Tropical mountain peatlands have received far less attention than lowland peat systems, and future land-cover trajectories have not previously been projected for the mapped Toba Highland Peatlands of North Sumatra, Indonesia. Using previously classified Landsat land-cover maps for 2014, 2019 and 2023, this study quantified land-cover transitions and projected land cover for 2028–2038 with an Artificial Neural Network–Cellular Automata (ANN–CA) model in MOLUSCE. The analysis used a nested design: a 65,603-ha landscape comprising Lintong Nihuta, Dolok Sanggul and Pollung districts (Humbang Hasundutan Regency), and the c. 6180-ha mapped peatland within it. Near-surface peat bulk density was measured at four cultivated sites (eight ring samples) as complementary field information. Cultivated land expanded from 24.8% to 44.0% of the landscape between 2014 and 2019 and remained at 43.2% in 2023, mainly at the expense of natural forest and open land. A 2023 hindcast agreed well with the classified reference map (Correctness 93.32%; Kappa Overall 0.896). Under the modeled conditions, cultivated land is projected to occupy 65.05% of the landscape and 94.75% of the mapped peatland by 2038, up from 70.61% of the peatland in 2023, while natural forest within the peatland declines to 0.64%. Dry bulk density ranged from 0.21 to 0.47 g cm⁻³; these site-level values do not represent peatland-wide conditions. The projections are conditional scenarios rather than forecasts, but they show that land-use pressure is concentrated within the peatland, where peat hydrology and the remaining non-agricultural cover should be priorities for monitoring and management.
Tropical peatlands store a significant portion of global soil carbon. However, fires in these ecosystems pose major monitoring challenges, particularly in detecting and distinguishing between flaming and smouldering phases. Distinguishing between these phases is crucial because the smouldering phase accounts for the majority of carbon and particulate emissions yet is far more difficult to detect. This review is the first to systematically link the physicochemical mechanisms of tropical peat combustion with remote sensing capabilities. Following the PRISMA 2020 protocol, 408 records were screened, and 23 articles were included to address three research questions: fire detection mechanisms, remote sensing methods and capabilities, and detection challenges. Phase detection relies on temperature-dependent radiation emissions. The flaming phase is best detected using shortwave infrared (SWIR), whereas the smouldering phase is best detected using thermal infrared (TIR) and longwave infrared (LWIR). The primary challenge lies in detecting the smouldering phase, which is difficult to identify yet most significant in terms of emissions. To address this, the study highlights high-temporal-resolution data such as VIIRS and recommends integrating the Tropical Peatland Combustion Algorithm index (ToPeCAI) with subpixel methods to enable accurate monitoring and classification of fire types at the local scale on a daily basis.
Climate change presents a significant threat to wheat production in the tropical highlands of southeastern Oromia, Ethiopia. The DSSAT-CERES-Wheat model was calibrated using three years (2023–2025) of field observations at Kulumsa and independently validated using observations from Bore and Sinana. Future simulations used four CORDEX-Africa climate-model combinations under SSP2–4.5 and SSP5–8.5 for the near-future (2037–2068) and far-future (2069–2100) periods. A randomized block design tested 24 combinations of sowing date, nitrogen rate, and cultivar. Simulations showed consistent yield declines under SSP2-4.5 and SSP5-8.5, with smaller reductions in the near future and sharper losses under SSP5-8.5 far future. Early planting consistently reduced yield losses by 15–25% relative to normal planting and 25–40% relative to late planting. Nitrogen application improved yields, though with diminishing returns; 69–92 kg N ha⁻¹ maintained near-baseline yields under SSP2-4.5, while benefits above 69 kg N ha⁻¹ were limited under SSP5-8.5. Varietal differences showed that Shaki outperformed Boru, showing higher baseline yields and smaller climate-induced reductions. Economic analysis indicated that early planting with 69–92 kg N ha⁻¹ for Shaki maximized profitability, with gross margins exceeding 258,000 ETB ha⁻¹ under SSP2-4.5 near future. Risk-averse farmers may prefer 69 kg N ha⁻¹ due to the small marginal gain from 92 kg N. No single practice fully offset losses under SSP5-8.5, highlighting the need for integrated strategies combining early planting, optimized nitrogen, and resilient cultivars. Location-specific optimization remains essential, and decision-support tools such as DSSAT can guide targeted adaptation. Overall, while climate change will reduce wheat yields, strategic management can substantially buffer impacts, though high-emission futures require urgent climate action and robust adaptation planning.
