016). In principle, using economic data on the
value of the land, it is a technique that can correct the bias in the approach of the production function (Salvo et al.,2013;
Journal of Business, Economics and Finance -JBEF (2017), Vol.6(4), p.336-347 Dumrul, Kilicarslan
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DOI: 10.17261/Pressacademia.2017.766 339
Chen et.al., 2013). The Ricardian approach is a remarkable tool for assessing the overall impact of climate change on a
specific geographical area.It has been applied to many different geographical areas in both developed and developing
countries (Salvo et al.,2013).Instead of examining the yield of certain crops, this approach examines how climate in
different regions affects the net rent or value of agricultural land.By directly measuring farm prices or incomes, the direct
effects of climate on the yields of different crops are explained.The Ricardian approach allows measuring the economic
value of different activities if markets work properly.Hence, the economic effects implied by the production function
approach ensures that it can be verified as to whether or not they are reoccurring (Mendelsohn et.al., 1994).Although not
explicitly mentioned, both short and long-term adaptations are included in Ricardian models.In other words, farmer
adaptations are taken into consideration in order to mitigate the adverse economic effects of climate change (Olesen and
Bindi, 2002). This approach is a cross-sectional method that measures the long-term effect of climate on agriculture by
regressing over a series of variables such as land value or net income per hectare.This approach has three advantages: “It is
relatively easy to guess, gives precise values geographically, catches adaptation” (Salvo et al., 2013). The Ricardian model
measures the impact of climate factors through their contribution to the prices of agricultural land. Nevertheless, a
Ricardian type model does not take into account time-independent, location-specific factors such as unobserved farming
skills and soil quality (Barnwal and Kotani, 2013). In addition, the Ricardian approach does not account for the effect of
unchanging variables on the region (such as carbon dioxide concentration, the effects of annual changes in the weather,
changes in climate change or extreme events, and future climates) (Salvo et al., 2013). Table 2 presents the literature
summary of the Ricardian Approach.
Table 2: Studies on The Impact of Climate Change on Agriculture: Ricardian (Hedonic) Approach
Author
Country/Period
Variables
Results
Mishra and Sahu
(2014)
Odisha (for all the nine
coastal districts)
1979-2009
Dependent Variable: Farm level net-
revenue.
Independent Variable: Rainfall,
Temperature
The study concluded that the July rainfall was useful
for the farm activity in Odisha. The study also
concluded that the increase in temperature for all
seasons had adverse effects on the agricultural
sector of coastal Odisha.
Salvo et al (2013)
Italian Alpin Region
2003–2007
Dependent Variable: Average net
revenue
Independent Variable: Average
temperature, average monthly rainfall
In contrast to the general beneficial effects of
climate change in the vast areas of Europe
(Germany and the UK), climate change has led to a
decline in average annual net income in the Alpine
region.
Deressa and
Hassan (2009)
Ethiopia
2050 and 2100
Dependent Variable: Net crop revenue
Independent Variable: Rainfall and
temperature, household, and soil
variables
Special Report on Emission Scenarios (SRES) the
climate variable such as temperature and
precipitation affected slightly net crop income. In
addition, it has also been observed that small
changes in temperature during the summer and
winter period negatively affect net crop revenue.
Kabubo-Mariara
and Karanja (2006)
Kenya
1988-2003
Dependent Variable: Net crop revenue
Independent Variable: Rainfall and
temperature
Global warming has an important influence on net
crop revenue in Kenya. However, the result is that
temperature is much more important than rainfall.
Deressa et al
(2005)
South Africa
(11 regions)
1977-1998
Dependent Variable: Sugar cane
production
Independent Variable: Rainfall,
temperature height and latitude
The study concluded that sugar cane production is
highly sensitive to climate change.
Gbetibouo and
Hassan (2005)
South Africa
(300 districts)
1970-2000
Dependent Variable:Net revenue per
hectare
Independent Variable: Rainfall,
temperature, soil types, labour,
population, irrigated land and
geographical coordinates
The results show that the production of field crops
is sensitive to marginal changes in temperature
compared to variations in rainfall. The increase in
temperature affects the net income positively, while
the effect of the decrease in rainfall is negative.
