Economic Impacts of Climate Change and Variability on Agricultural Production in the Middle East and North Africa Region
The accumulation of scientific evidences indicating that growing greenhouse gases will warm our planet becomes clearer. Higher temperature and changes in precipitation level will shrinkage crop yield in many countries. IPCC (2007) reported that most land areas will experience an increase in average temperature with more frequent heat waves, more stressed water resources and desertification. Stern and Treasury (2006) noted, that the “the poorest countries and populations” will bear the greatest costs of climate change. Therefore, the impact of climate change on agriculture has received increasing attention in the last decade literatures. Climate change coupled with population growth will deeply affect the availability and quality of water resources in the Middle East and North Africa (MENA) region (Alpert, Krichak, Shafir, Haim, & Osetinsky, 2008; Evans, 2010; Gao & Giorgi, 2008). In a similar way, Sowers and Weinthal (2010) argued that since most of the MENA region is arid and hyper-arid, slight changes in water accessibility and arable land have substantial consequences for human security.
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It is worth to take into account the climatic variability in addition to climate change in order to provide an integrated analysis of the impact of climate variables. Selvaraju and Baas (2007) stated that climate variability is the way climate fluctuates yearly above or below a long-term average value while climate change is the long-term continuous change (increase or decrease) to average weather conditions or the range of weather. In this study, we consider the possible impacts of climate changes and climate variability on agricultural production, with a focus on the region of Middle East and North Africa, where the deleterious impacts of climate change are generally projected to be greatest. In order to achieve such objective, Fixed Effect Regression (FER) is used to Estimate the agricultural production function using cross-section time series data of MENA countries. The advantages of panel data analysis are; getting actual responses is more informative to policy makers than results from field trials. Second, country fixed effects capture all additive differences between various countries (Stock & Watson, 2003).
2. Data Sources
In order to estimate the production function, cross-sectional time series (panel data) are used. The panel set consists of 20 MENA countries for the time period between 1961 and 2009 including Algeria, Bahrain, Egypt, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Sudan, Syrian Arab Republic, Tunisia, Turkey, United Arab Emirates, and Yemen. Table 1 shows the data description and data sources. Due to unavailability of the data for few countries, some observations are missing therefore panel data in the model are unbalanced. The data set consists of two variables group. The first is economics variables such as net agricultural production index number in international dollar, agricultural machinery, total fertilizers consumed, labors, and land. The second data subset is climatic variables like temperature and precipitation. The monthly climatic data were available by meteorological stations rather than by country as shown in Table 2. Therefore, it was necessary to calculate monthly country averages of climate variables and summed up into seasonal data.
|Agricultural production||1000 I$||Net agricultural Production Index Number (2004-2006 = 100)||FAO statistics|
|Agricultural machinery (tractors)||Number||Agricultural tractors, refer to total wheel, crawler or track-laying type tractors and pedestrian tractors used in agriculture.||FAO statistics|
|Fertilizers consumption||Ton nutrients||Total consumption of chemical fertilizers (N+P2O5+K2O)||International Fertilizer Industry Association|
|Livestock||Head||Buffaloes + cattle||FAO statistics|
|Labor||Million||Total economically active population||International Labor Organization (LABORSTA)|
|Land||1000 Hectare||Total area of cultivated land||FAO statistics|
|Temperature||Celsius||Monthly mean temperature||FAOClim-NET: Agroclimatic database management system|
|Precipitation||millimeter||Monthly mean precipitation||FAOClim-NET: Agroclimatic database management system|
3. Climate change and agriculture in Mena countries
According to the World Bank, The Middle East and North Africa is one of the regions that is most vulnerable to climate change, with the highest level of water scarcity in the world. The region has a total area of about 14 million km2, of which more than 87 per cent is desert. It is characterized by a high dependency on climate-sensitive agriculture and a large share of its population and economic activities are located in flood-prone urban coastal zones.
