Estimating Energy Concentrations in Wooded Pastures of NW Spain Using Empirical Models That Relate Observed Metabolizable Energy to Measured Nutritional Attributes
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Nutritional Characterization of Vegetation
2.3. Data Analysis to Obtain Prediction Equations of ME
2.4. Model Evaluation and Validation
3. Results
3.1. Nutritional Characteristics, Digestibility and ME (Based on IVDOM as Sole Predictor)
3.2. Relationships between Observed Metabolizable Energy and Nutritional Parameters
3.3. Prediction Equations of ME from Nutritional Parameters
3.4. Comparison of Predicted ME from Equations Developed in the Present Study and Using IVOMD as a Sole Predictor
4. Discussion
4.1. Establishment of the Prediction Equations
4.2. Accuracy and Applicability of the Prediction Equations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
VEGETATION TYPE | SPECIES | ADF | LIGNIN | CELLULOSE | SILICA | DM | IVOMD | CP | ME |
---|---|---|---|---|---|---|---|---|---|
Shrubs | |||||||||
O-Bl, O-My, O-Ru | Hedera helix | 431.88 | 158.38 | 264.86 | 8.62 | 305.95 | 621.53 | 82.58 | 9.76 |
n = 17 | 71.93 | 40.44 | 38.23 | 4.93 | 38.52 | 34.12 | 15.15 | 0.54 | |
16.66 | 25.53 | 14.43 | 57.17 | 12.59 | 5.49 | 18.34 | 5.49 | ||
O-Bl, O-My, O-Ru | Rubus sp. | 560.21 | 168.51 | 336.76 | 54.94 | 355.96 | 308.75 | 102.89 | 4.85 |
n = 16 | 93.87 | 38.87 | 59.48 | 39.07 | 62.14 | 55.80 | 21.83 | 0.88 | |
16.76 | 23.06 | 17.66 | 71.12 | 17.46 | 18.07 | 21.22 | 18.07 | ||
O-Bl, O-My | Erica arborea | 607.07 | 160.23 | 433.12 | 13.68 | 443.95 | 238.62 | 78.31 | 3.75 |
n = 13 | 68.18 | 28.04 | 73.61 | 12.59 | 73.15 | 49.03 | 11.10 | 0.77 | |
11.23 | 17.50 | 16.99 | 92.04 | 16.48 | 20.55 | 14.18 | 20.55 | ||
O-Bl, O-My | Ilex aquifolium | 570.89 | 203.71 | 356.53 | 10.96 | 377.62 | 477.79 | 83.87 | 7.50 |
n = 14 | 61.98 | 22.14 | 47.97 | 6.16 | 41.08 | 39.44 | 12.64 | 0.62 | |
10.86 | 10.87 | 13.45 | 56.23 | 10.88 | 8.26 | 15.08 | 8.26 | ||
O-Bl, O-My | Vaccinium myrtillus | 589.88 | 176.12 | 387.45 | 26.28 | 410.92 | 353.80 | 75.86 | 5.55 |
n = 10 | 94.46 | 32.90 | 69.99 | 27.88 | 42.06 | 59.78 | 8.70 | 0.94 | |
16.01 | 18.68 | 18.06 | 106.08 | 10.24 | 16.90 | 11.47 | 16.90 | ||
O-Bl, O-Ru | Lonicera periclymenum | 513.52 | 181.93 | 314.17 | 17.40 | 290.13 | 503.75 | 87.10 | 7.91 |
n = 12 | 61.18 | 18.12 | 57.94 | 13.16 | 56.18 | 91.97 | 31.67 | 1.44 | |
