Introduction

In recent years, the Intergovernmental Panel on Climate Change (IPCC)¹ and the Food and Agriculture Organization of the United Nations (FAO)² declared that the agricultural sector is the principal source of short-lived greenhouse gases (GHG), such as methane (CH₄) and nitrous oxide (N₂O).

Some strategies to mitigate methane emissions from dairy cattle include reducing the herd, changing bovine diet, using supplements, immunization against methanogenic archaea, and selecting animals with lower CH₄ production³.

The selection of low methane-producing animals requires knowledge about the genetic correlations between methane production and other characteristics of productive and economic importance⁴.

A selection index is a methodology that maximizes breeding for a specific trait⁵. Selection indices have been widely used to estimate the reproduction value of dairy cattle for individual and combined characteristics for selection purposes⁶.

In cattle and sheep, the variation of CH₄ emission has been demonstrated between individuals fed the same diet⁷. De Haas et al⁸ mentioned the possibility of selecting cows with low CH₄ emissions since genetic variation suggests that the reductions would be 11-26 % in 10 yr and could be even higher in a genomic selection program. However, the available information about the opportunities to mitigate enteric CH₄ through genetic improvement is scarce. Still, the genetic selection of animals with low methane emissions could affect economically important production traits.

This study aimed to estimate the response to selection through different selection indices between methane production and milk production and components in three dairy production systems in Mexico.

Material and methods

This study was carried out in three dual-purpose (DP) production units (PUs), two specialized tropical dairy (STD) PUs, and four family dairy (FD) systems (Table 1). Milk components and methane emissions were measured in 274 cows (98, 74, and 102 in the DP, STD, and FD systems, respectively).

Two of the three DP PUs are located in the Medellín de Bravo municipality, Veracruz, and have a tropical savanna climate, Aw(o), and an altitude of 12 m asl⁹. The annual mean temperature and precipitation are 25 °C and 1,460 mm⁹. The third DP PU and the STD PUs are located in the Hueytamalco municipality, Puebla, at an altitude of 240 m, with a tropical wet climate (Af(c)), mean annual temperature of 23 ºC, and mean annual precipitation ranging from 2,200 to 2,500 mm⁹.

The four FD PUs are located in the Tepatitlán municipality, Jalisco, at an altitude of 1,927 m. This location has a humid subtropical climate ((A)C(w1) (e)g) with an annual mean temperature and precipitation of 18 ºC and 715 mm⁹.

The DP systems mainly use cross-bred Bos taurus taurus and Bos taurus indicus. The most common Bos taurus indicus breeds are Brahman, Gyr, and Sardo Negro; as for Bos taurus taurus, the most common breeds are Holstein, Brown Swiss, and Simmental¹⁰.

One of the variants of tropical dairy systems is the STD. This system is characterized by using pure breeds, such as Holstein and Brown Swiss. Overall, STD management is similar to DP systems except for calf rearing, which is artificial, and milking, which is carried out without calf support¹⁰.

FDs are characterized by small production units that fluctuate from 3 to 30 cows. The production units are conditioned to small areas and adjacent to the housing units, called “backyard.” FDs can be intensive or semi-intensive according to the conditions of the cultivation field. Holstein is the most common breed. The technological level is considered scarce because producers do not carry out adequate feeding, reproductive, preventive, or breeding practices. This system lacks production records and has rudimentary facilities; manual milking is often performed. Feeding is based on grazing or the supply of forages and wastes from the producer's crops¹¹.

Results

The CH₄ emissions in the STD system were 0.08 mg of CH₄/ L; FD and DP systems produced 0.06 mg of CH₄/ L (Table 3). The average of the three systems was 0.065 mg of CH₄/ L. As for milk components, the average fat percentage in the three systems was 4.82 %; the values per system were 3.69 % in STD, 3.72% in FD, and 6.84 % in DP. The protein percentage in the STD, FD, and DP systems was 3.20, 3.29, and 3.21 %, respectively. When combining the three systems, the average protein percentage was 3.23 %.

