# Efficiency of Manchega Sheep Milk Intended for Cheesemaking and Determination of Factors Causing Inefficiency

^{1}

^{2}

^{*}

## Abstract

**:**

## Simple Summary

## Abstract

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Dataset and Sample Collection

#### 2.2. Laboratory Analysis

_{20}), curd firmness at 60 min (A

_{60}), and maximum curd firmness (A

_{max}). Curd yield (CY) was calculated after draining the whey and expressed in milligrams/100 mL of milk and dry curd yield (DCY) was obtained after desiccating the curds in a drying oven at 100 °C for 24 h. Somatic cell count (SCC) was determined on a Fossomatic FC (Foss Electric, Hillerød, Denmark) and a log

_{10}transformation was used to express it in somatic cell score (SCS). Finally, color indices of raw milk were measured with a PCE-CSM2 color meter (PCE Instruments, Southampton, UK) using the CIELAB color space, and variables included lightness (L*), red/green value (a*), blue/yellow value (b*), chroma (C*), and hue (h*).

#### 2.3. Modeling Curd Yield Performance of Manchega Sheep Milk

#### 2.4. Curd Yield Efficiency of Manchega Sheep Milk

#### 2.5. Determinants of Curd Yield Efficiency of Manchega Sheep Milk

## 3. Results and Discussion

#### 3.1. Efficiency of the Coagulation Process

_{1}and β

_{2}for fat and protein content [24]. As the sum of elasticities is less than 1, the production of curd is carried out with decreasing returns to scale; that is, if fat and protein content each increase by 1%, an extra amount of curd of less than 1% would be obtained. However, the sum of elasticities is practically 1 in the DCY model, indicating that the production process measured in dry matter follows constant returns to scale; that is, the percentage variations in both factors generate the same percentage increase in dried curd. These findings are relevant in order to economically optimize the cheesemaking process. If the important thing is to obtain solids, the combination of fat and protein that will minimize the cost of production will depend only on the price ratio of fat and protein, being independent of the level of production. If, by contrast, the product is measured fresh, then the volume of production should also be considered [24].

_{20}, which are indicative of a slower coagulation process.

#### 3.2. Factors Affecting the Coagulation Process

#### 3.2.1. Bivariate Associations

_{20}, and RCT, and negative for A

_{max}.

_{20}(r = 0.33), and lower A

_{max}(r = −0.30). These values would correspond to milk with “worse” coagulation parameters, resulting in a softer and more hydrated curd and poor drainage. Again, the CE model seems to be conditioned by the percentage of moisture retained in the curd, in agreement with Johnson et al. [27], who associated slow RCTs with an increase in moisture in the curd.

#### 3.2.2. Multivariate Analysis of Covariance (MANCOVA)

_{60}, and C* were not included in this model, as they strongly intensify multicollinearity. Eight variables were found to be significant factors for both CE and DCE coagulation efficiency models. The λ and F statistics suggest that the main factor causing inefficiency is Lac, followed by k

_{20}and A

_{max}. Cas, initial pH, and Season were also identified as causes of inefficiency, although they have a lesser impact. Lastly, and with a moderate effect, the model highlights Flock and SCS. The set of variables that most impacts the efficiency of the coagulation process is chemical composition (Lac and Cas), followed by coagulation properties (A

_{max}and k

_{20}), and finally the hygienic-sanitary quality of the milk (pH and SCS). Season had a moderate effect, while none of the colorimetric variables were significant.

#### 3.2.3. Generalized Linear Models (GLM)

_{max}, Casein, and initial pH. Other variables such as Season, Parity, and Flock showed a more moderate impact. Regression coefficients revealed that spring lactations, second and third lambing, pH, lactose content, and casein content had a positive effect on CE. Contrastingly, autumn lactations, high parity numbers (≥4), and A

_{max}showed a negative effect.

_{20}and SCS. Regression coefficients revealed a positive effect of pH, Lac, and Cas on DCE, while SCS and k

_{20}seemed to have a negative effect.