Bucknall (2007) classify the MENA countries into three groups on the subject of water source and availability. First group is countries have adequate quantities of renewable water, but the within-country and within year variations are problematically large including Iran, Lebanon, Morocco, and Tunisia. Second group is countries that have low levels of renewable water resources and highly dependent on non-renewable groundwater sources and supplies by desalination of sea water like Bahrain, Jordan, Kuwait, Libya, Oman, Qatar, Saudi Arabia, the United Arab Emirates, and Yemen. The last group is countries that mainly dependent on the inflow of transboundary rivers such as the Nile, the Tigris, and the Euphrates including Syria, Iraq, and Egypt.
Table 2 Descriptive Statistics for Aggregated climatic variables during the Period 1961-2009
|Temperature (c°)||Precipitation (mm/year)|
|Mean||Std. Dev.||Mean||Std. Dev.|
|Syrian Arab Republic||20||18.30||0.90||21.61||7.26|
|United Arab Emirates||13||27.56||1.33||5.47||5.11|
There are various models can be employed to assess the impact of climate change on agricultural production. Ricardian model, Agronomic model, and crop simulation models are most widely adopted models for the climate impact studies (Lee, Nadolnyak, & Hartarska, 2012). The Ricardian model estimates the examines the impact of climate and other variables on land values and farm revenues using cross-sectional data (Mendelsohn, Nordhaus, & Shaw, 1994). Crop Simulation Models (CSM) restrict the analysis to crop physiology and compare crop productivity for different climatic conditions (Salvo, Begalli, & Signorello, 2013). Because of the country level panel analysis, the production function model is adopted for the analysis in the present study.
To estimate the impact of climatic change on agriculture production in MENA countries, an empirical production function for country i at time t net agricultural production index is a function of some economic inputs (Frisvold & Ingram, 1995) and climatic variables: . Y represents the net agricultural production index,; M, F, L, A, and V are economic inputs which include agricultural machinery, fertilizer consumption, labor, cultivated area, and livestock respectively. T and represent temperature and precipitation. Number of agricultural tractors is used as proxy of agricultural capital stock and number of cattle and buffaloes is used as proxy of livestock production. For climatic variables temperature and precipitation, mean of the winter season (January, February, and March) , spring (April, May, and June), summer (July, August, and September), and Fall (October, November, and December) are involved in the model. Following (Barrios, Ouattara, & Strobl, 2008; Belloumi, 2014; Lee et al., 2012), The agricultural production model in the present study has the following specification form:
By taking the log on both sides, the fixed effect panel model is:
According to the fixed effect model, αi (i=1….n) is the unknown intercept for each country that absorb unabsorbed time variant effects and is a time varying effects. For climatic variables, both the linear and quadratic forms are integrated into the model in order to consider the nonlinear relationship between agricultural production and climatic variables.
As it is also sensible to estimate the impact of the variability of climatic variable along with the seasonal deviation and the mean temperature and precipitation, the squared of the mean differences of temperature and precipitation for each season observation is used in the second model. Then, This variability was measured by the seasonal coefficient of variation (CV) calculated as the seasonal ratio of the standard deviation to the mean of each climate variable for each country.
5. Results and discussion
Review different papers to strengthen the discussion Table 3 shows the results of fixed effects regression analysis in which we estimated the impact of agricultural inputs and climatic variables on agricultural production in MENA countries. The results show that the regression coefficient of temperature is positive and statistically significant in spring, summer, and fall seasons. By contrast, temperature in winter has negative coefficient at significance level of 0.01. Regarding the estimated parameters of precipitation, precipitation during spring showed negative impact at significance level of 1%.
The estimated parameters of nonlinear climatic variables indicated that each of the squared summer temperature has positive coefficient at significance level 0.05 while squared winter temperature has negative and significant impact at level of 0.05. In addition, squared spring precipitation showed positive influence.
