Meta-Regression of Pine Needle Fuel Loads and Combustion Efficiency for Forest Fire Risk Management in Uttarakhand

Chir pine (Pinus roxburghii Sarg.) forests of Uttarakhand, India, accumulate large quantities of needle litter (pirul) that constitute the primary surface fuel driving pre-monsoon forest fires across the Indian Himalayan Region. This study quantified fuel load distribution, burn efficiency (BE), and temporal combustion dynamics of pine needle litter through 53 controlled open-air combustion trials conducted over 22 collection days (20 May–12 June 2020), analyzed using a meta-regression framework. Mean pre-combustion fuel load was 7,847.5 ± 924.4 g sample⁻¹ and mean BE was 70.76 ± 5.20% (range: 54.82–81.54%). Neither fuel load nor collection day significantly predicted BE (both p > 0.05, R² < 0.04), indicating that within the pre-monsoon window, these covariates are not meaningful determinants of combustion completeness. Between-day heterogeneity was extremely high (I² = 96.9%, Cochran’s Q = 678.8, p < 0.001, τ² = 13.03), demonstrating that unmeasured meteorological variables principally ambient relative humidity and fuel moisture content drive virtually all real day-to-day variation in combustion performance. Post-combustion residue mass was significantly predicted by pre-combustion fuel load (r = 0.421, p = 0.002), providing a useful mass balance relationship for biomass energy applications. Total litter collected was 439.5 kg across 22 days, supporting the operational assumptions of the ‘Pirul Lao, Paisa Pao’ briquette scheme. These results demonstrate that static BE parameterization in fire danger rating systems is ecologically unsupported, and advocate for weather-coupled dynamic fire risk modelling incorporating real-time fuel moisture monitoring in Uttarakhand forest management.