EFFECT OF CLIMATE CHANGE ON COCOA PRODUCTION IN NIGERIA

EFFECT OF CLIMATE CHANGE ON COCOA PRODUCTION IN NIGERIA (1970-2017)
By
EGWUE, Ogechi Lynda
Department of Agricultural and Applied Economics, Faculty of Agriculture, Rivers State University, Nkpolu Oroworukwo, Port Harcourt.

Email: [email protected]; 08037321001
Presented At the International Conference on Economic Development and Sustainability At Rivers State University on 12th & 13th April, 2018
ABSTRACT
The study assessed the effect of climate change on cocoa output in Nigeria. Cocoa is a leading agricultural export of Nigeria and was a major foreign exchange earner for Nigeria in the 1950s and 1960s. There was a massive decline in cocoa production in 1972/73 season attributable to the drought that occurred that season which was the first major evidence of climate change in the Nigeria. This occurrence therefore made it important to assess the responsiveness of cocoa output to change in climate. Specifically, the study examined the trend of rainfall, temperature over the years and also assessed their effect on cocoa production in Nigeria. Data employed were national aggregates of cocoa production covering 1970 to 2017 and climate variables (temperature and rainfall) covering 1970-2017 collected from the Nigeria bureau of Statistics and world bank data catalogue respectively. Augmented Dickey Fuller unit root test was carried out on temperature, rainfall and cocoa output to test for the stationarity of each series. The result revealed that each series is non stationary at level but stationary at the second difference. Cointegration analysis was carried out to ascertain if the series are linked together in common long run equilibrium. The result revealed that the series are not cointegrated as the ADF statistics for the cointegration are -2.3739 and -1.6055 for “constant with trend” and “constant with no trend” situations respectively and these values do not fall within the range of the critical values. Ordinary Least Square method of regression was employed as against the error correction model as the series have been found to be unlinked in common long run equilibrium. The study revealed that rainfall amount and temperature have significant positive effect on cocoa production in both short and long run. 46.69% change in cocoa output is attributable to climate change while the remaining 53.31% is attributable to other factors outside of climate change. The study recommends that cocoa research agencies in Nigeria should look into other factors outside of climate change responsible for the fluctuations in production over the years. This is important because these other factors hold the key to explaining close to 54% of variation in Cocoa yield; research should be geared at producing much more prolific species of cocoa considering the suitability of our climate to its growth; and research should be geared towards constructing controlled environments where the temperature can be controlled to the levels for optimum production of cocoa.

Keywords: Cocoa, Climate, Export, Foreign Exchange,
1.0 INTRODUCTION
Climate change according to Adejuwon (2004) is the variation in the global or regional climates over a period time and it describes changes in the variability or average state of the atmosphere over time scales ranging from a decade to millions of years.

Climate is an important factor of agricultural productivity. Climate change is caused by the release of ‘greenhouse’ gases into the atmosphere. These gases accumulate in the atmosphere, which result in global warming. The related factors which cause changes in global climate such as temperature, precipitation and soil moisture, block the transmission of heat level (Wiah and Twumasi-Ankrah, 2017).

Cocoa is one of the most important tree crops of the humid tropical regions. The average world’s annual production of cocoa, from 2012 to 2015, is 3129 thousand tonnes, of which 72% comes from Africa (ICCO, 2015).Cocoa is a major cash crop of the tropical forest, most notably in West Africa where export earnings from its sales form a major part of the economy (Mayhew and Perry, 1998).

According to ICCO, (2008), cocoa is a leading agricultural export of Nigeria and was a major foreign exchange earner for Nigeria in the 1950s and 1960s. Nigeria was the second world largest cocoa producer in the 1960s producing between 250,000 to 308,000 metric tons for export yearly and generating about 50 percent of Nigeria’s revenue (Amos and Thompson, 2015). Nigeria’s share of the world output declined considerably in the 70s and 80s. This decline can be attributed to two major factors which are the investment in the oil sector in the 70s and 80s (FAO, 2015) and the drought of 1972/73 which was the first evidence of climate change in the region (Nwachukwu et al. 2012). As of 2010, Cocoa production accounted for only 0.3% of agricultural GDP. Nigeria is currently the world’s fourth largest producer of Cocoa, after Ivory Coast, Indonesia and Ghana (FAO 2015) and the third largest exporter, after Ivory Coast and Ghana (Verter, 2014).
Cocoa production in Nigeria witnessed a downward trend after 1970/1971 season when its export declined to 216,000 metric tonnes and this reduced Nigeria’s market share to about 6 percent to date and fifth world largest cocoa producer. (ICCO, 2008; Amos and Thompson, 2015). Currently, cocoa is grown in commercial quantity in nine State of the Federation. However, crop yield cocoa inclusive depends on climate and water supply generally (Dennett et al. 1981) and it is agreed that rainfall is an important climatic element for assessing water supply for agriculture in the tropics (Omotosho, 2001)
Therefore, since agricultural production in part of tropic is rain-fed, and recognizing that the constraints of rain-fed agriculture in Nigeria in particular as the erratic rain distribution (Jonathan et al., 2009), there is need to determine to what extent climate change affects cocoa production in Nigeria. This study aims to know the extent of the effects of climate change on cocoa production in Nigeria. The broad objective of this study was to assess the effect of climate change on cocoa production in Nigeria. The specific objectives of this study were to: examine the trend of rainfall and temperature in Nigeria over the years, examine the trend of cocoa yield in Nigeria over the years, assess the effect of climate variables on cocoa yield in Nigeria.

