




































Agriculture and Food 

Sciences Research 
ISSN 2411-6653 
Vol. 2, No. 2, 32-38, 2015 
http://www.asianonlinejournals.com/index.php/AESR 

  

 

* Corresponding Author 

 

 

32 

 

Climate Change and Agriculture: Modeling the Impact of 

Carbon   Dioxide Emission on Cereal Yield in Ghana 
 

Lawrence Amponsah
1*

 --- Glory Kofi Hoggar
2
 --- Samuel Yeboah Asuamah

3 

 
1
Department of General Agriculture, Sunyani Polytechnic, Ghana 

2
Department of General and Liberal Studies, Sunyani Polytechnic, Ghana 

3
Department of Marketing, Sunyani Polytechnic, Ghana 

 

Abstract 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
 

  

 

 
This work is licensed under a Creative Commons Attribution 3.0 License 

Asian Online Journal Publishing Group 

 

Contents 
1. Introduction ............................................................................................................................................................................... 33 

2 Research Methodology ............................................................................................................................................................... 34 

3. Empirical Results ...................................................................................................................................................................... 34 

4. Conclusions and Policy Recommendations ............................................................................................................................. 37 

References ...................................................................................................................................................................................... 37 

 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The objective of the paper is to contribute to the body of knowledge in the area of climate change 

and agriculture by examining the effect of carbon dioxide concentration (CO2) on cereal yield using 

autoregressive distributed lag models (ARDL). The research is based on quantitative, descriptive and 

cross-sectional research using secondary data obtained from World Bank data base for the period of 

1961-2010. The co-integration test indicates the series are co-integrated.  The results on the long run 

and shorts run elastically co-efficient indicate that there is significant negative link between CO2 and 

cereal yield. There significant positive long run and short run link between cereal yield and income 

(proxied by real gross domestic product). Policy makers and agriculture scientists and environmental 

scientists should put in place policies to reduce atmospheric temperature increase and pollution to 

benefit from CO2 fertilization in order to ensure food security. The findings indicate that income 

(proxied by real gross domestic product) positively affect cereal yield. The link between CO2 and 

cereal production should be examine in future studies current study considered cereal yield. 
 

Keyword: Climate change, CO2, Real gross product, Cereal yield. 

Jel Classification: Q54, Q56, Q57, Q58. 

 

http://creativecommons.org/licenses/by/3.0/


Agriculture and Food Sciences Research, 2015, 2(2): 32-38 

 

 

 

 

33 

 

1. Introduction 
The significant impact of changes in climatic conditions (an increased frequency of climate extreme events; 

altered precipitation and transportation regimes; higher temperatures; elevated CO2 Concentration)  agricultural 

production systems now and years to come in developed and developing economies [1]. The role of climate change 

in agriculture productivity is of much concern to researchers and policy makers [2]. This increased attention results 

from the fact that crop yield as well as the type of crops to grow is the function of climate change [2-4]. 

The role of agricultural sector in an economy such as job creation [5] provision of food security; provision of raw 

material for the industrial sector and supply of export product to generate foreign exchange makes research and 

policy makers spend a lots of resources in examining the effects of climate change on agriculture. Researchers [6] 

have reported that there is slower growth rate of agricultural sector over the years due to climate change and this 

becomes more worrisome given the current nature of urbanization and population growth rate. Since in the 

developing economies, such as Ghana, agricultural is based largely on the climatic condition [6, 7] there is the need 

to empirically examine the effect of climate change on agriculture so that the right adaptation measure could be put 

in place to ensure food security. 

Carbon dioxide (CO2) is among the influential climatic factors the influence crop yield and which has 

empirically attracted attention in the literature. According to researchers [8-12] CO2 acts as a fertilizing element 

(carbon dioxide fertilization) in the growth of crops classified as C3 and C4 crops. CO2 is reported to have both 

direct and indirect effect of plant growth by enhancing photosynthesis processes and changing the climate through 

greenhouse effect. 

