Predicting enteric methane emission in sheep using linear and non-linear statistical models from dietary variables

2016 ◽  
Vol 56 (3) ◽  
pp. 574 ◽  
Author(s):  
A. K. Patra ◽  
M. Lalhriatpuii ◽  
B. C. Debnath
Keyword(s):  
Organic Matter ◽  
Energy Intake ◽  
Methane Emission ◽  
Statistical Models ◽  
Prediction Error ◽  
Mean Square ◽  
Enteric Methane ◽  
Non Linear ◽  
Gross Energy ◽  
Linear Statistical Models

The objective of the present study was to develop linear and non-linear statistical models for prediction of enteric methane emission (EME) in sheep. A database from 80 publications, which included a total of 449 mean observations of EME measured on more than 1500 sheep, was constructed to develop prediction and evaluation of models of EME. Dietary nutrient composition (g/kg), nutrient or energy intake (kg/day or MJ/day) and digestibility (g/kg) of organic matter were used as predictors of EME (MJ/day). The dietary concentrations of neutral detergent fibre and crude protein, and feed intake, were 435 g/kg, 152 g/kg and 0.92 kg/day, respectively. The EME by sheep expressed as MJ/day and % of gross energy intake was 1.02 and 6.54, respectively. The simple linear equation that predicted EME with high precision and accuracy was EME = 0.208(±0.040) + 0.049(±0.0039) × gross energy intake (MJ/day), adjusted R2 = 0.86 with root mean-square prediction error of 22.7%, of which 93% was from random error and regression bias of 3.20%. Additions of dietary concentration of fibre and feeding level, and organic matter digestibility to the simple linear model improved the models. Among the non-linear equations developed, monomolecular model, i.e. EME = 5.699 (±1.94) – [5.699 (±1.94) – 0.133 (±0.047)] × exp[–0.021(±0.0071) × metabolisable energy intake (MJ/day)]; adjusted R2 = 0.90 and mean-square prediction error = 20.1%, with 96.3% random error, performed better than simple linear and other non-linear models. The equations developed in the present study will be useful for national methane inventory preparation, and for a better understanding of dietary factors influencing EME in sheep.

scholarly journals A Basic Model to Predict Enteric Methane Emission from Dairy Cows and Its Application to Update Operational Models for the National Inventory in Norway

Animals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1891
Author(s):  
Puchun Niu ◽  
Angela Schwarm ◽  
Helge Bonesmo ◽  
Alemayehu Kidane ◽  
Bente Aspeholen Åby ◽  
...  
Keyword(s):  
Dairy Cows ◽  
Energy Intake ◽  
Methane Emission ◽  
Neutral Detergent Fiber ◽  
Operational Model ◽  
National Inventory ◽  
Basic Model ◽  
Ch4 Production ◽  
Enteric Methane ◽  
Gross Energy

The aim of this study was to develop a basic model to predict enteric methane emission from dairy cows and to update operational calculations for the national inventory in Norway. Development of basic models utilized information that is available only from feeding experiments. Basic models were developed using a database with 63 treatment means from 19 studies and were evaluated against an external database (n = 36, from 10 studies) along with other extant models. In total, the basic model database included 99 treatment means from 29 studies with records for enteric CH4 production (MJ/day), dry matter intake (DMI) and dietary nutrient composition. When evaluated by low root mean square prediction errors and high concordance correlation coefficients, the developed basic models that included DMI, dietary concentrations of fatty acids and neutral detergent fiber performed slightly better in predicting CH4 emissions than extant models. In order to propose country-specific values for the CH4 conversion factor Ym (% of gross energy intake partitioned into CH4) and thus to be able to carry out the national inventory for Norway, the existing operational model was updated for the prediction of Ym over a wide range of feeding situations. A simulated operational database containing CH4 production (predicted by the basic model), feed intake and composition, Ym and gross energy intake (GEI), in addition to the predictor variables energy corrected milk yield and dietary concentrate share were used to develop an operational model. Input values of Ym were updated based on the results from the basic models. The predicted Ym ranged from 6.22 to 6.72%. In conclusion, the prediction accuracy of CH4 production from dairy cows was improved with the help of newly published data, which enabled an update of the operational model for calculating the national inventory of CH4 in Norway.

