Enteric methane mitigation strategies for ruminant livestock systems in the Latin America and Caribbean region: a meta-analysis.

Latin America and Caribbean (LAC) is a developing region characterized for its importance for global food security, producing 23 and 11% of the global beef and milk production, respectively. The region?s ruminant livestock sector however, is under scrutiny on environmental grounds due to its large contribution to enteric methane (CH4) emissions and influence on global climate change. Thus, the identification of effective CH4 mitigation strategies which do not compromise animal performance is urgently needed, especially in context of the Sustainable Development Goals (SDG) defined in the Paris Agreement of the United Nations. Therefore, the objectives of the current study were to: 1) collate a database of individual sheep, beef and dairy cattle records from enteric CH4 emission studies conducted in the LAC region, and 2) perform a meta-analysis to identify feasible enteric CH4 mitigation strategies, which do not compromise animal performance. After outlier?s removal, 2745 animal records (65% of the original data) from 103 studies were retained (from 2011 to 2021) in the LAC database. Potential mitigation strategies were classified into three main categories (i.e., animal breeding, dietary, and rumen manipulation) and up to three subcategories, totaling 34 evaluated strategies. A random effects model weighted by inverse variance was used (Comprehensive Meta-Analysis V3.3.070). Six strategies decreased at least one enteric CH4 metric and simultaneously increased milk yield (MY; dairy cattle) or average daily gain (ADG; beef cattle and sheep). The breed composition F1 Holstein × Gyr decreased CH4 emission per MY (CH4IMilk) while increasing MY by 99%. Adequate strategies of grazing management under continuous and rotational stocking decreased CH4 emission per ADG (CH4IGain) by 22 and 35%, while increasing ADG by 22 and 71%, respectively. Increased dietary protein concentration, and increased concentrate level through cottonseed meal inclusion, decreased CH4IMilk and CH4IGain by 10 and 20% and increased MY and ADG by 12 and 31%, respectively. Lastly, increased feeding level decreased CH4IGain by 37%, while increasing ADG by 171%. The identified effective mitigation strategies can be adopted by livestock producers according to their specific needs and aid LAC countries in achieving SDG as defined in the Paris Agreement

Rebrota do capim-marandu submetido ao ataque de cigarrinhas.

O objetivo deste experimento foi caracterizar e quantificar respostas de Brachiaria brizantha cv. Marandu sob níveis de infestações de cigarrinhas adultas do gênero Mahanarva (Hemiptera: Cercopidae). O ensaio foi realizado em casa de vegetação na Cidade de Piracicaba, SP, e seus tratamentos corresponderam a quatro níveis de infestações de cigarrinhas (5, 10, 20 e 40 insetos adultos vaso-1) mais o controle (sem cigarrinhas), distribuídos nas unidades experimentais (vasos) segundo delineamento inteiramente casualizado. O estudo compreendeu período de infestação pelo inseto e de rebrota das plantas. Avaliaram-se as seguintes variáveis-resposta: acúmulo de forragem, massa de raiz e teor de reservas orgânicas. Não foi observado efeito das cigarrinhas na massa das frações rebrote, resíduo e sistema radicular do capim-marandu (p > 0,05). A massa do resíduo e sistema radicular, assim como a concentração de carboidratos não estruturais nesses órgãos de acúmulo, sofreram influência apenas do período de rebrota (p < 0,0001). Já a concentração de nitrogênio total no resíduo e nas raízes foi influenciada pela interação entre níveis de infestação e período de rebrota (p < 0,0001 e p = 0,0521).201

Effect of grazing frequency on enteric methane emissions, output of milk constituents and milk yield.

