Impact of climate change on agricultural production of ...

Reg Environ Change DOI 10.1007/s10113-015-0774-5

ORIGINAL ARTICLE

Impact of climate change on agricultural production of Odisha (India): a Ricardian analysis Diptimayee Mishra • Naresh Chandra Sahu Dukhabandhu Sahoo

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Received: 8 May 2014 / Accepted: 14 February 2015 Ó Springer-Verlag Berlin Heidelberg 2015

Abstract The present study examines the climate sensitivity of the agricultural production of Odisha, a state at the east coast of India. The two climatic variables which have been taken for the study are temperature and rainfall. The study has adopted the Ricardian approach to assess the impact of climate change on the net revenue from agricultural production of Odisha. Panel regression model has been used to test the relationship between climate and other control variables on net revenue. Results of the study reveal that climate has significant influence on the agricultural production of Odisha. The possible future climate scenarios are found to have negative impact on the net revenue from agricultural production of Odisha towards the end of twenty-first century, which call for some policy attentions. Keywords Odisha  Climate change  Agriculture  Net revenue  Ricardian approach Introduction The changing climatic scenario and its impact on various sectors of the economy have emerged as one of the greatest Editor: Xiangzheng Deng.

Electronic supplementary material The online version of this article (doi:10.1007/s10113-015-0774-5) contains supplementary material, which is available to authorised users. D. Mishra (&)  N. C. Sahu  D. Sahoo School of Humanities, Social Sciences and Management, Indian Institute of Technology, Bhubaneswar 751007, Odisha, India e-mail: [email protected] N. C. Sahu e-mail: [email protected] D. Sahoo e-mail: [email protected]

challenges before the scientists and policy makers all over the world in twenty-first century. The impact of climate change is expected to be different in different parts of the globe (Stern 2007). Some regions and economic systems may explore positive impacts, whereas others may experience losses due to climate change (Antle 2008; Ninan and Satyasiba 2012). Researchers are of the view that the impact of climate change would be modest on the developed countries and many of them are going to gain from climate change in future (Mendelsohn et al. 1994; Mendelsohn and Dinar 2003). However, there is a general consensus among the researchers on the fact that there would be significant reduction in agricultural productivity in developing countries as a result of climate change (Bruinsma 2003; Cline 2007). Ciscar et al. (2012) found that the impacts of climate change would vary largely among the regions, with the developing regions like Africa, Asia, Latin America and India in particular experiencing the most negative effect of such change. The vulnerability due to the negative impact of climate change depends upon exposure, sensitivity and the adaptive capacity of the region to climate change (Schneider and Sarukhan 2001). Researchers in this field believe that the regions which are situated in tropical and sub-tropical climates are more exposed to the adverse impact of climate change (UNFCC 2007; Parry et al. 2007). Further, the adverse effect of climate change will fall heavily on climate sensitive agriculture sector (Nordhaus 1991; Cline 2007). Climatic variables act as direct inputs in agricultural production along with other inputs such as land, water, fertiliser, pesticides, etc. However, the effects of climatic variables become more pronounced on agriculture in regions where, agriculture is backward or primitive with less scope for technological adoption and transmission. The developing regions where poverty is a major concern and