According to Blanc [8] “greater CO2 concentration enhances CO2 assimilation by crops. This results in faster 

stomata closures and ultimately lower transpiration rates (i.e. crops loose less water). Therefore, CO2 Concentration 

increases are most beneficial in sudano-sahel, which has a warm and arid climate”. The implication for these 

theoretical frameworks is that crop yield is expected to have positive link with carbon dioxide emissions, especially 

C4 plants, which is a subject of empirical verification such as the current study. 

The empirical findings on the effects of the link between climate change and agricultural productivity are found 

in the works of various researchers [6, 8, 12-25]. Alaim [6] state that, rising concentration of CO2 may cause decline 

in agricultural productivity and “will act as a fuel to the higher prices of goods and services in the economy”. 

Blanc [8] reported that CO2 emissions have significant long run and short run impact on millet yield but not on 

cassava and maize yields. The results indicate that 1ppm CO2 increase in CO2 concentration leads to about 4.75% 

millet yields increase. At the regional level, a1ppm increase CO2 concentration leads to about 11.9% increase in 

millet yields. The results indicated that CO2 concentration insignificant impact on sorghum yields. 

In the study by Wang, et al. [26] the results indicate that without the direct effect of CO2 the estimated model 

indicated that all three climate change scenario examined significantly increase biomass production compared to the 

baseline scenarios whereas the combined effect (climate and CO2) increased biomass production much more, 

increased CO2 concentration of 220 and 120 ppm resulted in increased biomass production of 43-45% and 25%, 

respectively in the study. 

Other researchers such as Lobell and Field [14] used regression analysis and reported of positive plant responses 

to responses to CO2 fertilization in the 20 largest producing countries of the Northern Hemisphere. Adams [9] 

established positive plant yields results from CO2 fertilization. In a study by De Tafur, et al. [27] negative link was 

reported between CO2 and cassava yields. Fuhrer [28] states that “ Warning accelerate plant development and reduce 

grain-fill, reduces  nutrients use efficiency, and favors C4 weeds over C3 crops… and that warming again reduces 

the positive CO2 effect”. Cure and Acock [12] used experimental research to report of a positive relationship between 

maize yields and CO2 fertilization.  

 

1.1. The Agriculture Sector of Ghanaian Economy  
According to The State of Ghanaian Economy [29] the contribution of the agriculture sector to the Ghanaian 

economy in recent past has been low in relation to other sectors. For examples in 2011 the agriculture sector 

(25.65%) contributed least to the economic growth of the country followed by the industrial sectors (25.9%) and then 

the service sector (48.5%). The sector is still dominated by the peasant economy without any significant change since 

independent. Statistics indicate that growth rate of the sectors has been falling. For example in 2011 the growth rate 

was 5.3% but fell to 0.8% in 2011.  

The sector even experienced negative growth rate in 2007. The contribution to the positive growth rate in 2011 

after the rebounce in 2008 and 2009 are the subsectors such as cocoa (14%); livestock (5.1%) and the crop subsector 

(3.7%). Some of the subsectors (forestry and fisheries) have registered negative growth rate. The main cereal crops in 

Ghana are maize; sorghum; millet and rice. The performance of the cereals over the years has not been impressive. In 

2011 the total output of the cereal crops did not attain positive growth. In 2011 the output was 186,000 tonnes 

whereas in 2010 the output was 218,000 tonnes. Maize has been the leading subsectors of the cereal crops in terms of 

yields and cultivated hectares of land. 

The review of the literature and the agricultural sector indicates that the agricultural sector is important in 

relation to the economic development of any economy especially in small but open economy such as Ghana. 

Examination of factors that are responsible for the growth agricultural sectors in the empirical study is worth doing. 

The current paper models the long run and short run relationship between cereal yields and CO2. The review of the 

literature indicates mixed findings of the effects of CO2 on crop yield.  Few econometrics studies exist in relation to 

developing economies such as Ghana. The finding on cereal yields and CO2 has also been mixed in the literature [8]. 

The current paper fills the literature gap. Policy makers are provided with policy documents in order to adapt to the 

changes of climate change and to ensure food security.  Theories of fertilization and climate change are provided 

with further understanding. 