#### 3.2.4. Causes of Inefficiency

_{max}was evidenced as a cause of inefficiency.

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Bencini, R. Factors Affecting the Clotting Properties of Sheep Milk. J. Sci. Food Agric.
**2002**, 82, 705–719. [Google Scholar] [CrossRef] - Arias, R. Recuento de Células Somáticas y Calidad de La Leche de Oveja En Castilla-La Mancha. Ph.D. Thesis, Universidad de Castilla-La Mancha, Ciudad Real, Spain, 2009. [Google Scholar]
- Bonfatti, V.; Tuzzato, M.; Chiarot, G.; Carnier, P. Variation in Milk Coagulation Properties Does Not Affect Cheese Yield and Composition of Model Cheese. Int. Dairy J.
**2014**, 39, 139–145. [Google Scholar] [CrossRef] - Ng-Kwai-Hang, K.F.; Politis, I.; Cue, R.I.; Marziali, A.S. Correlations Between Coagulation Properties of Milk and Cheese Yielding Capacity and Cheese Composition. Can. Inst. Food Sci. Technol. J.
**1989**, 22, 291–294. [Google Scholar] [CrossRef] - Ikonen, T.; Morri, S.; Tyrisevä, A.-M.; Ruottinen, O.; Ojala, M. Genetic and Phenotypic Correlations Between Milk Coagulation Properties, Milk Production Traits, Somatic Cell Count, Casein Content, and PH of Milk. J. Dairy Sci.
**2004**, 87, 458–467. [Google Scholar] [CrossRef] [PubMed][Green Version] - Wedholm, A.; Larsen, L.B.; Lindmark-Månsson, H.; Karlsson, A.H.; Andrén, A. Effect of Protein Composition on the Cheese-Making Properties of Milk from Individual Dairy Cows. J. Dairy Sci.
**2006**, 89, 3296–3305. [Google Scholar] [CrossRef][Green Version] - Pazzola, M. Coagulation Traits of Sheep and Goat Milk. Animals
**2019**, 9, 540. [Google Scholar] [CrossRef][Green Version] - Caballero-Villalobos, J.; Perea, J.M.; Angón, E.; Arias, R.; Garzón, A. Coagulation Efficiency and Its Determinant Factors: A Case Study for Manchega Ewe Milk in the Region of Castilla-La Mancha, Spain. J. Dairy Sci.
**2018**, 101, 3878–3886. [Google Scholar] [CrossRef][Green Version] - Timmer, C.P. Using a Probabilistic Frontier Production Function to Measure Technical Efficiency. J. Political Econ.
**1971**, 79, 776–794. [Google Scholar] [CrossRef] - Angón, E.; García, A.; Perea, J.; Acero, R.; Toro-Mújica, P.; Pacheco, H.; González, A. Eficiencia Técnica y Viabilidad de Los Sistemas de Pastoreo de Vacunos de Leche En La Pampa, Argentina. Agrociencia
**2013**, 47, 443–456. [Google Scholar] - Toro-Mujica, P.; García, A.; Gómez-Castro, A.G.; Acero, R.; Perea, J.; Rodríguez-Estévez, V.; Aguilar, C.; Vera, R. Technical Efficiency and Viability of Organic Dairy Sheep Farming Systems in a Traditional Area for Sheep Production in Spain. Small Rumin. Res.
**2011**, 100, 89–95. [Google Scholar] [CrossRef] - Garzón, A.; Figueroa, A.; Caballero-Villalobos, J.; Angón, E.; Arias, R.; Perea, J.M. Derivation of Multivariate Indices of Milk Composition, Coagulation Properties, and Curd Yield in Manchega Dairy Sheep. J. Dairy Sci.