Quantification of methane emissions from Murrah buffaloes fed different energy diets during various temperature humidity index periods in a tropical environment

2019 ◽  
Vol 59 (1) ◽  
pp. 169
Author(s):  
Papori Talukdar ◽  
Shivlal Singh Kundu ◽  
Goutam Mondal
Keyword(s):  
Organic Matter ◽  
Methane Emission ◽  
Dry Matter ◽  
Winter Season ◽  
High Energy ◽  
Low Energy ◽  
Murrah Buffalo ◽  
Enteric Methane ◽  
Acid Detergent Fibre ◽  
Humidity Index

The objective of the present study was to quantify the enteric methane emission in Murrah buffalo heifers at high (summer) and low (winter) temperature humidity index (THI) period fed different energy level diets. Thirty-six growing Murrah buffalo heifers of average bodyweight (158.51 ± 16.5 kg) were distributed into three groups of six animals each separated based on their bodyweight and fed for the period of 120 days each during summer (high THI, 78–85) and winter (low THI, 50–61). The animals were fed on three different levels of metabolisable energy (ME) content and the Control ration (T1) having ME content according to ICAR (2013) and T2 and T3 were having 115% and 85% ME than the Control respectively, in total mixed-based ration. The SF6 tracer gas technique was used to quantify the enteric methane emission by the animals. Methane emission (g/day) of Control and the high energy (T1 and T2) group was lower (P < 0.05) than the low energy (T3) fed group in both seasons. Methane losses as percentage of gross energy intake was lower (P < 0.01) during the winter season. However, in the low energy treatment group (T3) at both seasons these values are higher than the IPCC recommended value (6.5%) for calculation of national inventory of greenhouse gas emission from enteric sources. In between season average daily gain (kg) was higher (P < 0.01) in the winter season and among the treatment groups it was higher (P < 0.01) in the high energy group (T1, T2). Higher (P < 0.01) digestibility of dry matter, organic matter, neutral detergent fibre and acid detergent fibre was reported in the Control and high energy-fed group. Whereas in the summer season digestibility of dry matter, organic matter, crude protein and acid detergent fibre was higher (P < 0.01) than in the winter season. It can be concluded that energy levels significantly (P < 0.05) affect methane emissions and was lower in the Control and high energy-fed group (T1 and T2). However, while quantifying methane emission in changing THI period at different seasons it did not show any significant variation.

An ensemble approach to forecast COVID-19 incidences using linear and non-linear statistical models

2021 ◽  
Vol 66 (3/4) ◽  
pp. 415
Author(s):  
Asmita Mahajan ◽  
Nonita Sharma ◽  
Firas Husham Almukhtar ◽  
Monika Mangla ◽  
Krishna Pal Sharma ◽  
...  
Keyword(s):  
Statistical Models ◽  
Ensemble Approach ◽  
Non Linear ◽  
Linear Statistical Models

Quantifying effects of grassland management on enteric methane emission

2016 ◽  
Vol 56 (3) ◽  
pp. 409 ◽  
Author(s):  
A. Bannink ◽  
D. Warner ◽  
B. Hatew ◽  
J. L. Ellis ◽  
J. Dijkstra
Keyword(s):  
Energy Intake ◽  
Dry Matter ◽  
Pearson Correlation ◽  
Grassland Management ◽  
Ch4 Emission ◽  
Enteric Fermentation ◽  
Grass Silage ◽  
Wide Range ◽  
Enteric Methane ◽  
Gross Energy