11.91 | 9.96 | 18.44 | 75.61 | 19.36 | 18.26 | 36.36 | 18.26 | ||
O-My | Genista florida | 586.20 | 169.50 | 406.00 | 10.70 | 460.00 | 490.00 | 179.80 | 7.69 |
n = 1 | |||||||||
O-Ru | Cytisus striatus | 419.9 | 69.6 | 348.2 | 2.1 | 336 | 500 | 153.1 | 7.85 |
n = 1 | |||||||||
O-Ru | Ruscus aculeatus | 545.03 | 129.30 | 404.78 | 10.93 | 427.17 | 375.67 | 93.78 | 5.90 |
n = 6 | 79.83 | 43.44 | 44.62 | 6.43 | 74.58 | 28.00 | 20.49 | 0.44 | |
14.65 | 33.59 | 11.02 | 58.83 | 17.46 | 7.45 | 21.85 | 7.45 | ||
Trees | |||||||||
O-Bl, O-My, O-Ru | Pyrus cordata | 567.73 | 150.12 | 377.03 | 40.56 | 415.93 | 362.40 | 74.22 | 5.69 |
n = 10 | 122.25 | 36.30 | 93.42 | 32.09 | 68.20 | 71.38 | 13.43 | 1.12 | |
21.53 | 24.18 | 24.78 | 79.11 | 16.40 | 19.70 | 18.10 | 19.70 | ||
O-Bl, O-My | Quercus robur | 640.70 | 218.75 | 403.90 | 18.05 | 460.00 | 320.00 | 90.54 | 5.02 |
n = 2 | 40.87 | 9.55 | 23.19 | 8.13 | 42.43 | 0.00 | 2.61 | 0.00 | |
6.38 | 4.36 | 5.74 | 45.05 | 9.22 | 0.00 | 2.88 | 0.00 | ||
O-Bl, O-Ru | Castanea sativa | 532.42 | 162.26 | 316.76 | 53.40 | 378.40 | 423.60 | 105.37 | 6.65 |
n = 5 | 100.72 | 22.55 | 67.43 | 44.23 | 52.73 | 45.62 | 14.60 | 0.72 | |
18.92 | 13.90 | 21.29 | 82.83 | 13.94 | 10.77 | 13.86 | 10.77 | ||
O-Bl | Betula alba | 657.55 | 152.20 | 400.95 | 104.35 | 370.50 | 315.00 | 108.75 | 4.95 |
n = 2 | 56.36 | 49.78 | 44.90 | 61.16 | 43.13 | 14.14 | 7.42 | 0.22 | |
8.57 | 32.71 | 11.20 | 58.61 | 11.64 | 4.49 | 6.83 | 4.49 | ||
O-My | Fagus sylvatica | 690.54 | 160.22 | 442.90 | 87.33 | 450.56 | 227.00 | 78.38 | 3.56 |
n = 5 | 74.40 | 64.49 | 86.31 | 53.67 | 50.90 | 11.45 | 18.18 | 0.18 | |
10.77 | 40.25 | 19.49 | 61.46 | 11.30 | 5.04 | 23.19 | 5.04 | ||
O-Ru | Frangula alnus | 513.30 | 187.84 | 309.56 | 15.88 | 325.20 | 563.40 | 110.33 | 8.85 |
n = 5 | 81.42 | 25.89 | 63.23 | 11.32 | 68.79 | 33.73 | 29.10 | 0.53 | |
15.86 | 13.78 | 20.43 | 71.27 | 21.15 | 5.99 | 26.38 | 5.99 | ||
O-Ru | Laurus nobilis | 608.64 | 146.88 | 425.02 | 36.72 | 435.40 | 346.20 | 71.65 | 5.44 |
n = 5 | 46.01 | 22.79 | 57.72 | 20.53 | 65.60 | 45.69 | 15.31 | 0.72 | |
7.56 | 15.52 | 13.58 | 55.90 | 15.07 | 13.20 | 21.37 | 13.20 | ||
Forbs | |||||||||
O-Bl, O-My, O-Ru | Teucrium scorodonia | 593.90 | 156.36 | 356.82 | 80.70 | 324.36 | 424.15 | 91.44 | 6.66 |
n = 13 | 80.14 | 47.04 | 52.19 | 47.54 | 60.25 | 78.72 | 23.05 | 1.24 | |
