The Effects of Climate Change on Iran's Sugarcane Production (Case study: Khuzestan Sugarcane)
Subject Areas : Climate change
Abdulah Rajabalinejad
1
,
Niv Nozari
2
,
Bita Rahimi Badr
3
1 - دانشجو دکتری ، گروه اقتصاد کشاورزی ، دانشگاه آزاد اسلامی واحد کرج.
2 - استاد و هیات علمی دانشگاه آزاد اسلامی واحد کرج و دانشگاه
هوستون آمریکا
3 - دانشیار دانشگاه آزاد اسلامی، واحد کرج، ایران
Keywords: Precipitation, Climate Change, temperature, dynamic ordinary least squares, sugarcane production in Khuzestan,
Abstract :
Climate change due to global warming cause a lot of concern that requires comprehensive and reciprocal world wide action. The agricultural sector is one of the most dependent sectors on the climate, in the production cycle and food security of Iran, with a dry and hot climate is at a higher risk of global damages for these climate changes. Moreever, the major sugarcane industries of Iran are located in Khuzestan province which climatic variables in this region have recorded drastic and increasing changes procedure. In this article the effects of climate change on sugarcane industry in Khuzestan are studied (1971-2020). The usage of Dynamic Ordinary Least Squares (DOLS), an econometric model, helped examining the effect of climate factors such as temperature and precipitation on production. The results depicts a nonlinear relation between the climatic factors temperature and precipitation and production. As a matter of fact, the nonlinear relation in the form of an inverted U-shape in the graph shows the importance of climate change on agricultural production. The government's ownership of Khuzestan's sugarcane cultivation and the assignment of exclusive rights should provide maximum productivity, but not achieving an ideal goal. In conclusion, because of the negative effects of climate change on sugarcane production, it, is highly recommended to 1) Limit human intervention in nature, 2) Utilize varieties of other crops which are more resilientistant to climate change, 3) Alternate the cultivation patteren,and finally (4) To consider supportive policies in this matter to cope with the effects of climate change .
The effects of climate change on Iran's sugarcane production
(Case study: Khuzestan sugarcane)
Abstract
Climate change due to global warming cause a lot of concern that requires comprehensive and reciprocal world wide action. The agricultural sector is one of the most dependent sectors on the climate, in the production cycle and food security of Iran, with a dry and hot climate is at a higher risk of global damages for these climate changes. Moreever, the major sugarcane industries of Iran are located in Khuzestan province which climatic variables in this region have recorded drastic and increasing changes procedure. In this article the effects of climate change on sugarcane industry in Khuzestan are studied (for a period of 1971-2020). The usage of Dynamic Ordinary Least Squares (DOLS), which is an econometric model, helped examining the effect of climate factors such as temperature and precipitation on production. The results depicts a nonlinear relation between the climatic factors temperature and precipitation and production. As a matter of fact, the nonlinear relation in the form of an inverted U-shape in the graph shows the importance of climate change on agricultural production. The government's ownership of Khuzestan's sugarcane cultivation and industry complex and the assignment of exclusive rights to sugarcane cultivation should provide prosperity and maximum productivity, but gradually an equation has been formed in the sugarcane system that achieving sustainable self-sufficiency in makes it an ideal goal. In conclusion, because of the negative effects of climate change on sugarcane production, it is highly recommended to 1) Limit human intervention in nature, 2) Utilize varieties of other crops which are more resilientistant to climate change, 3) Alternate the cultivation patteren,and finally (4) To consider supportive policies in this matter to cope with the effects of climate change.
Keywords: Climate change, sugarcane production in Khuzestan, temperature, precipitation, dynamic ordinary least squares.
JEL Classification: Q1, Q5, Q54.
Introduction
Climate change is one of the most important global challenges which requires comprehensive and mutual cooperation The amount of natural disasters and accidents has increased fivefold around the world. Climate change currently affect all regions on the planet in different ways, including warmer and colder seasons as well as shorter seasons, which will grow even in the coming decades (WMO, 2021). Climate change can drastically change the risk of flooding in large regional and temporal temperatures and increase the risk of flooding by changing the amount of rainfall, which is one of the most important features of climate change (Guo, Wu et al., 2020). The recent report of UN on climate change showsthat the daily lives of at least 3.3 billion people are "extremely lethal" to climate change.It arguesthatpeople are 15 times more likely to be affected by extreme weather conditions than in previous years (IPCC Report, 2022). In addition, climate-related disasters such as floods and droughts has the potential to displace large numbers of people which can exacerbate other geopolitical conflicts (UN Report, 2022).
