## 3 Results
### Land Cover Model Summary
This pattern of a fragmented agriarian landscape with some trees is evident across the landscape in Malawi where agro-forestry and soil amendment models increase tree cover while large contiguous plots of forest decline (also see 36). In such landscapes, one pathway in addressing sustainable land management is to understand the effectiveness of current delivery by agricultural extension in improving knowledge on landscape function and farming practices that increase landscape resilience [67] while also observing land-cover management at the forest/agriarian landscape interface [36; 61]. The projected LULC changes by 2050, reflecting agricultural cover-class dominance and a higher probability in Dedza (80% for transition to agriculture, 72% built-up, and 66% to barren land) are supported for Dedza by a previous study [39] that also predicted increases in agriculture, built-up and barren areas by 2025 attributed to unsustainable farmland practices and township growth. Despite Ntcheu's lower transition probability, there are still significant rates of change (43%) for agricultural land, particularly from shrub-woodland, forest-mixed, and significantly wetland classes, indicative of underlying landscape fragmentation and environmental degradation. Malawi's wetland conversion and degradation has been understudied given its growing importance to enhance food and livelihood security through supplemental dry-season irrigated farming in a predominantly rain-fed agricultural system, but one paper recognized wetland degradation caused by long-term pressures from poor farming practices, and population settlements on upland areas, which are predicted to increase in response to climate change [68]. Land-use management plans may need to be developed to equip communities and multiple stakeholders including government extension staff by first consolidating the diverse knowledge from multiple perspectives and emphasizing socio-economic and environmental benefits of functional landscapes such as wetlands to facilitate community engagement and promote sustainable land use management [69]. We acknowledge some study limitations associated with using high-medium spatial resolution remotely sensed satellite imagery to capture land-cover patterns, as well as to model biophysical and social drivers of change in heterogenous landscapes. This study uses 30 m spatial resolution imagery to model six explanatory drivers assumed to directly capture LULCC processes without consideration of indirect drivers such as climate change and assumes a business-as-usual scenario. Spectral signature separation across some land-cover classes such as between shrub-woodland and forest-mixed, and among wetland, agriculture, built-up, and the other class categories was difficult at this spatial resolution, although classification was significantly aided by primary ground truth data. The use of higher spatial resolution data would enhance the analytical rigor of future studies but as such data increase the temporal extent of historical data and as prices come down. Lastly, predictive modeling is data intensive and tries to capture multi-scale and multi-level driver interactions. However, some drivers such as social and economic drivers of LULC change, are not captured in remote sensing spectral sensors, thereby undermining the reliability of modeling outcomes [70]. Moreover, we mentioned previous studies in the introduction section that utilized integrated methodological approaches for land use retrieval to better understand rural to urban transition and urban growth prediction [24; 25] as well as the use of multiple integrated approaches and multi-sensor satellite imagery to capture unique heterogeneous landscape features that are non-existence in current land-cover products [26], this study also contributes to filling this methodological gap. Also noteworthy are limitations associated with the temporal framing of the image datasets.