Agriculture and Food Sciences Research, 2015, 2(2): 32-38 

 

 

 

 

34 

 

The general objective of the study is to contribute to the body of knowledge in the area of climate change and 

agriculture by modeling the stable long run relationship between carbon dioxide emissions and cereals yields 

empirically. Specifically, the paper examines; (a) The unit root properties of carbon dioxide emissions and cereal 

yields, (b) The co-integration relationship between carbon emission and cereals yield, (c) The long run and short run 

parameter estimate of the effect of carbon emission on cereal yield. 

The study is based on the following research questions; (a) What is the effect of carbon dioxide emissions on 

cereal yield? (b) What is the nature of long run and short run relationship between carbon dioxide emissions and 

cereals? The assumptions tested in the current research are; (a) Carbon dioxide emissions and cereals yield are co-

integrated, (b) Carbon emissions are key influential factors explaining cereal yield. 

Data for the study was obtained from World Bank database. The estimated model might suffer from errors in 

variables and data massaging. The study is descriptive in nature and as such, causality issues are not the focus of the 

current study. The period for the study is between 1961-2010. Structural breaks in unit root are not considered in the 

study. Other influential factors such as temperature and precipitation are not considered for non-availability of data. 

The rest of the study deals with the methods, empirical results conclusions and policy implications. 

The rest of the paper looks at the methodology, empirical results, discussion, and conclusion 

 

2 Research Methodology 
2.1. Design/Format/Strategy 

The study modeled the link between CO2 and cereal yield in a trivariate, quantitative, descriptive and cross-

sectional research format. Annual time series data for Ghana for the period 1961-2011 was used. The period was 

chosen for availability of data. The data used are CO2; cereal yield and economic growth (proxied by real gross 

domestic product). 

 

2.2. Unit root Test 
The examination of unit roots is based on the Kwiatkowski, et al. [30] (KPSS) and Augmented [31] (ADF) test 

models. The ADF model is based on the null assumption (H0) that there is a unit root in the levels of the series. The 

alternative hypothesis (H1) is that the series are stationary in levels. The KPSS model is based on the null assumption 

(H0) that there is no unit root (stationary) in the levels of the series. The alternatives hypothesis (H1) is that the series 

are unit root (stationary) in levels. 

 

2.3. Bound Testing Approach to Cointegration Model 
The cointegration test is based on the Autoregressive distributed lag model (ARDL) developed by Pesaran and 

Shin [32]. Series variables in a model are considered to be cointegrated if they are integrated of order one, 1(1) in the 

presence of non-zero vector which is integrated of order zero, 1(0). The non- stationary series with the same order of 

integration may be cointegrated if there exist some linear combination of the series that can be tested for stationarity. 

The cointegration analysis is based on the null hypothesis (Ho) that there is no cointegration among the series 

variables in the models against the alternatives hypothesis (Ha) that the variables are cointegrated. H0: b1=b2-b3=… 

=b k=0. The alternative hypothesis (Ha) is not Ho. The ARDL model estimated is assess for goodness of fit using 

various diagnostic test such as J-B Normality test. Breusch-Godfred LM test, ARCH LM test, white 

Heteroskedastically test, Ramsey RESET. The stability of the model parameters are examined using the cumulative 

sum of recursive residuals (CUSUM) and the cumulative sum of squares of recursive residuals (CUSUMSQ). 

 

2.4. The Model 
The conceptual model for the study state that cereal yield (CY) is a function of carbon dioxide emission (CO2) 

with real gross domestic product (RGDP) as the control variable. That is InCY= f (lnCO2, lnRGDP). The model is 

estimated in natural log form. 

 

3. Empirical Results 
The results are ADK; KPSS unit roots test results; cointegration test results; long run results; short run results; 

diagnostic test results and the stability test results. 

 

3.1. Time Series Plots of the Series Variables 
The plots of the series variables in levels and in first difference are shown in figures 1to6. The plot of the series 

in levels indicate the series are not stationary in levels but become stationary in first difference. This calls for formal 

examination of the stationary properties using the ADF test and the KPSS test. The results are presented in tables. 