Grazing management changes sward structure, affecting forage morphological characteristics and nutritive value, and ultimately animal performance and enteric methane (CH4) emissions. The objective of this study was to evaluate enteric methane emissions and animal performance of lactating dairy cows grazing elephant grass (Pennisetum purpureum Schum. cv. Cameroon). Treatments corresponded to strategies of rotational grazing characterized by two pre-grazing targets; 95% and maximum canopy light interception (LI95% and LIMax, respectively). Post-grazing target corresponded to 50% of the pre-grazing targets. Twenty-two midlactation Holstein × Jersey cows (488 ± 60 kg) were stratified by body weight, days in milk (126 ± 90 days), lactation number (2.3 ± 1.2), and daily milk yield (20.3 ± 2.6 kg d-1) in a completely randomized design (n = 11). The 2.5 ha experimental area was divided into two sets of 18 paddocks (700 m2). Concentrate was offered twice daily before milking based on the average milk production of each group (1 kg of concentrate:3 kg of milk). Enteric CH4 emissions were collected using the sulfur hexafluoride (SF6) tracer gas method. Dry matter intake (DMI) was determined using titanium dioxide as a marker. Sampling occurred during the grazing season from December 2015 to April 2016. Results were analyzed using the PROC MIXED of SAS (? = 0.05). There were no treatment effects on DMI (18.4 kg d-1, on average; P = 0.090) nor on daily CH4 emissions (304.9 g d-1 on average; P = 0.136). Therefore, there were no treatment effects on enteric CH4 emissions per unit of feed consumed (17.3 g CH4 kg DMI-1). However, cows grazing LI95% swards had greater milk (17.5 vs 14.6 kg d-1; P = 0.043), protein (0.55 vs 0.47 kg d-1; P = 0.029), fat (0.66 vs 0.55 kg d-1; P = 0.027), and milk solids yield (2.15 vs 1.79 kg d-1; P = 0.019). Consequently, the LI95% target resulted in lower enteric CH4 emissions per unit of milk produced (16.7 vs 23.4 g CH4 L-1, P = 0.002), per unit of milk protein (528.1 vs 703.5 g CH4 kg-1; P = 0.003), per unit of milk fat (437.9 vs 606.5 g CH4 kg-1; P = 0.001), and per unit of milk solids yield (135.2 vs 186.1 g CH4 kg-1; P = 0.001). Grazing management based on the LI95% pre-grazing target resulted in increased output of milk constituents and milk yield, whilst reducing CH4 emissions per unit of final product. These results are likely associated with increased forage nutritive value in LI95% swards, since no effects of pre-grazing targets were observed on DMI

Strategic grazing management and nitrous oxide fluxes from pasture soils in tropical dairy systems.

Greenhouse gases emissions are considered one of the most important environmental issues of dairy farming systems. Nitrous oxide (N2O) has particular importance owing to its global warming potential and stratospheric ozone depletion. The objective of this study was to investigate the influence of two rotational grazing strategies characterized by two pre-grazing targets (95% and maximum canopy light interception; LI95% and LIMax, respectively) on milk production efficiency and N2O fluxes from soil in a tropical dairy farming system based on elephant grass (Pennisetum purpureum Schum. cv. Cameroon). Results indicated that LI95% pre-grazing target provided more frequent defoliations than LIMax.Water-filled pore space, soil and chamber temperatures were affected by sampling periods (P1 and P2). There was a significant pre-grazing target treatment × sampling period interaction effect on soil NH4 + concentration, which was most likely associated with urinary-N discharge. During P1, there was a greater urinary-N discharge for LI95% than LIMax (26.3 vs. 20.9 kg of urinary-N/paddock) caused by higher stocking rate, which resulted in greater N2O fluxes for LI95%. Inversely, during P2, the soil NH4 + and N2O fluxes were greater for LIMax than LI95%. During this period, the greater urinary-N discharge (46.8 vs. 44.8 kg of urinary N/paddock) was likely associated with longer stocking period for LIMax relative to LI95%, since both treatments had similar stocking rate. Converting hourly N2O fluxes to daily basis and relating to milk production efficiency, LI95% was 40% more efficient than LIMax (0.34 vs. 0.57 g N&#727;N2O/kg milk·ha). In addition, LI95% pre-grazing target decreased urea-N loading per milk production by 34%. Strategic grazing management represented by the LI95% pre-grazing target allows for intensification of tropical pasture-based dairy systems, enhanced milk production efficiency and decreased N-N2O emission intensity.Made available in DSpace on 2019-10-25T18:08:43Z (GMT). No. of bitstreams: 1 StrategicGrazingManagement.pdf: 919309 bytes, checksum: 86b425d4eed29c3fe7da37652bc030f3 (MD5) Previous issue date: 2019bitstream/item/203704/1/StrategicGrazingManagement.pd

