Prediction and evaluation of methane emission of growing cattle diets in Vietnam based on fecal near infrared reflectance spectroscopy 

Tran Hiep, Dang Vu Hoa*, Vu Chi Cuong* and Nguyen Xuan Trach

Faculty of Animal Sciences and Aquaculture
 Hanoi University of Agriculture - Vietnam
hiep26@yahoo.com
*National Institute of Animal Sciences - Hanoi - Vietnam

Abstract

The objective of the study was to estimate methane emission based on growing cattle diet intake and to develop prediction model for methane emission based on fecal NIRS that can be applied in practice. An experiment was carried out during 6 months (February – September) in the experimental station of National Institute of Animal Sciences (Vietnam). Five growing cattle were used in a 5 x 5 Latin square (5 diets x 5 periods) design and fed 5 diets based on forages and by-products used in northern Vietnam (NaOH treated rice straw, Napier grass, cassava leaf meal, cassava root meal, cotton cake and molasses). Feed intake was measured to estimate methane emission using an equation developed by Moe and Tyrell (1980). Fecal samples were scanned twice by a Foss NIRsystem 5000 monochromator to collect spectra. Methane-fecal pair database was then used to perform different mathematical techniques in WinISI software to select best model for methane prediction.

Methane emissions (liters/kg DMI) were  negatively correlated to DMI intake. Methane production was positively correlated with NDF (r = 0.75) and ADF (0.69) but negatively correlated with ME concentration (r = - 0.34), CP content (r = - 0.0) and ADL content ( r = - 0.14). As a consequence, methane emission were lower with a diet containing cassava leaf plus molasses (49.3 liters/kg DMI) compared with a diet containing only Napier grass (63.2 litres/kg DMI). It was also shown that the energy loss from methane emissions was about 12.7%.

Keywords: Fecal NIRS, growing cattle, methane emission.

Introduction

While carbon dioxide receives the most attention as a factor in global warming, there are other gases to consider, including methane. In an effort to combat global warming, reducing methane emissions is an attractive target. Firstly, methane has a global warming potential 300 times that of carbon dioxide (Aydinalp and Cresser 2008). Secondly, methane is broken down quite rapidly in the atmosphere; within 9-15 years (FAO 2006). Therefore a fall in methane emission would quickly result in a reduction in atmospheric greenhouse gas concentration.

Methane production in the digestive tract of ruminants, called enteric fermentation, is one of the major sources of global methane emissions. According to the recent FAO report ‘Livestock’s Long Shadow’, enteric methane emissions amount to almost 86 million  tonnes of methane each year (FAO 2006). With an extra 17.5 million  tonnes of methane produced from manure, livestock are responsible for 37% of anthropogenic methane, in which buffaloes and cattle contribute about 80% of the global methane emissions from domestic livestock annually (FAO 2006; Aydinalp and Cresser 2008). It is thus necessary to estimate methane emissions from ruminant in order to find out methods to reduce methane production from these animals.

However, determination of methane emission in ruminant has met with much difficulty due to cost of modern equipment and technical skills (Johnson 1995; McCaughey et al 1997, 1999). Some measurement techniques based on diet intake and quality have been developed (Benchaar et al 2001; Boadi and Wittenberg 2002; Hegarty 1998; Kebreab et al 2008; Mills et al 2003; Wilkerson 1995) but the application is limited due to the large variation of feed types in ruminant diets under farm conditions that present complications in assessing feed intake, especially in tropical countries such as Vietnam. So, a simple technique for methane emission that could be applied on farm is needed.   

Materials and methods

Location

The in vivo experiment was done at the experimental station of National Institute of Animal Sciences (NIAS).  

Animals and diets

The experiment involved 5 growing cattle of 115-140 kg. Each animals was housed in a tie-stall to allow intake measurement. Five diets were formulated using forages and by-products available in north Vietnam (Table 1 and Table 2).

Experimental design

A 5 x 5 Latin square (5 diets x 5 periods) design was used. Each period lasted for 3 weeks (2 for adaptation and 1 for measurement).

Intake measurement

For each animal, the daily forage and concentrate intake was individually determined. Forage refusals were weighed the next morning.  

Sampling procedure

Approximately 500 g on a fresh matter basis of each ingredient were collected every methane estimating day. Fecal samples were collected directly from the rectum in the next morning. They were then dried in a forced-air oven at 70°C for 48 h to determine DM content. All dried samples were ground through a 1 mm screen and stored in closed plastic boxes at room temperature before NIRS analyses.

Chemical composition

Chemical composition of each feed (ash, CP, NDF and ADF) were predicted according to a large NIRS database and equations from Gembloux (Belgium) and Cirad (France) databases. For concentrate feeds, composition and feeding value data were collected from the local suppliers.

