## 5 Conclusions  
This study sought to advance knowledge in quantifying spatial-temporal gains and losses in LULC across Dedza and Ntcheu districts of Central Malawi, and modeled land transitions using driver variables among the land-cover classes and produced predictions of land cover to 2050. The study used a mixture of remote sensing and ancillary datasets, geospatial analysis and land-cover modeling based on Markov chains to generate spatially explicit land cover and transition products to advance knowledge and inform policy within and beyond the study sites and regions.  
Our study informs stakeholders of spatial-temporal patterns of LULC change and its varying drivers across Dedza and Ntcheu districts. Modeled changes and future projections show significant landscape changes and a need to develop landscape-level policies that raise awareness among farming communities and policy makers. Enhanced understanding of land-use change, especially in areas that are projected to experience the most significant land transitions, can better inform policy interventions on sustainable land and other local natural resource management strategies (e.g., mandatory sub-district and district development plans) as well as shape national level policies. They could also inform the design of socio-economic incentives to encourage sustainable agricultural production and improve multifunctional use of the landscapes and enhance the targeting of conservation and restoration interventions to reduce or reverse land degradation and mitigate climate change.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs14143477/s1](https://www.mdpi.com/article/10.3390/rs14143477/s1)]([https://www.mdpi.com/article/10.3390/rs14143477/s1](https://www.mdpi.com/article/10.3390/rs14143477/s1)), Table S1: A Confusion matrix summary 2001 for Dedza district. (* 100 value accuracy has been rounded off). Table S2: Confusion matrix summary 2009 for Dedza district (* 100 value accuracy has been rounded off). Table S3: Confusion matrix summary 2019 for Dedza district. (* 100 value accuracy has been rounded off). Table S4: Confusion matrix summary 2001 for Ntcheu district. Table S5: Confusion matrix 2009 for Ntcheu district. Table S6: Confusion matrix 2019 for Ntcheu district. (* 100 value accuracy has been rounded off).  
Conceptualization, L.M.M. and J.P.M.; methodology, L.M.M.M.; writing--original draft preparation, L.M.M.; writing and writing--review and editing, S.S., L.C.Z., J.P.M., J.Q. and L.M.M.; funding acquisition, J.P.M. and S.S. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the support of the American People provided to the Feed the Future Innovation Lab for Sustainable Intensification through the United States Agency for International Development (USAID). The contents are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government. Program activities are funded by USAID under Cooperative Agreement No. AID-OAA-L-14-00006.  
Data Availability Statement:Data associated with this manuscript are available via Harvard Dataverse ([PERSON] et al., 2022) and from the corresponding author, [PERSON], upon reasonable request.  
**Acknowledgments:** The authors also wish to thank [PERSON] and [PERSON] also involved with Michigan State University, Department of Plant, Soil, and Microbial Sciences, East Lansing, Mich., and Africa RISING project for their involvement in field work preparation and guidance. We thank [PERSON], [PERSON], [PERSON] and [PERSON] (deceased) of Africa RISING for their support in data collection. We credit Drs. [PERSON] and [PERSON] giving us permission to adapt the diagram flow chart from the article ([PERSON] and [PERSON] 2017).  
**Conflicts of Interest:** The authors declare no conflict of interest.