International Journal of Food and Agricultural Economics ISSN , E-ISSN: Vol. 5 No. 2, 2017, pp

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1 Internatonal Journal of Food and Agrcultural Economcs ISSN , E-ISSN: Vol. 5 No. 2, 2017, pp Abstract LONG RUN DETERMINANTS OF CEREAL PRODUCTION IN ETHIOPIA: DOES CO2 EMISSION MATTER? Solomon Bzuayehu Wasse Kobe Unversty, Dvson of Food and Envronmental Economcs, Japan. & Bahrdar Unversty, Department of Agrcultural Economcs, Ethopa. Emal: 147a171a@stu.kobe-u.ac.jp The study employed vector error correcton model to examne the long run relatonshp between clmate change and cereal producton usng tme seres data ( ) n Ethopa. The contegratng equaton shows that the parameters ncluded n the model are jontly sgnfcant at 1% sgnfcant level. The study revealed that a 10% percent ncrease n CO 2 emsson would have 2.75 % cereal producton loss n the country. However, land and fertlzer were found to have postve and sgnfcant long-term mpact on cereal producton n the country. The forecasted cereal producton - usng contngent equaton shows that cereal producton s expected to grow annually by 2.8%, on average, for the next 10 years. As a concludng remark, efforts towards reducng CO 2 should be strengthened to further enhance the cereal producton growth n the country. Moreover, provdng fertlzer for the farmers wth a reasonable prce on due tme s decsve to beneft from ntensve agrculture. Key Words: Cereal crops, CO 2 emsson, clmate change, Ethopa, vector error correcton model. Jel Codes: Q18 1. Introducton Intergovernmental Panel on Clmate Change (IPCC) defnes clmate change as a change n the state of the clmate that can be dentfed by changes n the mean and/or the varablty of ts propertes, due to natural varablty or as a result of human actvty (IPCC, 2007). Carbon doxde (CO 2) whch s amongst the major greenhouse gases s consdered as a drvng factor of clmate change (Pervez, Ghulam & Khan, 2015) manly because the rse n the concentraton of CO 2 n the atmosphere s lkely to cause an ncrease n temperature (Neenu, Bswas & Rao, 2013). And ths s among the most mportant problems facng the world agrculture. However, there has been a tendency n the clmate economcs lterature n recent years (IPCC 2007; Khan, Kumar, Hussan & Kalra, 2009) to downplay ths rsk, and even to argue that a couple of degree celsus ( C) warmng mght beneft world agrculture (Clne 2008). Scentfcally, the drect effect of CO 2 postvely benefts plants n two ways (Pervez, et al., 2015). Frst, t ncreases the photosynthess process n plants termed as carbon doxde fertlzaton effect (Khan, et al. 2009). Second, ncreased level of CO 2 n the atmosphere decreases the transpraton by partally closng of stomata and hence declnes the water loss by plants. Both aspects enhance the water use effcency of plants causng ncreased growth. But researchers n the other group (e.g. ICPP, 2007; Neenu, et al. 2013) showed that the mpact of clmate change s dfferent for dfferent geographc areas. Specfcally, the ncrease 109