Methane emission estimation

The total methane production was estimated using an equation developed by Moe and Tyrell (1980): [CH4 litres/day = 86.1+67.0*Cell+43.9* Hemi+12.9*Starch and Sugar]; (kg ingested/day on DM basis)].

NIRS Measurements and Spectral Treatments

All samples were scanned twice at 2 nm intervals over the 1100 – 2498 nm wavelengths by a Foss NIRsystem 5000 monochromator (Silver Spring, MD, USA). The samples were scanned using closed cells, and NIR absorbance data were recorded as mean log 1/Reflectance values (log 1/R). Mathematical treatment of the spectral data was performed using WinISI software (Infrasoft International; Shenk 1992) to evaluate standardized Mahalanobis distance (H). Before calibration and validation was performed, mathematical pretreatments, standard normal variate and detrend, and different derivative pretreatments were applied to spectral data.  

Model calibration

The models were developed using estimated methane production database (reference database) and fecal spectra database using WinISI software. Firstly, methane production and fecal spectra were paired. Then, methane-fecal pair database was used to perform different mathematical techniques to select best model for methane emission.

Model evaluation

The statistical parameters used to evaluate the accuracy of the calibration and validation dataset were standard error of prediction (SEP), relative standard error of prediction (RSEP%), ratio performance deviation (RPD), and coefficient of determination (R²). 

Where n = number of pairs; Oi = observed values of the diet component i; Pi = predicted values of the diet component i by the calibration or validation model; SS Error = sum of squares of error; SS Total = total variation in the model; SD = standard deviation of the original data.

The SEP and RSEP show the robustness of the model. A prediction model was considered satisfactory if R² > 0.8, and 10% £ RSEP £ 20%, good if R² > 0.8 and RSEP < 10%, and excellent if R² > 0.9 and RSEP < 10%.

Statistical analysis

The data from each experiment were analyzed by the General Linear Model option in the ANOVA program of SAS system Software (version8.0).

Results and Discussion

Diet intake

Dietary composition and intake are given in Table 3 and indicate that the quality of molasses (MO) supplement diet (MU-CLM) was highest based on its low fiber content but high protein content and energy concentration. Conversely, the quality of NaOH treated rice straw (RS) basal diet supplemented with cassava leaf (CL) (RS-CLM) was lowest due to its high fiber but low protein and energy concentration. However, the soybean cake supplement increased significantly protein and energy of the RS basal diet (RS-SBM compared with RS-CLM). Otherwise, the supplement of both cassava root meal (CR) and cassava leaf to the Napier grass (NG) basal diet reduced fiber and protein contents (NGCLM-CRM compared with N).

The high and similar levels of DMI intake were found for RS basal diets (RS-CLM) and NG supplemented with both CR and CL (NGCLM-CRM). The diet using solely NG and diet using MU-CLM provided much lower level of intake compared with the others (about 27% lower). CL, SC and/or CR supplement improved significantly level of intake of growing cattle, especially on the low quality diet. As a consequence, these supplements increased considerably fiber intake. However, CP intake was much improved only with RS basal diet supplemented with SC (RS-SBM).

Methane emission 

Variation of methane emission over 5 different diets is shown in table 4. High and similar levels of total methane emission were found in RS-CLM diets. The lower level of methane was emitted from NG and MU-CLM. According to Giger-Reverdin et al (2000), methane emission increased with level of intake. So, it is expected that the three diets that had high intake must emit more methane.  

In terms of rate of methane emission, expressed as liters of methane/kg DMI, MU-CLM produced least methane per unit DM intake. Giger-Reverdin et al. (2000) reported that methane emission (liters/kg DMI) reduces by the increase in both the level of intake and concentrate proportion in the diet. That means methane efficiency is improved as the increase in intake. Here, N and 5 had low intake but methane efficiency was oppositely obtained. MU-CLM provided highest efficiency whereas N had lowest efficiency. This illogicality was caused by different diet quality. MU-CLM containing high protein and energy concentration could provide high digestibility. On the contrary, other diets containing high fiber content theoretically provided low digestibility. The increase in the retention time of feed in rumen facilitates the activity of methanogenic bacteria and as a consequence, the methane emission increased.

These findings are similar to results reported by Lassey et al (2002) and Primavesi et al (2004). They found that high fiber forages emitted more methane than lower fiber forages due to their lower digestibility. So, supplements of protein and energy sources such as CL, SC, MO to the low quality forage (RS) in this study could improve feed conversion ratio and decrease methane due to the increase in digestibility of the whole diet. Figures 1 and 2 illustrate that the methane emissions per unit DM intake were reduced with level of intake but increased with the increase in fiber content.