Agriculture, as one of the mostmost important economic sectorof the global community,is affected profoundly by climate change.The so called’ecosystem of agriculture’referring to food security, human health, and environmental protection is vulnerable to climate change. And also,climate change has a profound effect on agricultural ecosystems and posed serious threats to food security,human health, and and environmental protection. Almost all economic sectors are affected by the climate, but the agricultural sector is the most dependent within others (Sivakumar, 2021). Therefore, the agricultural productivity affected by climate change through change in precipitation pattern, change in planting and harvesting date, will increase in temperature, evaporation and transpiration (Amirnjad and Asadpour, 2016). According to predictions, global warming have significant effects on the agricultural economy through changes in temperature, carbon dioxide, runoff, frost, precipitation and the interaction of these elements (Wani, Mahdi et al., 2020). The economic effects of climate change are manifested in the form of changes in the performance, production and supply of agricultural products and its impact on food security, as well as long-term changes in climate parameters that affect the profitability and income of farmers (Amirenjad and Asadpour, 2016). Therefore, the agricultural sector is vulnerable both economically and physically from the change of weather factors such as temperature and precipitation (Benhin, 2008) and the change in the pattern of these two variables can reduce the yield of the crop during the harvest time (Ben Zaied, 2013). By 2050, the amount of demand for food is predectited to be increased by 70. Exessive exploitation of existing resources, pollution of soil and water resources, and excessive use of poisons and chemical fertilizers will lead to the escalation of instability (Vahdati, et al., 2020).
The Islamic Republic of Iran has a hot and dry climate, and many agricultural areas are suffering under the conditions of water scarcity and water stress, and the agricultural sector of Iran accounts for more than 8% of the Gross Domestic Product (GDP). Due to its special ecological structure, Iran is more sensitive to environmental changes and more vulnerable than other countries in the world. Therefore, occurrence of climate changes in these regions seems to have significant effects on agricultural production systems (Amirenjad and Asadpour, 2016). Therefore, climate change is reffered as the biggest threat to sustainable development due to the severe damage it causes to natural resources, environment, human health, food security and economic activities. (Rahimi, Malekian et al., 2019). However, in addition to possible impact of climate change on the agricultural sector of the country, the sanction factor can also affect various economic sectors, including the agricultural sector (Madani, 2021).
Sugarcane is a tropical crop, and continues to grow in hot, humid conditions unless terminated by flowering. The normal life cycle of sugarcane is about 15 to 18 months. A maximum temperature of 27 to 38 degrees Celsius is necessary in all phases of its growth. An optimal temperature of 32 to 38 degrees Celsius is needed for germination. Above 38°C, the rate of photosynthesis decreases. Sufficient amount of water is equally essential for crop development (Kelkar, Kulkarni et al., 2020).
In recent years, climate change and abnormal rains have led to severe weather events, floods, and droughts. Several researches in Iran have shown a significant increase in annual temperature (Hemadi, Jamei et al., 2011). In dry areas of the country such as Khuzestan province, almost 100% of agricultural production is obtained from irrigated agriculture. (Shahbazi, 2019) Considering the importance and necessity of the subject and the few researches that have been done in this regard, the local and regional effects of climate fluctuations on the evaporation and transpiration of agricultural products are of particular importance. These changes are especially important in fertile and agricultural areas such as Khuzestan (Shahbazi, 2019).
The studies conducted in Iran, like other areas around the world, show an increasing pattern in annual temperature. A research was conducted in Khuzestan Plain to investigate the effect of increasing temperature on sugarcane water consumption, based on the analysis of annual temperature time series and the theoretical basis of evaporation and transpiration and crop water demand. The results of the annual time series analysis showed an increase of 3.7 °C in temperature for a period of 100 years. The sensitive analysis of water demand model showed 14, 8, 4 and 2.7% change in potential evaporation and transpiration for every 1% change in temperature, wind speed, sunshine hours and relative humidity parameters, respectively. The increase in the rate of potential evaporation and transpiration due to the increase in temperature was 2.04, 2.01, 1.52 and 2.23 mm per year in Behbahan, Ahvaz, Dezful and Karkheh regions, respectively (Hemadi, Jamei et al. 2011). Also, the study of precipitation statistics over the past 50 years in the southwest basin, including the two basins of Karun and Karkheh in Khuzestan province, shows significant changes in the pattern of precipitation and the creation of rain waves and floods (Special Committee of the National Flood Report, 20182019).
