A Meta-Analytic Approach to Predict Methane Emissions from Dairy Goats Using Milk Fatty Acid Profile
Abstract
:1. Introduction
2. Materials and Methods
3. Results
R2 = 0.46; RMSEP = 3.16 g/kg DMI (20.1%)
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Author | n1 | Method 2 | Breed 3 | DIM 4 | Forage Type |
---|---|---|---|---|---|
[11] | 9 | RC | M-G | 57 | Alfalfa hay |
[12] | 6 | RC | M-G | 60 | Alfalfa + oat hay |
Alfalfa hay + olive by-product silage | |||||
Alfalfa hay + tomato surplus silage | |||||
[13] | 10 | OCM | M-G | LL | Alfalfa hay |
[14] | 10 | OCM | M-G | LL | Alfalfa hay |
Maralfalfa hay | |||||
[15] | 10 | OCM | M-G | LL | Alfalfa pellets |
Lemon leaves pellets | |||||
[16] | 10 | OCM | M-G | ML | Alfalfa pellets |
Orange leaves pellets | |||||
[17] | 6 | SF6 | AxB | n.s. | Berseem hay |
[18] | 8 | OCM | M-G | ML | Alfalfa hay |
[19] | 8 | RC | M-G | 13 | Alfalfa hay |
[20] | 3 | RC | S | 106 | Italian ryegrass silage |
[21] | 3 | RC | S | 106 | Native pasture |
Pasture hay | |||||
Non-forage diet | |||||
[22] | 8 | RC | M-G | ML | Alfalfa hay |
Mean | Minimum | Maximum | Standard Deviation | |
---|---|---|---|---|
Body weight (kg) | 44.9 | 34.0 | 55.0 | 6.07 |
Dry matter intake (kg/d) | 1.75 | 0.99 | 2.69 | 0.443 |
CP 1 intake (kg/d) | 0.30 | 0.18 | 0.47 | 0.076 |
NDF 2 intake (kg/d) | 0.57 | 0.32 | 1.15 | 0.205 |
EE 3 intake (kg/d) | 0.06 | 0.01 | 0.19 | 0.041 |
NFC 4 intake (kg/d) | 0.61 | 0.20 | 1.01 | 0.262 |
Milk yield (kg/d) | 1.92 | 0.94 | 3.69 | 0.831 |
Milk fat (%) | 4.86 | 2.96 | 6.90 | 1.103 |
Palmitic acid (% in milk fat) | 29.8 | 20.5 | 44.5 | 6.20 |
Methane (g/d) | 28.1 | 12.3 | 68.8 | 14.33 |
Methane (g/kg dry matter intake) | 15.7 | 9.0 | 26.6 | 4.36 |
Mean | Minimum | Maximum | Standard Deviation | |
---|---|---|---|---|
Palmitic acid (% in milk fat) | 29.4 | 20.5 | 44.5 | 6.77 |
Acetate (mol/100 mol VFA) | 62.1 | 53.5 | 67.6 | 4.23 |
Propionate (mol/100 mol VFA) | 15.4 | 11.1 | 26.7 | 3.73 |
Butyrate (mol/100 mol VFA) | 16.8 | 10.6 | 22.4 | 2.72 |
CH4 (g/kg dry matter intake) | 14.8 | 9.0 | 21.1 | 3.37 |
CH4C16:0 (g/kg dry matter intake) | 15.4 | 10.8 | 23.4 | 3.55 |
CH4VFA (g/kg dry matter intake) | 11.6 | 6.7 | 14.0 | 2.07 |
RMSEP 1 g/kg DMI | RMSEP % | Mean Bias | Linear Bias | BCF 2 | Pearson r | CCC 3 | |
---|---|---|---|---|---|---|---|
CH4C16:0 | 2.36 | 16 | 3.49 p = 0.13 | −0.27 p = 0.07 | 0.98 | 0.77 | 0.76 |
CH4VFA | 4.41 | 30 | 6.62 p = 0.11 | −0.29 p = 0.39 | 0.54 | 0.43 | 0.23 |
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Requena, F.; Peña, F.; Agüera, E.; Marín, A.M. A Meta-Analytic Approach to Predict Methane Emissions from Dairy Goats Using Milk Fatty Acid Profile. Sustainability 2020, 12, 4834. https://doi.org/10.3390/su12124834
Requena F, Peña F, Agüera E, Marín AM. A Meta-Analytic Approach to Predict Methane Emissions from Dairy Goats Using Milk Fatty Acid Profile. Sustainability. 2020; 12(12):4834. https://doi.org/10.3390/su12124834
Chicago/Turabian StyleRequena, Francisco, Francisco Peña, Estrella Agüera, and Andrés Martínez Marín. 2020. "A Meta-Analytic Approach to Predict Methane Emissions from Dairy Goats Using Milk Fatty Acid Profile" Sustainability 12, no. 12: 4834. https://doi.org/10.3390/su12124834