As expected, production inputs showed significant and positive relation with agricultural production except machinery and fertilizers consumption. As inputs and agricultural production are in logarithmic form, the regression coefficients reflect the production elasticity of each input. Therefore, 1 percent increase in each input of livestock, labor, and land, with keeping all other inputs the same, leads to increase in agricultural production by 0.16%, 0.98%, and 0.91% respectively.
|Winter Temperature Squared||-0.0024*||0.0010||0.014|
|Spring Temperature Squared||0.0002||0.0016||0.892|
|Summer Temperature Squared||0.0043*||0.0019||0.028|
|Fall Temperature Squared||-0.0005||0.0010||0.643|
|Winter Precipitation Squared||-5.0600E-06||5.1400E-06||0.325|
|Spring Precipitation Squared||3.8800E-06||6.2400E-06||0.535|
|Summer Precipitation Squared||1.5300E-05*||7.6600E-06||0.047|
|Fall Precipitation Squared||-3.4000E-06||4.7100E-06||0.470|
The results of Fixed Effects Regression analysis of climate variability as explanatory variables and agricultural production are presented in Table 4. The results suggest that temperature variability in fall season seems to have significant and positive relation with agricultural production while it has negative relation in spring. Squared variability of temperature during winter and summer seasons have significant and negative relation. Furthermore, variability of winter precipitation have positive and significant relation. Likewise, the regression coefficient of squared variation of winter and summer precipitation showed significant and positive relation with agricultural production..
|Winter Temperature Squared||-0.0825*||0.0408||0.044|
|Spring Temperature Squared||0.0204||0.0370||0.581|
|Summer Temperature Squared||-0.0571**||0.0216||0.008|
|Fall Temperature Squared||-0.0071||0.0487||0.884|
|Winter Precipitation Squared||0.0221**||0.0062||0.000|
|Spring Precipitation Squared||-0.0020||0.0034||0.558|
|Summer Precipitation Squared||0.0005*||0.0003||0.044|
|Fall Precipitation Squared||0.0056||0.0042||0.18|
Marginal Impact analysis
The excepted marginal effects of climatic change and variability on agricultural production appraised at the mean are calculated by the first-order differentiation of the equation 2 to temperature and precipitation respectively:
The elaticities of climate change and variability of temperature and precipitation are derived from equations (3) and (4) respectively by dividing both equation (3) on and equation (4) on . therefore, the elasticities can be computed as :
Where and refer to temperature change or variability and precipitation change or variability respectively.
The marginal impact of climate change and climate variability on agricultural production in the MENA region are presented in Table 5. The impact and the elsticities of Climate change and climate variability are calculated using the regression coefficient and mean values of temperatures and precipitation. The results indicate that increase of temperature in winter season has negative impact on agricultural production as one percent increase in temperature during winter season will lead to a decrease in agricultural production value by 1.12 percent. Instead, increasing the temperature during the other seasons showed positive impact. Temperature variability negative impact on agricultural production during winter and spring as one percent increase of temperature variability, will lead to about 0.09 and 0.14 percent decrease in agricultural production.
In regard to the impact precipitation changes, the results confirmed that increasing precipitation during winter and fall season have negative impact on agricultural production in MENA countries while it has positive impact in spring and summer seasons. Moreover, the results of the impact of precipitation variability showed that precipitation variability has negative impact during winter and summer seasons, whereas one percent increase of precipitation variability will lead to decrease in agricultural production in the MENA region by 0.037 and 0.013 percent respectively. However, precipitation variability showed positive impact during the season of spring and fall.
|Climate change||Climate Variability|
|Marginal impact||Elasticity||Marginal impact||Elasticity|
Alpert, Pinhas, Krichak, Simon O, Shafir, Haim, Haim, David, & Osetinsky, Isabella. (2008). Climatic trends to extremes employing regional modeling and statistical interpretation over the E. Mediterranean. Global and Planetary Change, 63(2), 163-170.
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