Industrial production of sugarcane in Iran, with a significant share of GDP and job creation, has an important role in the economy and is considered a strategic product that can pave the way for sugar self-sufficiency in the country. To investigate the effect of climate change on the economy of sugarcane, it is necessary to calculate the effects of this change on the amount of production, the area under cultivation, the amount of supply and also the price index of the product. The range of economic effects caused by climate change, of which the sugarcane economy is also a part of it, will alternately affect the methods of agriculture and sugarcane, environmental policies and international wide-ranging measures. Moreover, its feedback will have its special effect on intensification or reduction of global warming and the way to possible control climate change. Sugarcane is one of the strategic products in Iran's agriculture that can pave the way for the country to achieve self-sufficiency in sugar production. Khuzestan province, with about 70% of sugarcane production in Iran, has the first production rank (Amili, 2013), that’s why Khuzestan province has been chosen to study the effect of climate change.
Considering the production of sugarcane is of particular importance in the world and in Iran, and it is considered as one of the strategic products which its production rate has been directly affected by climate change, so this study seeks to answer the following questions:
1- Is the total production of sugarcane in Iran affected by the average annual temperature?
2- Does the variable of precipitation affect the yield (or production) of sugarcane in Khuzestan province?
Considering that 77% of agricultural products of the country are produced in semi-arid areas and climate changes can affect sugarcane production in these areas, study and research in this area will be important. Therefore, in recent years, many studies have been conducted on the effect of climate change on the agricultural sector, but the studies on sugarcane have been very limited.
Various studies have been conducted on how climate change affects sugarcane production and evaluate its consequences on the sugarcane industry, and how changes in climate lead to changes in primary production. However, few studies consider how climate change translates into industry-wide impacts and economic consequences across the sugarcane value chain (Linnenluecke, Nucifora et al. 2018). Improving productivity as one of the most important sources of economic growth means using efficient production for all resources including labor,capital, and energy (Eskandari, ZeraatKish et al., 2022).
From the review of 90 studies published under the titles of articles, proceedings and book chapters, 61 are evaluations of the observed or predicted effects of climate change on sugarcane production, which largely lead to different conclusions about how the increase in air temperature affects or atmospheric carbon dioxide levels affect sugarcane production. A total of 17 adaptation studies have focused on observed or predicted impacts of climate change, such as management practices or agricultural practices, but there is limited evidence on successful adaptation outcomes. In addition, a separate stream of articles discusses the reduction of energy use and greenhouse gas emissions in the sugarcane production process, often with a view to reducing environmental impacts (Linnenluecke, Nucifora et al., 2018).
The research of Azizi et al (2022) by using of panel data and dynamic ordinary least squares (DOLS) method and estimated the threshold levels of temperature and rainfall has confirmed an inverted U shaped relationship between climate change variables and irrigated barely yield. The threshold levels of temperature and rainfall are estimated to be 15.48 ºC and 239mm, respectively; beyond these threshold levels, increase in temperature and rainfall has a kegative impact on barely yield in Iran. The long term elasticity of temperature shows that the yield will be reduced by an increase in temperature in the long run. Same is happen for precipitation and barely yield (Azizi, Zarei et al., 2022).
Climate change is expected to have important consequences for sugarcane production in the world, especially in the developing countries because of relatively low adaptive capacity, high vulnerability to natural hazards, and poor forecasting systems and mitigating strategies. Sugarcane production may have been negatively affected and will continue to be considerably affected by increases in the frequency and intensity of extreme environmental conditions due to climate change. The degree of climate change impact on sugarcane is dependent on geographic location and adaptive capacity (Zhao and Li, 2015).
Bakshi et al. (2021) investigated the effects of climate change in semi-arid regions on the market (price, income, production, export and import) of sugarcane products. Also, in order to simulate the effects of climate change, they calculated the yield of agricultural crops, including wet and dry wheat, wet and dry barley, and seed corn, using temperature and precipitation variables and yield response coefficientsand then the demand function of different products was calculated using estimated elasticity of the demand function. The results of the research showed that climate changes until 2025 will cause different consequences such as changes in yield, and in cultivated area and available water in selected products, which will increase the price of most of these products.