2 Long Run Determnants of Cereal n temperature, whch could happen due to ncreasng CO 2 concentraton, has a negatve mpact n tropcal and subtropcal regons but postve n temperate regons. Hence, ths research was motvated to study the net effect of CO 2 emsson on cereal producton of Ethopa a country whch les wholly n tropcs wth a pror negatve premse. Ethopan economy s heavly dependent on agrculture. And, despte all the challenges, the growth of agrcultural sector n Ethopa has been wtnessed n the last decade (IFDC, 2012; World Bank, 2014). Dfferent researchers assocate the sources of agrcultural growth to dfferent factors, such as land and labor expanson (Fantu, Guush & Alemayehu, 2015), total road network (Worku, 2011), and area under rrgaton, usage of manure and access to mproved varety (Gutu, Bezabh & Menstu, 2012). However, research on estmatng the economc mpacts of CO 2 emssons are lmted (Dawt, et al., 2016). Most mportantly, the fact that the agrculture s domnantly ran-fed makes the possble consequence of clmate change worse. Moreover, even though there are studes (e.g. Dawt, et al. 2016; Joshua, Esn, & Kamal, 2016; Tonkaz, Dogan & Kocygt, 2010) done on dentfyng the (long term and short term) causes of agrcultural producton both n Ethopa and elsewhere, the long run effect of CO 2 emsson on producton and productvty s less studed. The purpose of ths research s two-fold. Frst, the study examnes the net effect of CO 2 emsson on total cereal producton n Ethopa. Second, t also attempts to dentfy the longrun determnants of cereal producton n the country. The current study gves specal attenton to cereal producton manly for two reasons. () despte the producton potental n the country, Ethopa has mported 900 thousand metrc ton of wheat annually for the last three consecutve years to fll the demand and supply gap n the domestc market (Abu, 2015). () cereals account 87% of the total crop producton n the country (CSA, 2015). Hence, mprovng cereal producton wll have an mmense mpact n terms of enhancng the agrcultural productvty n general and total gran supply n partcular. The remanng secton of the paper develops as follows. The next secton explans the data and data source. Secton three llustrates estmaton strategy and secton four dscuss results of the study. Secton fve concludes wth polcy mplcatons. 2. Data and Descrptve Statstcs The study prmarly uses a tme seres data (1962 to 2014) from the onlne avalable World Bank database. However, Ethopan Central Statstc Authorty (CSA) and publshed journals are also consulted. Total cereal producton s measured n thousands of metrc tons. The average cereal producton, for the study perod, s thousand of metrc ton. As presented n Appendx 1, the producton shows radcal ncrement startng from In 2014, the total cereal producton n the country was 23,607 thousand metrc ton. Land allocated for cereal producton was relatvely lower from late 1970 to 1990 but has shown an ncreasng trend wth yearly fluctuatons after 2000 (Appendx 2). On average, a total of nearly 6.4 mllon hectares of land was allocated for cereal producton n the country. The fertlzer consumpton n the country has ncreased from 1200 metrc ton n 1961 to 326,583 metrc ton n And the average consumpton of fertlzer, for the study perod, s 131,112 metrc ton. In ths study, CO 2 emsson refers to those stemmng from the burnng of fossl fuels and the manufacture of cement. And the carbon doxde produced durng consumpton of sold, lqud, and gas fuels and gas flarng (World Bank, 2015). There has been an ncreasng trend n the total amount of CO 2 emtted every year, compared to less than 400 kloton emsson 110

3 S.B. Wasse level n the begnnng to the study perod (Appendx 1). The result shows that, over the study perod, on average, there s an emsson of kloton of CO 2 annually. Table 1. Defnton and Summary of Varables Varables Unt Observaton Mean Std. Dev. Cereal Producton 1000 metrc ton Land 1000 Hectare Fertlzer Metrc ton CO 2 emsson Klo ton (Kt) Source: Own computaton from World Bank data ( ) 3. Estmaton Strategy The man nterest of ths study s to examne the mpact of CO 2 emsson on total cereal producton. Consderng the lack of consstent long years of tme seres data on some of the agrcultural nputs (e.g. seed and labor) the study used fertlzer and land together wth CO 2 emsson as major determnants of cereal producton n Ethopa. Hence, the conventonal Cobb Douglas producton functon can be specfed as: LnY LnL LnF LnCO e. (1) t o 1 t 2 t 3 2 t t where Ln denotes to natural log, Y refers to total cereal producton n thousands of metrc ton; L s area of land allocated for cereal producton measured n thousands of hectares; F s total fertlzer used n metrc ton; CO 2 s total carbon doxde emsson measured n Kt; β 0, β 1, β 2 and β 3 are parameters to be estmated; t ndexes year and e s an error term. The standard regresson technques, such as ordnary least squares (OLS), requre the varables to be covarance statonary. A varable s covarance statonary f ts mean and all ts autocovarances are fnte and do not change over tme. Instead of beng covarance statonary, many economc tme seres varables are not statonary at levels (StataCorp., 2013b). And f all varables are not statonary at levels, an alternatve estmaton method should be used. To solve the possble problem of msspecfcaton, f any, an error correcton model called Vector Error Correcton (VEC) s employed. Followng Engle and Granger (1987) the error correcton model can be specfed as: m m m m LnYt o LnY LnL t LnF t LnCO t t t t 1 (2) where βs are parameters to be estmated, represents the change (e.g. Y t -Y t-1), m s the number of lags, δ s the speed of adjustment parameter, ε t-1 s one-perod lag error correcton term; η refers to an error term wth zero mean. The long run equaton can be extracted from the error correcton model, as the error correcton term s the OLS resdual serous of the long run contegraton equaton: 111