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agriculture is the major livelihood option for the people, measuring the impact of climate change on agriculture in these regions is very important in view of attaining food security and reducing poverty. The focus of the present study is to assess the impact of climate change on the agricultural production of Odisha. Odisha is a state of India which is situated in the eastern coast along Bay of Bengal. The state is located in a tropical climatic zone which is characterised by high temperature and high humidity. It experiences a medium to high rainfall and, short and mild winter. The incidence of poverty is a major concern of the state. Tendulkar Committee (2009) of the Planning Commission of India has reported that the poverty headcount ratio of Odisha is 37 % in 2009–2010 against the national average of 29.8 %. Further, concentration of poor is higher in rural areas than in urban areas, i.e. rural poverty is around 39.2 %, whereas urban poverty is 25.9 %. In terms of the multidimensional poverty index, about 63.2 % of the people in Odisha live below poverty line. The state is also facing other challenges with respect to poor human development indicators like health, literacy rate and basic household amenities (UNDP 2011). More than 60 % of people of the state depend directly or indirectly on climate sensitive agriculture sector. Share of agriculture in net state domestic product (NSDP) has decreased from 56.91 % in 1960–1961 to 19.95 % during 2010–2011 at 2004–2005 constant prices. On the other hand, the percentage of workforce engaged in agriculture was 73.8 % in 1961 which has declined to 61.81 % in 2011. Further, it has been observed that in the last 50 years, the food production has decreased by 40 % in the state (Economic Survey of Government of Odisha 2009–2010, 2012–2013). It is empirically verified that in the developing countries or regions where agriculture is the main stay of people’s livelihood, improvement of agricultural productivity is the critical entry-point for an effective reduction in the level of poverty (Christiaensen et al. 2006). Thus, there is an urgent need to foster agricultural production of Odisha that can reduce the incidence of rural poverty in the state. The state is also required to prepare itself for future agricultural production under varying climatic conditions. It has been predicted that the climate (temperature and rainfall) may get worse in Odisha. Studies on the state’s rainfall and temperature variations reveal that the summers are getting longer and the intensity of rainfall has increased in the state (Orissa Climate Change Action Plan 2010–2015). Murari Lal, a lead author of the Intergovernmental Panel on Climate Change (IPCC), in his speech to the global scientific body working on climate change held that ‘‘the state is definitely heating up’’. He concluded that ‘‘Orissa’s weather conditions are warnings of global warming’’ (Mahapatra 2006). The full impact of climate change may

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not show up immediately. It will trigger changes slowly but certainly. Realising the need to increase the growth of agricultural production and the increased recognition of the worsening climatic conditions of Odisha, it is required to examine the impact of climate change on Odisha’s agriculture. The present study aims at examining the sensitivity of Odisha’s agricultural production to two important climatic variables such as temperature and rainfall and simulates the impact of the possible future climatic scenarios on the agricultural production. A brief review of literature pertaining to the approaches to measure the impact of climate change on agriculture has been made in the following section.

Approaches adopted to assess the impact of climate change on agriculture: some review of literature Over the years, a number of studies have tried to assess the impact of climate change on agriculture. The approaches that are followed in climate change impact studies on agriculture can be broadly classified into two major types: general equilibrium approach and partial equilibrium approach. The structure of the general equilibrium approach takes simultaneous equilibrium in all the interconnected sectors of the economy (Levin 2006). Computable general equilibrium (CGE) model is one of the general equilibrium models that have been used by various researchers in this field (Rosenzweig and Parry 1994; Zhai et al. 2009; Ciscar et al. 2012). However, the application of general equilibrium models in case of developing countries has some limitations. The major limitations in the execution of the models include problems relating to data consistency and calibration, specification of parameters and functional forms, lesser availability of the high skill needed to develop and use the models, absence of statistical tests for the model specification, and the complexities involved in the CGE models (Gillig and McCarl 2002). On the other hand, partial equilibrium approach is widely used in case of the developing countries. It rests on the analysis of a part or a subset of the overall economy. The partial equilibrium approach used in the literature can be analysed in two ways. The first one is based on crop growth simulation approach, and the second one is the econometric approach or the Ricardian approach (Deressa 2007). Studies that have used the crop growth simulation approach, examine the direct impact of bio-physical and other attributes like soil characteristics on the crop growth under controlled experimental set-up (Darwin et al. 1995; Fischer et al. 2005). However, this approach does not take into account the adaptation made by the farmer to the changing climate. On the other hand, Ricardian approach analyses the impact of climate and weather on agriculture by taking land values