Ghafari et al. (2019) investigated the effect of climate change on the economic growth of Iran's sugarcane sector in the form of a dynamic calculable general equilibrium model based on the social accounting matrix of 2019. The results showed that taking into account the decrease in rainfall in the twenty-years horizon until 2030, the amount of production, consumption, investment and export inthe sugarcane sector will decrease by 4.469, 5.025, 4.462 and 13.770 percent respectively, but the amount of Imports in this sector will increase by 504.5%. Considering the adverse effects that climate change has on the macro variables of the sugarcane sector, it is necessary for the government to take appropriate measures to support this sector in unfavorable climatic conditions.
Linnenluecke investigated the effect of climate change on Australian sugarcane production. In this study, they have used the climatic information of the country including the maximum and minimum temperature and precipitation during the years 1964 to 2012. Their results showed that during the study period, annual CO2 emission along with maximum temperature has a negative effect and minimum temperature has a significant positive effect on Australian sugarcane production (Linnenluecke et al., 2018).
In a study, Kalkar et al. investigated the possible effects of climate change on the production of three crops, sugarcane, cotton and rice in India. The analysis of the results of this research shows that climate change has led to a significant decrease in the production of these three major crops, namely sugarcane, cotton and rice (Kelkar, Kulkarni et al., 2020).
In a study, Tokunaga et al investigated the effects of climate change on agricultural production in Japan using dynamic panel analysis. In this study, by review of the data of 1995-2006 for 8 regions of Japan, the effects of three climatic variables of temperature, solar radiation and precipitation on the production of agricultural products were analyzed using the production function. The results showed that an increase of one degree Celsius in the average annual temperature reduces rice production by 5.8% in the short term and by 3.9% in the long term (Tokunaga et al,, 2015).
The above studies are some examples of studies conducted in recent years in relation to the effect of climate change on sugarcane production. According to the studies conducted in the country and abroad, what has been used as important climatic variables are temperature and precipitation variables, which have been used in this research too.
In this research, the most important climatic factors on sugarcane production were investigated by using the production function, and the data were estimated as a "time series" using the Dynamic Least Squares (DOLS) method. Climatic data including: average temperature and total precipitation has been compiled from the averaging of Khuzestan synoptic station in a consecutive period of 50 years (1971-2020).
Materials and methods
Mathematical modelling, is widely used to study farm-level adaptations. There are numerous examples of different types of mathematical models that are used to identify various aspects of farm adaptations. Examples of these modelling techniques are: decision models such as the agent-based model (ABM) and the decision support model (DSM); and optimization models such as linear programming (LP), non-linear programming (NLP) and mixedinteger programming models (MIP). The ABM and DSM are useful tools for examining adaptation measures on farms (Shrestha, Barnes et al., 2016).
In this study, climatic factors affecting sugarcane production have been investigated using the production function. We know that the production function shows the relationship between the consumed input and the produced output at different levels of input consumption. The general form of the production function is as equation (1) (Amirnjad and Asadpour,2016-2017):
(1)
In equation (1); Y represents the amount of production and X represents the factors of production (various types of labor, capital and materials, respectively). If in production of a product, is considered both, managed production factors, and unmanageable production factors, then the production function will be in the form of equation (2):
Y=f (X1, X2, X3) (2)
In relation (2); X1 is the vector of production inputs, X2 is the climatic factors of temperature, and precipitation, and X3 is the level of technology has been used. In this study, according to the studies of (Linnenluecke et al, 2020) and (Kelkar et al, 2020), the relation (2) is used which both studies are based on the important climatic variables of temperature and precipitation.
(3)
In relation (3), Lyt; the natural logarithm of the country's total sugarcane production, Lraint; The natural logarithm of total annual precipitation, (rain2)t; The natural logarithm of the second power of the total annual precipitation, Ltempt; The natural logarithm of the average annual temperature, (Ltemp2)t; The natural logarithm of the second power of the average annual temperature is considered.
It should be noted that in function (3) to show the relationship between the climatic variables of temperature and precipitation with the total production of sugarcane, the square power of these variables has been used, so that in the long t,the relationship is not estimatedonly linearly but a more accurate estimate is obtained.