2.0 LITERATURE REVIEW
The Ricardian Cross-sectional Analysis
This approach explores the relationship between land values or net revenue and climate variables (usually temperature and precipitation) on the basis of statistical estimates from farm survey or country-level data. The “traditional? Ricardian analyses implicitly account for contemporaneous farm level adaptations. Using crop revenue as weights, the study predicts an increase in land values as a result of climate change in the US. This result was corroborated by the estimates of Reinsborough (2003) study on Canada. Using farmers’ perceptions of land value across eleven African countries, Maddison et al. (2006) forecast damaging climate change impacts for Africa while acknowledging large disparities across countries. They observe that countries with warmer climates suffer greater losses.

Nwachukwu et al. (2012) examined the relative effect of climate change on cocoa productivity in Nigeria. Overall, rainfall recorded a significant negative coefficient while that of temperature was positive coefficient, implying decreasing rainfall with rising temperature. More so, rainfall and its squared term were the only significant climatic variables influencing the productivity of cocoa. Chizari et al. (2017) carried out a similar study on the effect of the climate change philosophy on the productivity of cocoa in Malaysia. According to the study, climate change is arguably one of the most important factors influencing agricultural production in developing countries such as Malaysia. The study applied the autoregressive distributed lag (ARDL) co-integration approach over the periods (1980 – 2014). Amos and Thompson (2015) also contributed their own quota to the subject matter in their study titled”Climate Change and the Cocoa Production in the Tropical Rain Forest Ecological Zone of Ondo State, Nigeria”. The study examined whether or not there is long run equilibrium relationship between Cocoa yield and climate change (i.e. Rainfall, Temperature and Humidity). However, some of the empirical studies relied only on the OLS without checking the time series characteristics of the variables and evidence of long-run relationship. Again, model adopted by most of the studies followed a narrow path due to the explanatory variables in the model. This study however seeks to adopt a much more effective approach in measuring the effect of climate change on cocoa production in Nigeria. The tests for stationarity and evidence of co-integration will be conducted in the course of this study. This study will extend the time frame covered by previous studies by spanning from 1970 to 2017 and limiting the scope to Nigeria for its usage in making policy in the long-run. These will help in filling the gap identified in previous studies.

3.0 METHODOLOGY
3.1 Research Design
Owing to the nature of this study, a quasi-experimental research approach was utilized. This is necessitated due to the nature of variables under investigation which are not subject to manipulation.

3.2 Data collection method and sources
Data for this study was purely secondary data. Time series data of annual rainfall amount and annual average temperature in Nigeria spanning from 1970-2017 was obtained from the world bank data catalogue while data on Nigeria’s annual cocoa spanning across the same time period (1970-2017) was obtained from the Nigeria bureau of Statistics.

3.3 Method of Data Analysis
This study employed OLS regression as technique of data analysis. For proper diagnosis of the data and determination of the statistical significant of the estimated parameters, several tests such as the t test, f test and R2 were employed while cointegration analysis and trend analysis were carried out to check if variables are linked in long run equilibrium and to observe the trend of variables.

4.0 RESULTS AND DISCUSSION
Trend analysis on rainfall and average temperature data in Nigeria between 1970 and 2017 as shown in table 4.1 reveals that the increasing trend of rainfall is not statistically significant (t=0.3971) while that of average temperature is statistically significant at 1% level of significance.

The result of the Augmented Dickey Fuller (ADF) unit root tests for the climate variables is summarized in Table 4.3. According to these results, the null hypothesis of a unit root cannot be rejected at conventional (10%, 5%, or 1%) significance levels for cocoa yield, rainfall and average temperature at level, but is rejected at the 1% significance level for cocoa, annual rainfall and average temperature in second difference. These results imply that each series is non stationary at level but stationary at the second difference.

Accordingly, it can be concluded that cocoa yield, annual rainfall and average temperature are I(1) series. For the tests, a constant term and time trend are included. To conserve space, only the results of the unit root tests with both constant term and time trend variable included are reported in Table 4.3.

The critical values of the ADF t-statistic as reported by SHAZAM, the econometric software package used for performing the unit root tests, are –3.96, -3.41 and –3.13 at the 1%, 5% and 10% levels of significance, respectively.