A Ricardian analysis

or land revenue into account. It was the original contribution of Ricardo who observed that the value of land would reflect its net productivity (Deressa 2007). This approach to the study of the impact of climate change on agriculture was first used by Mendelsohn et al. (1994) as a cross-sectional analysis for the agriculture of USA where land value is used as the dependent variable. However, in many developing countries due to market imperfections, land values cannot be obtained in a synchronised way. Annual net revenues per hectare have been used as an alternate way in most of the studies pertaining to the developing countries. The rationale for this is that land value can be represented as the present value of a future stream of net revenues (Dinar et al. 1998). It reflects how variations in climatic variables change land value or net revenue taking adaptations into account. Since adaptations made by farmers play a major role in climate change impact analysis on agricultural production, Ricardian approach has been adopted in the present study. This approach has been widely used in studies relating to developing countries (Gbetibouo and Hassan 2005; Deressa 2007; KabuboMariara and Karanja 2007; Kurukulasuriya and Mendelsohn 2008). Although research on the impact of climate change on agriculture in developing countries is growing, still there is a dearth of studies in case of India and particularly in case of Odisha. In case of India, few studies (Kumar and Parikh 2001; Guiteras 2007) have been carried out which conclude a decline in agricultural production with changing climatic scenario in future. It has also been observed that there will be regional differences, with the eastern part of the country experiencing a positive return in agricultural production as a result of climate change. However, there is hardly any study in literature that has particularly focused on the impact of climate change on the agriculture sector of Odisha taking all the 30 districts of the state with updated climatic and agricultural data. Therefore, the present study is an endeavour towards extending the literature by examining the impact of climate change on the net revenue from agricultural production of Odisha taking all the 30 districts with an updated climatic and agricultural data set.

Research design In this section, the coverage, data source and the methods used in the present study have been elaborated. Coverage and data source There are 30 administrative districts in Odisha and these districts constitute the 30 cross-sectional units. The study is based on secondary data collected at district level. Monthly

averages of the temperature data (which is the average of monthly maximum temperature and monthly minimum temperature) and monthly total of rainfall data have been collected from the Indian Meteorological Department (IMD) for all the meteorological stations of the state for 30 years from 1979 to 2009. Then, the station level data have been converted into district level data by taking a weighted regression for all the stations that come within 100 km radius from the geographical centre of the district. The weight has been assigned according to the distance of the stations from the geographical centre of the district (i.e. the inverse of the square root of the distance). Agricultural input, output and the required data on prices have been collected for the period 1993–2009(for 17 years), from the district statistical handbooks prepared by the Directorate of Agriculture and Statistics of Government of Odisha and the various census reports. The crops that have been taken for the study are: paddy, wheat, maize, millet, greengram, blackgram, horsegram, sesame, groundnut, mustard, potato, jute and sugarcane. Methods As discussed earlier, the study has used the Ricardian approach for the measurement of the impact of climate change on Odisha’s agriculture. Ricardian technique rests on the assumption that the farmers maximise net revenues per hectare. Let the total product of land be Q which depends on the input variables as follows. Qi ¼ f ðN; C; S; EÞ

ð1Þ

where, Qi represents the production of crop i, N is a vector of purchased inputs except value of land, C is the vector of climatic variables, S represents the soil types, E is the economic factors. Let Pi and Pn be the prices of crops and purchased inputs, respectively. Then, the net revenue (NR) of land will be NR ¼ RPi Qi  RPn N

ð2Þ

Research in agronomic studies reveals that different crops have different ideal temperature and rainfall zones (Thompson 1962; Kurukulasuriya and Mendelsohn 2008). Temperature and rainfall levels below or above such optimal ranges may harm productivity. Therefore, there is the possibility of a nonlinearity in the relationship between net revenue and climatic variables. To capture this nonlinearity in the climate response function, various studies have used the quadratic and interaction terms of the climate variables. For the present study, the presence of nonlinearity among the climatic variables and net revenue has been verified through scatter plots. Since the deviation of temperature and

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rainfall from the normal levels may affect productivity, the present study has included the deviation of weather variables. Further, interaction terms of the climatic variables have also been included because the same amount of rainfall can have different effects if accompanied by varying levels of temperature and vice versa (Stallings 1961). The expression after including all these variables has been given by the following equation. NR ¼a þ b1i Ti þ b2i dTi þ b3i Ti2 þ b4i Ri þ b5i dRi þ b6i R2i þ b7i Ri Ti þ b8 CLTVTR þ b9 BULCK þ b10 TRCTRS þ b11 DP þ b12 LP þ b13 HYV þ b14 IRGN þ b15 SOIL