To achieve this goal, the dynamic ordinary least squares (DOLS) method has been used. Because this is a common methodfor examining long-term relationships between dependent and explanatory variables of the model, and is considered change in weather conditions over a period of time, acoording to the definition of climate change.Using the DOLS method can be beneficial in examining this phenomenon (Ben Zaied, 2013).
The DOLS method is one of the dynamic composite data model estimation methods proposed by Stock and Watson (1993). In this method, by applying adjustments in the ordinary least squares method, the reaction of a dependent variable to the variability of the independent variables is investigated (Alizadeh and Golkhandan, 2013). One of the most important advantages of this method compared to other cointegration vector estimators is that it can be used in small samples and prevents the creation of simultaneous bias and has a normal asymptotic distribution (Fatras et al., 1390). Also, it is necessary to remember that Cao Weiqiang (2000) shows that this method is more efficient and allows reliable statistical inferences. In this method, the equation (4) is used to estimate the long-term coefficients (Fiqh Majidi and Ebrahimi, 2013):
(4) 
In relation (4), P; indicating past and future trends (precedence or delay), ∆X (i,t-j); Difference of explanatory variable with interval, ∆X(i,t+j); Difference of explanatory variable with future trends, γj; Coefficients of breaks or past trends, δj; Coefficients of future trends, uit; the error of estimating the long-term dynamic relationship, Yit; is the dependent variable.
The study area is located in the west part of Khuzestan province (32°11'.01" to 31°06'.89" N and 91 47°72'.16" to 48°26'.37" E) in southwest part of Iran. The data of this study is in the form of time series and for the period of 1971-2020..To collect data,have been used of the information related to Central Bank of the Islamic Republic of Iran, the Ministry of Agricultural Jihad, the Sugar Organization and the Meteorological Organization of the country. Due to the lack of access to provincial production data, national data was used. Also, Eviews9 software was used to estimate the model.
Results
To understand the variables used in the study, the statistical characteristics of the variables are shown in Table (1). In this table (1), the minimum, maximum, average and standard deviation values of the variables are summarized.
Table (1) statistical characteristics of the variables used in the study for the sugarcane product
Standard Deviation | Mean | Maximum | Minimum | Variable Description | Variable Name | ||||||
2169/264 | 3151/652 | 7800 | 578 | Sugarcane production (tons) | Y | ||||||
0.61 | 25/33 | 26/89 | 23/72 | Annual average temperature (Celsius) | TEMP | ||||||
80/29 | 254/1 | 470/23 | 93/96 | Total annual precipitation (mm) | RAIN | ||||||
Source: research findings
The figures in table (1) show that the minimum and maximum production of sugarcane in Khuzestan during the investigated period of 1971 to 2020 is equal to 578 and 7800 tons in 1971 and 2017, respectively. Also, the maximum average annual temperature of this province is 26.89 degrees Celsius for 2010 and the minimum temperature is 23.72 degrees Celsius for 1977. For the rainfall variable, the maximum total annual rainfall is 470.23 mm, which corresponds to 1997 and the lowest rainfall corresponds to 2010 with 93.96 mm.
Chart 1. Annual average precipitation and temperature of the studied period
In graph 1, the average annual precipitation and temperature of the studied period shows that, temperature variables have been increasing since 1997 and moving towards the top of the graph. At the same time, the average annual rainfall in the country has faced a decreasing trend. Decrease in rainfall itself will be a factor for the mutual intensification of temperature and side effects on the environment and agriculture. An increase in average seasonal temperature can shorten the growing period of many agricultural crops and hence reduce yield. In the long term, climate change can affect agriculture in several ways, such as quantity and quality of agricultural products in terms of productivity, growth rate, photosynthesis and transpiration rate, moisture availability, etc.
In the following, before estimating the model, first, the stationarity of the variables has been checked by means of the widely used augmented Dickey–Fuller test (ADF) tests generalized Dickey-Fuller test, and the summary of the results of this test is shown in Table No. (2) for the logarithm of the investigated variables at the level and after the first differentiation.