**2021**, 104, 8618–8629. [Google Scholar] [CrossRef] [PubMed] - Figueroa Sánchez, A.; Perea Muñoz, J.; Caballero-Villalobos, J.; Arias Sánchez, R.; Garzón, A.; Angón Sánchez de Pedro, E. Coagulation Process in Manchega Sheep Milk from Spain: A Path Analysis Approach. J. Dairy Sci.
**2021**, 104, 7544–7554. [Google Scholar] [CrossRef] [PubMed] - Greene, W. Maximum Likelihood Estimation of Econometric Frontier Functions. J. Econom.
**1980**, 13, 27–56. [Google Scholar] [CrossRef] - McDonald, J. Using Least Squares and Tobit in Second Stage DEA Efficiency Analyses. Eur. J. Oper. Res.
**2009**, 197, 792–798. [Google Scholar] [CrossRef] - Iliyasu, A.; Mohamed, Z.A. Evaluating Contextual Factors Affecting the Technical Efficiency of Freshwater Pond Culture Systems in Peninsular Malaysia: A Two-Stage DEA Approach. Aquac. Rep.
**2016**, 3, 12–17. [Google Scholar] [CrossRef][Green Version] - Stukalin, Y.; Einat, H. Analyzing Test Batteries in Animal Models of Psychopathology with Multivariate Analysis of Variance (MANOVA): One Possible Approach to Increase External Validity. Pharmacol. Biochem. Behav.
**2019**, 178, 51–55. [Google Scholar] [CrossRef] - Carey, G. Multivariate Analysis of Variance (MANOVA) II: Practical Guide to ANOVA and MANOVA for SAS. 1998. Available online: http://ibgwww.colorado.edu/~carey/p7291dir/handouts/manova2.pdf (accessed on 28 November 2022).
- Wang, M.; Zhou, Y.; Tan, G.Z. Multivariate Analysis of Variance (MANOVA) on the Microstructure Gradient of Biomimetic Nanofiber Scaffolds Fabricated by Cone Electrospinning. J. Manuf. Process.
**2019**, 44, 55–61. [Google Scholar] [CrossRef] - Roldan-Valadez, E.; Piña-Jimenez, C.; Favila, R.; Rios, C. Gender and Age Groups Interactions in the Quantification of Bone Marrow Fat Content in Lumbar Spine Using 3T MR Spectroscopy: A Multivariate Analysis of Covariance (Mancova). Eur. J. Radiol.
**2013**, 82, e697–e702. [Google Scholar] [CrossRef] - Kiebel, S.J.; Mueller, K. The General Linear Model. In Brain Mapping; Elsevier: Amsterdam, The Netherlands, 2015; pp. 465–469. [Google Scholar]
- Quintana, Á.R.; Perea, J.M.; García-Béjar, B.; Jiménez, L.; Garzón, A.; Arias, R. Dominant Yeast Community in Raw Sheep’s Milk and Potential Transfers of Yeast Species in Relation to Farming Practices. Animals
**2020**, 10, 906. [Google Scholar] [CrossRef] - Alin, A. Multicollinearity. Wiley InterdiScip. Rev. Comput. Stat.
**2010**, 2, 370–374. [Google Scholar] [CrossRef] - Aiyar, S.; Dalgaard, C.-J. Accounting for Productivity: Is It OK to Assume That the World Is Cobb–Douglas? J. Macroecon.
**2009**, 31, 290–303. [Google Scholar] [CrossRef] - Vacca, G.M.; Cipolat-Gotet, C.; Paschino, P.; Casu, S.; Usai, M.G.; Bittante, G.; Pazzola, M. Variation of Milk Technological Properties in Sheep Milk: Relationships among Composition, Coagulation and Cheese-Making Traits. Int. Dairy J.