 

3.2. The ADF Model 
The unit root results based on the ADF test are reported in Table 1and Table 2.  The result of the ADF test for 

unit root in logarithm show that the series are non- stationary in intercept and trend. The null hypothesis of unit root 

was accepted for the series variables. 

 
Table-1. ADF stationary test results with a constant and time trend 

Variables (logarithm) T-ratio ADF P-value Results 

CO2 -0.523027 0.9826 Not stationary 

CY -1.74441 0.7315 Not stationary 

RGDP 0.0578461 0.9969 Not stationary 

                                      Source: Author’s computation, 2014 

 



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35 

 

Taking logarithm of the first difference of the series and testing these with intercept and time trend does make 

the series stationary. That is, the null hypothesis of unit root was rejected. The series become stationary after first 

difference. These results indicate that the series exhibit unit root processes and are integrated of order one, 1(1) and 

order zero, 1(0). The results are reported in Table 3 

 
Table-2. ADF stationary test result with constant and time trend 

Variables( 1
st
 diff. of Logarithm) T-ratio ADF P-value Results 

ΔlnCO2 -6.01131 1.89e-006 stationary 

ΔlnCY -4.83595 0.0003711 stationary 

ΔlnRGDP -4.7488 0.0005364 stationary 
                               Source: Author computation, 2014 

 

3.3. The KPSS Model 
The KPSS test results are presented in table 4. The KPSS is a reversed test for unit root. It is used in the current 

study for confirmation of the stationary properties of the series. The series were examined in their logarithm form. 

The results confirm that of the ADF test results. 

 
Table-3. KPSS stationary test results with a constant and time trend 

Variables (logarithm) T-stats Results 

CO2 0.28804(<0.01) Not stationary 

CY 0.232247(<0.01) Not stationary 

RGDP 0.306493(<0.01) Not stationary 

                    Source: Author computation, 2014 

                    Note: 1%; 5% and 10% Critical values are 0.213, 0.149 and 0.121 respectively 

 
Table-4. KPSS stationary test results with constant and trend 

Variables( 1
st
 diff. of Logarithm T-stats Results 

ΔlnCO2 0.065819(>0.1) stationary 

ΔlnCY 0.0478982(>0.1) stationary 

ΔlnRGDP 0.100559(>0.1) stationary 
                  Source: Author computation, 2014 

                  Note: 1%; 5% and 10% Critical values are 0.212, 0.149 and 0.122 respectively 
 

In summary, the test results from the ADF and the KPSS indicate that the series exhibit unit root processes and 

are integrated of order one, 1(1) and order zero, 1(0). The detection of unit roots in the series indicate that shocks to 

the series will have permanent effects and not transitory effects. The results indicate that cointegration can be 

performed. 

 

3.4. Cointegration Analysis using Autoregressive Distributed Lag (ARDL) Model/Bound Approach 

to Cointegration 
In this section of the paper the results of the relationship between carbon dioxide emissions, infant mortality and 

real gross domestic product ate presented. The dependent is variable is infant mortality whereas dioxide emission and 

real gross domestic products are the independent variables. The results on the bound test, long run and short run 

parameters are presented in table 5, table 6 and table 7. 

 

3.4.1. Bound Approach to Cointegration for Infant Mortality (FM) 
The results presented reported in table 5 indicate significant cointegration between carbon dioxide emissions 

(CO2); cereal yield (CY) and real gross domestic product (RGDP) in Models 1, 2 and 3. In model 1, the F- statistics 

value of 11.0045 is greater than the critical value of the upper bounds at the 90%, 95%, and 99% levels of 

significance, which is an indication of cointegration between carbon dioxide emissions, cereals yields, and real gross 

domestic product with carbon emissions as the dependent variable. In model2, the F-calculated value of 4.4938 is 

greater than the critical values of the upper bounds at the 90% and 95% levels of significance, which are indication 

of cointegration between carbon dioxide emissions, cereals yield, and real gross domestic product with carbon 

emissions as the dependent variable. In Model 3, the F-calculated value of 8.9438 is greater than the critical values of 

the upper bounds at the 90%, 95%, and 99% levels of significance, which is indication of cointegration between 

carbon dioxide emissions, cereal yields, and real gross domestic product with real gross domestic product as the 

dependent variable. The null assumption of no cointegration is rejected in models 1, 2 and 3. Models 1 are estimated 

for long run and short run parameters (elasticity coefficient). 