Table (2) Stationary results for variables at level and first order difference
Variable
| Variable in level | First order difference | |||
Generalized Dickey-Fuller test | Generalized Dickey-Fuller test | ||||
t statistic | Possibility | t statistic | Possibility | ||
LY | -1.51 | 0.52 | -7.09*** | 0.000 | |
Lrain | 06.88- | 0.000 | -9.59*** | 0.000 | |
Lrain2 | -6.89 | 0.000 | -6.69*** | 0.000 | |
LtempP | -6.89 | 0.06 | -12.80*** | 0.000 | |
Ltemp2 | -2.88 | 0.06 | -12.82*** | 0.000 | |
Source: research findings
According to the results of the unit root tests in table (2), in short, except for the variable of precipitation, which is stationary at the probability level of one percent, the rest of the variables are nonstationary at the level of one percent and contain a single root, so that their First order difference are at one percent level in all cases of mana or I(0). Estimation of the model in the state of unknown variables leads to the creation of false regression in the model; to avoid reliance on false regression, there are differentiating methods and cointegration tests. Besides, the KPSS test also had similar results. Therefore, if there is an accumulation between the unknown variables in the model, the results of the estimation of the model will be reliable. In this article, the method provided by Johanson is used to check the cointegration test in the model. The null hypothesis in this test is the absence of co-accumulation or long-term relationship. The results of which are shown in table (3):
Table (3) Johanson test results
The number of cointegration equations | Eigenvalue | Statistics Trace | Critical limit 0.05 | Probe |
Zero* | 0.51 | 91.60 | 69.82 | 0.000 |
One | 0.30 | 32.32 | 29.79 | 0.087 |
Two | 0.22 | 15.06 | 15.49 | 0.197 |
Three | 0.05 | 2.54 | 75843 | 0.110 |
* The absence of cointegration is rejected. Source: research findings
According to table (3), at a significance level of one percent, it indicates the existence of a convergent vector. After proving the existence of co-accumulation in the model, the long-term relationship is estimated.There are different methods to estimate the long-term pattern,however in this study, as previously stated, the fully modified ordinary least squares (DOLS) method was used. The estimation results of the DOLS model are shown in Table (4).
Table (4) DOLS model results
Probability value | t statistic | Standard deviation | Coefficient value | Variable | ||||
(0.000) | 4.259 | 1155.560 | 4922.278** | Ltemp | ||||
(0.000) | -4.241 | 178.932 | -758.855** | Ltemp2 | ||||
(0.086) | 1.786 | 9.147 | 16.344* | Lrain | ||||
(0.060) | -1.964 | 0.845 | -1.660* | Lrain2 | ||||
(0.000) | -4.323 | 1853.368 | -8013.186** | C | ||||
R2= 81 |
|
|
| |||||
Sources: Research findings (*, ** and *** are significant at 10, 5 and 1 percent level, respectively)
According to the estimation results shown in table (4), the estimated coefficient of determination is 0.81, which means that 81% of the changes in the dependent variable are explained by the explanatory variable of the model. Also, according to the results, both climatic variables of rainfall and average annual temperature have shown a non-linear relationship with production, and according to the sign of the first and second power coefficients of the variables, this non-linear relationship is in the form of an inverted U. In fact, the climatic variable of temperature and rainfall have a maximum point in such a way that before the maximum temperature, an increase in temperature and precipitation leads to an increase in production and after that it causes a decrease in production. This return threshold point in the logarithmic model is determined by equation (5) (Hosseninasab & Paykari, 2012):
(5) 
In relation (5), X is the maximum temperature or precipitation (return threshold), α1 is the coefficient of the variable first power and α2 is the coefficient of the variable second power.
Therefore, according to equation (5), the maximum annual temperature point for sugarcane is 25.62 degrees Celsius. This means that, before the temperature reach at 25.8 Celsius, an increase in the average annual temperature leads to an increase in sugarcane production, and after a temperature of 25.8 Celsius, an increase in temperature leads to a decrease in production. Also, according to equation (5), the maximum point of precipitation has been obtained for sugarcane. That is, before the total annual rainfall of 137.40 ml, an increase in rainfall leads to an increase in sugarcane production, and after 137.40 ml, an increase in rainfall leads to a decrease in production.
Discustion??? Discussion
Climate changes, which have existed since the past, in recent decades due to their intensification as a result of human activities have caused concerns at the international level, and have had many effects on agricultural production around the world. In fact, today climate change is one of the most important environmental problems in the world and its importance is greater in the agricultural sector than in other sectors and since the country's agricultural production system has little flexibility to changes in technology and capital, and so the sensitivity of this sector make it more and more vulnerable to climate change. Considering this importance, in this study, the effects of climatic variables of average annual temperature and total annual precipitation of Khuzestan have been investigated. To achieve this goal, the dynamic ordinary least squares (DOLS) method has been used. Because this model is a method for examining long-term relationships between the dependent and explanatory variables of the model, and according to the definition of climate change, which is a change in weather conditions over a period of time, using this method can be beneficial.