ð3Þ

where, NR = (VQ - VN)/TA or, the net revenue per hectare NR is the difference of the value of production of crops VQ and the value of purchased inputs (excluding land) VN weighted by the total area under crops (crops under consideration) for a district. Value of a crop is estimated taking the product of the total production of a crop in a year and the farm level harvest price of that crop. The total production of a crop is obtained by multiplication of area under the crop with the corresponding yield estimates. Values of purchased inputs include the expenditure on fertilisers, expenditure on agricultural labourers and expenditure on seeds. i refers to the seasons. One year has been divided into four seasons: summer, rainy, winter and spring. The average of the months of April, May and June is represented as summer season. Rainy season is the average of the months of July, August and September. The average of months of October, November and December is represented as winter season, and spring season represents the average of the months of January, February and March. T and R are the normal temperature and rainfall, respectively. Climate normals are used to summarise or describe the average climatic conditions of a particular location. A climate normal is defined, by convention, as the arithmetic mean of a climatological element computed over three consecutive decades (WMO 1989). T2 and R2 are the quadratic specifications of the climatic variables. dTi and dRi represent the year wise deviation of the weather from the normal climatic variables for each of the seasons. RiTi represents the seasonal interaction terms of climatic variables. CLTVTR is year wise number of cultivators per hectare for a district. BULCK is year wise number of bullocks per hectare for a district. TRCTRS is the number of tractors per hectare per year for a district. DP is the year wise density of population of a district. LP is the year wise proportion of literate people of a district. High-yielding variety (HYV) represents proportion of area under HYV seeds. IRGN represents the proportion of area under irrigation. SOIL represents the ten soil types of Odisha.

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There are ten types of soils found in Odisha corresponding to ten agro-climatic zones of the state. These are mixed red and yellow, red loamy, coastal alluvial, deltaic alluvial and laterite, laterite and brown forest, red and laterite, red soil, red and black, red heavy textured colour, and laterite mixed red and black soils (Odisha Agricultural Statistics 2008–2009). Dummy variables have been used to represent the soil types corresponding to the respective districts. Nine soil dummies have been included for the regression. All the values used in Eq. (3) have been deflated using agricultural gross domestic product (GDP) deflator (at 2004–2005 constant prices). The variables (density of population, literacy proportion, number of bullocks, agricultural labourers and cultivators) for which continuous annual data are not available interpolation technique is used to make the data annual. For estimating the functional relationship among the variables shown in Eq. (3), panel regression model has been used. Before estimating Eq. (3), the presence of quadratic relationships among the climatic variables and net revenue has been tested. Further, the presence of multicollinearity among the independent variables has been checked through the cross-correlation table. Before using panel regression, it is pertinent to ensure that the data can be pooled. Chow test (1960) which is one of the poolability tests has been used, to verify the poolability/homogeneity of the data. After ensuring poolability of the data, Hausman test (1978) has been used to choose between fixed effect and random effect panel models (Green 2003). On the basis of Hausman test, Eq. (3) is estimated with random effect model. Finally, using partial derivatives the marginal effect of each of the climatic variables, i.e. temperature and rainfall on net revenue have been estimated. Using these marginal effects, the study simulates the impact of climate change on Odisha’s agriculture, for some possible future climatic scenarios. All these results and analysis have been discussed in the following section. Results and analysis Test carried out to verify the presence of nonlinearity among the dependent variable and the climate variables The presence of nonlinear relationship among net revenue and the seasonal climatic variables has been verified through plotting scatter plots. From the scatter plots, it is revealed that none of the seasonal temperature variables have a significant quadratic relationship with net revenue. This implies that there is a narrow range of observed change in average temperature variables for the period in the study area. Thus, only the linear terms of the seasonal

A Ricardian analysis

temperature variables have been chosen for the study. However, it is revealed from the figures that rainy season and winter season rainfall variables have a quadratic relationship with the net revenue. Therefore, for the present study, we have included the quadratic terms of only two seasonal climatic variables, i.e. the rainy season rainfall and winter season rainfall for a possible nonlinear relationship. These plots have been shown through Fig. 1a–h.