4 Long Run Determnants of Cereal LnY t o LnLt LnFt LnCO (3) t t where, γ 0, γ 1, γ 2, and γ 3 are parameters to be estmated; ρ s the dsturbance term wth expected value of zero and others are as defned earler. 4. Result and Dscusson Result of the conventonal producton functon, presented n table 2, shows that land and fertlzer are the determnants of cereal producton n Ethopa. But CO 2 emsson doesn t have a detectable effect on the total cereal producton. The model output revealed that the varables explan more than 85% of the varaton n cereal producton. Table 2. Estmated producton functon usng OLS Varables(log) Coeffcent Std. Err. t value Land *** Fertlzer *** CO 2 emsson Constant Number of observatons 51 Adj. R Note: *** refers to 1% sgnfcance level. However, ths result has a strong pror assumpton all varables consdered n the model are statonary at level. Hence, t s mportant to test the statonarty of the varables to argue that results usng the ordnary least square regresson are robust. Graphng the varables over tme can help to observe the trend of the varables as an ndcaton of statonarty. As shown n fgure 1, cereal producton at level (the fgure on the left) was not statonary. But after dfferencng (the fgure on the rght) the varable was nearly statonary. 112 Fgure 1. Trends of level and dfferenced cereal producton n Ethopa ( ) Despte the graphcal representaton, all the varables consdered n the model are tested aganst statonarty usng Augmented Dckey-Fuller (ADF) test the common method of

5 S.B. Wasse testng unt root wth a constant and trend. The decson crteron of testng statonarty s to compare the test statstcs wth the conventonal level of crtcal values. And f the value of test statstcs n absolute value s greater than 5% crtcal value, the null hypothess of no statonarty wll be rejected mplyng that the varable s statonary. Table 3. Statonarty Test of Varables Level Frst dfference Order of Varable (log) test statstc 5% crtcal value test statstc 5% crtcal value ntegraton Cereal Producton *** I (1) Land *** I (1) Fertlzer *** I (1) CO 2 emsson *** I (1) Note: *** refers to 1% sgnfcance level. Statonary test, presented n table 3, shows that all varables are not statonary at levels, mplyng that one cannot use ordnary least square estmaton method. Hence, the study employs VEC model whch account for non-statonarty at level. The most common methods to transform the varables to meet statonarty condton nclude dfferentatng, detrendng and transformng to the log form. For ths research combnaton of log transformaton and dfferencng methods are used to change the varables nto ther statonary form. The VEC model requres all varables to be non-statonary at level but statonary at frst dfference. Accordngly, the unt root test done usng ADF proves that t s approprate to use VEC model n ths study. The next step n estmatng the long-run relatonshp s, to determne the lag length. Gonzalo (1994) ndcate that under specfyng the number of lags n a VEC models can sgnfcantly ncrease the fnte sample bas n the parameter estmates and leads to seral correlaton. Hence, I estmate the lag usng dfferent selecton crtera (Table 4). Table 4. Lag Selecton Crtera Lag Cereal Producton length LR AIC HQIC SBIC 0 NA * -3.34* -2.86* * Notes: *shows the optmum lag chosen by the crtera; LR refers to log lkelhood; AIC = Akake nformaton crteron; SIC = Schwarz nformaton crteron; HQIC = Hannan-Qun nformaton crteron. The SBIC and HQIC methods measure the dscrepancy between the gven model and the true model, whch of course, has to be mnmzed. And they have a theoretcal advantage over other crtera of lag selecton (StataCorp., 2013b). Three out of four lag selecton crtera 113