**2019**, 97, 5–14. [Google Scholar] [CrossRef] - Stocco, G.; Pazzola, M.; Dettori, M.L.; Paschino, P.; Summer, A.; Cipolat-Gotet, C.; Vacca, G.M. Effects of Indirect Indicators of Udder Health on Nutrient Recovery and Cheese Yield Traits in Goat Milk. J. Dairy Sci.
**2019**, 102, 8648–8657. [Google Scholar] [CrossRef] [PubMed] - Johnson, M.E.; Chen, C.M.; Jaeggi, J.J. Effect of Rennet Coagulation Time on Composition, Yield, and Quality of Reduced-Fat Cheddar Cheese. J. Dairy Sci.
**2001**, 84, 1027–1033. [Google Scholar] [CrossRef] [PubMed] - Casati, M.R.; Cappa, V.; Calamari, L.; Calegari, F.; Folli, G. Effects of the Season on Milk Yield and on Some Milk Characteristics in Cows. Sci. Tec. Latt. -Casearia
**1998**, 49, 7–25. [Google Scholar] - Tyrisevä, A.M.; Ikonen, T.; Ojala, M. Repeatability Estimates for Milk Coagulation Traits and Non-Coagulation of Milk in Finnish Ayrshire Cows. J. Dairy Res.
**2003**, 70, 91–98. [Google Scholar] [CrossRef] - Cipolat-Gotet, C.; Cecchinato, A.; Pazzola, M.; Dettori, M.L.; Bittante, G.; Vacca, G.M. Potential Influence of Herd and Animal Factors on the Yield of Cheese and Recovery of Components from Sarda Sheep Milk, as Determined by a Laboratory Bench-Top Model Cheese-Making. Int. Dairy J.
**2016**, 63, 8–17. [Google Scholar] [CrossRef] - Sevi, A.; Albenzio, M.; Marino, R.; Santillo, A.; Muscio, A. Effects of Lambing Season and Stage of Lactation on Ewe Milk Quality. Small Rumin. Res.
**2004**, 51, 251–259. [Google Scholar] [CrossRef] - Novotná, L.; Kuchtík, J.; Šustová, K.; Zapletal, D.; Filipčík, R. Effects of Lactation Stage and Parity on Milk Yield, Composition and Properties of Organic Sheep Milk. J. Appl. Anim. Res.
**2009**, 36, 71–76. [Google Scholar] [CrossRef] - Weber, F. El Desuerado Del Coágulo. In Proceedings of the El Queso; Eck, A., Ed.; Omega S.A.: Barcelona, Spain, 1990; pp. 21–33. [Google Scholar]
- Poulsen, N.A.; Buitenhuis, A.J.; Larsen, L.B. Phenotypic and Genetic Associations of Milk Traits with Milk Coagulation Properties. J. Dairy Sci.
**2015**, 98, 2079–2087. [Google Scholar] [CrossRef][Green Version] - Stocco, G.; Summer, A.; Cipolat-Gotet, C.; Malacarne, M.; Cecchinato, A.; Amalfitano, N.; Bittante, G. The Mineral Profile Affects the Coagulation Pattern and Cheese-Making Efficiency of Bovine Milk. J. Dairy Sci.
**2021**, 104, 8439–8453. [Google Scholar] [CrossRef] [PubMed] - Cipolat-Gotet, C.; Cecchinato, A.; De Marchi, M.; Bittante, G. Factors Affecting Variation of Different Measures of Cheese Yield and Milk Nutrient Recovery from an Individual Model Cheese-Manufacturing Process. J. Dairy Sci.
**2013**, 96, 7952–7965. [Google Scholar] [CrossRef] [PubMed] - Bittante, G.; Cipolat-Gotet, C.; Malchiodi, F.; Sturaro, E.; Tagliapietra, F.; Schiavon, S.; Cecchinato, A. Effect of Dairy Farming System, Herd, Season, Parity, and Days in Milk on Modeling of the Coagulation, Curd Firming, and Syneresis of Bovine Milk. J. Dairy Sci.
**2015**, 98, 2759–2774. [Google Scholar] [CrossRef] [PubMed][Green Version]