Given the nature and structure of functional form of variables of the study, it is likely that the variables may be collinear. However, multicollinearity in the presence of square and interaction terms can safely be ignored because the p value of those terms will not be affected by the

(b)

Net Revenue (in rupee)

Net Revenue (in rupee)

(a)

Temperature (0C)

Temperature (0C)

(d) Net Revenue (in rupee)

Net Revenue (in rupee)

(c)

Temperature (0C)

Temperature (0C)

(f)

Net Revenue (in rupee)

Net Revenue (in rupee)

(e)

Rainfall (in millimeter)

Rainfall (in millimeter)

(h) Net Revenue (in rupee)

(g) Net Revenue (in rupee)

Fig. 1 Analysis to verify nonlinear relationship among the net revenue and seasonal climate variables. a Temperature of spring season, b Temperature of summer season, c Temperature of rainy season, d Temperature of winter season, e Rainfall of spring season, f Rainfall of summer season, g Rainfall of rainy season, h Rainfall of winter season

Tests for the presence of multicollinearity among the independent variables

Rainfall (in millimeter)

Rainfall (in millimeter)

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presence of multicollinearity (Allison 2012). Further, correlation can be reduced with the use of panel data structure. To check the presence of multicollinearity, the study has estimated the cross-correlation table of the independent variables (except the quadratic and interaction terms) which has been given as an electronic supplement. Perusal of the table reveals that the partial correlation coefficients among the variables are very low. The highest partial correlation coefficient exists between rainfall of spring and summer season (0.668). With such magnitude of correlation coefficient, though it is possible to estimate the regression coefficients, these coefficients may not be consistent. Therefore, in the present study, spring season rainfall variable has been dropped. Summer season rainfall has been retained for the study as it is the planting period for the Kharif crops and plays important role in plant growth. The correlation coefficients among the other independent variables are significant, but the magnitude is low. Since in the present study panel data structure is used, the coefficients will be consistent in spite of the presence of significant low correlation among the independent variables. Result of Chow test The null hypothesis of the Chow test assumes homogeneous slope and intercept coeffeicients for the different cross-sections. If the null hypothesis is rejected, the slopes and intercepts of the groups are not homogeneous and are not poolable. The expression for the poolability test has been given in the following equation.   P K SSE2  SSE2 FðK; NT  NKÞ ¼ P R 2  UR ð4Þ SSEUR NðT  KÞ where SSE2R = sum of squares error of pooled regression or P the restricted model, SSE2UR = sum of the sum of squares error from the unrestricted model (summation of the sum of squares from the individual cross sections), K = the number of restrictions (often equal to K, i.e. all parameters), N = the number of cross sections or groups, T = number of time periods, K = number of parameters in the model. The estimated F value of the poolability test is 0.98 which is less than the critical value of F, i.e. 1.43 at 0.05 % level of significance. Therefore, the null hypothesis of ‘homogeneous slopes and intercepts’ is not rejected and the data set is poolable. Result of Hausman test The Hausman test has been used to choose between the fixed effect and random effect panel regression models. The null hypothesis for the test is that, ‘random effects would be consistent and efficient’ versus the alternative

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hypothesis that ‘random effects would be inconsistent’. The test statistic H is a measure of the difference between the two estimates which has been expressed through the following equation.  0   H ¼ b^FE  b^RE ½VFE  VRE 1 b^FE  b^RE ð5Þ where, b^FE is the coefficient vector from the fixed effect estimator; b^RE is the coefficient vector from the random effect estimator; VFE is the covariance matrix of the fixed effect estimator; VRE is the covariance matrix of the random effect estimator. It is found from the result that Prob [ v2 = 0.1151 which is [0.05 (conventional 5 % level of significance). Therefore, the study does not reject the null hypothesis (i.e. random effects would be consistent and efficient) and adopted the random effect model for the analysis. Results of random effect model of panel regression After going through all the above-mentioned tests of the data set, the Eq. (3) is estimated with random effect panel regression model and the results are given in Table 1. Perusal of the table reveals that the climatic variables which positively affect net revenue from agricultural production of Odisha are rainy season rainfall, deviation of rainy season rainfall, winter season rainfall, summer season temperature, rainy season temperature, winter season temperature and the interaction of climatic variables of spring season. On the other hand, the climatic variables which negatively affect the net revenue are summer season rainfall, spring season temperature, deviation of winter season temperature, and interaction terms of temperature and rainfall of summer, rainy and winter seasons. Odisha has two major cropping seasons such as Kharif season and Rabi season. The duration of Kharif cropping season is from July to October during the south-west monsoon. For the Kharif crops the planting, growing and harvesting times occur in summer, rainy and winter seasons, respectively. The Rabi season starts with the onset of north-east monsoon in October. The planting, growing and harvesting stages of the Rabi crops occur in winter, spring and summer seasons, respectively. Summer season is the planting period of the Kharif crops. The negative effect of summer season rainfall on net revenue might be due to the reason that growths of seeds are adversely affected with higher rain. On the other hand, rise in temperature during this season has a positive effect on net revenue. Rise in temperature protects the seeds from insect attacks. However, the interaction term of the two climatic variables of summer season has a negative effect on the net revenue. This implies that the positive net revenue during this season depends on optimum combination