6 Long Run Determnants of Cereal recommend usng one lag n estmatng the level of contegraton n VEC model. Hence, for ths study lag order of one s used to estmate the contegratng model. The fact that the tme seres varables under consderaton are non-statonary at level mples that the regresson result usng these varables n the classcal framework leads to spurous regresson unless ther lnear combnaton produces a statonary resdual (Gujarat, 2004). Hence, a Johansen test of contegraton s appled to test for potental long term relatonshp amongst the varables. Test of contegraton presented n table 5 confrmed that the varables are contegrated. Table 5. Johansen Multvarate Test of Contegraton Maxmum Rank (r) 114 Null Hypothess Parameters Trace stat. (5% Crtcal Value) Max stat. (5% Crtcal Value) 0 Ho: r = ** (47.2) 40.17(27.07) ** 1 Ho: r (29.68) ( Ho: r (15.4) 7.63 (14.07) 3 Ho: r (3.76) 0.01 (3.76) 4 Ho: r Note: ** The statstcs value s less than the 5% crtcal value and hence reject Ho. There are two test statstcs n Johansen test of contegraton, trace statstcs and max statstcs. The decson rule for both statstcs s that when these statstcs values are greater than the 5% crtcal value, the null hypothess sayng there s at least r level of contegraton, startng from r = 0 of no contegraton, wll be rejected. Accordngly, result from both statstcs rejects the null hypothess of no contegraton, confrmng the optmum maxmum rank to be one. Table 6. Summary Result of VEC Models Dependent Varable (Δ Ln) Constant ECT_1 R 2 Ch 2 Cereal Producton 0.054*** -0.50*** *** Land Fertlzer 0.11** *** CO 2 emsson 0.07** ** Note: *** and ** refers to 1% and 5% sgnfcance level respectvely. Table 6 presents the summary of VEC models whch take each varable both the dependent and explanatory varables n equaton 1 as a dependent varable to estmate ndependent regresson outputs. The VEC model allows the long-run behavor of the endogenous varables to converge to ther long-run equlbrum whle allowng a wde range of short-run dynamcs (Mukhtar & Resheed, 2010). In confrmaton wth ths, the coeffcent of the error correcton term (ECT_1) of cereal producton do have the expected negatve sgn and t s sgnfcant at 1% sgnfcance level. Hence, for ths model n the short run, cereal producton s adjusted by 50% of the past year devaton from equlbrum. Ths confrms the stablty of the model. The Johansen test of contegraton confrms that there s a long-term relatonshp between varables, hence the estmaton of the determnants of cereal producton, cannot be estmated

7 S.B. Wasse usng the conventonal OLS method rather an error correctng model. Accordngly, VEC model results of the long run relatonshp between cereal producton and exogenous (land, fertlzer and CO 2 emsson) varables s presented n table 7 below. The values n table (7) are subject to the Johansen normalzaton restrcton, n a sense the coeffcents whch are very close to zero and one are constraned to be zero and one respectvely, for normalzaton whch wll yeld the same maxmzed log lkelhood (StataCorp., 2013b). The contegraton equaton ndcates that the three parameters ncluded n the model are jontly sgnfcant at 1% sgnfcant level, wth Ch 2 value of Moreover, post-estmaton tests (Appendx 3) confrmed that there s no enough evdence at 5% sgnfcance level to reject the null that clams no autocorrelaton and errors are normally dstrbuted. Hence, generally, the output and tests ndcate that the model fts well and results are robust. Table 7. Contegratng Equaton of Long Term Determnants of Cereal Producton Beta (log) Coeff. Std. Err P Value Cereal producton 1 Land *** Fertlzer *** CO 2 emsson *** Constant Ch 2 value *** Parameters 3 Observatons (years) 52 Note: *** refers to 1% sgnfcance level. More formally, the long run contegraton equaton can be wrtten as: Producton Land 0.247Fertlzer 0.275CO 2 Table 7 presents the man results of ths study. CO 2 emsson whch s the man aggravatng factor of clmate change has a negatve and sgnfcant mpact on cereal producton n Ethopa. Specfcally, ncrease n the total CO 2 emsson by 10 percentage ponts s assocated wth 2.75% declne n the total cereal producton n the country. Ths result s n lne wth Joshua et al. (2016), Amponsah, Glory, and Samuel (2015) and Dawt et al. (2016) but contrary to Tonkaz et.al. (2010). Gven the pror premse of the negatve mpact of clmate change on developng countres economy, as stated n IPCC (2007), t was not surprsng that ts mpact on cereal producton of Ethopa s negatve. Agrculture s the most vulnerable sector to clmate change (Harry, Kaser, Rha, Davd & Radha, 1993; Pervez et al. 2015) and unless measures are taken to curb CO 2 emsson, agrcultural productvty would fall dramatcally, especally n developng countres (Clen, 2008). However, Chrstoph et al. (2015) got a promsng result sayng aggressve mtgaton could elmnate up to 81% of the negatve mpacts of clmate change on bophyscal agrcultural productvty globally by the end of the century. Hence, the current effort of the Ethopan government n mplementng Clmate Reslent Green Economy (CRGE) strategy whch s planned to foster development and sustanablty whle lmtng greenhouse gas emssons by 2030 should be strengthened. 115