Mendelsohn and
Dinar (2003)
USA
1997
Dependent Variable: Farmland value
Independent Variable: Rainfall and
temperature
The paper shows that the value of irrigated cropland
is not sensitive to precipitation and increases in
value with temperature.
Chang (2002)
Taiwan
1977-1996
Dependent Variable: 60 crops
Independent Variable: Rainfall and
temperature
The study was concluded that climate change has a
significant effect on crop yield.
Journal of Business, Economics and Finance -JBEF (2017), Vol.6(4), p.336-347 Dumrul, Kilicarslan
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DOI: 10.17261/Pressacademia.2017.766 340
2.3. Advanced Ricardian (Panel Data) Approach
In recent studies, a panel data approach is used to estimate the impact of rainfall and temperature change on agricultural
production. This approach takes into account the fluctuations that occur randomly year-to-year in the weather conditions
(Deschenes and Greenstone, 2007).The panel data approach assesses the impact of climate change on average yield and
yield variability. There are two types of panel data approach found in the literature. These are fixed effect method and
random effect method (Gumel et. al., 2016; Guiteras, 2007). The panel data approach, which considers fixed effects, has the
advantage of controlling factors that are time invariant and unobservable (such as farmer quality or unobservable soil
quality) at the regional level. Moreover, contrary to the approach of production function, the data about the real field
results are used rather than the results in the laboratory environment. This means that the estimates obtained from the
panel data will reflect the farmers’ regulations within the year (such as changes in inputs or sowing techniques) (Guiteras,
2007). The random effect model assumes that there is no correlation between unobserved and timely independent
variables and independent variables. If this assumption is neglected, the fixed effect model will provide a more unbiased
assessment. For this reason, the fixed effect model gives a better estimate (Gumel et. al., 2016). Table 3 presents the
literature summary of the Panel Data Approach.
Table 3: Studies on The Impact of Climate Change on Agriculture: Panel Data Approach
Author
Country/Period
Variables
Results
Loum and
Fogarassy
(2015)
Gambia
1960-2013
Dependent Variable: Cereals
production (Maize and Millet)
Independent Variable: Rainfall,
temperature, CO2, fertilizer and
area planted
A marginal increase or decrease in both rainfall and
temperatures may negatively affect cereals productivity. Carbon
dioxide has positive effects on crop yield.
Sarker et al
(2014)
Bangladesh
1972-2009
Dependent Variable: Various
types of rice (Aus, Aman ve Boro)
Independent Variable: Rainfall,
average maximum and minimum
temperature.
Average maximum temperature: Aus and Aman rice are a risk-
augmenting factor and Boro rice is a risk-reducing factor.
Average minimum temperature: Boro rice is a risk-augmenting
factor and Aus and Aman rice are a risk-reducing factor. Rainfall:
Aman rice is a risk-augmenting factor and Boro and Aus rice are
a risk-reducing factor.
Dasgupta
(2013)
66 countries
1971-2002
Dependent Variable: Maize and
rice yields
Independent Variable: Rainfall,
temperature.
Climate change affects the amount of maize and rice production
negatively. The increase in the variability of the climate variables
has a greater negative effect on the countries with lower
productivity for rice.
Barnwal and
Kotani (2013)
India
1971-2004
Dependent Variable: Kharif and
Rabi rice yields
Independent Variable: Rainfall,
temperature.
The monsoonic crop (Kharif) is more sensitive to temperature
and precipitation, while the winter crop (Rabi) is quite resistant
to changes in climate variability.
Dell et al.
(2012)
125 Countries
1950-2003
Dependent Variable: GDP
Independent Variable: Average
temperature and rainfall
The increase in temperature is greatly reducing the economic
growth in poor countries and reducing growth rates. The
increase in temperature leads to a decrease in agricultural
production, industrial production and political stability.