**Figure 1.**Frequency distribution (histogram) of curd efficiency (CE) in Manchega sheep milk (n = 967).

**Figure 2.**Frequency distribution (histogram) of dry curd efficiency (DCE) in Manchega sheep milk (n = 967).

**Figure 3.**Frequency distribution of curd efficiency (CE) and dry curd efficiency (DCE) in Manchega sheep milk (n = 967).

**Figure 4.**Bivariate associations (Pearson correlations) between curd efficiency (CE) and dry curd efficiency (DCE) and the quantitative factors evaluated (n = 967).

**Figure 5.**Bivariate association (ANOVA or Student’s t) between curd efficiency and the categorical factors evaluated (mean ± standard error). Left column = CE, right column = DCE. “a”, “b”, and “ab”: Means without a common letter are significantly different at p < 0.05 according to Student’s t test or SNK post hoc test (n = 967).

**Table 1.**Adjusted regression models between the observed values ($Y$) for curd yield (CY) and dry curd yield (DCY), and fat (${X}_{1}$) and protein (${X}_{2}$) content of Manchega sheep milk (n = 967).

Model | Parameter | Coefficient | S.E. |
---|---|---|---|

Curd yield (CY) | data | ||

α | 5.3885 | 0.2765 | |

β_{1} | 0.4833 | 0.0185 | |

β_{2} | 0.4106 | 0.0351 | |

White test | 0.831 | ||

Chow test | 0.948 | ||

Kolmogorov–Smirnov test | 0.432 | ||

Durbin–Watson test | 0.064 | ||

ANOVA | <0.001 | ||

Adjusted R^{2} | 61.97 | ||

Mean absolute error (MAE) | 2.93 | ||

Dry curd yield (DCY) | |||

α | 1.8721 | 0.0597 | |

β_{1} | 0.4804 | 0.0116 | |

β_{2} | 0.5171 | 0.0221 | |

White test | 0.949 | ||

Chow test | 0.567 | ||

Kolmogorov–Smirnov test | 0.231 | ||

Durbin–Watson test | 0.770 | ||

ANOVA | <0.001 | ||

Adjusted R^{2} | 82.98 | ||

Mean absolute error (MAE) | 0.65 |

**Table 2.**Description of the variables used to estimate yield efficiency and causes for inefficiency in Manchega sheep milk (n = 967).