A Ricardian analysis Table 1 Results of panel regression Variables Summer rainfall

Coefficients -62.27*

Deviation summer rainfall

0.029

Rainy rainfall

6.95*

Rainy rainfall squared Deviation rainy rainfall

-0.003 0.140***

Standard error 5.54 0.037 2.75 0.0002 0.01

Winter rainfall

24.78***

5.73

Winter rainfall squared Deviation winter rainfall

-1.934 -0.047

0.022 0.031

-85.59**

6.16

Spring temperature Deviation spring temperature

0.079

0.047

Summer temperature

22.44*

3.98

Deviation summer temperature

-0.486

0.053

Rainy temperature

49.40*

2.34

Deviation rainy temperature

1.193

0.053

Winter temperature

78.51**

2.71

Deviation winter temperature

-3.09**

1.48

2.76*

1.21

Spring temperature 9 Spring rainfall Summer temperature 9 Summer rainfall

-0.034*

0.01

July temperature 9 July rainfall

-0.24*

0.104

Winter temperature 9 Winter rainfall

-0.846***

0.29

Population density

0.047

0.034

Literacy proportion Cultivators

-1.01** 4.15

0.324 0.166

Tractors

256.035*

13.086

Bullocks

-31.636*

10.207

Hyv

12.122

Irrigation

40.856**

5.088 11.074

sdm1

-36.103*

15.486

sdm2

156.136

10.629

sdm3

305.438

11.710

sdm4

-35.976

10.712

sdm5

-21.585

sdm6

-44.270*

18.62

sdm7

-39.117*

21.24

sdm8

-42.295**

16.18

sdm9

21.239

Constant R2 2

Wald v

Prob [ v2

-775.6

9.532

4.497 25.410

0.54 304.72 0.0000

*, **, and *** represent 10, 5, and 1 % level of significance respectively

of the two climatic variables. During the months of July, August and September (or, rainy season), monsoon rain helps the Kharif crops to grow. In this season, temperature also has positive effect on net revenue. Further, the increase in the yearly deviation term of the rainfall variable

has a positive effect on the net revenue of the state in the rainy season. This implies that the normal rainfall has a positive effect on the net revenue, whereas more than normal rainfall may harm the agricultural production and reduce the net revenue. However, the interaction variable of the two climatic variables of rainy season affects net revenue negatively. This reflects that the effect of climatic variables on net revenue in rainy season depends on an ideal combination of both the climatic variables. The effect of rainfall of winter season which is the harvesting period of Kharif crops and planting period for Rabi crops is positive on the net revenue of the state. Temperature of winter season has a positive effect on net revenue which might have caused for the possible reason that temperature rise during this period helps the Rabi crops like blackgram, greengram, etc. from the insect attacks and also helps the Kharif crops in the ripening process. However, the interaction effect of the climatic variables of winter season is negative indicating that the marginal effects of both temperature and rainfall depend on the occurrence of each other. Among the control variables, the effect of irrigation and tractor are found to be positive, whereas the effects of literacy and number of bullocks per hectare are found to be negative. With the advance of technology, currently, tractors are replaced for bullocks under farm mechanisation for the agricultural production activity. Increased use of tractors and increase in area under irrigation reflects farm mechanisation and it helps in increasing agricultural production. Therefore, it affects net revenue positively. The negative effect of literacy rate on net revenue may be due to the fact that, for the educated people, the benefit is lower from agriculture than other occupational activities. This may induce the educated people to shift to other occupational activities than agriculture in the face of various uncertainties. Furthermore, from the ten types of soil, four soil types are revealed to have significant negative effect on net revenue. These are: mixed red and yellow, red and laterite, red, and, red and black. These soils are mostly found in the districts like Sundargarh, Deogarh, Koraput, Nawarangpur, Malkangiri, Kalahandi and Nuapada. These types of soils are generally acidic in nature and are deficient in nitrogen and water in-soluble Phosphate. This could be the reason for negative effect of these kinds of soils on net revenue from agricultural production of Odisha. In the next section, the marginal effects of temperature and rainfall on net revenue have been examined to uncover the long-run impact of climate change on net revenue. Impact of climate change on net revenue of Odisha The impact of climate change on agriculture in the future depends upon the future climatic conditions. In order to