Akram (2012)
8 Asian
Countries
1972-2009
Dependent Variable: GDP,
Growth rate, added value of
agriculture, industry and service
sectors. Independent Variable:
Rainfall, temperature, population
and urbanization
The effect of temperature and rainfall increase on GDP is
negative. These effects are higher in the agricultural sector
compared to the manufacturing and service sectors.
Lobell et al.
(2011)
USA
1980-2008
Dependent Variable: Four crops
(maize,wheat,rice, and soybeans)
Independent Variable: Rainfall
and temperature
Climate change shows that maize and wheat production
decreased by 3.8% and 5.5%, respectively.
Brown et al.
(2010)
133 Countries
1961-2003
Dependent Variable: GDP growth,
added value of agricultural and
industrial GDP, poverty
headcount ratio
Independent Variable: Rainfall
and temperature
The increase in the amount of rainfall affects the share of the
agricultural sector in GDP positively, while the increase in
temperature affects the negative direction.
Guiteras
(2007)
India
1961-1999
(200 districts)
Dependent Variable: Agricultural
outcome
Independent Variable: Rainfall,
temperature, urbanization, soil
quality.
During the 2010-2039 period, crop yields are reduced by 4.5-9%
due to climate change. In the absence of long-term adaptation in
the 2070-2099, the yield is predicted to decrease by 25% or
more.
Journal of Business, Economics and Finance -JBEF (2017), Vol.6(4), p.336-347 Dumrul, Kilicarslan
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DOI: 10.17261/Pressacademia.2017.766 341
2.4. Time Series Approach
To examine the relationship between climate variables and the yield of agricultural products, this approach suggests the
use of past time series data on yield and climate variability.The time series approach has been extensively used to assess
the impact of climate variables on the yield of various crops at global, country or regional level (Maharjan and Joshi, 2012).
Such an analysis assumes that changes in management are either unrelated to climate or originate from climate. In other
words, product yields respond in the same way to rapid and gradual climate changes. These models provide a quantitative
assessment of uncertainties. In order to minimize or possibly reverse the adverse effects of climate change, farmers change
the cropping system as the climate changes. In addition, adaptation is expected to be a few years behind the climate
trends. Because it is difficult to distinguish climate trends from natural variability and the disaggregated nature of farmer
decisions (when farmers make decisions about adaptation, they are independent of each other) (Maharjan and Joshi, 2012).
Therefore, the observed data can be used when time series analysis is performed. However, in this regression analysis all
possible variables affecting yields such as irrigation coverage, input use, labor utilization should be found. Therefore, in such
cases, the estimation using the time series analysis is more suitable (Maharjan and Joshi, 2012). Table 4 presents the
literature summary of the Time Series Approach.
Table 4: Studies on The Impact of Climate Change on Agriculture: Time Series Approach
Author
Country/Period
Variables
Results
Rahim and
Puay (2017)
Malaysia
1983-2013
Dependent Variable: GDP
Independent Variable:
Farmland, temperature,
rainfall
It has been concluded that there is a long run cointegration
relationship between variables in the study. There is a one-way
causality relation from rainfall, temperature and agricultural land to
GDP.
Bayraç and
Doğan
(2016)
Turkey
1980-2013
Dependent Variable:
Agricultural GDP
Independent Variable: CO2
emissions, agricultural
yield, temperature, rainfall
In the study, changes in agricultural yield and rainfall have positive
effects on agricultural GDP, and negative effects on CO2 emissions and
temperature changes. Moreover, the negative effect of temperature
changes on the agricultural sector is more than the positive effect of
changes in rainfall amount. For this reason the overall impact of
climate change on the agricultural sector is negative.
Zaied and
Zouabi
(2015)
Tunisia
1980-2012
Dependent Variable: Olive
in tons
Independent Variable:
Rainfall, temperature,
labor and capital stock
In long-term semi-arid areas, the olive output decreases with
increasing temperature.
Amponsah
et.al (2015)
Ghana
1961-2010
Dependent Variable: Cereal
yield
Independent Variable:CO2,
real GDP
The results indicate that there is significant negative link between CO2
and cereal yield. There significant positive long run and short run link
between cereal yield and income
Alam (2013)
India
1971-2011
Dependent Variable: Cereal
yield
Independent Variable: CO2,
economic growth
There is a positive and significant relationship between cereal yield and
economic growth, while there is a negative and significant relationship
between CO2 emissions and economic growth.