The h² estimated for CH₄ emissions during milking using the univariate model was 0.19. Similarly, the h² for fat percentage was 0.39 and 0.18 for protein percentage.

However, the h² estimated using bivariate models was 0.32±0.245 for CH₄ emissions during milking and 0.46±0.278 for fat percentage. The h² for protein percentage was similar to the one estimated with the univariate model. However, the h² of CH₄ is similar to the one found in the bivariate analysis with fat percentage (0.35). The genetic correlations between milk fat and protein percentage and CH₄ emissions during milking were -0.090 ± 0.080 and -0.18 ± 0.575, respectively.

Table 4 and Figure 1 show the accuracy of the selection indices (rHI) and the genetic gain (∆g). In all the selection indices, the decrease of CH₄ emissions during milking does not negatively affect milk components. Moreover, the rHI of the most accurate indices are those where CH₄ emissions during milking are selected.

Figure 1 Precision of the selection indices and genetic gain for CH₄ and milk components 

In the indices with greater rHI, indices from 1 to 10, this variates from 15.06 to 19.58 in five generations; this would result in CH₄ reductions ranging from 0.021 to 0.020 mg/L, fat percentage increases ranging from 0.027 to 0.030 and from 0.072 to 0.093 for protein percentage. These results indicate that milk production decreases when CH₄ emissions decrease. For the remaining indices, the changes in CH₄ emissions during milking, fat percentage, and protein percentage were not significant; however, the rHI is lower.

Discussion

The CH₄ emissions during milking estimated in this study are lower than those reported by Bell et al¹⁷; this is possibly due to diet heterogeneity. In the farms analyzed in this study, animals are fed by grazing; in the specialized systems, animals are fed a concentrate-based diet. The h² of CH₄ production during milking estimated in this study are similar to those reported by other authors with metabolic chambers^18,19 or even with prediction equations⁸. These results coincide with those reported by other authors using similar CH₄ measuring methodologies²⁰; this suggests that CH₄ during milking is a trait that could be potentially used in breeding programs because it can be easily incorporated into production control programs and requires unsophisticated equipment compared to respiratory chambers; additionally, this equipment can be mobilized to places of difficult access. The fat percentage h² estimated in this study is higher than those observed in other studies^21-22. The protein percentage h² is lower than the 0.23 reported by Othmane et al²³; this may be due to the inherent heterogeneity in dairy production units depending on the herd's diet and race.

The genetic correlations between CH₄ emissions and milk components suggest genetic antagonism. However, the estimates in this study were not different from zero. The CH₄ and grams of milk fat correlation observed by Pszczola et al⁴ is higher (0.21) than that reported in this study; the correlation between CH₄ and grams of milk protein in this study was similar to the one reported by Lassen et al²⁴ (0.39). In both cases, the opposite sign. It is important to mention that this difference is due to the units of measurement since milk production and the percentages of milk components have a negative correlation. In contrast, milk production and the content of its components have a positive correlation²⁵. Currently, the reduction of CH₄ emissions through genetic selection has been proposed; this could reduce dairy cattle CH₄ emissions in 10 yr between 11 and 26 %⁸; 5 % in beef cattle²⁶.

The selection indices performed by Kandel et al²⁷ include fat and protein production, which were positively correlated with CH₄ production. Considering the units of measurement, their result is similar to the one reported in this study since the correlation between milk production and the percentage of milk components is negative. In contrast, the correlation between milk production and the content of milk components is positive²⁷.

Conclusions and implications

The genetic correlations between CH₄ emissions and milk components (fat and protein percentage) suggest that a breeding program aimed to simultaneously decrease CH₄ emissions and increase the percentages of milk components is feasible. In other words, these results show that it is possible to genetically select animals to reduce CH₄ emissions without negatively affecting milk composition; this is confirmed by the genetic gains per generation predicted by the selection indices. The above, with CH₄ reductions during milking in five generations ranging from 0.013 to 0.021 mg/L, without decreasing fat and protein percentages.