Central Malawi’s degraded land expanded as recovery lagged behind pressure on farms and soils
Land degradation in Malawi’s Central Region worsened across the study window, and the numbers leave little room for optimism. Using the UNCCD framework inside the Trends.Earth tool, investigators estimated the SDG 15.3.1 indicator and found that degraded land rose from 26.8% in the 2001–2015 baseline period to 41.2% during 2016–2022.
The shift is not just a statistical change. It signals a region where the demands of cultivation, settlement, and resource extraction are outrunning the land’s ability to recover.
Across the full 2001–2022 period, 27.1% of land remained degraded, 62.4% stayed stable, and 9.4% improved. Those figures matter because they capture the balance between decline and restoration over time, rather than a one-year snapshot that might miss slow-moving environmental damage. In a region where most land is tied to agriculture and where rain-fed production remains the norm, loss of productive ground translates quickly into food insecurity and livelihood strain.
Productivity losses outpaced restoration in the monitoring period
The sharpest movement came in the productivity subindicator. Land classified as degraded in productivity fell from 74.5% in the baseline period to 29.3% in the monitoring period, while stable productivity crept up from 1.1% to 1.9%. The wording may look counterintuitive at first glance, but the SDG framework uses relative change across classes to define whether land has shifted from one state to another against the baseline.
Even with the reported decline in degraded productivity class, the overall SDG indicator still pointed toward continued net degradation. The practical reading is straightforward: some areas improved, but not enough to offset the broader losses in land condition elsewhere in the Central Region.
A second punchline sits in the land cover data. Land cover remained overwhelmingly stable at 96.7% across the study period. That stability does not mean the system was healthy. It means the dominant land cover classes persisted while the underlying condition of the land changed through productivity loss and subtle soil decline, a pattern that can be easy to miss when analysts look only at maps of vegetation or land use.
Soil organic carbon stayed mostly stable, but croplands and wetlands showed losses
Soil organic carbon, one of the three core subindicators in the UNCCD methodology, remained largely stable at more than 97%. Even so, slight losses appeared in cropland, grassland, and wetland areas. Those losses are important because SOC acts as a proxy for soil health, water-holding capacity, and long-term fertility. When carbon stocks slip, even modestly, soils become less resilient to drought, erosion, and repeated cultivation.
In a region that depends heavily on agriculture, that matters on the ground. Soils with depleted organic carbon are less able to sustain yields, absorb rainfall efficiently, or resist crusting and erosion. Over time, small losses accumulate into lower resilience, especially where deforestation, overgrazing, bushfires, and poor farming practices continue to press on the same land base.
The study area itself helps explain why these trends are so consequential. The Central Region spans about 35,592 km², includes nine districts, and contains Lilongwe, the national capital. It also houses about 7.5 million people, roughly 43% of Malawi’s population. Most residents depend on rain-fed agriculture, so degraded soil is not an abstract environmental concern; it is a direct threat to household production and regional food security.
Why the land degradation neutrality framework matters for Malawi
The UNCCD’s Land Degradation Neutrality concept rests on a simple requirement: land that is degraded must be balanced by land that is restored, so the overall stock of healthy land does not fall. This study shows that balance has not yet been achieved in the Central Region. Degradation still exceeds recovery, and that gap is visible in the widening share of degraded land during the monitoring period.
The methodology matters here because it links three biophysical signals rather than relying on one. Land cover captures changes in vegetation and land use, land productivity tracks the functional output of ecosystems, and soil organic carbon reflects soil condition. When viewed together, these indicators provide a stronger picture of land health than any one metric alone. A field may look unchanged from above while productivity erodes beneath the surface.
That is precisely the sort of problem remote sensing is built to catch. By combining satellite-derived land cover, productivity dynamics, and soil carbon in a GIS workflow, the analysis turns dispersed environmental pressure into a coherent regional map of risk. Decision-makers can then identify priority zones for intervention rather than spreading restoration efforts thinly across the entire region.
Targeted restoration will matter more than broad reassurance
The data point toward specific interventions rather than generic calls for sustainability. Areas showing persistent degradation need tighter land management, while zones with recent improvement deserve protection so they do not slide backward. Because the region’s economy depends heavily on agriculture, land restoration strategies will need to speak to farming systems directly: soil conservation, reduced deforestation, better land-use planning, and protection against erosion and bushfire damage.
The study also carries a policy warning. Stable land cover does not guarantee stable land function, and a region can appear visually intact while productivity and soil condition erode underneath. For planners, that means restoration targets should be set with the SDG 15.3.1 indicator in mind, not just with tree cover or land-use maps.
The analysis stops short of field validation and does not measure every local driver in detail, leaving room for finer-grained district studies. Even so, the central message is firm: in Malawi’s Central Region, degradation is still advancing faster than restoration, and the window for reversing that pattern will depend on where interventions are concentrated first.