# Relationship between Somatic Cell Score and Fat Plus Protein Yield in the First Three Lactations in Spanish Florida Goats

^{1}

^{2}

^{*}

## Abstract

**:**

^{2}oscillated between 0.272 and 0.279 for SCS and 0.099 and 0.138 for FPY. The genetic correlation between SCS and FPY was negative and medium (−0.304 to −0.477), indicating that a low-SCS EBV is associated with a genetic predisposition to high FPY production. Our results showed that given the magnitude of h

^{2}for SCS and its r

_{g}with FPY, the SCS could be used as a selection criterion to increase resistance to mastitis, thus obtaining an improved dairy and cheese aptitude in this breed.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Phenotypic Data and Pedigree

^{3}to >10,000 × 10

^{3}) were excluded. In the end, we used for the variance component analysis a total of 340,654 TDs recorded in the first 43 weeks of lactation of the first three kiddings from January 2006 to November 2019, from 27,479 daughters of 941 sires and 16,243 dams, of which 8788 were in the data vector. The pedigree contained all the known ancestors of the phenotyped animals, with a total of 36,144 animals. The variables analyzed were SCC transformed into SCS = log2 (SCC) + 3 [16] and the total daily amount of fat plus protein (FPY) expressed in grams.

#### 2.2. Statistical Analysis

_{i}8618 levels), litter size (LS

_{j}4 levels), kidding number (NP

_{k}3 classes) and lactation length expressed in weeks (DIM

_{wl}43 weeks).

_{1}and y

_{2}represent the phenotypic values for SCS and FPY, respectively. The elements b

_{i}represent the same fixed effects mentioned previously; µ

_{i}are the vectors for the additive genetic random effect and p

_{i}is the permanent environmental random effect due to repetitions of the same observations in the animal. The matrices X

_{i}, Z

_{i}and W

_{i}are incidence matrices connecting the fixed and random effects to the vector of dependent variables. Finally, e

_{i}represents the random vector of the error.

_{wI}). The representation of this model was similar to the RM, but in the RRM, the elements of a Legendre polynomial of order r were included as covariates. (Φr) of order r, as a fixed covariate in X

_{i}and a random covariate in Z

_{i}, was included to estimate the evolution of the (co)variance components between both dependent variables along the lactation trajectory expressed as DIM

_{wi}.

_{1}) and fpy (X

_{2}), respectively, ${\mathsf{\sigma}}_{{\mathsf{\mu}}_{12}}$ is their covariance, I

_{W}and l

_{n}are identity matrices and A is the denominator of the kinship relationship. The genetic heritability parameters (h

^{2}

_{xi}) and the genetic correlations between both dependent variables (r

_{g12}) were estimated according to classical formulas [18].

^{2}

_{xi}; r

_{g12}and EGV

_{i}for each i

^{th}point of DIM

_{wi}. Traits which are expressed longitudinally require an additional procedure presented by Jamrozik and Schaeffer [19], although basically the structure of expected (co)variances and estimates was similar:

_{i}by ${\mathsf{\alpha}}_{\mathrm{i}}$, which is a linear function for SCS and FPY throughout the lactation expressed in terms of a Legendre polynomial ${(\Phi}_{\mathrm{d}})$. The term $\otimes $ is a symbol of the Kronecker operator and ${\mathrm{C}}_{\mathsf{\alpha}}$ is a matrix containing the eigen elements of the polynomial used, while the rest of the terms are the same as previously presented. The matrix ${\mathrm{C}}_{\mathsf{\alpha}}$t has a complex structure consisting of four square submatrices ${\mathrm{C}}_{\mathsf{\alpha}}=\left[\begin{array}{cc}{\mathrm{c}}_{\mathsf{\alpha}1}& {\mathrm{c}}_{\mathsf{\alpha}12}\\ {\mathrm{c}}_{\mathsf{\alpha}21}& {\mathrm{c}}_{\mathsf{\alpha}2}\end{array}\right]$, with, on the diagonal (${\mathrm{c}}_{\mathsf{\alpha}1}$ and ${\mathrm{c}}_{\mathsf{\alpha}2}$), the genetic (co)variance components for each trait, with the elements corresponding to a polynomial of order r = 2, which best fits the data, and, outside the diagonal (${\mathrm{c}}_{\mathsf{\alpha}12}={\mathrm{c}}_{\mathsf{\alpha}21}$), the covariances between all the terms of each variable. The structure of each of these submatrices was:

_{w}trajectory were estimated by:

## 3. Results

#### 3.1. Phenotypic Parameters

#### 3.2. Genetic Parameters

#### 3.2.1. Repeatability Model

_{Xi}of each variable in the three kiddings also showed a pattern of positive relationships within the variable but of moderate magnitude, while there was a slight antagonism between SCS and FPY (ranging from −0.307 for the first kidding to −0.592 for the third one).