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assess the impact of future climatic conditions on the ecosystem, some hypothetical climatic scenarios have been developed by taking into account the different factors which affect climate. The impact of climate change on future agricultural production can be evaluated with some tentativeness. This can be done by estimating two different net revenues from agricultural productions, i.e. one is the estimation of net revenue with the existing climatic conditions and the other estimation is with the future possible climate scenarios. The difference between these two could be attributed to the impact of climate change on net revenue. This part of the study proceeds with the predicted climate change scenarios to get an idea about the future change in climatic variables for Odisha. The Third Assessment Report of IPCC has enabled the assessment that climate sensitivity is likely to be in the range of 2–4.5° C with a best estimate of about 3° C, and is very unlikely to be \1.5° C towards the end of twenty-first century. Climate sensitivity has been defined by IPCC as the equilibrium global average surface warming following a doubling of CO2 concentration. Karmacharya et al. (2007) used the outputs of eleven GCMs (Global Circulation Models) obtained from IPCC data distributed centre and the ICTP (International Center for Theoretical Physics) PWC (Physics of Weather and Climate) group. Taking 1961–1990 as the reference period, the study has developed climate change scenarios corresponding to three IPCC scenarios (B1, A1B and A2) for South Asia as a whole as well as for Central Himalayan Region for three time periods: 2020s, 2050s and 2080s. The results of the study for South Asia show that there will be a rise in temperature as well as increase in precipitation in all the three climate change scenarios towards 2080s. Rise in temperature will vary from a 2.3 to 3.9 °C and the precipitation will rise from 61.5 to 98.2 mm under the three climate scenarios in 2080s. All these climate change scenarios for the future settle at a rising temperature and an increasing level of precipitation for South Asia. Assuming the projections made for climate change of South Asia by Karmacharya et al. (2007) will hold for Odisha, the rise in temperature of Odisha may vary from 2 to 4° C and rainfall may rise above 15 % towards 2080s (above current level based on data of the present study). Since climate scenarios are the probable situations and are uncertain, the present study has simulated the impact of change in climate scenarios for different combinations of climatic variables (rainfall and temperature) within the range given by Karmacharya et al. (2007). To find out the marginal effect of each of the climatic variables, i.e. temperature and rainfall on net revenue, the study has estimated the partial derivatives of the two climatic variables. Using Eq. (3) and the results of panel regression, the present study has derived the

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equations for the marginal effects which can be expressed as follows. Marginal effect of temperature is: ¼ b1i þ b2i þ 2b3i þ b7i Ri Marginal effect of rainfall is: ¼ b4i þ b5i þ 2b6i þ b7i Ti