Prediction of enteric methane production and yield in sheep using a Latin America and Caribbean database

Methane (CH4) produced from enteric fermentation in ruminants has a noticeable impact on climate change. Prediction models are an alternative to current laborious and costly in vivo CH4 measurement techniques. The objectives of this study were to: (1) collate a database of individual sheep records from CH4 emission studies conducted in the Latin America and Caribbean (LAC) region; (2) identify key variables for predicting CH4 production (g/d) and CH4 yield [g/kg of dry matter intake (DMI)]; (3) develop and cross-validate these newly-developed models; and (4) compare models’ predictive ability with equations currently used to support national greenhouse gas (GHG) inventories in the LAC region. After removing outliers, the final database retained 219 individual sheep records from 11 studies, 48.2% of the original database. Models were developed using a sequential approach, by incrementally adding different variables with increasing complexity. Production and yield of CH4 were predicted by fitting mixed-effects models with a random effect of study. The predictive accuracy of fitted CH4 prediction models was evaluated using a leave-one-out cross-validation. Overall, increasing model complexity improved the predictive performance of CH4 production and yield equations. Feed intake was the most important predictor of sheep CH4 production. Our best-developed CH4 production models outperformed Tier 2 equations from the Intergovernmental Panel on Climate Change (IPCC) in the growing lambs and mature sheep subsets, whereas they performed slightly worse in the complete subset. Methane yield can be predicted using dietary forage content only, or with an increased complexity model combining body weight, feeding level, and dietary forage content. The use of the newly-developed models rather than IPCC Tier 2 equations can substantially improve the accuracy of GHG inventories from LAC countries

Prediction of enteric methane production and yield in dairy cattle using a Latin America and Caribbean database.

ABSTRACT: Successful mitigation efforts entail accurate estimation of on-farm emission and prediction models can be an alternative to current laborious and costly in vivo CH4 measurement techniques. This study aimed to: (1) collate a database of individual dairy cattle CH4 emission data from studies conducted in the Latin America and Caribbean (LAC) region; (2) identify key variables for predicting CH4 production (g d&#8722;1) and yield [g kg&#8722;1 of dry matter intake (DMI)]; (3) develop and cross-validate these newly-developed models; and (4) compare models' predictive ability with equations currently used to support national greenhouse gas (GHG) inventories. A total of 42 studies including 1327 individual dairy cattle records were collated. After removing outliers, the final database retained 34 studies and 610 animal records. Production and yield of CH4 were predicted by fitting mixed-effects models with a random effect of study. Evaluation of developed models and fourteen extant equations was assessed on all-data, confined, and grazing cows subsets. Feed intake was the most important predictor of CH4 production. Our best-developed CH4 production models outperformed Tier 2 equations from the Intergovernmental Panel on Climate Change (IPCC) in the all-data and grazing subsets, whereas they had similar performance for confined animals. Developed CH4 production models that include milk yield can be accurate and useful when feed intake is missing. Some extant equations had similar predictive performance to our best-developed models and can be an option for predicting CH4 production from LAC dairy cows. Extant equations were not accurate in predicting CH4 yield. The use of the newly-developed models rather than extant equations based on energy conversion factors, as applied by the IPCC, can substantially improve the accuracy of GHG inventories in LAC countries

Prediction of enteric methane production and yield in dairy cattle using a Latin America and Caribbean database

