# Intake time as potential predictor of methane emissions from cattle.

John F. Ramirez-Agudelo, Carlos S. Escobar-Restrepo, Rafael Munoz

Tamayo, Sandra L. Posada-Ochoa, Ricardo Rosero-Noguera

► To cite this version:

John F. Ramirez-Agudelo, Carlos S. Escobar-Restrepo, Rafael Munoz Tamayo, Sandra L. Posada-Ochoa, Ricardo Rosero-Noguera. Intake time as potential predictor of methane emissions from cattle.. 2022. hal-03604356

HAL Id: hal-03604356

https://hal.inrae.fr/hal-03604356

Preprint submitted on 10 Mar 2022

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Intake time as potential predictor of methane emissions from
cattle

John F. Ramirez-Agudelo¹*, Carlos S. Escobar-Restrepo¹, Rafael

Muñoz-Tamayo², Sandra L. Posada-Ochoa¹, Ricardo Rosero-Noguera¹

¹Grupo de Investigación GRICA, Facultad de Ciencias Agrarias, Universidad de Antioquia, Calle 70 No. 52-21, AA 1226, Medellín, Colombia

² Université Paris-Saclay, INRAE, AgroParisTech, UMR Modélisation Systémique Appliquée aux Ruminants, 75005, Paris, France.

*Corresponding author: johnf.ramirez@udea.edu.co

**Abstract**

Measuring enteric methane (CH₄) produced by cattle is central for the construction of national greenhouse gas inventories and for devising strategies to mitigate the environmental footprint of ruminant livestock. Efforts to estimate or measure enteric CH₄ emissions in cattle include techniques such as the Green-Feed system, laser detectors, respiration chambers, and the SF6 technique. However, using these tools on a large scale is complicated and expensive. Therefore, it is necessary to develop alternative methods to estimate CH₄ emissions from cattle. The objective of this work was to evaluate the potential use of intake time (IT) to estimate enteric CH₄ emissions in cattle. The CH₄ emissions of seven steers were
measured in respiration chambers. A computer vision technique, called Object Detection (OD), was used to estimate IT. The images recorded from the top of the feeder were used for OD. Additionally, the dry matter intake (DMI) was measured by the difference between the offered and rejected food each day. CH₄ emissions were predicted by two linear equations using DMI or IT as explanatory variables. The equation using IT versus that one using DMI presented lower root mean square error (RMSE, 13.8 vs. 20.2 g/d), relative prediction error (RPE, 9.1% vs. 13.3%) and RMSE to standard deviation of observed values ratio (RSR, 0.38 vs. 0.56), respectively. Our results indicated that, under our experimental conditions, IT is a potential predictor of enteric CH₄ emissions in cattle. However, more experimental data are needed to validate our findings.

**Keywords:** Greenhouse gasses inventories, modelling, Object Detection technique, Precision Livestock Farming

# 1. Introduction

Methane (CH₄) emitted from livestock contributes substantially to global warming (Gerber et al., 2013). To establish the amount of CH₄ emitted from livestock, and specifically from cattle, several methods have been developed: open-circuit respiration chambers (Pinares and Waghorn, 2012), sulfur hexafluoride tracer (SF6) (Zimmerman, 1993) and GreenFeed (C-Lock Inc. Rapid City, SD, USA). Unfortunately, the implementation of
those methods on a large scale is hampered by their high cost and the need
for advanced equipment. Farms and animal lab facilities around the world,
with few exceptions, are not equipped to measure cattle CH₄ emissions
because these devices require advanced instrumentation, expensive
maintenance, and highly skilled labor.

Complementary to experimental-based techniques, mathematical models
have been accepted by IPCC (Shukla et al., 2019) to estimate the total
amount of CH₄ emitted by cattle. Predictive models of enteric CH₄ emissions
are mainly based on feed intake and chemical composition (Sauvant et al.,
2011; Ramin and Huhtanen, 2013; Benaouda et al., 2019). Under the
assumption that food composition is well known, the estimation accuracy of
the models will depend mainly on the accuracy of feed intake determination.

