Feed quality, digestibility, and methane emission trade-offs among conventional and unconventional fodder resources in smallholder dairy systems of Rwanda.

Description

Smallholder dairy production in Rwanda is constrained by seasonal shortages of nutritionally adequate fodder, affecting production and potentially increasing enteric methane emissions. This study evaluated feed quality, digestibility, intake potential, energy supply, and methane emission trade-offs among conventional and unconventional fodder resources available during the dry season across two agro-ecological zones of Rwanda. Feed quality, digestibility, intake, energy, and methane-related parameters were estimated using simulation models, while multivariate and correlation analyses assessed relationships among nutritional attributes and methane production. Significant differences were observed among fodder classes in nutritional composition, digestibility, energy value, and predicted methane production (P < 0.05). Crude protein ranged from 5–26.5%, NDF from 42–69%, metabolizable energy from 4.68–13 MJ/kg DM, and predicted methane production from 12.8–32 g/day. Legumes showed superior feed quality, with higher CP, mineral content, digestibility, and energy value and lower NDF and methane production than grasses, shrubs, and crop residues. Methane production was positively correlated with NDF (r = 0.70; P < 0.001) and negatively correlated with non-fiber carbohydrates (r = −0.77), net energy for lactation (r = −0.56), and metabolizable energy (r = −0.47; P ≤ 0.001). Overall, high-quality legume fodders demonstrated a favorable trade-off between nutrient supply, digestibility, and methane emission potential, supporting their integration into sustainable smallholder dairy feeding systems.

Steps to reproduce

On-farm data collection was conducted during the period of dry season (June to September 2023). 29 common individual fodder samples were collected in both regions, each representing a replication. Samples of 250 g each of the most common fodder used in the two contrasting regions were collected, and analyzed for their nutritive value, including DM, Ash, OM, CP, NDF, ADF, EE, DMI, and RFV, Gas volume at 24hours, DDM, OMD, OMDm, NEL, TDN, and ME, and CH4. Laboratory analysis of feed samples Twenty-nine (29) feed samples collected in duplicate were analysed for DM, Ash, OM, CP, NDF, ADF, and Gas volume at 24hours, in the analytical laboratory of Rubona station of Rwanda Agriculture and Animal Resource Development Board (RAB). The DM content was determined by oven-drying the samples at 105 °C for 96 hours (George W Latimer 2023). Ash and organic matter (OM) were determined by igniting samples at 500oC in a muffle furnace for 4 hours (George W Latimer 2023). Protein was analysed by the Kjeldahl method (George W Latimer 2023). NDF was determined using ANKOM fibre bags F57, 25 micro porosities, where samples were rinsed using neutral detergent solution (NDS) or Acid detergent solution (ADS) for two hours, after which they were dried in a muffle furnace for another two hours for acid detergent fibre (ADF) (George W Latimer 2023). In vitro dry matter digestibility was determined according to the procedure of Tilley and Terry (1963). A buffer solution was prepared identical to cattle saliva according to Jayanegara et al. (2019). It was mixed with rumen liquor at a 2:1 volume. Diffusion of carbon dioxide (CO2) was thoroughly connected to the system to maintain the anaerobic fermentation. Approximately 100 ml of the mixture was added to the samples previously placed in syringes using a lab revolver (George W Latimer 2023). The syringes were transferred to an oven set at 39 °C, similar to rumen conditions, for 24 hours. Gas readings were done at 0,2,4,6,8,10,12,16,20, and 24 hours (Jayanegara et al. 2019). Methane production estimation Methane emission potential was estimated using the prediction equations developed by Ramin and Huhtanen (2013). The model was applied considering a standard lactating dairy cow with an average body weight of 433 kg, representing the typical dairy animal size under the study conditions. Methane production was estimated based on predicted dry matter intake (DMI), organic matter digestibility (OMDm), ether extract intake, and the proportion of non-fiber carbohydrates relative to total fermentable carbohydrates. The one-sided and two-sided methane prediction equations were applied, and the final methane output was expressed as liters per day (L/day) and converted to daily methane production per animal. The model has an adjusted root mean square error (RMSE) of 21.1 L/day (Ramin and Huhtanen 2013).

Institutions

• University of Nairobi

  Nairobi County, Nairobi

Categories

Licence