Variable | Description | Units | Mean | S.D. |
---|---|---|---|---|

Yield efficiency | ||||

CY (Y_{1}) | Curd yield | g/100 mL | 26.80 | 6.10 |

DCY (Y_{2}) | Dried curd yield | g/100 mL | 11.18 | 2.34 |

WR (Y_{3}) | Water retention (CY-DCY) | g/100 mL | 15.71 | 4.11 |

Fat (X_{1}) | Fat content in milk | g/100 mL | 6.48 | 1.84 |

CP (X_{2}) | Protein content in milk | g/100 mL | 5.69 | 0.81 |

Random factors | ||||

Flock (F_{1}) | Flock of origin | 1 to 4 | - | - |

Fixed factors | ||||

SOL (F_{2}) | Stage of lactation | 1 to 3 | - | - |

Syneresis (F_{3}) | If A_{60} < A_{max} | Yes or no | - | - |

Prolificacy (F_{4}) | Number of lambs | 1 to 3 | - | - |

Season (F_{5}) | Season of lambing | Autumn or spring | - | - |

Parity (F_{6}) | Lactation number | 2 to 5 or more | - | - |

Covariable factors | ||||

DMY (F_{7}) | Daily milk yield | mL | 1.093 | 474.3 |

pH (F_{8}) | pH | −log[H^{+}] | 6.61 | 0.29 |

SCS (F_{9}) | Somatic cell score | log_{10} (10^{3} cells/mL) | 5.24 | 0.62 |

WTS (F_{10}) | Whey total solids | % | 11.19 | 2.47 |

TS (F_{11}) | Milk total solids | g/100 mL | 17.95 | 2.36 |

Lac (F_{12}) | Lactose content in milk | g/100 mL | 4.88 | 0.38 |

Cas (F_{13}) | Casein content in milk | g/100 mL | 4.51 | 0.69 |

RCT (F_{14}) | Rennet clotting time | min | 21.39 | 11.48 |

A_{60} (F_{15}) | Curd firmness at 60 min. | mm | 38.26 | 11.11 |

A_{max} (F_{16}) | Maximum curd firmness | mm | 42.14 | 9.16 |

k_{20} (F_{17}) | Rate of curd aggregation | min | 24.72 | 12.40 |

L* (F_{18}) | Lightness | [0, 100] | 82.66 | 2.34 |

a* (F_{19}) | Red/Green value | [−60, +60] | −2.55 | 0.81 |

b* (F_{20}) | Blue/Yellow value | [−60, +60] | 5.51 | 1.65 |

C* (F_{21}) | Chroma or saturation | (a*^{2} + b*^{2})^{1/2} | 4.67 | 2.05 |

h* (F_{22}) | Hue | tan^{−1} (b*/a*) | −0.55 | 0.28 |

Variable | CE | DCE |
---|---|---|

CY (Y_{1}) | 49.57 | 26.76 |

DCY (Y_{2}) | 14.92 | 16.76 |

WR (Y_{3}) | 34.65 | 10.00 |

Fat (X_{1}) | 9.47 | 7.81 |

CP (X_{2}) | 4.89 | 6.35 |

Flock (F_{1}) | 4 | 1 |

SOL (F_{2}) | 1 | 3 |

Syneresis (F_{3}) | No | Yes |

Prolificacy (F_{4}) | 1 | 1 |

Season (F_{5}) | Spring | Autumn |

Parity (F_{6}) | 3 | 3 |

DMY (F_{7}) | 1.420 | 1.300 |

pH (F_{8}) | 6.61 | 6.56 |

SCS (F_{9}) | 5.95 | 6.12 |

WTS (F_{10}) | 1.79 | 1.71 |

TS (F_{11}) | 15.36 | 20.02 |

Lac (F_{12}) | 4.88 | 4.94 |

Cas (F_{13}) | 3.93 | 5.12 |

RCT (F_{14}) | 19.45 | 5.30 |

A_{60} (F_{15}) | 50.0 | 47.76 |

A_{max} (F_{16}) | 50.0 | 54.26 |

k_{20} (F_{17}) | 22.15 | 7.00 |

L* (F_{18}) | 83.19 | 85.00 |

a* (F_{19}) | −2.37 | −2.32 |

b* (F_{20}) | 4.25 | 0.80 |

C* (F_{21}) | 4.86 | 2.45 |

h* (F_{22}) | 4.22 | 1.90 |

Variable | Wilks λ | F | p |
---|---|---|---|

Flock (F_{1}) | 0.949 | 8.49 | <0.001 |

SOL (F_{2}) | - | - | >0.05 |

Syneresis (F_{3}) | - | - | >0.05 |

Prolificacy (F_{4}) | - | - | >0.05 |

Season (F_{5}) | 0.973 | 13.22 | <0.001 |

Parity (F_{6}) | - | - | >0.05 |

DMY (F_{7}) | - | - | >0.05 |

pH (F_{8}) | 0.977 | 11.62 | <0.001 |

SCS (F_{9}) | 0.981 | 9.22 | <0.001 |

WTS (F_{10}) | - | - | >0.05 |

TS (F_{11}) | - | - | >0.05 |

Lac (F_{12}) | 0.935 | 33.71 | <0.001 |

Cas (F_{13}) | 0.960 | 20.18 | <0.001 |

RCT (F_{14}) | - | - | >0.05 |

A_{60} (F_{15}) | - | - | >0.05 |

A_{max} (F_{16}) | 0.964 | 18.12 | <0.001 |

k_{20} (F_{17}) | 0.941 | 30.52 | <0.001 |

L* (F_{18}) | - | - | >0.05 |

a* (F_{19}) | - | - | >0.05 |

b* (F_{20}) | - | - | >0.05 |

C* (F_{21}) | - | - | >0.05 |

h* (F_{22}) | - | - | >0.05 |

Factors | Coefficients | S.E. | F Value | p Value | FIV |
---|---|---|---|---|---|