 
Table-5. Test for cointegration relationship 

Critical bounds of the F-statistic: intercept   

 

Models 

90% level 95% level 99% level 

1(0)             1(1) 

2.915           3.695 

1(0)       1(1) 

3.538     4.428 

1(0)  1(1) 

5.155  6.265 

Computed F-stats decision 

1.FlnCY(lnCY/lnCO2, lnRGDP 11.0045[0.001***] cointegrated 

2.FlnCO2(lnCO2/lnCY, lnRGDP) 4.4938[0.034**] cointegrated 

3.FlnRGDP(lnRGDP/lnCO2, lnCY 8.9438[0.003***] cointegrated 
Source: Author computation, 2014: Note: critical value are obtained from Pesaran, et al. [33] and Narayan [34]:  
Note*** and** denote significance at 1% and 5% levels of significance. 

 



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36 

 

3.4.2. Results of Long- Run Elasticities of ARDL Model  
The long-run relation between carbon dioxide emissions, cereals yields and real gross domestic product was 

estimated with cereal yield as the dependent variable. The results are reported in table 6. The results indicated that 

carbon dioxide concentration is significant (at 10% level) explanatory variable of cereal yield in the long run. The 

results indicate that increase in carbon dioxide emissions (CO2) by 1% will lead to a decrease in cereal yields by 

about 54.67%. There is positive significant long run relationship between cereal yield (CY) and real gross domestic 

product (RGDP) in the model estimated. The results indicate that increase in real gross domestic (RGDP) by 1% will 

lead to an increase in cereal yield by about 76.41%. 

 
Table-6. Estimated Long run Coefficients. Dependent variable is lnFM 

Variable Coefficient Std.Error T-ratio P-value 

Constant 6.2748 1.8826 3.3330 0.002*** 

Trend 0.035723 0.011348 3.1479 0.003*** 

InCO2 -0.54665 0.29027 -1.8833 0.066* 

InRGDP 0.76406 0.23174 3.29711 0.002*** 
Author’s computation, 2014: ARDL (1) selected based on Akaike Information criterion.  

Note: *** and * denotes statistical significance at the 1% and 10% levels significantly. 

 

3.4.3. Result of Short-Run Elasticities of ARDL Model  
The results of the short-run dynamic equilibrium relationship coefficients estimated with trend and error 

correction term (ecm) are reported in table 7. The results on the nature of the short run coefficient are not different 

from that of the long run parameters. The results indicate that carbon dioxide concentration is statistically significant 

determinants of cereal crop yields in the short run. The results indicate that increase in carbon dioxide emissions 

(CO2) by 1% will lead to a decrease in cereal yield by about 31.03%. There is significant positive short run 

relationship between cereal yield (CY) and real gross domestic product (RGDP) by 1%will lead to an increase in 

cereal yield by about 43.37%. The error correction term is statistically significant and does have theoretical expected 

sign which is negative. The coefficient of -0.56767 indicate that, after 1 percent deviation or shock to the system, the 

long run equilibrium relationship of infant mortality is quickly re-established at the rate of about 56.77% per annum. 

The value does indicate moderate adjustment rate. 

 
Table-7. Short-run representation of ARDL model. ARDL (1) selected based on Akaike Information criterion. Dependent variable: ΔlnFM 

Variable Coefficient Std.Error T-ratio P-value 

Trend 0.020279 0.0059051 3.4341 0.001*** 

ΔInCO2 -0.310331 0.14638 -2.1199 0.040** 

ΔInRGDP 0.43373 0.14503 2.9906 0.005*** 

Ecm(-1) -0.56767 0.13033 -4.3557 0.000*** 
Source: Author’s computation, 2014.  

Note: *** and* denotes statistical significance at the 1% and 10% levels significantly. 