Başoğlu and
Telatar
(2013)
Turkey
1973-2011
Dependent Variable:
Agricultural GDP
Independent Variable:
Rainfall, Temperature,
population, number of
diploma from secondary
education.
The results indicate that precipitation has a positive impact on
agricultural GDP, while temperature has a negative impact.
3.DATA AND METHODOLOGY
Following the recent literature on economic impacts of climate change on agriculture, we take Aagricultural GDP as
dependent variable and temperature and rainfall as the main independent variables. Data is transformed in logarithmic
form as it provides consistent, better and efficient results. Annual time series data is utilized for the period of 1961-2013.
All time series are taken from the World Bank, World Development Indicator database. The empirical model is given below.
Aggricultural GDP= ƒ (rainfall and temperature) (1)
Journal of Business, Economics and Finance -JBEF (2017), Vol.6(4), p.336-347 Dumrul, Kilicarslan
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DOI: 10.17261/Pressacademia.2017.766 342
The functional form of the model will be as:
(2)
Where AGDP is the agricultural GDP as measured Agriculture, value added (% of GDP), Rain is rainfall as measured mm and
Temp is the temperature as measured °C . t is the time trend and
t
is white noise error term. The parameters β1
and β2 and are the long-run elasticities of Agricultural GDP with respect to rainful and temperature, respectively.
We have employed the Autoregressive Distributed Lag (ARDL) bounds testing approach developed by Pesaran and Shin
(1999) and Pesaran, Shin and Smith (2001) to ascertain the long-run relationship between Aggicultural GDP, Rainfall and
Temperature. The ARDL approach has several advantages. First, The ARDL approach is that it can be used even in cases
when different variables have different orders of integration. Second, when compared to the Johansen and Juselius
cointegration test, the ARDL test ensures more consistent estimates in the case of small samples. Third, given that it is free
of residual correlation, the ARDL testcan handle the eventual phenomenon of endogeneity among variables (Marques et al.,
2016). Fourth, short-run adjustments can be integrated with the long-run equilibrium in ARDL by deriving the error
correction mechanism (ECM) via simple linear transformation without trailing the information about long-run (Ali et al.,
2017).
The mathematical representation of the ARDL approach is as follows
(3)
Where
represents change. n is the optimum delay lengths. The existence of cointegration relationship between
variables from Eq. 3 is examined by testing the significance of the lagged levels of variables using the F-statistic or Wald-
coefficient test. Pesaran et al. (2001) propose testing
0: 6540
H
which means that we cannot reject the
absence of cointegration, against the alternative
0: 6541
H
, which implies that the hypothesis of the
existence of such a relationship cannot be rejected.
ARDL approach is based on two steps. First step, one is to determine the existence of a long run cointegrating relationship
among the variables by using the Wald-coefficient test or F-statistics and by comparing them with critical values set out by
Pesaran et al. (2001). Pesaran et al. (2001) reported two types of critical values: lower bounds and upper bounds. The
critical values for the I(0) variables are referred to as lower-bound critical values while the critical values for the I (1)
variables are referred to as upper-bound critical values. If the calculated F-Statistic is higher than the upper bounds, it
means the null of hypothesis of no co-integration is rejected, indicating evidence of a long-run cointegratin relationship
between the variables, regardless of the order of integration of the variables. If calculated F-statistic is below the lower
bound, we cannot reject the null hypothesis of cointegration, indicating the absence of a long-run equilibrium relationship.
If calculated F-statistic is between lower and upper bounds, a conclusive inference could not be made without knowing the
order of integration of the underlying regressors. The second step is estimation of long-run and short-run coefficient.
According to the estimation results of ARDL calculate long term coefficients. In order to investigate the short-run
relationship between the variables, the error correction model based on the ARDL approach is established as follows.