According to the results, both climatic variables of precipitation and average annual temperature have shown a non-linear relationship with production. According to the sign of the first and second power coefficients of the variables, this relationship is non-linear and in the form of an inverted U. In fact, the climatic variables of temperature and precipitation have a maximum point in such a way that before the maximum temperature, an increase in temperature and precipitation leads to an increase in production and after that it causes a decrease in production.
The significant relationship between temperature and other agricultural products of the country in the research of Zarkari et al. (2014), Momeni and Sabeti (2013), Alijani et al. (2011), Sultana et al. (2009) and Linneluk et al. (2019) have also been seen.
Conclusion
According to the findings of this research, first of all, the factors that lead to an increase in temperature should be avoided, because the non-linear relationship between temperature and sugarcane production indicates that the temperature after the maximum point leads to a decrease production in sugarcane. Therefore, it is recommended to take necessary measures to prevent the rising temperature, such as reforestation and environmental protection, and also the use of clean energy such as solar and wind energy can lead to decrease increasing trend of temperature.
Policy-making to reduce the effects of climate change in the agricultural sector should be reviewed and implemented in the form of sustainable development programs, because climate change has a reciprocal effect on human life. One of the most important tools for achieving sustainable development is the creation of a social culture, optimal consumption management, and the use of compatible technologies and how to use them; humans use technology in the form of a system that itself affects the environment.
In general, considering that the phenomenon of climate change and global warming is happening and the results of this research have also confirmed and shown the effectiveness of production in relation to climate variables; Both climate variables used in this study, i.e. temperature and precipitation,significently affect on sugarcane production. Therefore, the research findings can be used in providing strategic plans for policy makers in the face of climate change. For example, by improving and developing new types of seeds, which are able to withstand severe weather changes, and using temperature-resistant cultivars and varieties, as well as investigating alternative cultivation, effective measures can be taken to deal with the phenomenon of climate change. .
At the same time, it is necessary for the government to take appropriate measures to support this sector in unfavorable climatic conditions.
Acknowledgment
In the end, the authors of the article consider it necessary to appreciate the cooperation and advice of Dr. Maryam Asadpour Kurdi to improve and enrich the text of the article.
References
1. Ali Bakshi, H., A. Durandish and M. Sabouhi Saboni (2020). "Investigating the effects of climate change in semi-arid regions on the market of agricultural products." Agricultural Economy and Development (Agricultural Sciences and Industries) Summer 2019, Volume 34, Number 2; 127-148. (Persian)
2. Alijani, F., Karbasi, A., R. and Mozafari Masan, M. (2011). Investigating the effect of temperature and rainfall on the yield of water wheat in Iran. Agricultural Economics and Development, 76:143-166. (Persian)
3. Alizadeh, Mohammad and Golkhandan, Abulqasem (2013). "Testing the Leviathan hypothesis for selected developing countries," two quarterly journals of regional economy and development, p. 21, p. 8, pp. 50-80. (Persian)
4. Amirnejad, Asadpour. Kurdi (2017). "Investigating the effects of climate change on Iran's wheat production." Agricultural Economics Research 9(35): 163-182. (Persian)
5. Azizi, J., N. Zarei and S. Ali (2022). "The short-and long-term impacts of climate change on the irrigated barley yield in Iran: an application of dynamic ordinary least squares approach." Environmental Science and Pollution Research 29(26): 40169-40177.
6. Ben Zaied, Y. (2013). Long Run Versus Short Run Analysis of Climate Change Impacts on Agriculture. Working Paper 808.
7. Benhin, J. K.A. (2008). South African crop farming and climate change: An economic assessment of impacts.Global Environmental Change, 18: 666-678.
8. Benlloch-Gonzalez, M., Bochicchio, R. and Berger, J. (2014). High temperature reduces the positive effect of elevated CO2 on wheat root system growth. Field Crops Research, 165: 71-79.
9. Climate Change 2022: Impacts, Adaptation and Vulnerability (ipcc.ch)
10. Eskandari, S., Y. ZeraatKish, R. Moghaddasi and J. Azizi (2022). "Determinant factors energy efficiency and emission of pollutants Co2 & So2 in Iran's agricultural sector." International Journal of Environmental Science and Technology 19(3): 1717-1728.