 

3.4.4. Results of Diagnostic Tests 
The diagnostic tests of the short-run estimation to examine the reliability of the results of the error correction 

model are reported in table 8. The null hypothesis of no serial correlation is not rejected using the Lagrange 

multiplier test and F-statistics. The reset test showed evidence of incorrect functional specification of the model. The 

estimated model did not passed the normality test. The model passed Heteroscadasticity test indicating the variances 

are constant over time. The R
2
(0.81733) and the adjusted R

2
(0.80034) in the Table are an indication of very well 

behave model. The coefficient indicate approximately 81.73% of the variations in cereal yield are attributed to 

carbon dioxide emissions and real gross domestic product. 

 
Table-8. Short run diagnostic tests of ARDL model 

Test statistics LM Version F Version 

A: Serial correlation CHSQ(1)=.15519[0.694] F(1, 42) =0.13623[0.714] 

B: Functional form CHSQ(1)= 5.5570[0.018] F(1, 42)=5.4990[0.024] 

C: Normality CHSQ(2)=12.1834[0.002] Not applicable 

D: Heteroscedasticity CHSQ(1)=1.4482[0.222] F(1, 46)= 1.4718[0.23] 

A: Language multiplier test of residual serial correlation 

B: Ramsey’s RESET test using the square of the fitted value 

C: Based on a test of test skewness and kurtosis of residuals 

D: Based on the regression of squared residual on squared fitted values 
               Source: Author’s computation, 2014 

 

The stability of the long run estimates was determine by employing the cumulative sum ( CUSUM) and 

cumulative sum of squares (CUSUMG) procedures. This was determined using the residual of the error-correction 

model indicate by equation (1). The CUSUM test of stability determines the methodological arrangements of the 

estimate and its null hypothesis states the coefficients are stable. The null assumption is rejected when the CUSUM 

surpasses the given critical boundaries, which demonstrate unstable nature of the estimates. The CUSUMG 

determines the stability of the variance. Both tests indicate that the estimate and the variance were stable as the 

residuals and the squared residuals fall within the various 5% critical boundaries. The null assumptions are rejected 

in both tests. 

 



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37 

 

4. Conclusions and Policy Recommendations  
The objectives of the paper have been achieved. The long run link between CO2 and cereal yield has been 

examined. The series variables in the estimated model are cointegrated. The long run and short run elasticities are 

statistically significant but do not have the expected signs. The CO2 elasticity of cereal yields is negative in the long 

run and short run. The findings of the current study are in support of the findings of previous studies reported in the 

literature [6, 8, 27, 35-37]. 

Blanc [8] reported of insignificant effect of CO2 concentration on maize yield. Fuhrer [28] indicate that the 

benefits of CO2 may be lost in warmer climate. According to J., et al. [37] crop yield (wheat and rice) is decreased as 

a results of high temperature (Above 32) and that higher temperature offset an increase in yield resulting increase 

atmospheric CO2 concentration. Amthor [36] indicated benefits of CO2 fertilization is largely offsets by pollutants 

and nutrients limitation. De Tafur, et al. [27] reported of negative link between CO2 concentration and cassava 

yields. The findings are in consistent with the findings of researchers [12, 14, 26] who reported of positive significant 

link between CO2 concentration and crop yield. 

Policy makers, agriculture scientists, and environmental scientists should put in place policies to reduce 

atmospheric temperature increase and pollution to benefits from CO2 fertilization in order to ensure food security. 

Agriculture practices should ensure there is no issue of limiting nutrient crop farming. There should by intensive 

agriculture that will save the potential to adapt to changing climate conditions. Farmers should adapt policies such as 

providing irrigation or increasing its efficiency, maintaining or improving flood control, encouraging agronomic 

research. The findings indicate that income (proxies by real gross domestic product) positively affect cereal yield. 

Economic policy makers should put in place policies to ensure economic growth so as to be able to attain increase 

cereal yield. Future studies should examine causality issues since the current study is descriptive in nature. The link 

between CO2 and cereal production should be examined in future studies since the current study considered cereal 

yields.  