(4)
Where ECM(-1) term is a lagged value of the residual of model in which the long-term relationship is obtained. ECM(-1) is
the speed of adjustment parameter which is expected to be negative.
ttt TempRainAGDP
210
tttt
n
iit
n
iit
n
it
RainTempAGDP
RainTempAGDPAGDP
161514
0
3
0
2
1110
tt
n
iit
n
iit
n
itECMRainTempAGDPAGDP
14
3
0
3
2
0
2
1
1110
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DOI: 10.17261/Pressacademia.2017.766 343
4. FINDINGS AND DISCUSSION
Before testing whether the Agricultural GDP, rainfall and temperature are cointegrated, we investigated the order of
integration of each series. Two different unit root tests were used to assess the integration order of the series: (i) the
Augmented Dickey-Fuller (ADF) test; (ii) the Phillips Perron (PP) test.
Table 5: Unit Root Tests Results
ADF Unit Root Test
Phillips-PerronUnit Root Test
Order of integration
Variables
Level
First Difference
Level
First Difference
AGDP
-3.185633
(-3.498692)
-6.785477
(-3.500495)
-3.377705
(-3.498692)
-7.491863
(-3.500495)
I(1)
Rain
-6.574099
(-3.498692)
-
-6.539586
(-3.498692)
-
I(0)
Temp
-6.270889
(-3.498692)
-
-6.249822
(-3.498692)
-
I(0)
Note: Intercept and trend model with 5% significance level.
The study applied the unit root test on the natural logarithms of the variables in level and first difference forms as shown in
Table 5, Order of integration is a mixture of I (0) and I (1). In other words, the results indicate that rainfall and temperature
are stationary at level while aggricultural GDP is stationary at first difference. In Table 6 contains ARDL cointegration test
results. Critical values for F-Statistic are presented in Pesaran et al. (2001). Also for small samples size, that are useful for 30
to 80 observations, these critical values were recalculated in Narayan (2005).
Table 6: ARDL (3, 4, 1) Cointegration Test Results
Note: k shows the number of explanatory variables. Critical values for the bound test were taken from Case IV in Pesaran et al. (2001) and
Narayan(2005) (Pesaran et al. 2001; Narayan, 2005).
As seen in Table 6, the calculated F statistic values are above the critical values. This implies that there is a long-run
relationship between the mentioned variables in the period covered. Long term coefficients calculated according to the
estimation results of ARDL (3,4,1) model are shown in Table 7. The results of long run estimates are presented in Table 7.
The results show that the temperature has a positive and significant impact on the agricultural GDP, in the long run. The
coefficient of temperature implies that an increase of 1% in temperature, it will be cause of 1.472% in aggricultural GDP in
the long run in Turkey. However, the results show that the rainfall has a negative and significant impact on the agricultural
GDP, in the long run. The coefficient of rainfall implies that an increase of 1% in rainfall leads to a decrease of 1.032% on
agricultural GDP, in the long run in Turkey.
Test statistic
Value
k
F Statistics
7.585593
2
Critical Value Bounds (Peseran et al 2001)
Significance
I0 Bound
I1 Bound
10 %
3.38
4.02
5%
3.88
4.61
1%
4.99
5.85
Critical Value Bounds (Narayan 2005)
Significance
I0 Bound
I1 Bound
10 %
3.573
4.288
5%
4.225
5.030
1%
5.805
6.790
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Table 7: Long-Run ARDL Estimates
Dependent variable is the natural log of Aggricultural GDP
Regressor
Coefficient
T-statistics (Probability)
InTemp
1.472744
2.621136 (0.0126)*
lnRain
-1.032954
-2.006958 (0.0521)**
Trend
-0.042544
-24.984897 (0.0000)*
Diagnostic test statistics
R2
0.991970
Adj.R2
0.989583
F-statistic
415.5261 (0.000000)
Durbin-Watson
2.028564
Serial Correlation
0.8971(0.8204)
Normality
1.361629(0.506205)
Heteroscedasticity
0.0659(0.0839)
Note: * and ** indicate significance levels of 5% and 10%, respectively.