Dry Matter Intake (DMI) is the primary predictor of enteric CH₄ emissions (Appuhamy et al., 2016; Charmley et al., 2016; Hristov et al., 2018). However, the individualized measurement of DMI, especially for grazing animals or in group-housing systems, is an expensive task (Seymour, et al., 2019). As an alternative to the direct measurement of DMI, new technologies based on sensors and Machine Learning algorithms allow the DMI to be inferred satisfactorily using the time that animals spend eating (Intake Time, IT), both indoors (Barker et al., 2018) and grazing (Mattachini et al., 2016).

Taking into account that IT has a close relationship with DMI, and that this in
turn has a close relationship with CH₄ emissions, it is possible to infer that
IT has the potential to infer CH₄ emissions in a similar way as DMI. Based on this premise, Muñoz-Tamayo et al. (2019) evaluated theoretically the use of IT as CH₄ emissions predictor. The authors found that the accuracy of enteric CH₄ predictions using IT was similar to the accuracy obtained when DMI is used as a predictor in a dynamic model. The application of mathematical models using IT as predictors can be useful to improve the ruminant's enteric CH₄ inventories, in particular in countries lacking advanced experimental infrastructures. The objective of this work was to evaluate the potential of IT as a predictor of CH₄ emission.

## 2. Material and methods

### 2.1. Location and animals

Seven Angus x Brahman crossbreed steers (body weight: 240.7 +/- 23.1 kg; age: 477 +/- 42 days) were housed at the calorimetry laboratory of Universidad de Antioquia (Antioquia, Colombia). This laboratory is located at an altitude of 2480 m, with 16°C average temperature and coordinates 6°26'43.1"N 75°32'43.0"W. During 60-days before the measurement of CH₄ emissions, the animals were individually housed in 2.5-m² covered pens (Figure 1) and adapted to the diet and to the respiration chamber facility.
<figure><img src="image_2.png" /><figcaption>Figure 1. Calorimetry laboratory, Universidad de Antioquia. A. Monitoring room. B. Two respiration chambers for CH₄ measurements. C. Individual pens.</figcaption></figure>

## 2.2. *Diet and feeding*

The diet consisted of hay grass (*Digitaria decumbens* Stent.) and supplement (corn-cottonseed-soybean meal) in a 60:40 ratio. Animals were fed *ad libitum* twice a day, at 09:00 and 15:00 h, throughout the experimental period. The daily amount offered was adjusted such that the rejected food was 10% of the offer. Diet composition is presented in Table 1.

## 2.3. CH₄ emissions

The calorimetry laboratory has two respiration chambers made in galvanized steel (Figure. 1). Internal dimensions of the chambers were 2.6 m wide, 3.7 m length, 2.3 m height, resulting in 22.1 m³ total volume. The chambers have side windows (1.40 x 1.30 m) to allow visual contact between animals, and are provided with feeder, automatic drinker, fan to
ensure gas mixing, air conditioning and dehumidifier to guarantee thermoneutrality and welfare conditions.

<table><caption>**Table 1.** Chemical composition of hay grass (*Digitaria decumbens* Stent) and supplement.</caption><thead><tr><th>Nutrient (%)<sup>1</sup></th><th>Hay</th><th>Concentrate supplement<sup>2</sup></th></tr></thead><tbody><tr><td>DM</td><td>89.0</td><td>87.8</td></tr><tr><td>CP</td><td>6.0</td><td>24.5</td></tr><tr><td>NDF</td><td>59.0</td><td>13.8</td></tr><tr><td>Ash</td><td>8.7</td><td>10.6</td></tr><tr><td>GE (kcal/kg DM)</td><td>4200</td><td>4450</td></tr></tbody></table>

During the measurement of CH₄ emissions, the air was removed from the chambers at 500 L/min using a mass-flow system (Flowkit 2000, Sable Systems International, Las Vegas, NV, USA). CH₄ concentration in the extracted air column was measured every second with an infrared analyzer (MA-10, Sable Systems International, Las Vegas, NV, USA). The Exp-UI2 software (Sable Systems International, Las Vegas, NV, USA) automatically calculated CH₄ emissions as liters per minute. The results were corrected for standard temperature and pressure. Figure 2 shows the respiration chamber design and the sampling for CH₄ quantification.