 

References 
[1] P. Calanca and J. Furhrer, Swiss agriculture in a changing climate: Grassland production and it economic value, in: Haurie, A. and 

Viguier, L. (Ed.). The coupling of climate and economic dynamics- essays on integrated assessment. Advances in global change 

research vol. 22. Dordrecht, NL: Springer, 2005. 

[2] Y. B. Zaied, "Long run versus short run analysis of climate impacts on agriculture," Economic Research Forum (ERF), vol. 1, p. 14-

55, 2013. 

[3] R. Mendelsohn, W. D. Nordhaus, and D. Shaw, "The impact of global warming on agriculture: A Richardian analysis," American 

Economic Review, vol. 84, pp. 753-771, 1994. 

[4] G. Fischer, M. Shah, H. Van Velthuizen, and F. Nachtergaele, Global agro-ecological assessment for agriculture in the 21st century: 

Methodology and results. Rome, Italy: International Institute for Applied Systems Analysis Laxenberg, Austria and Food and 

Agriculture Organization of the United Nations, 2001. 

[5] World Bank, World development report 2008: Agriculture for development world bank. Washington D.C: World Bank, 2007. 

[6] M. Q. Alaim, "Climate change, agricultural productivity and economic growth in India: The bounds test analysis," International 

Journal of Applied Research and Studies (JJARS), vol. 2, pp. 1-14, 2013. 

[7] K. S. K. Kumar and J. Parikh, "Indian agriculture and climate sensitivity," Global Environmental Change, vol. 11, pp. 147-154, 

2001. 

[8] E. Blanc, "The impact of climate change on crop production in Sub-Saharan Africa," PhD Thesis, 2011. 

[9] J. M. Adams, Vegetation-climate interaction: How vegetation makes the global environment. Berlin: Springer Praxis Books, 2007. 

[10] D. Walker, Green plants. London: Evans Brothers Ltd, 2006. 

[11] W. J. Maunder, Dictionary of global climate change. London: UCL Press Ltd, 1992. 

[12] J. D. Cure and B. Acock, "Crop responses to carbon dioxide doubling: A literature survey," Agricultural and Forest Meteorology, 

vol. 38, pp. 127-145, 1986. 

[13] C. Lippert, T. Kirmly, and J. Aurhbacer, "A Richardian analysis of the impact of climate change on agriculture in Germany," Climate 

Change, vol. 97, pp. 593-610, 2009. 

[14] D. B. Lobell and C. B. Field, "Estimation of the carbon dioxide (CO2) fertilization effect using growth rate anomalies of CO2 and 

crop yields since 1961," Global Change Biology, vol. 14, pp. 39-45, 2008. 

[15] G. Lang, "Where are Germany’s grains from Kyoto? Estimating the effects of global warming on agriculture," Climate Change, vol. 

84, pp. 423-439, 2007. 

[16] W. Schlenker, W. Hanamann, and A. Fisher, "The impact of global warming on U.S agriculture: An econometric analysis of optimal 

growing conditions," Review of Economics and Statistics, vol. 88, pp. 113-125, 2006. 

[17] J. D. Derner, H. B. Johnson, B. A. Kimball, P. J. Pinter Jr, H. W. Polley, C. R. Tischler, T. W. Bouttons, R. L. LaMorte, G. W. Wall, 

N. R. Adam, S. W. Leavitt, M. J. Ottman, A. D. Matthias, and T. J. Brooks, "Above – and below-ground responses of C3-C4 species 

mixtures to elevated CO2 and soil water availability," Global Change Biology, vol. 9, pp. 452- 460, 2003. 

[18] F. N. Tubiello and F. Ewert, "Simulating the effects of elevated CO2on crops: Approaches and applications for climate change," 

European Journal of Agronomy, vol. 18, pp. 57-74, 2003. 

[19] G. Fischer, H. Van Velthuizen, M. Shah, and F. Nachtergaele, Global agro-ecological assessment for agriculture in the 21st century: 

Methodology and results. Rome, Italy: International Institute for Applied Systems Analysis Laxenberg, Austria and Food and 

Agriculture Organization of the United Nations, 2002. 

[20] B. A. Kimball, K. Kobayashi, and M. Bindi, "Responses of agricultural crops to free-air CO2 enrichment," Advances in Agronomy, 

vol. 77, pp. 293-368, 2002. 