13.49 | 30.08 | 14.62 | 58.91 | 18.57 | 18.56 | 25.21 | 18.56 | ||
O-Bl, O-My | Asphodelus albus | 369.10 | 128.10 | 236.30 | 4.70 | 118.76 | 789.80 | 179.58 | 12.40 |
n = 5 | 102.80 | 67.49 | 45.06 | 1.89 | 30.99 | 19.52 | 43.78 | 0.31 | |
27.85 | 52.68 | 19.07 | 40.17 | 26.09 | 2.47 | 24.38 | 2.47 | ||
O-Bl, O-My | Other forbs* | 463.48 | 146.00 | 276.35 | 41.04 | 193.18 | 598.00 | 145.55 | 9.39 |
n = 4 | 131.41 | 49.88 | 74.97 | 48.10 | 54.77 | 136.77 | 53.81 | 2.15 | |
28.35 | 34.16 | 27.13 | 117.20 | 28.35 | 22.87 | 36.97 | 22.87 | ||
Grasses | |||||||||
O-Bl, O-My, O-Ru | Grasses** | 655.15 | 170.52 | 419.23 | 65.39 | 324.30 | 423.71 | 100.14 | 6.65 |
n = 21 | 111.95 | 50.14 | 70.66 | 34.32 | 113.39 | 93.91 | 28.61 | 1.47 | |
17.09 | 29.40 | 16.85 | 52.48 | 34.96 | 22.16 | 28.57 | 22.16 |
VEGETATION TYPE | SPECIES | ADF | LIGNIN | CELLULOSE | SILICA | DM | IVOMD | CP | ME |
---|---|---|---|---|---|---|---|---|---|
Shrubs | |||||||||
AH, CH | Erica australis | 718.55 | 166.05 | 526.98 | 25.50 | 512.28 | 162.08 | 60.11 | 2.54 |
n = 12 | 28.40 | 19.31 | 29.32 | 9.54 | 69.94 | 36.11 | 7.53 | 0.57 | |
3.95 | 11.63 | 5.56 | 37.41 | 13.65 | 22.28 | 12.53 | 22.28 | ||
AH, CH | Halimium lasianthum | 716.90 | 171.38 | 457.31 | 88.19 | 406.07 | 207.25 | 75.58 | 3.25 |
n = 16 | 55.57 | 41.48 | 39.68 | 38.44 | 83.75 | 34.12 | 12.38 | 0.54 | |
7.75 | 24.20 | 8.68 | 43.58 | 20.62 | 16.46 | 16.38 | 16.46 | ||
AH, CH | Pterospartum tridentatum | 590.91 | 183.32 | 397.76 | 9.81 | 541.32 | 349.00 | 74.47 | 5.48 |
n = 9 | 35.02 | 17.36 | 31.84 | 7.10 | 48.14 | 32.92 | 8.09 | 0.52 | |
5.93 | 9.47 | 8.00 | 72.32 | 8.89 | 9.43 | 10.86 | 9.43 | ||
AH | Calluna vulgaris | 652.47 | 132.91 | 452.75 | 66.80 | 459.17 | 245.91 | 62.10 | 3.86 |
n = 11 | 99.68 | 26.21 | 59.36 | 33.95 | 61.63 | 32.85 | 8.32 | 0.52 | |
15.28 | 19.72 | 13.11 | 50.83 | 13.42 | 13.36 | 13.40 | 13.36 | ||
AH | Daboecia cantabrica | 644.73 | 128.99 | 428.35 | 87.38 | 403.63 | 245.38 | 85.06 | 3.85 |
n = 8 | 113.87 | 42.47 | 67.90 | 41.25 | 56.65 | 53.43 | 15.32 | 0.84 | |
17.66 | 32.93 | 15.85 | 47.22 | 14.04 | 21.78 | 18.02 | 21.78 | ||
AH | * Erica spp. | 657.54 | 155.24 | 476.52 | 25.72 | 456.55 | 205.21 | 65.42 | 3.22 |
n = 29 | 72.63 | 25.19 | 57.66 | 15.68 | 73.66 | 46.32 | 8.36 | 0.73 | |
11.05 | 16.23 | 12.10 | 60.96 | 16.13 | 22.57 | 12.78 | 22.57 | ||
AH | ** Ulex spp. | 677.14 | 144.55 | 522.38 | 10.18 | 434.90 | 392.30 | 92.73 | 6.16 |