<footnote n="1">¹ Composition expressed as percentage of the dry matter (DM); CP= Crude protein; NDF= Neutral detergent fiber; Ash= Inorganic matter; GE= Gross energy (kcal/kg DM).</footnote>

<footnote n="2">² Supplement: corn (35.4%), soybean meal (35%), cotton seed (15%), urea (0.6%) molasses (8%) and mineralized salt (6%).</footnote>
<figure><img src="image_3.png" /><figcaption>Figure 2: Methane analyzer and respiration chamber design: 1. Air inlet. 2. Air outlet. 3. Metallic pen. 4. Feeder and drinker. 5. Air conditioning. 6. Fan. 7. Dehumidifier</figcaption></figure>

Before measuring the CH₄ produced by the animals, the analyzer was calibrated with commercially prepared gas of known concentration (0.098%). The animal's CH₄ emissions were adjusted according to the Recovery Factors (RF) of each chamber. To estimate the RF in each chamber, pure CH₄ was injected at a known rate (0.25 L/min) for 2 h. The amount of gas injected was determined gravimetrically. RF were 0.87 and 0.90 for chamber 1 and 2, respectively. CH₄ emissions from each animal were measured for three consecutive days.

## 2.4. Intake time (IT) measurements

To identify eating activity, a video camera was placed over the feeder inside each chamber (Figure 3). It was assumed that animals only visit the feeder to eat. To establish if the animal was eating or not, the object detection model YOLOv5x was used (Jocher et al., 2020). This model is a
convolutional neural network (CNN, or ConvNet), based on the YOLO model (AlexeyAB/darknet) that, in simple terms, divides an input image into multiples grids, and use some of these cells for object detection if the center of an object falls into a grid cell. In general, the YOLO-based models have been trained using several image databases (e.g COCO dataset) to detect 80 different classes of objects, including horse, sheep, and cow. Given that in this work just one object class was necessary (i.e. cow), the model was fine-tuned to increase the cow detection accuracy.

Considering that all the video monitoring was taken under the same environmental conditions (e.g. illumination, distance from the feeder), the images data set extracted from the videos (>2 million) was visually inspected to find 1000 images with eating animals. This image sub-set was randomly selected from both chambers and all measured days. In each selected image, the software Labellmg (Tzutalin, 2018) was used to find the area where the object of interest (steer head) is located. This process is named “Image Labelling” and as result, 1000 text files with the object coordinates were obtained. The images, their corresponding labeling information, and the free resources in Google Colab were used to re-train the YOLOv5x following the Jocher et al. (2020) instructions. Finally, the re-trained YOLOv5x was used to calculate the IT per animal per day in the 2 chambers.
<figure><img src="image_4.png" /><figcaption>Figure 3: Example of eating activity detection in the chamber.</figcaption></figure>

## 2.5. Dry matter intake (DMI)

Simultaneously with the quantification of CH₄ emissions, the DMI was determined. The DMI was daily calculated as the difference between the feed offered and rejected. Samples of food offered and refused were collected daily, and stored at -10°C for subsequent dry matter analysis (AOAC 930.15, 1990).

## 2.6. Model equations and performance indicators

We developed two linear equations to estimate CH₄ emission. The impact of the feed intake was either represented by the DMI or IT. The model performance was assessed by the calculation of the following statistical indicators:
The root mean square error RMSE:

$$RMSE = \sqrt{\frac{1}{n}\left(\frac{\sum_{i=1}^{n}(O_i - P_i)^2}{n}\right)}$$

where n is the number of observations, Oᵢ is the ith observed value and Pᵢ is the ith model predicted value.

