Assessment of GHG emissions in dairy production systems based on existing feed resources through the GLEAM model under different climatic zones of Bangladesh and their mitigation options.

Objective: The current study evaluated the greenhouse gas (GHG) emissions of dairy cattle through the Global Livestock Environmental Assessment Model (GLEAM) model and illustrated potential mitigation strategies by modifying nutrition interventions. Materials and Methods: A semi-structural questionnaire was developed to calculate dairy animal GHG emissions. This study comprised 40 farmers from four districts: river basin (Pabna), drought prone (Chapainobabganj), floodplain (Nilphamari), and saline-prone (Sathkhira) areas. Ten lactating cows (two cows from each farmer) were also selected to collect information on feeding practices, feed resources, feed intake (roughages and concentrate), water intake, and productive and reproductive parameters for 7 days at each site during two seasons: dry (November– February) and wet (June–October). Results: The GHG emissions from the river basin area were significantly (p < 0.05) higher due to low-quality roughages (75%), whereas CH4 /kg of milk production was the lowest (77.0 gm). In contrast, the area that frequently experiences drought showed a different pattern. For instance, the generation of CH4 from enteric fermentation was 1187.4 tons/year, while the production of CH4 and N2O from manure management was 323.1 tons/year and 4.86 tons/year, respectively. In comparison to other climatic areas, these values were the lowest because the supply of green grass was twice as abundant as in the other climatic areas (40%). The quantity of CH4 /kg of milk produced in an area susceptible to drought did not vary. Conclusion: Implementing feeding systems in drought-prone areas is a successful approach to reducing GHG emissions in the dairy industry in Bangladesh. Consequently, implementing feed-balancing techniques can enhance productivity and foster environmentally sustainable animal production. J. Adv. Vet. Anim. Res., 11(3): 664–674, September 2024http://doi.org/10.5455/javar.2024.k816