8 Long Run Determnants of Cereal Both conventonal nputs, land and fertlzer, consdered n the model were found to have a postve and sgnfcant effect on cereal producton. As shown n table 7, an ncrease n land by 10 percentage ponts s attrbuted to 13 percentage ponts ncrease n total cereal producton. Smlarly, ncreasng the fertlzer consumpton by 10 percentage ponts rase the total cereal producton by 2.4 percentage ponts. Ths result confrms the study done by Fantu et al. (2015) whch clams land and labor expanson over tme to be mportant contrbutng factors for the ncreasng agrcultural producton n Ethopa. However, as the avalable arable land s becomng ncreasngly scarce, ncrease n producton wll be drven largely by ntensfcaton of nputs rather than expanson of land area (IFDC, 2012). Ths makes fertlzer consumpton a key element n ncreasng agrcultural producton n general and cereal producton n partcular. Fnally, after checkng all potental econometrc problems, cereal producton for the perod s forecasted by the predcted contngent equaton usng STATA verson 13. The total cereal producton n Ethopa s expected to ncrease, on average, annually by 2.8% for the next 10 years. Accordngly, the total cereal producton n Ethopa wll be 18,639 thousand metrc ton as of Table 8. Forecast of Cereal producton trend ( ) Producton forecast Coeffcent* Std. Err. P value Year *** Constant *** Adj_R Number of Obs. 10 Note: *** refers to 1% sgnfcance level; * OLS estmaton usng the projected yeld as dependent varable. 5. Concluson and Recommendatons Ths study was done wth the objectve of analyzng the determnants of cereal producton, wth specal attenton to CO 2 emsson, n Ethopa. The post estmaton result showed that the results are robust. The contegratng equatons normalzed relatve to cereal producton show that the three parameters (land, fertlzer and CO 2 emsson) ncluded n the model are jontly sgnfcant at 1% sgnfcant level. The study revealed that CO 2 emsson has a negatve and sgnfcant mpact on long-run total cereal producton n Ethopa. But land and fertlzer were found to have postve and sgnfcant long-term mpact, as expected. Fnally, the future cereal producton was forecasted by the predcted contngent equaton. Accordngly, the total cereal producton s expected to ncrease, on average, by 2.8% annually for the next 10 years. It should be noted that ths research gnores the role of technologcal progress to reduce the negatve mpact of clmate change on agrcultural producton. As concludng remark, the government should strengthen ts effort to mplement the green economy strategy to reduce possble effect of CO 2 emsson on the total gran producton and supply n the economy by adoptng envronment-frendly substtutes of energy sources and mplementng command and/or control systems for excess emsson of CO 2 by manufacturng frms. Moreover, gven that the total avalable land s lmted, efforts towards ncreasng future cereal producton and productvty should beneft from ntensve use of nputs (fertlzer). Hence, t s very crucal to plan on the ways to provde fertlzer for the farmers wth a reasonable prce on due tme. 116

9 S.B. Wasse Acknowledgment The author would lke to thank Professor Htosh Kusakar and Dr. Masahro Summoto, of Kobe Unversty, and Professor Chuck. A. Arze of Texas A&M Unversty - Commerce, for ther consderable and valuable comments durng the preparaton of the paper. The author would lke to acknowledge World Bank for provdng the data free of cost. References Abu, T. (2015). Gran and Feed Annual Report. (GAIN Report Number ET-1503). Retrevedfromhttp://gan.fas.usda.gov/Recent%20GAIN%20Publcatons/Gran%20and %20Feed%20Annual_Adds%20Ababa_Ethopa_ pdf. Amponsah, L., Glory, K.H. & Samuel, Y.A. (2015). Clmate Change and Agrculture: Modelng the Impact of Carbon Doxde Emsson on Cereal Yeld n Ghana. Agrculture and Food Scences Research 2(2): CSA (2015). Agrcultural Sample Survey Tme Seres Data for Natonal and Regonal Level From 1995/ /15. Central Statstcs Agency of Ethopa, Adds Ababa. Chrstoph, M., Joshua, E., James, C., Delphne, D., Chrstan, F., Thomas, A.M., & Erwn, S. (2015). Implcatons of clmate mtgaton for future agrcultural producton. Envron. Res. Lett do: / /10/12/ Clne, W.R., (2008). Global Warmng and Agrculture. Fnance and Development. Peterson Insttute for Internatonal Economcs and the Center for Global Development. Washngton DC. Retreved from Dawt, W.M., Zerayehu, S.E., & Tsegaye, G.G. (2016). The Impact of CO2 Emssons on Agrcultural Productvty and Household Welfare n Ethopa: A Computable General Equlbrum Analyss. Workng paper Seres, Ethopa: Envronment fordevelopment.retrevedfromhttp:// ns/efd-dp pdf Engle, R.F., & Granger, C.W.J. (1987). Co-ntegraton and error correcton: Representaton, estmaton, and testng. Econometrca 55(2): Fantu, N., Guush, B., & Alemayehu, S.T. (2015). Agrcultural Growth n Ethopa ( ): Evdence and Drvers. IFPRI workng paper 81. Adds Ababa, Ethopa: Internatonal Food Polcy Research Insttute. FDRE (2011). Ethopa s Clmate Reslent Green Economy: Green Economy Strategy. Adds Ababa, Ethopa: Federal Democratc Republc of Ethopa. Retreved from Gonzalo, J., (1994). Fve alternatve methods of estmatng long-run equlbrum relatonshps. Journal of Econometrcs 60: Gujarat, D. (2004). Basc Econometrcs, Fourth Edton. McGraw-Hll Companes. Gutu, T., Bezabh, E. & Mengstu, K. (2012). A tme Seres Analyss of Clmate Varablty and Its Impacts on Food Producton n Northern Shewa Zone n Ethopa. Afrcan Crop Scence Journal 20 (2): Harry, M., Kaser, S.J., Rha, D.S., Davd, G.R. & Radha, S. (1993). A Farm-Level Analyss of Economc and Agronomc Impacts of Gradual Clmate Warmng. Amercan Journal of Agrcultural Economcs 75: IFDC (2012). Ethopan Fertlzer Assessment. Internatonal Fertlzer Development Centre. Alabama, USA: IFDC. Retreved from IPCC (2007). Clmate Change 2007: Synthess Report. Contrbuton of Workng Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Clmate 117