#### 3.2.2. Random Regression Model

_{wi}scale and each kidding is represented in Figure 2. Genetic variances and h

^{2}estimates showed similar shapes, with a decreasing trend as DIM

_{w}increases and a slight increase in the middle of lactation. In the same way, the within-trait genetic correlations across the DIM

_{wi}scale and number of kiddings were different from unity.

## 4. Discussion

^{2}lie within the published range of the literature available for goat species, with estimates of h

^{2}for SCS of between 0.18 and 0.32 (Rupp et al. [26] and Arnal et al. [27] in Alpine and Saanen breeds; Scholtens et al. [15] in New Zealand breeds). It should be noted, however, that previous references were not homogeneous with regard to how to quantify the level of SCC, and a wide range of models were also used.

^{2}for total fat plus protein production have shown ranges of h

^{2}between 0.04 and 0.40, as published by numerous authors, in goat species (Rupp et al. [26] in Alpine and Saanen breeds; García-Peniche et al. [28] and Castañeda-Bustos et al. [29] in US breeds; Scholtens et al 2019 [15] in New Zealand breeds; Arnal et al 2019 [27] in Alpine and Saanen breeds). The results of this study for both variables were within the lower levels of the above references, while the same trends can be seen in the wide variability of genetic origin that could be exploited in a selection program and the negative but weak relationships between SCS and milk components. However, this negative correlation does not coincide with the farmer’s viewpoint, at least on those farms with higher production levels, which could lead us to think that production levels in this breed are not yet high enough for the production stress suffered by the animals to compromise their immune response capacity (i.e., cause a lower resistance to udder infection). However, Figure 1 shows that the inverse relationship between the two variables at the phenotypic level follows the same tendency as at the genetic level. An increase in the number of somatic cells might be related to natural desquamation by simple repletion of the udder in highly productive animals, without there being a defensive inflammatory reaction caused by bacterial growth and without being related to a higher propensity of genetic origin.

^{2}from 0.12 to 0.25 [32], while Arnal et al. [21] found an h

^{2}of between 0.10 and 0.155; both studies showed slight increases in the middle of lactation. Applying the same statistical model, Arnal et al. [21] presented values of h

^{2}from 0.14 to 0.23 for total fat and protein production in the three kiddings of Alpine goats in France, with both variables showing higher values of h

^{2}in the middle of lactation. Similar trends were indicated by Zumbach et al. [31], but with higher levels of h

^{2}, from 0.28 to 0.47, while Oliveira et al. [33] showed results of h

^{2}from 0.40 to 0.50 for fat and protein percentages, respectively, recorded along the lactation scale.

_{g}) between different points in the lactation, the trends for each variable were very similar for each kidding, with a decreasing response with distance between lactation stages. This same response was presented by Zumbach et al. [31] (r

_{g}≈ 0.48 to 0.80 for total protein and r

_{g}≈ 0.37 to 0.73 for total fat) in different goat breeds in Germany; Arnal et al. [21], with (r

_{g}≈ 0.50 to 0.85) for fat and total protein in Alpine goats from France, while Oliveira et al. [33] presented the same pattern (r

_{g}≈ 0.50) for percentages of fat and protein in Saanen and Alpine goats from Brazil. The problem is even more complex if we consider the r

_{g}between SCS and FPY, which were median but negative throughout lactation. References of this type are scarce, however: Rupp et al. [26] presented values of r

_{g}≈ −0.13 to − 0.20, which are very close to those in Figure 2, following the same trend as in dairy sheep [23]. In general, the relationships between milk components in goat species tend to be negative, according to several references presented in tables in the article by Scholtens et al. [15].

_{g}between milk production traits and SCS are scarce and have only been reported for fat yield and protein yield separately, using a repeatability model. Rupp et al. [26] observed negative r

_{g}between SCS and FY and PY (−0.13 and −0.04, respectively) in the French Saanen breed. Conversely, other studies have reported positive correlations between these traits, varying between 0.06 and 0.23 (Rupp et al. [26] in Alpine goats; Valencia-Posadas et al. [34] in Nigerian Dwarf goats).

## 5. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Least-squares means of somatic cell score (SCS) and fat plus protein yield (FPY) across lactation length (in weeks) and number of kiddings in the Florida breed.