The relative prediction error RPE:

$$RPE = \left( \frac{RMSE}{Xo} \right) * 100$$

where Xo is the mean of the observations.

The mean absolute error MAE:

$$MAE = \frac{1}{n}\sum_{i=1}^{n}|O_i - P_i|$$

The mean absolute percent error MAPE:

$$MAPE = \frac{1}{n}\sum_{i=1}^{n}\left|\frac{O_i - P_i}{O_i}\right| * 100$$
The RMSE to standard deviation of observed values ratio RSR:

$$RSR = \frac{RMSE}{So}$$

where So is the standard deviation of the observed values.

The coefficient of determination R²:

$$R^2 = \frac{\sum_{i=1}^{n} (O_i - P_i)^2}{\sum_{i=1}^{n} (O_i - Xo)^2}$$

The Lin's concordance correlation coefficient CCC (Lin, 1989):

$$CCC = \frac{2\sigma_{xp}}{\sigma_x^2 + \sigma_p^2 + (Xo - Po)^2}$$

where Po is the mean of the model predictions, and σ_x², σ_p² are the variances for the observations and the model predictions. The covariance σ_xp is calculated as:

$$\sigma_{xp} = \frac{1}{n} \sum_{i=1}^{n} (O_i - Xo)(P_i - Po)$$
### 3. Results and discussion

Table 2 shows the summary of the experimental data used for model construction. The mean DMI was 6.3 kg/d and the average CH₄ emissions per animal was 152.0 g/d. The mean IT recorded by image analysis was 5.4 h/animal/day. We observed that some animals eat food that falls outside the image area, which can have a negative impact on the precision of IT determination with image analysis in our experimental framework.

<table><caption>**Table 2.** Dry matter intake (DMI), intake time (IT) and CH₄ emissions data used for model construction.</caption><thead><tr><th>Animal</th><th>DMI (kg/d)</th><th>IT (h/d)</th><th>CH<sub>4</sub> (g/d)</th></tr></thead><tbody><tr><td>1</td><td>6.3</td><td>6.3</td><td>179.1</td></tr><tr><td>2</td><td>5.9</td><td>3.7</td><td>132.7</td></tr><tr><td>3</td><td>6.1</td><td>5.6</td><td>148.3</td></tr><tr><td>4</td><td>6.7</td><td>6.5</td><td>189.3</td></tr><tr><td>5</td><td>6.0</td><td>5.0</td><td>141.6</td></tr><tr><td>6</td><td>8.6</td><td>7.0</td><td>184.8</td></tr><tr><td>7</td><td>4.6</td><td>3.7</td><td>88.3</td></tr><tr><td>Mean±SD<sup>1</sup></td><td>6.3±1.2</td><td>5.4±1.3</td><td>152.0±36</td></tr></tbody></table>

#### 3.1. CH₄ emission

Figure 4 shows a typical pattern of the IT and CH₄ emission for an animal in our facility. CH₄ emission starts to increase when feed is offered. Compared

<footnote n="1">¹SD= standard deviation</footnote>
with other studies reporting experimental data on the dynamic emissions of CH₄ (Crompton et al., 2011; Troy et al., 2015; Moate et al., 2018; Muñoz-Tamayo et al., 2019), our data suggest a possible buffer effect of the respiration chamber that hampers to capture the fluctuation pattern of CH₄ missions.