10 Long Run Determnants of Cereal Change [Core Wrtng Team, Pachaur, R.K & Resnger, A. (eds.)]. Intergovernmental Panel for Clmate Change. Geneva, Swtzerland: IPCC. Joshua, C.H., Esn, C. & Kamal, P.U. (2016). Clmate Change and Its Impact on Wheat Producton n Kansas. Internatonal Journal of Food and Agrcultural Economcs 4(2): Khan, S.A., Kumar, S., Hussan, M.Z. & Kalra, N. (2009). Clmate Change, Clmate Varablty and Indan Agrculture: Impacts Vulnerablty and Adaptaton Strateges. In S.N. Sngh (ed.). Clmate Change and Crops (pp ). Berln: Sprnger. Mukhtar, T. & Rasheed, S. (2010). Testng Long Run Relatonshp between Export and Import: Evdence from Pakstan. Journal of Economc Cooperaton and Development 31(1): Neenu, S. Bswas, A.K. & Rao, A.S. (2013). Impact of Clmatc Factors on Crop Producton - A Revew. Agr. Revews 34 (2): Pervez, Z.J., Ghulam, S. & Khan, N.U. (2015). Impact of Clmate Change on Wheat Producton: A Case Study of Pakstan. The Pakstan Development Revew 49(4): StataCorp., (2013a). Stata: Release 13. Statstcal Software. College Staton, TX: StataCorp LP. StataCorp, (2013b). Stata Tme Seres Reference Manual. College Staton, TX: StataCorp LP. Sraj, M. (2014). Prvate nvestment and Economc Growth: Evdence from Ethopa (Unpublshed master`s thess). Mekele Unversty, Ethopa. Tonkaz, T., Dogan, E. & Kocygt, R. (2010). Impact of Temperature Change and Elevated Carbon Doxde on Wnter Wheat (Trtcum Aestvum L.) Grown Under Sem-Ard Condtons. Bulgaran Journal of Agrcultural Scence 16 (5): Worku, I. (2011). Road Sector Development and Economc Growth n Ethopa (EDRI Workng Paper 4). Adds Ababa, Ethopa: Ethopan Development Research Insttute. World Bank. (2015). World Bank Development Indcators Data Base. Retreved from: (Accessed on 01 June 2016). 118

11 S.B. Wasse Appendx Appendx 1 Trend of Cereal Producton and CO2 Emsson ( ) Fertlzer Consumpton (Metrc ton) Year Cereal producton n Thousands of Metrc ton Fertlzer Consumpton (Metrc ton) CO2 emsson (Kt) Appendx 2 Land Allocated for Cereal Producton n 1000 Hectares ( ) Year Appendx 3 Post Estmaton Dagnostcs Descrpton Ch 2 P value LM test of autocorrelaton Normalty Test Ch 2 P value Skewness Kurtoss J-Bera test Skewness test Kurtoss test Source: Own computaton (

12 Long Run Determnants of Cereal 120