Estimation of Ruminal Methane Production From Measurement of Volatile Fatty Acid Production

Hegarty, R. S.; Nolan, JV

doi:10.1007/978-1-4020-6133-2_4

Cited by 14 publications

(23 citation statements)

References 26 publications

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“…Jayanegara et al [7] used both stoichiometric equations to predict CH 4 from VFA concentrations in vitro, and found that indeed, the equations overpredicted CH 4 , likely due to a much lower observed H recovery (observed range of 28.9% to 56.2%) compared to the recoveries assumed by the models (100% and 90%). In agreement with Jayanegara et al [7], when the stoichiometric equations [8,9] were applied to the current test dataset, CH 4 i was overpredicted (observed CH 4 i (mmol/L) = 11.6 ± 2.44, using [8], predicted CH 4 i = 16.2 ± 3.07; using [9], predicted CH 4 i = 14.0 ± 2.68) and had poor CCC evaluation statistics (0.135 and 0.227 for [8,9], respectively). For the test dataset, the average H 2 recovery, calculated according to [31], was 80%, a value that is substantially lower than the theoretical recovery rates [8,9], and also different from those observed by Jayanegara et al [7], indicating the potential value of an empirical approach, such as those developed in our work.…”

Section: Discussionsupporting

confidence: 89%

“…To predict CH 4 (in vivo or in vitro) based on stoichiometry principles alone and considering a H recovery of 100%, Hegarty and Nolan [8] proposed the equation: CH 4 (mmol/L) = 0.5AC + 0.5BT − 0.25PR − 0.25VL (where all VFAs are expressed in mmol/L). Similarly, Moss et al [9], considering a H recovery of 90%, proposed the equation: CH 4 (mmol/L) = 0.45AC − 0.275PR + 0.40BT (where all VFAs are expressed in mmol/L).…”

Section: Discussionmentioning

confidence: 99%

“…The resulting H is utilized by methanogens to reduce CO 2 to H 2 O (CO 2 + 8H → CH 4 + 2H 2 O). However, predicting CH 4 from the above stoichiometric equations is only valid if (1) these VFAs are the only end-products of fermentation, (2) no free H 2 accumulates or escapes, (3) the microbial digestion process is strictly anaerobic, and (4) H 2 is not used in other reactions (e.g., reduction of sulphates to sulphides, or saturation of double bonds in fatty acids) [8]. In practice, the production of CH 4 will be less than the stoichiometry prediction given by the above equations, because these assumptions are generally not held.…”

Section: Discussionmentioning

confidence: 99%

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Application of Meta-Analysis and Machine Learning Methods to the Prediction of Methane Production from In Vitro Mixed Ruminal Micro-Organism Fermentation

Ellis

Aláiz‐Moretón

Navarro-Villa

et al. 2020

Animals

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Simple Summary: In vitro gas production systems are regularly utilized to screen feed ingredients for inclusion in ruminant diets. However, not all in vitro systems are set up to measure methane (CH 4 ) production, nor do all papers report in vitro CH 4 . Therefore, the objective of this study was to develop models to predict in vitro production of CH 4 , a greenhouse gas produced by ruminants, from in vitro gas and volatile fatty acid (VFA) production data, and to identify the major drivers of CH 4 production in these systems. Meta-analysis and machine learning (ML) methodologies were applied to predict CH 4 production from in vitro gas parameters. Meta-analysis results indicate that equations containing apparent dry matter (DM) digestibility, total VFA production, propionate, valerate and feed type (forage vs. concentrate) resulted in best prediction of CH 4 . The ML models far exceeded the predictability achieved using meta-analysis, but further evaluation on an external database would be required to assess their generalization capacity. The models developed can be utilized to estimate CH 4 emissions in vitro. Abstract:In vitro gas production systems are utilized to screen feed ingredients for inclusion in ruminant diets. However, not all in vitro systems are set up to measure methane (CH 4 ) production, nor do all publications report in vitro CH 4 . Therefore, the objective of this study was to develop models to predict in vitro CH 4 production from total gas and volatile fatty acid (VFA) production data and to identify the major drivers of CH 4 production in these systems. Meta-analysis and machine learning (ML) methodologies were applied to a database of 354 data points from 11 studies to predict CH 4 production from total gas production, apparent DM digestibility (DMD), final pH, feed type (forage or concentrate), and acetate, propionate, butyrate and valerate production. Model evaluation was performed on an internal dataset of 107 data points. Meta-analysis results indicate that equations containing DMD, total VFA production, propionate, feed type and valerate resulted Animals 2020, 10, 720 2 of 18 in best predictability of CH 4 on the internal evaluation dataset. The ML models far exceeded the predictability achieved using meta-analysis, but further evaluation on an external database would be required to assess generalization ability on unrelated data. Between the ML methodologies assessed, artificial neural networks and support vector regression resulted in very similar predictability, but differed in fitting, as assessed by behaviour analysis. The models developed can be utilized to estimate CH 4 emissions in vitro.Keywords: in vitro gas production; methane; rumen; feed; meta-analysis; machine learning; neural network IntroductionGlobally, greenhouse gas (GHG) emissions from the agriculture, forestry and other land use (AFOL) sector account for~23% of the global anthropogenic GHG total emissions [1], with enteric methane (CH 4 ) from fermentation in the forestomach of ruminants representing 32%-40% of ...