Enteric methane (CH4) from ruminants is the major driver of global warming and climate change. Successful mitigation efforts entail accurate estimation of on-farm emission and prediction models can be an alternative to current laborious and costly in vivo CH4 measurement techniques. This study aimed to: (1) collate a database of individual dairy cattle CH4 emission data from studies conducted in the Latin America and Caribbean (LAC) region; (2) identify key variables for predicting CH4 production (g d−1) and yield [g kg−1 of dry matter intake (DMI)]; (3) develop and cross-validate these newly-developed models; and (4) compare models' predictive ability with equations currently used to support national greenhouse gas (GHG) inventories. A total of 42 studies including 1327 individual dairy cattle records were collated. After removing outliers, the final database retained 34 studies and 610 animal records. Production and yield of CH4 were predicted by fitting mixed-effects models with a random effect of study. Evaluation of developed models and fourteen extant equations was assessed on all-data, confined, and grazing cows subsets. Feed intake was the most important predictor of CH4 production. Our best-developed CH4 production models outperformed Tier 2 equations from the Intergovernmental Panel on Climate Change (IPCC) in the all-data and grazing subsets, whereas they had similar performance for confined animals. Developed CH4 production models that include milk yield can be accurate and useful when feed intake is missing. Some extant equations had similar predictive performance to our best-developed models and can be an option for predicting CH4 production from LAC dairy cows. Extant equations were not accurate in predicting CH4 yield. The use of the newly-developed models rather than extant equations based on energy conversion factors, as applied by the IPCC, can substantially improve the accuracy of GHG inventories in LAC countries

Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries

On-farm methane (CH4) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH4 have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH4 emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH4 inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (∼ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH4 emission (g d−1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH4 yield (g kg−1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH4 emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH4 prediction for beef cattle in inventories of LAC countries based either on more or less readily available data.Fil: Congio, Guilhermo F.S.. Universidade de Sao Paulo; BrasilFil: Bannink, André. University of Agriculture Wageningen; Países BajosFil: Mayorga, Olga L.. Corporación Colombiana de Investigación Agropecuaria; ColombiaFil: Rodrigues, João P.P.. Universidade Federal Rural do Rio de Janeiro; BrasilFil: Bougouin, Adeline. University of California at Davis; Estados UnidosFil: Kebreab, Ermias. University of California at Davis; Estados UnidosFil: Carvalho, Paulo C.F.. Universidade Federal do Rio Grande do Sul; BrasilFil: Berchielli, Telma T.. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Mercadante, Maria E.Z.. São Paulo Agribusiness Technology Agency; BrasilFil: Valadares-Filho, Sebastião C.. Universidade Federal de Viçosa; BrasilFil: Borges, Ana L.C.C.. Universidade Federal de Minas Gerais; BrasilFil: Berndt, Alexandre. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; BrasilFil: Rodrigues, Paulo H.M.. Universidade de Sao Paulo; BrasilFil: Ku Vera, Juan C.. Universidad Autonoma de Yucatan (uady);Fil: Molina Botero, Isabel C.. Universidad Nacional Agraria La Molina; PerúFil: Arango, Jacobo. Centro Internacional de Agricultura Tropical; ColombiaFil: Reis, Ricardo A.. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Posada Ochoa, Sandra L.. Universidad de Antioquia; ColombiaFil: Tomich, Thierry R.. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; BrasilFil: Castelán Ortega, Octavio A.. Universidad Autónoma del Estado de México; MéxicoFil: Marcondes, Marcos I.. Washington State University; Estados UnidosFil: Gómez, Carlos. Universidad Nacional Agraria La Molina; PerúFil: Ribeiro Filho, Henrique M.N.. Universidade Do Estado de Santa Catarina; BrasilFil: Gere, José Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; ArgentinaFil: Ariza-Nieto, Claudia. Corporación Colombiana de Investigación Agropecuaria; ColombiaFil: Giraldo, Luis A.. Universidad Nacional de Colombia; ColombiaFil: Gonda, Horacio Leandro. Sveriges Lantbruksuniversitet (slu);Fil: Cerón Cucchi, María Esperanza. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires; ArgentinaFil: Hernández, Olegario. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires; ArgentinaFil: Ricci, Patricia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires; ArgentinaFil: Hristov, Alexander N.. State University of Pennsylvania; Estados Unido