Data on the effect of grassland management on the nutritional characteristics of fresh and conserved grass, and on enteric methane (CH4) emission in dairy cattle, are sparse. In the present study, an extant mechanistic model of enteric fermentation was evaluated against observations on the effect of grassland management on CH4 emission in three trials conducted in climate-controlled respiration chambers. Treatments were nitrogen fertilisation rate, stage of maturity of grass and level of feed intake, and mean data of a total of 18 treatments were used (4 grass herbage treatments and 14 grass silage treatments). There was a wide range of observed organic matter (OM) digestibility (from 68% to 84%) and CH4 emission intensity (from 5.6% to 7.3% of gross energy intake; from 27.4 to 36.9 g CH4/kg digested OM; from 19.7 to 24.6 g CH4/kg dry matter) among treatment means. The model predicted crude protein, fibre and OM digestibility with reasonable accuracy (root of mean square prediction errors as % of observed mean, RMSPE, 6.8%, 7.5% and 3.9%, respectively). For grass silages only, the model-predicted CH4 correlated well (Pearson correlation coefficient 0.73) with the observed CH4 (which varied from 5.7% to 7.2% of gross energy intake), after predicted CH4 was corrected for nitrate consumed with grass silage, acting as hydrogen sink in the rumen. After nitrate correction, there was a systematic under-prediction of 18%, which reduced to 9% when correcting the erroneously predicted rumen volatile fatty acid (VFA) profile (RMSPE 15%). Although a small over-prediction of 3% was obtained for the grass herbages, this increased to 14% when correcting VFA profile. The model predictions showed a systematic difference in CH4 emission from grass herbages and grass silages, which was not supported by the observed data. This is possibly related to the very high content of soluble carbohydrates in grass herbage (an extra 170 g/kg dry matter compared with grass silages) and an erroneous prediction of its fate and contribution to CH4 in the rumen. Erroneous prediction of the VFA profile is likely to be due to different types of diets included in the empirical database used to parameterise VFA yield in the model from those evaluated here. Model representations of feed digestion and VFA profile are key elements to predict enteric CH4 accurately, and with further evaluations, the latter aspect should be emphasised in particular.

Methane emissions, feed intake, and total tract digestibility in lambs fed diets differing in fat content and fibre digestibility

2019 ◽  
Vol 99 (4) ◽  
pp. 858-866 ◽  
Author(s):  
E.J. McGeough ◽  
L.C.G. Passetti ◽  
Y.H. Chung ◽  
K.A. Beauchemin ◽  
S.M. McGinn ◽  
...  
Keyword(s):  
Organic Matter ◽  
Dry Matter ◽  
Latin Square ◽  
Methane Emissions ◽  
Fat Content ◽  
Differential Impact ◽  
Neutral Detergent Fibre ◽  
Enteric Methane ◽  
High Fibre ◽  
Gross Energy

This study determined enteric methane (CH4) emissions, intake, and apparent total tract digestibility of diets varying in fibre digestibility and fat content. A Latin square design with two levels of fat [2.0% and 6.0% dry matter (DM); low and high] and two levels of fibre digestibility [low fibre digestibility (LFbD) or high fibre digestibility (HFbD)] was used. Higher dry matter intake (DMI) was observed (P < 0.01) for LFbD versus HFbD diets (2.56 vs. 2.14 kg d−1, respectively), with no effect of fat. Fibre, DM, and organic matter digestibility were higher (P < 0.01) for HFbD than LFbD diets. Increasing fat did not affect intake or digestibility of DM or dietary constituents but there was a fibre digestibility × fat content interaction (P < 0.01) for fat digestibility. There was also a fat content × fibre digestibility interaction (P < 0.05) for CH4 (g kg−1 DMI, organic matter intake, neutral detergent fibre intake, and percent gross energy intake), with emissions being higher when fat was added to the HFbD than the LFbD diet. The CH4 emissions per kilogram of neutral detergent fibre (NDF) digested were higher (P < 0.01) for the HFbD than the LFbD diet. Methane emissions were increased by the HFbD diet, but inclusion of fat had a differential impact on CH4 emissions as a proportion of DMI or NDF intake in diets differing in fibre digestibility.

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