The cointegration test was carried out using ADF approach. From the results in Table 4.4, the numbers in parentheses for the ADF test are the optimal lag lengths, which are determined using Akaike information Criterion (AIC). The critical values of the ADF t-statistic as reported by SHAZAM, the econometric software package used for performing the cointegration tests, are -4.96, -4.42, and -4.13 at the 1%, 5% and 10% levels of significance, respectively, for regressions with a constant but no trend. If both a constant and trend are included, the critical values of the ADF t-statistic are -5.25, -4.72 and -4.43 at the 1%, 5% and 10% levels of significance, respectively.

The ADF statistics for the cointegration are -2.8144 and -1.3775 for constant with trend and constant with no trend respectively. The values do not fall within the range of the critical values therefore are not significant.

Since the results of the unit root tests on the OLS residuals of the cointegration regression does not reject the null hypothesis of a unit root in favor of the stationary alternative even at the 10% significance level, we conclude that the series are not cointegrated. In other words, they are not linked in common long run equilibrium.

The result of the semi-log form shows that the coefficient of multiple determination (R2) is 0.4981 (49.81%) implying that rainfall amount, average temperature and time jointly explained 49.81% of variation in cocoa yield. Consequently, the interpretation of the results of the regression indicates the following:
Rainfall (TAR) is positively related to cocoa yield implying that as rainfall increases cocoa yield increases. A unit increase in rainfall amount keeping all other explanatory variables constant would lead to 5.328 units increase in cocoa yield. This effect is not statistically significant at 10% level of probability.

Temperature (MAT) is positively related to cocoa yield implying that as temperature increases cocoa yield increases. A unit increase in temperature keeping all other explanatory variables constant would lead to 6.578 units increase in cocoa yield. This is in line with the a priori expectation. This effect is statistically significant at 1% level of significance (p=0.001).

Time period (TM) shows a positive relationship with cocoa yield which is statistically significant at 5% (p=0.032).
The F-ratio which determines the overall significance of the regression is statistically significant at the 1% level as F-calculated value (14.558) is far higher than F-tabulated value. It can therefore be concluded that climate change significantly affects cocoa yield.

Table 4.1: Descriptive and trend analysis of data on climate from 1970 – 2017
Rainfall (mm) Temperature (oC)
Mean 1117.81 27.03
Standard deviation 97.92 0.41
Maximum value 1341.31 27.83
Minimum value 875.69 26.18
Trend coefficient 0.436 (0.3971) 0.022xxx (6.761)
Correlation coefficient 0.06 0.714xxx
R2 of trend model 0.36 50.96
Functional form Linear Linear
*** significant at 1%
Value in parenthesis represent the t statistics for the trend analysis
Source: Computed by the Author with Shazam Econometrics software
Table 4.3: Augmented Dickey- Fuller (ADF) test results
Variables Level 1st difference 2nd difference Order
Cocoa -2.9070 -2.4751 -4.2459*** I(1)
Rainfall amount -2.3715 -3.8992** -5.4098*** I(1)
Temperature -3.1052 -2.6827 -7.3013*** I(1)
*** significant at 1% level of significance
**significant at 10% level of significance
Source: Computed by the Author with Shazam Econometrics software
Table 4.4: Summary of the Augmented Dickey Fuller Co-integration test result
Regress and Condition ADF test R2 Durbin Watson AIC
Cocoa yield Constant, no trend -1.3775(2) 0.4446 0.8156 22.431
Constant, trend -2.8144 (1) 0.7360 0.5238 21.310
Source: Computed by the Author with Shazam Econometrics software
Table 4.5: Result of the OLS regression using the semi log functional form
Variables Coefficient Std Error T ratio P value
Dependent Variable = Cocoa yield
Constant -7.14 2.242 -3.184 0.003
Rainfall (TAR) 5.328 4.030 1.322 0.193
Temperature(MAT) 6.578 2.090 3.147 0.003
Time period (TM) 4.695 2.12 2.210 0.032
R2 0.4981 Adjusted R2 0.4639 F ratio 14.558 Source: Computed by the Author with Shazam Econometrics software
5.0 CONCLUSION AND RECOMMENDATIONS
This study focused attention on the effect of climate change on cocoa output in Nigeria. The findings indicated that rainfall amount and average temperature has significant positive effect on cocoa production in both short and long run. 49.81% change in cocoa output is attributable to climate change while the remaining 50.19% is attributable to other factors outside of climate change. The significant positive impact of rainfall and average temperature on cocoa yield is consistent with the findings of Omolaja et al. (2009) that favourably high temperature promote flowering intensity in cocoa and ultimately result in better yield. The trend analysis reveals that there has been a significant increase in temperature over the years.

Considering the findings of this study, the following recommendations are proffered.
i.The Cocoa research agencies in Nigeria should look into other factors outside of climate change responsible for the fluctuations in production over the years. This is important because these other factors hold the key to explaining close to 54% of variation in Cocoa yield.
ii.Research should be geared at producing much more prolific species of cocoa considering the suitability of our climate to its growth.

iii.Research should be geared towards constructing controlled experiments where the temperature can be increased to optimum levels for cocoa production.

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