Datasets for correlation dynamics of cocoa production in South Western Nigeria

In the Nigeria economy, cocoa production has been of great importance. This buttresses the fact that cocoa as a product is the leading agricultural export of Nigeria, leaving the country currently as the world fourth largest producer of cocoa, after Ivory Coast, Indonesia and Ghana and the third largest exporter, after Ivory Coast and Ghana. Hence, there is need for the agricultural sector expansion, effective predictive models and reliable price mechanism. This article examines tonnes of cocoa production dataset of the Nigeria agricultural sector for the period of twenty-four (24) years spanning between 1993 to 2016. The Correlation dynamics examined includes the autocorrelation features as affected by the production rate within the considered time interval. The degree of similarity between the dataset and the corresponding lagged version of itself over successive time interval is measured using a serial correlation test while the results mostly favour negative correlation showing that large current values correspond to small values at the specified lag. These dataset can effectively serve as good candidate for agricultural product modelling in terms of forecasting.


a b s t r a c t
In the Nigeria economy, cocoa production has been of great importance. This buttresses the fact that cocoa as a product is the leading agricultural export of Nigeria, leaving the country currently as the world fourth largest producer of cocoa, after Ivory Coast, Indonesia and Ghana and the third largest exporter, after Ivory Coast and Ghana. Hence, there is need for the agricultural sector expansion, effective predictive models and reliable price mechanism. This article examines tonnes of cocoa production dataset of the Nigeria agricultural sector for the period of twentyfour (24) years spanning between 1993 to 2016. The Correlation dynamics examined includes the autocorrelation features as affected by the production rate within the considered time interval. The degree of similarity between the dataset and the corresponding lagged version of itself over successive time interval is measured using a serial correlation test while the results mostly favour negative correlation showing that large current values correspond to small values at the specified lag. These dataset can effectively serve as good candidate for agricultural product modelling in terms of forecasting. &

Subject area
Agricultural Sciences More specific subject area Cocoa production Type of data Within this data article.

Value of the data
The present data will be of great usefulness for the determinants of cocoa production in Nigeria. The dataset will help to know the trend and pattern of cocoa production output over the time.
The dataset can be used for agricultural product modelling and forecasting. The dataset will aid in budget planning since cocoa production contributes immensely to the country's economy.

Data
The datasets used in this work contains tonnes of cocoa production of the Nigeria agricultural sector for the period of twenty-four (24) years. This is presented in Table 1 followed by Fig. 1. Related literature on cocoa production and Nigerian economy includes the references in [1][2][3][4][5]. Predictive and forecasting approaches are of great importance in any production sector [6]. In addition to the mathematical formula (1), the data are processed/analysed via the statistical software (SPSS). Fig. 1 shows a graphical view of disease infested cocoa pods (Right), and non-infested cocoa pods (left). The healthy nature of cocoa trees have significant effect on the production of cocoa beans.

Design, and methodology
In addition to the statistical software used in the data analysis, is the mathematical model defined as follows: where x m 1 and x m 2 are the means of the first N−1 and the last N−1 observations respectively. Eq. (1) represents the correlation coefficient computed between one time series and the same series lagged by one or more time units. Such correlation model is a good candidate for examining the relationship existing between adjusted volatilities in the market and the investment settings [7][8][9][10].

Data analysis
Here, the outcomes of the analysed data are presented in Tables 2-5     a The underlying process assumed is independence (white noise). b Based on the asymptotic chi-square approximation.

Analysis overview
From the analysis, making reference to ACF in Fig. 2, it is pointed out that all the 16 coefficients are below the two-sided error limits. Only 6 out of the 16 are above the zero bar. From Fig. 3, the PACF shows that 12 out of the 16 coefficients are below the zero bar. Hence, there is a greater need to improve the trend model with regard to cocoa production in Nigeria. The ACF plot indicates significant autocorrelation and that the data are not stationary. Since stationary conveys the idea of the mean and standard deviation holding still and not shifting. The plot shows that the differenced data appear to be stationary and do not exhibit seasonality. Though, using regular differencing, the seeming trends can be adjusted by computing the difference between every two successive values.