## 3 Results
### Land Cover Model Summary  
The Land Change modeler (TerrSet Software, Version 19.0.5) analyzes explanatory variables of land-cover change and simulates predictions of future changes. For accuracy, the model uses a backward stepwise constant procedure as described in Section 2.5, to evaluate how well driver variables can predict change [48]. Land change modeling outcomes show each land class transition map, with the potential of change between two classes, as well as future change projection. MLP modeling revealed moderate land-cover transition model accuracies across Dedza and Ntcheu landscapes based on the sub-model skill measure value that represents the overall accuracy of how well the predictor variables explain change in the past. Dedza's sub-model accuracies varied between 56% and 58%, with the highest accuracy associated with sub-model transitions from the forest-mixed to agriculture (58.1%) and from barren to agriculture (58%) sub-models. The wetland to agriculture sub-model had slightly lower accuracy (56%) (Table 8). Each sub-model predicted varying degrees of transition and persistence. The wetland class skill measure revealed high persistence rates of 0.5909. The class skill measure (ratio) can be less than 0.1 or as high as 0.99 [49]. The wide range between the different land-cover classes may be due to variances affecting their ratio, such as land-cover classification accuracy as change is not controlled entirely by the drivers modeled [49]. RMS values for training and testing the model are within a range of 0.48-0.49 which is acceptable as RMS values between 0.2 and 0.5 indicate that the model can predict the data relatively accurately [49]. In Ntcheu (Table 9), the highest accuracy sub-model (67%) is associated with the transition from wetlands to agriculture, followed by the forest-mixed to agriculture, barren to agriculture and shrub-woodland to agriculture transitions. Their accuracies ranged between 60% and 59% while moderate rates of transition were predicted from wetland to agriculture at 0.53 and from forest-mixed to agriculture at 0.31 class skill values. Additionally, the RMS values ranged from 0.45 to just below 0.50. This indicates that the model can reliably predict transitions as the RMS values fall in the upper range of the recommended 0.20-0.50 range [16]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sub-Model**} & \multirow{2}{*}{\begin{tabular}{c} **Class Skill** \\ **Measure (Ratio)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Sub-Model** \\ **Accuracy (\%)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Sub-Model** \\ **Skill Measure** \\ \end{tabular} } & \multirow{2}{*}{**Training**} & \multirow{2}{*}{**Testing**} \\ \hline Forest-mixed to Agriculture & & & 58.13 & 0.1626 & 0.4918 & 0.4925 \\ Transition: Forest-mixed to & \(-\)0.0654 & & & & \\ Agriculture & Persistence: Forest-mixed & 0.3956 & & & \\ \hline Barren to Agriculture & & 57.95 & 0.1589 & 0.4865 & 0.4886 \\ Transition: Barren to Agriculture & 0.0338 & & & & \\ Persistence: Barren & 0.284 & & & & \\ \hline Wetland to Agriculture & & 55.80 & 0.116 & 0.4946 & 0.4946 \\ Transition: Wetland to Agriculture & \(-\)0.198 & & & & \\ Persistence: Wetland & 0.5909 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Sub models in the Multi-Layer Perceptron (MLP) using the six explanatory variables and model performance summary for Dedza district (2001–2019). Dedza's land transitions show generally low probabilities of land transition (10-40%). Areas with a high probability of transition (70-90%, and red in color in Figure 8), were limited in spatial scope, mostly to eastern Dedza and for transitions to agriculture. Land transitions from forest-mixed or barren to agriculture (30-70%) are likely to occur in central Dedza locations, while land transitions from wetland to agriculture had a probability of 40-60%.