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Application of Meta-Analysis and Machine Learning Methods to the Prediction of Methane Production from In Vitro Mixed Ruminal Micro-Organism Fermentation

Ellis

Aláiz‐Moretón

Navarro-Villa

et al. 2020

Animals

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“…Therefore, the proportions of acetate, butyrate and propionate determine the amounts of available H 2 in the rumen to be used by methanogens. By this relation, CH4 emission can stoichiometrically be calculated from the respective VFA (Moss et al, 2000;Hegarty and Nolan, 2007).…”

Section: Introductionmentioning

confidence: 99%

Assessment of Methane Estimation From Volatile Fatty Acid Stoichiometry in the Rumen in Vitro

Jayanegara

Ikhsan

Toharmat

2013

J. Indonesian Trop. Anim. Agric.

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Mikroba rumen mendegradasi komponen pakan dan menghasilkan sejumlah produk metabolit seperti asam lemak terbang (volatile fatty acids, VFA), karbon dioksida, amonia dan metana (CH 4). Hidrogen metabolik dalam bentuk proton tereduksi digunakan dalam pembentukan CH 4 dan juga dalam sintesis VFA. Dengan demikian, konsentrasi VFA di rumen berhubungan secara stoikiometri dengan emisi CH 4. Tujuan penelitian ini adalah untuk mengevaluasi kesesuaian emisi CH 4 antara hasil eksperimen dengan prediksi dari komposisi VFA. Dua buah model stoikiometri yang memprediksi CH 4 dari VFA dievaluasi, yakni model Moss et al. (2000) dan model Hegarty dan Nolan (2007). Data yang digunakan merupakan data mentah dari sebuah publikasi yang menggunakan sampel berupa 27 hijauan pakan tropis. Prediksi galat dilakukan dengan cara menghitung root mean square prediction error (RMSPE). Hasil menunjukkan bahwa model estimasi Moss et al. (2000) memiliki RMSPE yang lebih rendah dibandingkan dengan model Hegarty dan Nolan (2007), yakni 8.01% vs 10.73%. Variasi tinggi rendahnya nilai emisi CH 4 dapat diestimasi secara cukup akurat menggunakan kedua model stoikiometri. Penyesuaian nilai estimasi dengan mempertimbangkan H 2 recovery dapat mengurangi bias secara signifikan. Dari penelitian ini dapat disimpulkan bahwa model Moss memiliki akurasi yang lebih baik dalam memprediksi emisi CH 4 dari komposisi VFA dibandingkan dengan model Hegarty dan Nolan.

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“…This error can be overcome by making separate injections or infusions of labelled acetate and butyrate on separate days to quantify the interconversion. Procedures for estimating VFA production in ruminants and advantages and disadvantages of the method are described in detail in Hegarty and Nolan (2007). According to these authors, while there is no maximum time for infusions, a 9 h infusion, with sample collection over the final 6 h can give a good estimate of daily production if animals are given their daily ration in equal hourly portions.…”

Section: Energy Availability From a Feed In The Rumenmentioning

confidence: 99%

Role of Tracer techniques in Animal Nutrition- A review

Ponnuvel¹

2016

Int. J. Adv. Res. Biol. Sci

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Estimation of Ruminal Methane Production From Measurement of Volatile Fatty Acid Production

Cited by 14 publications

References 26 publications

Application of Meta-Analysis and Machine Learning Methods to the Prediction of Methane Production from In Vitro Mixed Ruminal Micro-Organism Fermentation

Application of Meta-Analysis and Machine Learning Methods to the Prediction of Methane Production from In Vitro Mixed Ruminal Micro-Organism Fermentation

Assessment of Methane Estimation From Volatile Fatty Acid Stoichiometry in the Rumen in Vitro

Role of Tracer techniques in Animal Nutrition- A review

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