# Information and Communication Technologies and Agricultural Production: New Evidence from Africa
## 4 Results and Discussion
### Unit Roots Test of the Variables  
It is customary to test the stationarity or otherwise before conducting an autoregressive distributed lag modeling [80; 81; 82]. This paper applied the Pesaran Cross-sectional Augmented Dickey-Fuller (CADF) and Im-Pesaran-Shin unit-root tests to determine stationarity of our dataset. The result of the unit-root test is presented in Table 6. The result shows that crop production index, livestock production index, mobile phone subscriptions, domestic credit provided to the private sector, and rural population were stationary at level under the Pesaran Cross-sectional Augmented Dickey-Fuller (CADF) test, while mobile phone subscriptions and rural population were stationary at level under the Im-Pesaran-Shin unit-root test. Individuals using internet, fixed telephone subscriptions, and net national income per capita have unit roots under the Pesaran Cross-sectional Augmented Dickey-Fuller (CADF) test, while crop production index, livestock production index, individuals using internet, fixed telephone subscriptions, net national income per capita, and domestic credit provided to the private sector have unit roots under the Im-Pesaran-Shin unit-root test. However, all the variables were stationary at the first difference under the Pesaran Cross-sectional Augmented Dickey-Fuller (CADF) and Im-Pesaran-Shin unit-root  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **CD-Test** & \(p\)**-Value** \\ \hline InY1 & 44.00 & 0.000 \\ InY2 & 43.01 & 0.000 \\ InX1 & 85.04 & 0.000 \\ InX2 & 87.02 & 0.000 \\ InX3 & 7.99 & 0.000 \\ InX4 & 67.91 & 0.000 \\ InX5 & 42.08 & 0.000 \\ InX6 & 23.19 & 0.000 \\ \hline \hline \end{tabular}  
Variable list: Y1 = crop production index; Y2 = livestock production index; X1 = individuals using the internet (% of population); X2 = mobile cellular subscriptions (number of persons); X3 = fixed telephone subscriptions (number of persons); X4 = adjusted net national income per capita (current USD); X5 = domestic credit to private sector by banks (% of GDP); X6 = rural population (% of total population).  
\end{table}
Table 4: Cross-section dependence test.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Variance Inflation Factor (VIF)** \\ \hline lnX1 & 1.872 \\ lnX2 & 3.467 \\ lnX3 & 1.891 \\ lnX4 & 3.452 \\ lnX5 & 2.564 \\ lnX6 & 2.090 \\ \hline \hline \end{tabular}  
Variable list: Y1 = crop production index; Y2 = livestock production index; X1 = individuals using the internet (% of population); X2 = mobile cellular subscriptions (number of persons); X3 = fixed telephone subscriptions (number of persons); X4 = adjusted net national income per capita (current USD); X5 = domestic credit to private sector by banks (% of GDP); X6 = rural population (% of total population).  
\end{table}
Table 5: Multicollinearity test using variance inflation factor (VIF).  
tests. This suggests that the panel ARDL model is appropriate in this case to model the impact of the independent variables on the dependent variable. Since all the variables were stable at first differencing, a further test to establish if there was a cointegration between the variables was conducted using the Pedroni test and Westerlund test.