<figure><img src="image_5.png" /><figcaption>Figure 4. Example of intake time and CH₄ emission patterns. The line represents CH₄ emissions pattern (gr per minute) and the bars represent intake time pattern (minutes in 20 minutes intervals)</figcaption></figure>

### 3.3. Performance of mathematical models

Table 3 shows the resulting linear equations obtained when using DMI and IT as predictors and the respective statistical performance indicators. For the model with DMI as predictor, the estimated slope is 12% higher than the slope estimated for Angus steers and 23% higher than the slope estimated for Brahman steers in the work of Charmley et al. (2016).
As observed, IT is a better predictor than DMI under our experimental conditions for all statistical performance indicators. Models with higher CCC and lower RPE, MAPE and RSR present better predictive performance. The RPE for IT (9.1) indicates satisfactory predictability (Fuentes-Pila et al., 1996). Likewise, the MAPE value for IT (8.2) is associated with a high predictive capacity (Mayer and Butler, 1993). Based on these parameters, it is observed that the model that incorporates IT for CH₄ estimations presents superior performance compared with DMI.

<table><caption>**Table 3.** Statistical indicators of model performance.</caption><thead><tr><th rowspan="2">Statistical indicator<sup>1</sup></th><th colspan="2">Models<sup>2</sup></th></tr><tr><th>CH<sub>4</sub> = 24.07*DMI</th><th>CH<sub>4</sub> = 27.95*IT</th></tr></thead><tbody><tr><td>RMSE</td><td>20.2</td><td>13.8</td></tr><tr><td>RPE</td><td>13.3</td><td>9.1</td></tr><tr><td>MAE</td><td>16.9</td><td>10.9</td></tr><tr><td>MAPE</td><td>11.5</td><td>8.2</td></tr><tr><td>RSR</td><td>0.56</td><td>0.38</td></tr><tr><td>R<sup>2</sup></td><td>0.63</td><td>0.84</td></tr><tr><td>CCC</td><td>0.78</td><td>0.92</td></tr></tbody></table>

<footnote n="1">¹RMSE: root mean square error; RPE: relative prediction error, MAE: mean absolute error; MAPE: mean absolute percent error; RSR: RMSE to standard deviation of observed values ratio; R²: coefficient of determination; CCC: Lin's concordance correlation coefficient.</footnote>

<footnote n="2">²CH₄: methane (g/d); DMI: dry matter intake (kg/d); IT: Intake time (h/d).</footnote>
Hristov et al. (2018), using DMI as an independent variable, obtained CCC between 0.69 and 0.74 and RSR between 0.64 and 0.67. In the present work, both models resulted in higher CCC and lower RSR. McBride's (2005) indicated that CCC values lower than 0.90 and fluctuating between 0.90 and 0.95 are associated with poor and moderate predictive capacity, respectively. Accordingly, the model with IT had a better predictive capacity than the model with DMI.

Although it is widely accepted that DMI can be used to predict CH₄ emissions, the association between these variables depends on the DMI range used to establish the relation. Higher ranks are associated with higher coefficients of determination (R²) (Hristov and Melgar 2020). Hristov et al. (2018) revealed that the relationship was higher for the respirometric chamber (R² = 0.58), with DMI ranges between 3.9 and 33.5 kg/d.

Benaouda et al. (2019) evaluated the performance of various CH₄ prediction models in dairy cattle, including the Charmley et al. (2016) model, which includes DMI as predictor. This model occupied a medium position, with root mean square prediction error (RMSPE), RSR and CCC of 22.8%, 0.81 and 0.68, respectively. In the present work, the best performance parameters for the model including IT are explained by the lower RMSE and the higher Pearson correlation coefficient between CH₄ emissions and IT (R = 0.92), with respect to DMI (R = 0.80).

Figure 5 displays the comparison of the experimental data against the model predictions and the respective residuals. Determination of IT in the
field fulfills the characteristics of low cost and individual level accuracy discussed by Negussie et al. (2017). Accordingly, our work suggests that IT is a potential proxy of CH₄ emissions on a large scale. However, more experimental data are needed to validate our findings.

