Measured ammonia emissions from tropical and subtropical pastures: A comparison with 2006 IPCC, 2019 Refinement to the 2006 IPCC, and EMEP/EEA (European Monitoring and Evaluation Programme and European Environmental Agency) inventory estimates.

Agriculture is the largest source of ammonia (NH3) emissions. As NH3 is an indirect greenhouse gas, NH3 measurements are crucial to improving greenhouse gas emission inventory estimates. Moreover, NH3 emissions have wider implications for environmental and human health. Only a few studies have measured NH3 emissions from pastures in the tropics and subtropics and none has compared emissions to inventory estimates. The objectives of this study were to (1) measure NH3 emissions from dairy pastures in tropical and subtropical regions; (2) calculate NH3 emissions factors (EF) for each campaign; and (3) compare measured EF with those based on the 2006 Intergovernmental Panel on Climate Change (IPCC) Tier 1, 2019 Refinement to the 2006 IPCC Tier 1, and the European Monitoring and Evaluation Programme/European Environmental Agency (EMPE/EEA) Tier 2 inventory estimates. Pasture NH3 emissions were measured on 3 dairy farms in Costa Rica. On each dairy, NH3 emissions were measured twice during the wet season and once during the dry season using a micrometeorological integrated horizontal-flux mass-balance method. Emissions were measured from excreta (dung and urine) deposited by grazing cattle and the subsequent application of organic (slurry) or synthetic fertilizer (ammonium nitrate or urea). Measured EF for all campaigns [from grazing cattle excreta and any subsequent slurry or fertilizer application; 4.9 ± 0.9% of applied nitrogen (mean ± SE)] were similar to those of the EMEP/EEA Tier 2 approach (6.1 ± 0.9%; mean ± SE) and 4 times lower than 2006 IPCC and 2019 Refinement to 2006 IPCC Tier 1 default estimates (17.7 ± 1.4 and 18.2 ± 0.9%, respectively; mean ± SE). Measured EF for excreta deposited on pasture and excreta both deposited on pasture and slurry application [3.9 ± 2.1 and 4.2 ± 2.1% (mean ± 95% CI), respectively] were 5 times lower than default EF assumed by 2006 IPCC and 2019 Refinement to 2006 IPCC methodology (both 20 and 21%, respectively), whereas EMEP/EAA estimates were similar [6.0 and 4.6 ± 0.3% (mean ± 95% CI), respectively]. This suggests an overestimation of EF from excreta deposited on pasture and slurry applications in tropical and subtropical regions by IPCC methodologies. Furthermore, rainfall, which is not included as a parameter in the current EMEP/EEA Tier 2 methodology, appeared to reduce NH3 emissions, suggesting that accounting for this in the inventory methodologies could improve inventory estimates.

Methane Emission, Carbon Footprint and Productivity of Specialized Dairy Cows Supplemented with Bitter Cassava (Manihot esculenta Crantz).

The objective of this research was to determine the effect of cassava (Manihot esculenta Crantz) supplementation on enteric methane (CH4) emissions, carbon footprint, and production parameters in dairy cows. Daily concentrate supply for Jersey and Jersey * Holstein breeds was evaluated in four treatments (T): T1: 100% commercial concentrate; T2: 70% concentrate + 30% cassava leaves; T3: 70% concentrate + 30% cassava roots; and T4: 70% concentrate + 15% cassava leaves + 15% cassava root chips. Measurements of CH4 emissions were performed using the polytunnel technique. Average daily dry matter intake ranged from 7.8 to 8.5 kg dry matter (DM). Cassava leaves were characterized by a high crude protein (CP) content (171 g CP/kg DM), with 5 times more neutral detergent fiber (NDF) content than cassava root (587 vs. 108 g NDF/kg DM). Average enteric CH4 emissions per animal ranged from 194 to 234 g/d (p > 0.05). The carbon footprint was reduced by replacing 30% of the concentrate with cassava leaves and/or roots. Energy-corrected milk production was 1.15 times higher in Jersey * Holstein animals than Jersey cows (47 vs. 55 kg). Therefore, supplementation with cassava leaves and/or roots is a nutritionally and environmentally sustainable strategy.

Environmental impact of primary beef production chain in Colombia: Carbon footprint, non-renewable energy and land use using Life Cycle Assessment.

In Colombia, the beef production chain accounts for approximately 11.6 million cattle heads and annually produces 933 million kg of the beef carcass. There are no life cycle assessment (LCA) studies that have evaluated the environmental performance of Colombian beef systems. The present study aimed to estimate the carbon footprint (CF), non-renewable energy use, and land use of 251 cow-calf and 275 fattening farms in Colombia. The study also aimed to identify the main hotspots of adverse environmental impacts and propose possible mitigation options and their cost-effectiveness. The impact categories were estimated using the 2006 IPCC and the 2019 Refinement to 2006 IPCC guidelines, databases, and locally estimated emission factors. The functional units used were 1 kg fat and protein corrected milk (FPCM) and 1 kg live weight gain (LWG), leaving the farm gate. Three methods of allocating environmental burdens to meat and milk products were applied: economic, energy, and mass allocation. The adoption of improved pastures was considered a mitigation measure, and an economic assessment was performed to estimate the relative cost-effectiveness of its establishment. A principal component multivariate analysis and a Hierarchical Clustering on Principal Components were performed. The economic allocation method assigned a greater environmental burden to meat (83%), followed by energy content (80%) and mass production (73%). The largest sources of GHG emissions were enteric fermentation and manure deposited on pasture. Both cow-calf and fattening systems had a cluster of farms with better productivity, pasture and cattle management practices, and environmental performance. The CF for meat could be reduced by 33 to 56% for cow-calf and 21 to 25% for fattening farms, by adopting improved pastures. Therefore, our results suggest that GHG emissions can be reduced by adopting improved pastures, better agricultural management practices, efficient fertilizer usage, using the optimal stocking rate, and increasing productivity.