Random factors | |||||

Flock (F_{1}) | - | - | 8.99 | <0.001 | 1.7 |

Fixed factors | |||||

Season (F_{5}) | - | - | 14.87 | <0.001 | 1.7 |

Spring | 0.012 | 0.0032 | - | - | - |

Autumn | −0.012 | 0.0032 | - | - | - |

Parity (F_{6}) | - | - | 2.62 | 0.049 | 1.7 |

2 | 0.0074 | 0.0042 | - | - | - |

3 | 0.0068 | 0.0043 | - | - | - |

4 | −0.0069 | 0.0049 | - | - | - |

≥ 5 | −0.0073 | 0.0044 | - | - | - |

Covariable factors | |||||

pH (F_{8}) | 0.0952 | 0.0174 | 29.85 | <0.001 | 1.3 |

Lac (F_{12}) | 0.0763 | 0.0099 | 58.96 | <0.001 | 2.4 |

Cas (F_{13}) | 0.0341 | 0.0052 | 42.88 | <0.001 | 2.1 |

A_{max} (F_{16}) | −0.0017 | 0.0002 | 49.38 | <0.001 | 1.2 |

Factors | Coefficients | S.E. | F Value | p Value | FIV |
---|---|---|---|---|---|

Random factors | |||||

Flock (F_{1}) | - | - | 9.00 | <0.001 | 1.7 |

Covariable factors | |||||

pH (F_{8}) | 0.0756 | 0.0161 | 21.99 | <0.001 | 1.8 |

SCS (F_{9}) | −0.0045 | 0.0014 | 9.56 | 0.002 | 1.2 |

Lac (F_{12}) | 0.0541 | 0.0065 | 68.32 | <0.001 | 1.7 |

Cas (F_{13}) | 0.0185 | 0.0033 | 31.27 | <0.001 | 1.5 |

k_{20} (F_{17}) | −0.0007 | 0.0002 | 18.23 | <0.001 | 1.8 |

**Table 7.**Summary of the determinants for curd efficiency (CE and DCE) according to MANCOVA and GLM (n = 967).

MANCOVA | CE (GLM) | DCE (GLM) | |||
---|---|---|---|---|---|

Variable | F | F | Effect | F | Effect |

Flock (F_{1}) | 8.49 | 5.58 | - | 13.30 | - |

Season (F_{5}) | 13.22 | 8.48 | Spring > Autumn | ns | - |

Parity (F_{6}) | ns | 2.93 | 2 and 3 > 4 and 5 or more | ns | - |

pH (F_{8}) | 11.62 | 25.92 | Positive | 21.99 | Positive |

SCS (F_{9}) | 9.22 | ns | - | 9.56 | Negative |

Lac (F_{12}) | 33.71 | 68.18 | Positive | 68.32 | Positive |

Cas (F_{13}) | 20.18 | 30.01 | Positive | 31.27 | Positive |

A_{max} (F_{16}) | 18.12 | 51.10 | Negative | ns | - |

k_{20} (F_{17}) | 30.52 | ns | - | 18.23 | Negative |

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Garzón, A.; Perea, J.M.; Arias, R.; Angón, E.; Caballero-Villalobos, J. Efficiency of Manchega Sheep Milk Intended for Cheesemaking and Determination of Factors Causing Inefficiency. *Animals* **2023**, *13*, 255.
https://doi.org/10.3390/ani13020255

**AMA Style**

Garzón A, Perea JM, Arias R, Angón E, Caballero-Villalobos J. Efficiency of Manchega Sheep Milk Intended for Cheesemaking and Determination of Factors Causing Inefficiency. *Animals*. 2023; 13(2):255.
https://doi.org/10.3390/ani13020255

**Chicago/Turabian Style**

Garzón, Ana, José M. Perea, Ramón Arias, Elena Angón, and Javier Caballero-Villalobos. 2023. "Efficiency of Manchega Sheep Milk Intended for Cheesemaking and Determination of Factors Causing Inefficiency" *Animals* 13, no. 2: 255.
https://doi.org/10.3390/ani13020255