oðNRÞ oT

oðNRÞ oR

ð6Þ

ð7Þ

The results reveal that the marginal effect of temperature on net revenue depends on the occurrence of rainfall at that period and the vice versa. This implies there is endogenous relationship between the two variables. The total effect of these two marginal effects is the effect of climate on agriculture at a particular time period. Now, the impact of climate change for some hypothetical scenarios in future is traced by examining the net change in net revenue from the current level to the simulated figures for the future climate scenarios. The study has estimated the impact of climate change from a mild climate change scenario of 2° C rise in temperature and 5 % increase in rainfall to a high climate change scenario of 4° C rise in temperature and 15 % increase in rainfall. Percentage changes in net revenue due to the changed climatic scenarios from the current level have been given in Table 2. Results unfold that with 5 % rise in the level of rainfall, rise in temperature by 2 °C, 3 and 4 °C may result in -4, 2.67 and 9.57 % changes in net revenue, respectively. Further rise in rainfall to 15 % with the same levels of rise in temperature may result in -40, -33.35, -26.46 % change in net revenue, respectively. This implies that the marginal impact of temperature depends upon the amount of rainfall and vice versa for a particular time period. There are some optimum combinations of these variables which may have positive effects on agricultural production. The estimated results reveal that increase in rainfall of 5 % and temperature of 3 and 4 °C may positively influence the agricultural production, with the net revenue increasing to 2.67 and 9.57 % respectively. However, further increase in rainfall to 15 % may harm the agriculture sector and reduce the net revenue from agriculture. This reflects, under the assumed future climate scenarios for Odisha, climate change is definitely going to have a negative impact on the agriculture sector of Odisha towards the end of the twentyfirst century. Conclusion and policy implications To sum up, the study aimed to explore the impact of climate change on the agricultural production of Odisha. The results revealed that most of the climatic and control

A Ricardian analysis Table 2 Impact of diverse climate change scenarios on net revenue Possible climate change scenarios

Marginal impact of change in rainfall on net revenue (%)

Marginal impact of change in temperature on net revenue (%)

Net impact of change in temperature and rainfall on net revenue (%)

2 °C rise in temperature and 5 % increase in rainfall

-18

14.46

-4

2 °C rise in temperature and 15 % increase in rainfall

-56.77

16

-40

3 °C rise in temperature and 5 % increase in rainfall

-18.51

21

2.67

3 °C rise in temperature and 15 % increase in rainfall

-56.59

23.23

-33.35

4 °C rise in temperature and 5 % increase in rainfall

-18.33

27.9

9.57

4 °C rise in temperature and 15 % increase in rainfall

-56.41

29.95

-26.46

variables have significant influence on net revenue from agricultural production of the state. It is found that optimum combinations of temperature and rainfall are needed to increase the net revenue from agricultural production of Odisha. Using the marginal effect of the two climatic variables, the study estimated the impact of change in climate on the net revenue from agricultural production of the state under some possible future climate change scenarios. The impact of climate change on net revenue depends on the net impact of both rainfall and temperature in future. Net revenue may increase when 5 % increase in rainfall is combined with 3 or 4 °C rises in temperature. However, for the other possible climate change scenarios, there may be negative changes in the net revenue. The fourth assessment report of IPCC reveals that towards the end of twenty-first century climate may change towards the upper range (more than 3 °C rise in temperature and more than 7 % rise in rainfall) for South Asia (Cruz et al. 2007). From the results of the present study, it is found that such a climate change scenario can affect agricultural production of Odisha adversely. The results of the study indicate the need to give special importance to agriculture sector of Odisha which is vulnerable to climate change. In this connection, agricultural extension programs should be promoted in the state. The personnel of the extension program will help timely dissemination of information to the farmers on the climatic conditions as well as the planting dates and the selection of crops which can help the farmers to adapt to the likely adverse impact of climate change in Odisha. Research should be promoted towards developing agricultural technologies (improved varieties of seeds and its wide application, irrigation facilities, and modern techniques of production) to withstand the volatile changes in climate. The regression result of the effect of literacy rate reveals that it influences net revenue negatively. This has serious adverse implications for the attainment of growth with sustainable development. A

state like Odisha needs both high literacy rate as well as higher agricultural production. Therefore, to make the two goals complementary to each other, proper steps should be taken to make agriculture more profitable to the educated farmers. These steps include better marketing facilities for agricultural production, improving various social security measures, creating other employment opportunities in the rural areas and, providing incentives and encouragement to the educated rural people to stay in agricultural activities. In addition, soil management practices have to be adopted to combat the negative influence of the four types of soil (mixed red and yellow, red and laterite, red, and, red and black). These soils require proper application of organic manure and balanced use of fertilisers. Since groundnut and pulses are the preferred production items in those acidic soils, these crops should be given priority in those areas. In the light of adverse impact of climate change on agricultural production of Odisha, adaptation to climate change by the government, non-government organisations as well as by the farmers can help reduce this adverse effect to some extent. Last but not the least, any actions towards protecting the environment from changing to a high climate change scenario are welcome steps in this direction.

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