[21] R. M. Adams, B. H. Hurd, S. Lenhart, and N. Leary, "Effects of global climate change on agriculture: An interpretative review," 

Climate Research, vol. 11, pp. 19-30, 1998. 

[22] R. M. Adams, R. A. Fleming, C. C. Chang, B. A. McCarl, and C. Rosenzweig, "A reassessment of the economic effects of global 

climate change on U.S. agriculture," Climate Change, vol. 30, pp. 147-167, 1995. 

[23] N. J. Rosenberg, Towards an integrated assessment of climate change: The MINK study vol. 173. Boston: Kluwer Academic 

Publishers, 1993. 

[24] C. Rosenzweig and M. Parry, "Potential impact of climate change on world food supply," Nature, vol. 367, pp. 133-138, 1994. 

[25] W. X. Wang, B. Vinocur, and A. Atman, "Plant response to drought, salinity and extreme temperatures: towards genetic engineering 

for stress tolerance," Plant, vol. 218, pp. 1-14, 2003. 

[26] H. Wang, Y. He, B. Qian, B. McConkey, H. Cutforth, T. McCaig, G. McLeod, R. Zentner, C. Campbell, R. DePauw, R. Lemke, K. 

Brandt, T. Lui, X. Qin, G. Hoogenboom, J. White, and T. Hunt, "Impact of climate change on wheat production for ethanol in 

Southern Saskatchewan, Canada, climate change issues," presented at the World Renewable Energy Congress 2011 Sweden, 2011. 

[27] S. M. De Tafur, M. A. El-Sharkawy, and F. Calle, "Photosynthesis and yield performance of cassava in seasonally dry and semiarid 

environment," Photosynthesis, vol. 33, pp. 249-257, 1997. 



Agriculture and Food Sciences Research, 2015, 2(2): 32-38 

 

 

 

 

38 

 

[28] J. Fuhrer, "Agro-ecosystems responses to combinations of elevated CO2, Ozone and global climate change," Agric. Ecosyst. 

Environ., vol. 97, pp. 1-20, 2003. 

[29] The State of Ghanaian Economy, ISSER. Legon: University of Ghana, 2011. 

[30] D. Kwiatkowski, P. C. B. Philips, P. Schmidt, and Y. Shin, "Testing the null hypothesis of stationary against the alternative of unit 

root," Journal of Econometrics, vol. 54, pp. 159-178, 1992. 

[31] D. A. Dickey and W. A. Fuller, "Distribution of the estimators for autoregressive time series with a unit root," Econometrica, vol. 49, 

pp. 1057-1072, 1981. 

[32] M. H. Pesaran and Y. Shin, An autoregressive distributed lag modelling approach to cointegration analysis. London: Cambridge 

University Press, 1999. 

[33] H. M. Pesaran, Y. Shin, and R. J. Smith, "Bounds testing approaches to the analysis of long-run relationships," Journal of Applied 

Econometrics, vol. 16, pp. 289-326, 2001. 

[34] P. K. Narayan, Reformulating critical vlues for the bounds F-statistics approach to cointegration: An application to the tourism 

demand model for Fiji. Discussion papers. Department of economics. Victoria, Australia: Monash University, 2004. 

[35] S. P. Long, E. A. Ainsworth, A. D. B. Leakey, and P. B. Morgan, "Global food security," Philos. Trans. R Soc. Lond Biol. Sci., vol. 

360, pp. 2011-2020. Doi: 10.1098/rstb.2005.1749, 2005. 

[36] J. S. Amthor, "Effects of atmospheric CO2 concentration on wheat yield: Review of results from experiment using various 

approaches to control CO2 concentration," Field Crop Res., vol. 73, pp. 1-34. Doi: 10.1016/S0378- 4290(01)00179-4, 2001. 

[37] G. J., B. Walker, W. Steffen, J. Canadell, and J. S. I. Ingram, Managed production systems. In the terrestrial Biosphere and Global 

change: Implications for natural and managed systems. Cambridge, UK: Cambridge University Press, 1999. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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