The bottom part of Table 7 contains diagnostic test results of the selected ARDL (3, 4, 1) model. The adjusted R2 value of
99% suggests that rainfall and temperature jointly explain a significant part of the variation in agricultural GDP. The JB test
for normality indicates that the residuals are distributed non-normal. Furthermore, from the results of the Breusch-Godfrey
serial correlation LM test and the Breusch-Pagan-Godfrey heteroscedasticity test, we fail to reject the null-hypotheses of no
serial correlation and no heteroscedasticity of the residuals. In other words, the functional form of the model is normal,
there is no serial correlation and heteroscedasticity in our model. The residuals are normally distributed.
Next the results of the short run ARDL estimate and the coefficient of the error correction terms are presented in Table 8.
Table 8: Short-run ARDL Estimate
Dependent variable is the natural log of Aggricultural GDP
Variable
Coefficient
T-statistics (Probability)
D(LNAGGDP(-
1))
0.170216
1.289254 (0.2053)
D(LNAGGDP(-
2))
0.336495
2.364978 (0.0234)
D(LNRAIN)
-0.043435
-0.669609 (0.5073)
D(LNRAIN(-1))
0.428389
4.134152 (0.0002)
D(LNRAIN(-2))
0.379731
4.160083 (0.0002)
D(LNRAIN(-3))
0.182407
2.239388 (0.0312)
D(LNTEMP)
0.490778
3.290868 (0.0022)
C
2.467922
5.633993 (0.0000)
ECM(-1)
-0.548492
-5.727352 (0.0000)
The important outcome of the short run dynamics is the calculation of the coefficient of ECM.The lagged error correction
coefficients, ECMt-1 are correct in sign, and significant in both cases verifying the established co-integrating relationships
among the variables (Jalil et al., 2013). The ECM coefficient is negative and statistically significant. The coefficient of ECMt-1
shows the speed of the adjustment back to the long-run equilibrium after a short run shock. For example, the coefficient of
ECMt-1 is 0.5484. This implies, nearly 55% of the disequilibria of the previous year's shock adjusting back to the long run
equilibrium in the current year.
5. CONCLUSION
In recent years, the effects of climate change on important variables such as agriculture, industry, human health, energy
demand and economic growth are being increasingly investigated. Climate change modifies the distribution of a set of
climate variables including temperature, rainfall, humidity, wind speed, sunlight duration, and evaporation. In recent years,
a number of studies have been conducted around the world on the impacts of climate change. Climate change affects
various sectors such as agriculture, food production, fisheries, livestock, forestry, foreign trade, tourism, health,
construction, logistics and finance-insurance. However, among these sectors, agriculture is a very sensitive sector to climate
Journal of Business, Economics and Finance -JBEF (2017), Vol.6(4), p.336-347 Dumrul, Kilicarslan
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DOI: 10.17261/Pressacademia.2017.766 345
change. The effects of climate change can arise by influencing production factors and their productivity, prices and
international trade patterns. Furthermore, the effects of climate change on the countries may be different. These effects,
which differ from country to country, can also change agricultural competitiveness. As a result of climate change, both
winners and losers can emerge. In this study, the literature on economic effects of climate change in agriculture is
presented in consideration of four different approaches (Production function approach, Ricardian approach, Panel data
approach, Time series approach). In these studies, it is observed that especially temperature and rainfall are used as the
two most important indicators of climate change for agricultural production. In this study, important climate variables such
as temperature and precipitation were used to evaluate the effects of climate change. The effects of climate variables on
aggricultural GDP in Turkeywere estimated using the Autoregressive Distributed Lag (ARDL) approach. In this study, the
increase in precipitation affects agricultural GDP positively, while the increase in temperature has a negative effect on
agricultural GDP. In order to minimize the adverse effects of climate change in Turkey, which is one of the largest countries
in the world in terms of agricultural land, it is important to establish policies, strategies, plans and programs to combat
climate change. In addition, the production of agricultural products suitable for the increase in temperature in Turkey
should be supported and the farmers should be aware of the adaptation to climate change. Further research on agricultural
production in Turkey should take into account the impact of other climate indicators (such as solar radiation, lig ht length,
humidity, socio-economic and sea level).
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