<figure><img src="image_6.png" /><figcaption>Figure 5: Experimental data of CH₄ emissions are compared against the predictive CH₄ production when using DMI (A) and IT (B) as predictor variables. The solid line is the isocline.</figcaption></figure>

## 4. Conclusion

Our work indicated the potential of using IT as a predictor of CH₄ emissions, since IT can be estimated using different technologies (e.g. computer vision). Our result is of great relevance for estimation of CH₄ emissions at large scale, in particular for conditions when accurate estimations of DMI are not feasible.
## Ethical considerations

This study was approved by the Ethics Committee for Animal Experimentation at Universidad de Antioquia (act 76, May 2012, Medellín-Colombia).

## Acknowledgements

John F. Ramirez-Agudelo was supported by a grant from the Ministerio de Ciencia, Tecnología e Innovación - Colombia (projects 58414, 66737). We acknowledge the support of ECOS Nord Colombia-France in the framework of the project Suivi et simulation des émissions de gaz à effet de serre dans les systèmes de production laitière.

## Author contributions

**J.F. Ramirez-Agudelo:** Conceptualization, Methodology, Software, Investigation, Data Curation, Writing - Original Draft,; **C.S. Escobar-Restrepo:** Conceptualization, Methodology, Investigation, Writing - Original Draft; **R. Muñoz-Tamayo:** Formal analysis, Methodology, Writing - Review & Editing, Funding acquisition; **S.L. Posada-Ochoa:** Conceptualization, Methodology, Supervision, Writing - Review & Editing; **R. Rosero-Noguera:** Conceptualization, Methodology, Supervision, Project administration, Funding acquisition.
ORCIDs

J.F. Ramirez-Agudelo: 0000-0002-2169-7260

C.S. Escobar-Restrepo: 0000-0002-3955-484X

R. Muñoz-Tamayo: 0000-0002-9266-4132

S.L. Posada-Ochoa: 0000-0002-4615-0552

R. Rosero-Noguera: 0000-0002-3238-8842

References

AlexeyAB/darknet. https://github.com/AlexeyAB/darknet

Appuhamy, J. A., France, J., & Kebreab, E. (2016). Models for predicting enteric methane emissions from dairy cows in North America, Europe, and Australia and New Zealand. *Global Change Biology*, *22*(9), 3039-3056.

Barker, Z. E., Diosdado, J. V., Codling, E. A., Bell, N. J., Hodges, H. R., Croft, D. P., & Amory, J. R. (2018). Use of novel sensors combining local positioning and acceleration to measure feeding behavior differences associated with lameness in dairy cattle. *Journal of dairy science*, *101*(7), 6310-6321.

Benaouda, M., Martin, C., Li, X., Kebreab, E., Hristov, A. N., Yu, Z., ... & Eugène, M. (2019). Evaluation of the performance of existing mathematical models predicting enteric methane emissions from
ruminants: Animal categories and dietary mitigation strategies. *Animal feed science and technology*, 255, 114207.

Charmley, E. S. R. O., Williams, S. R. O., Moate, P. J., Hegarty, R. S., Herd, R. M., Oddy, V. H., ... & Hannah, M. C. (2015). A universal equation to predict methane production of forage-fed cattle in Australia. *Animal Production Science*, 56(3), 169-180.

COCO dataset. https://cocodataset.org

Crompton, L. A., Mills, J. A., Reynolds, C. K., & France, J. (2011). Fluctuations in methane emission in response to feeding pattern in lactating dairy cows. In *Modelling nutrient digestion and utilisation in farm animals* (pp. 176-180). Wageningen Academic Publishers, Wageningen.

Fuentes-Pila, J., DeLorenzo, M. A., Beede, D. K., Staples, C. R., & Holter, J. B. (1996). Evaluation of equations based on animal factors to predict intake of lactating Holstein cows. *Journal of Dairy Science*, 79(9), 1562-1571.

Gerber, P. J., Steinfeld, H., Henderson, B., Mottet, A., Opio, C., Dijkman, J., ... & Tempio, G. (2013). *Tackling climate change through livestock: a global assessment of emissions and mitigation opportunities*. Food and Agriculture Organization of the United Nations (FAO).