Figure 1a. Correlation between total DM intake and methane emission rate (NaOH-treated rice straw + CLM)	Figure 1b. Correlation between total DM intake and methane emission rate (NaOH-treated rice straw + soybean meal)
Figure 1c. Correlation between total DM intake and methane emission rate (napier grass)	Figure 1d. Correlation between total DM intake and methane emission rate (napier grass + CRM + CLM)
Figure 1e. Correlation between total DM intake and methane emission rate (molasses + CLM)

Figure 2. Correlation between total DM intake, NDF content and methane emission rate

Energy loss

Estimates of energy loss from methane are presented in table 5. The energy loss from methane emissions varied from 10 to 14% of the gross energy intake, lowest in MU-CLM  and highest in RS-CLM diets. The supplement of CR and CL to tge NG basal diet reduced the energy loss from methane. The results are slightly higher than those reported by Johnson et al. (1993) and Kujawa (1994). They found that the energy loss from methane varied from approximately 2 to 12% of GE intake.

Diet intake and methane prediction (Local calibration)

Methane emission from ruminant is affected by factors concerning diet quality and digestibility (intake, concentrate level, type of carbohydrate, feed processing, addition of lipids and nitrate, alterations to the microflora…) (Giger-Reverdin et al. 2000). So, any approach to evaluate these factors should be used to estimate methane emission.

The fecal NIRS, as illustrated by many studies, could predict accurately intake, diet quality and digestibility (Boval et al. 2004).  Awuma (2003) reported that the voluntary organic matter intake (OMVI) and digestibility in cattle could be predicted by fecal NIRS with high R² (> 0.8) low RSEC (6%). For sheep, Fanchone et al. (2007) estimated the OMVI with an acceptable accuracy with RSEC equal to 12%. More recently, Decruyenaere et al. (2008) demonstrated that the OMVI of ruminants was successfully predicted by fecal NIRS equations with R² ranging from 0.80 to 0.90, and low RSEC (8%). Concerning diet quality, Lyons and Stuth (1992) found that fecal NIRS could predict diet quality (OMD, CP) with equal or better accuracy than NIRS equations using forage spectra and that it had the same accuracy as conventional analysis methods. Gibbs et al. (2002) also indicated that fecal NIRS profiling can be developed to distinguish total diet quality from animals fed forage resources of varying quality and concentrates in different proportions. So, fecal NIRS method could be promisingly used to develop prediction model for methane emission.

The calibration statistics of equations for diet intake and methane emission are summarized in Table 6 and Figure 3. Results show that relative standard error of prediction (RSEP) and coefficient of determination (R²) for all variables were about 8.07% and 0.77, respectively.

The highest predictive capacity was found for equations concerning DM intake (DMI equation and methane efficiency equation (liters/kg DMI)). These equations had low RSEP (9.28% and 4.81%) and good R² (0.83 and 0.85), respectively for DMI and methane emissions rate, expressed by liters/kg DMI. 

 

Figure 3a. Regression plot of predicted values vs observed values of DMI	Figure 3b. Regression plot of predicted values vs observed values of total CH4
Figure 3c. Regression plot of predicted values vs observed values of CH4/NDF	Figure 3d. Regression plot of predicted values vs observed values of CH4/ADF

 

According to Fuentes-Pila et al (1996, 2003) the precision of a prediction model is considered as good, acceptable or unsatisfactory when RSEC is lower than 10%, between 10% and 20% or higher than 20% of the mean observed values, respectively. Moreover, the R² and RPD values of a prediction model must be higher than 0.80 and 2.0, respectively (Valdés et al 2006, Williams, 2004). The present predictive statistics showed good prediction capacity of equations concerning DMI.  Other equations showed less satisfactory capacity of prediction. This was probably due to the limited data for developing prediction equations based on fecal NIRS. Therefore, it is needed to enlarge the data base in order to make good predictive capacity of these equations.  

Conclusions

• The supplement of CL, CR and SC increased significantly the intake of a high-fibre forage diet, reduced methane emissions, and as a consequence increased feed utilisation efficiency. In term of rate of methane per unit DM intake, the supplement of CL or SC to RS basal diet showed lower values than on the NG diet. These findings are significant for cattle feeding systems based on forages.

• The fecal NIRS method provides an opportunity to develop prediction models for methane emissions from dairy cattle. These advantages are without any additional requirements for details of diet composition. We conclude that this technique can be used as a rapid and efficient tool for diet monitoring. However, it is also needed to enlarge the data base to make prediction equations more accurate. Finally, a portable system of spectra measurements directly on farms would be interesting to facilitate this approach.

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