11. Feqh Majidi, A, and Ebrahimi, S. (2021). Applied econometrics of panel data using Eveiws, Noor Alam. (Persian)
12. Fitros, M, Nemati, M, and Akbari Sh, A. (2010), "Iran's Human Development Index in 2010," Political-Economic Magazine, No. 284. (Persian)
13. Food and Agriculture Organization (FAO) Annual Report 2014.
14. Ghafari, I, Akbari, G, Ahmad and K. Kalai (2019). "The effect of climate change on the economic growth of Iran's agricultural sector (the dynamic calculable general equilibrium model approach)." Agricultural Economics and Development 32(4): 333-342. (Persian)
15. Guo, Y., Y. Wu, B. Wen, W. Huang, K. Ju, Y. Gao and S. J. T. L. P. H. Li (2020). "Floods in China, COVID-19, and climate change."
16. Hemadi, K., M. Jamei and F. Z. Houseini (2011). "Climate change and its effect on agriculture water requirement in Khuzestan plain, Iran." Journal of Food, Agriculture & Environment 9(1): 624-628. (Persian)
17. IMF: The International Monetary Fund Annual Report 2019.
18. Kao, C., Chiang, M.H. (2000). On the estimation and inference of a cointegrated regression in panel data. Advances Econometrics, 15: 179–222.
19. Kelkar, S. M., A. Kulkarni and K. K. Rao (2020). "Impact of climate variability and change on crop production in Maharashtra, India." Curr. Sci 118(8): 1235-1245.
20. Linnenluecke, M. K., N. Nucifora and N. Thompson (2018). "Implications of climate change for the sugarcane industry." Wiley Interdisciplinary Reviews: Climate Change 9(1): e498.
21. Madani, K. (2021). "Have International Sanctions Impacted Iran’s Environment?" World 2(2): 231-252. (Persian)
22. Momeni, S. and beauty, m. (2013) Potential effects of climate change on agriculture in Fars province. Economy and Development Journal. 3:169-179. (Persian)
23. Rahimi, J., A. Malekian and A. Khalili (2019). "Climate change impacts in Iran: assessing our current knowledge." Theoretical and Applied Climatology 135: 545-564. (Persian)
24. Shahbazi, N. (2019). "Investigation of changes in precipitation and evaporation and transpiration of agricultural products in Khuzestan province under the influence of climate change." Journal of Water and Soil Conservation Research 25(6): 123-139. (Persian)
25. Shrestha, S., A. Barnes and B. V. Ahmadi (2016). Farm-level modelling: Techniques, applications and policy, CABI.
26. Shrestha, S., A. Barnes and B. V. Ahmadi (2016). Farm-level modelling: Techniques, applications and policy, CABI.
27. Sivakumar, M. (2021). Climate change, agriculture adaptation, and sustainability. Climate resilience and environmental sustainability approaches, Springer: 87-109.
28. Stock, J. H. and Watson, M. W. (1993). A simple estimator of cointegrating vectors in higher order integrated systems, Econometrica, 61: 783-820.
29. Sultana, H., Ali, N., Iqbal, M. M. and Khan, A. m. (2009). Vulnerability and adaptability wheat production in different climatic zones of Pakistan under climate change. J. of Climatic Change, 94:123– 142.
30. Tokunaga, S., Okiyama, M. and Ikegawa, M. (2015). Dynamic Panel Data Analysis of the Impacts of Climate Change on Agricultural Production in Japan. Japan Agricultural Research Quarterly, 49(2):149-157.
31. University of Tehran; https://news.ut.ac.ir/fa/news/15620
32. Vahdati, Koresh, Sarikhani and Saadat (2020). "A review of the sustainable development of the agricultural sector in Iran and the world." Journal of Strategic Researches in Agricultural Sciences and Natural Resources 5(1): 19-32. (Persian)
33. Wani, S., S. Mahdi, S. Samoon, G. Hassan and S. Dar (2020). "CLIMATE CHANGE-ITS IMPACT ON AGRICULTURE." 02: 82-86.
34. Wmo: World Meteorological Organization; https://public.wmo.int/en/media/press-release/wmo-new-climate-report-clarion-call-urgent-action
35. Zhao, D. and Y.-R. Li (2015). "Climate change and sugarcane production: potential impact and mitigation strategies." International Journal of Agronomy 2015.