Hristov, A. N., & Melgar, A. (2020). Relationship of dry matter intake with enteric methane emission measured with the GreenFeed system in dairy cows receiving a diet without or with 3-nitrooxypropanol. *animal*, 14(S3), s484-s490.
Lawrence, I., & Lin, K. (1989). A concordance correlation coefficient to evaluate reproducibility. *Biometrics*, 255-268.

Mattachini, G., Riva, E., Perazzolo, F., Naldi, E., & Provolo, G. (2016). Monitoring feeding behaviour of dairy cows using accelerometers. *Journal of Agricultural Engineering*, 47(1), 54-58.

Mayer, D. G., & Butler, D. G. (1993). Statistical validation. *Ecological modelling*, 68(1-2), 21-32.

McBride, G. B. (2005). A proposal for strength-of-agreement criteria for Lin's concordance correlation coefficient. *NIWA client report: HAM2005-062*, 45, 307-310.

Moate, P. J., Williams, S. R. O., Deighton, M. H., Hannah, M. C., Ribaux, B. E., Morris, G. L., ... & Wales, W. J. (2018). Effects of feeding wheat or corn and of rumen fistulation on milk production and methane emissions of dairy cows. *Animal Production Science*, 59(5), 891-905.

Munoz-Tamayo, R., Agudelo, J. R., Dewhurst, R. J., Miller, G., Vernon, T., & Kettle, H. (2019). A parsimonious software sensor for estimating the individual dynamic pattern of methane emissions from cattle. *animal*, 13(6), 1180-1187.

Negussie, E., de Haas, Y., Dehareng, F., Dewhurst, R. J., Dijkstra, J., Gengler, N., ... & Biscarini, F. (2017). Invited review: Large-scale indirect measurements for enteric methane emissions in dairy cattle: A review of proxies and their potential for use in management and breeding decisions. *Journal of Dairy Science*, 100(4), 2433-2453.
Hristov, A. N., Kebreab, E., Niu, M., Oh, J., Bannink, A., Bayat, A. R., ... & Yu, Z. (2018). Symposium review: uncertainties in enteric methane inventories, measurement techniques, and prediction models. *Journal of dairy science*, **101**(7), 6655-6674.

Pinares-Patiño, C. S., & Waghorn, G. (2012). Technical manual on respiration chamber designs. *Wellington, New Zealand: Ministry of Agriculture and Forestry*.

Ramin, M., & Huhtanen, P. (2013). Development of equations for predicting methane emissions from ruminants. *Journal of dairy science*, **96**(4), 2476-2493.

Sauvant, D., Giger-Reverdin, S., Serment, A., & Broudiscou, L. (2011). Influences of diet and rumen fermentation on methane production by ruminants. *INRA Productions Animales*, **24**(5), 433-446.

Seymour, D. J., Cánovas, A., Baes, C. F., Chud, T. C. S., Osborne, V. R., Cant, J. P., ... & Miglior, F. (2019). Invited review: Determination of large-scale individual dry matter intake phenotypes in dairy cattle. *Journal of dairy science*, **102**(9), 7655-7663.

Shukla, P. R., Skeg, J., Buendia, E. C., Masson-Delmotte, V., Pörtner, H. O., Roberts, D. C., ... & Malley, J. (2019). Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems.

Troy, S. M., Duthie, C. A., Hyslop, J. J., Roehe, R., Ross, D. W., Wallace, R. J., ... & Rooke, J. A. (2015). Effectiveness of nitrate addition and
increased oil content as methane mitigation strategies for beef cattle
fed two contrasting basal diets. *Journal of Animal Science*, 93(4),
1815-1823.

Tzutalin. https://github.com/tzutalin/labellmg

Zimmerman, P. R. (1993). *U.S. Patent No. 5,265,618*. Washington, DC: U.S. Patent and Trademark Office.