**Figure 2.**Evolution of variance components, heritabilities and genetic correlations across lactations and each parity for somatic cell score (SCS) and fat plus protein yield (FPY) using a random regression model in the Florida breed.

**Figure 3.**Evolution of the genetic correlations between somatic cell score (SCS) and fat plus protein yield (FPY) throughout lactation and in each parity using a random regression model in the Florida breed.

**Figure 4.**Frequency distribution of estimated genetic values (EGVs) for somatic cell score (SCS) and fat plus protein yield (FPY) up to 240 days of lactation and variation in shapes of the lactation curves of the 500 best animals, using a random regression model in the Florida breed.

**Figure 5.**Correlations between the estimated genetic values (EGVs) of somatic cell score (SCS) and fat plus protein yield (FPY) estimated by the repeatability and random regression models throughout lactation for each kidding in the Florida breed.

**Table 1.**Descriptive statistics of the data analyzed (standard deviation in parenthesis) in the Florida goat breed.

Kidding Number | ||||
---|---|---|---|---|

First | Second | Third | Total | |

Number of test-day records | 145,816 | 118,039 | 76,799 | 340,654 |

Number of animals | 25,430 | 19,268 | 12,599 | 27,749 |

Number of sires | 939 | 900 | 807 | 941 |

Number of dams | 15,215 | 12,150 | 8406 | 16,243 |

Average SCS (×10^{3}) | 11.86 (1.65) | 12.36 (1.61) | 12.72 (1.54) | 12.22 (1.65) |

Average daily FPY (grams) | 168.1 (64.1) | 211.0 (79.1) | 219.9 (83.2) | 194.6 (77.6) |

**Table 2.**Estimation of genetic parameter components for somatic cell score (SCS) and total daily fat plus protein yield (FPY) in the Florida goat breed using a bivariate repeatability model.

Lactation Number | ||||||||
---|---|---|---|---|---|---|---|---|

First | Second | Third | Total | |||||

SCS | FPY | SCS | FPY | SCS | FPY | SCS | FPY | |

Genetic variance | 0.283 | 452.9 | 0.291 | 539.0 | 0.273 | 522.4 | 0.264 | 497.8 |

Phenotypic variance | 1.024 | 3275 | 1.066 | 4850 | 0.979 | 5287.5 | 1.051 | 4379.0 |

Heritability | 0.276 ± 0.02 | 0.138 ± 0.01 | 0.272 ± 0.02 | 0.111 ± 0.01 | 0.279 ± 0.02 | 0.099 ± 0.01 | 0.246 ± 0.01 | 0.105 ± 0.01 |

Genetic correlation | −0.304 ± 0.03 | −0.308 ± 0.04 | −0.477 ± 0.06 | −0.371 ± 0.04 | ||||

Repeatibility | 0.486 ± 0.001 | 0.202 ± 0.01 |

**Table 3.**Correlations between the expected genetic values estimated according to the repeatability model in the Florida goat breed: above the diagonal, correlations between kiddings for SCS; below the diagonal, correlations between kiddings for FPY and on the diagonal, correlations for each kidding between SCS and FPY.

First | Second | Third | |
---|---|---|---|

First | −0.307 | 0.552 | 0.418 |

Second | 0.366 | −0.423 | 0.609 |

Third | 0.197 | 0.358 | −0.592 |

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**MDPI and ACS Style**

Jiménez-Granado, R.; Molina, A.; Sánchez Rodríguez, M.; Ziadi, C.; Menéndez Buxadera, A.
Relationship between Somatic Cell Score and Fat Plus Protein Yield in the First Three Lactations in Spanish Florida Goats. *Dairy* **2024**, *5*, 1-12.
https://doi.org/10.3390/dairy5010001

**AMA Style**

Jiménez-Granado R, Molina A, Sánchez Rodríguez M, Ziadi C, Menéndez Buxadera A.
Relationship between Somatic Cell Score and Fat Plus Protein Yield in the First Three Lactations in Spanish Florida Goats. *Dairy*. 2024; 5(1):1-12.
https://doi.org/10.3390/dairy5010001

**Chicago/Turabian Style**

Jiménez-Granado, Rocío, Antonio Molina, Manuel Sánchez Rodríguez, Chiraz Ziadi, and Alberto Menéndez Buxadera.
2024. "Relationship between Somatic Cell Score and Fat Plus Protein Yield in the First Three Lactations in Spanish Florida Goats" *Dairy* 5, no. 1: 1-12.
https://doi.org/10.3390/dairy5010001