n = 20 | 61.63 | 31.95 | 46.48 | 7.90 | 77.22 | 55.38 | 15.73 | 0.87 | |
9.10 | 22.10 | 8.90 | 77.55 | 17.76 | 14.12 | 16.96 | 14.12 | ||
CH | Halimium umbellatum | 630.3 | 194.3 | 414.38 | 21.58 | 448.88 | 304.25 | 68.50 | 4.78 |
n = 4 | 35.81 | 8.46 | 31.91 | 11.41 | 43.91 | 35.91 | 5.47 | 0.56 | |
5.68 | 4.35 | 7.70 | 52.90 | 9.78 | 11.80 | 7.99 | 11.80 | ||
Forbs | |||||||||
AH | Asphodelus albus | 417.77 | 143.70 | 267.20 | 6.88 | 172.83 | 767.33 | 151.30 | 12.05 |
n = 3 | 127.68 | 76.73 | 46.06 | 6.08 | 8.98 | 13.20 | 16.73 | 0.21 | |
30.56 | 53.39 | 17.24 | 88.35 | 5.20 | 1.72 | 11.06 | 1.72 | ||
AH | Potentilla erecta | 542.44 | 186.64 | 322.46 | 33.34 | 319.38 | 415.60 | 101.62 | 6.52 |
n = 5 | 126.42 | 72.53 | 49.64 | 22.30 | 80.65 | 88.38 | 26.53 | 1.39 | |
23.31 | 38.86 | 15.39 | 66.88 | 25.25 | 21.26 | 26.11 | 21.26 | ||
Grasses | |||||||||
AH, CH | Agrostis curtisii | 700.28 | 188.81 | 452.55 | 58.88 | 407.10 | 392.75 | 78.25 | 6.17 |
n = 8 | 33.31 | 17.56 | 29.17 | 17.44 | 142.54 | 49.94 | 23.04 | 0.78 | |
4.76 | 9.30 | 6.45 | 29.62 | 35.01 | 12.72 | 29.45 | 12.72 |
VEGETATION TYPE | ADF | LIGNIN | CELLULOSE | SILICA | DM | IVOMD | CP | ME |
---|---|---|---|---|---|---|---|---|
O-Bl | ||||||||
Mean | 579.27 | 173.01 | 369.54 | 36.79 | 348.16 | 431.70 | 94.24 | 6.78 |
Min | 276 | 56.70 | 192.50 | 2.60 | 117.40 | 140.00 | 49.38 | 2.20 |
Max | 755.3 | 253.00 | 536.30 | 147.60 | 585.00 | 803.00 | 208.50 | 12.61 |
SD | 110.47 | 42.41 | 84.64 | 37.80 | 96.93 | 141.60 | 32.52 | 2.22 |
CV | 19.07 | 24.51 | 22.90 | 102.74 | 27.84 | 32.80 | 34.51 | 32.80 |
O-My | ||||||||
Mean | 562.98 | 164.81 | 359.03 | 39.11 | 372.00 | 400.72 | 88.12 | 6.29 |
Min | 258.80 | 74.40 | 162.50 | 0.20 | 80.00 | 180.00 | 54.94 | 2.83 |
Max | 755.30 | 260.20 | 519.40 | 150.80 | 533.70 | 806.00 | 205.50 | 12.65 |
SD | 118.31 | 39.79 | 85.10 | 41.14 | 99.94 | 153.32 | 30.43 | 2.41 |
CV | 21.01 | 24.15 | 23.70 | 105.17 | 26.87 | 38.26 | 34.53 | 38.26 |
O-Ru | ||||||||
Mean | 541.61 | 155.90 | 347.27 | 38.42 | 338.33 | 449.02 | 98.75 | 7.05 |
Min | 266.30 | 32.60 | 204.80 | 0.40 | 190.00 | 230.00 | 58.44 | 3.61 |
Max | 698.50 | 213.20 | 480.20 | 147.90 | 559.00 | 715.00 | 153.60 | 11.23 |
SD | 103.67 | 42.25 | 74.90 | 41.11 | 89.32 | 128.78 | 26.67 | 2.02 |
CV | 19.14 | 27.10 | 21.57 | 107.01 | 26.40 | 28.68 | 27.01 | 28.68 |
AH | ||||||||
Mean | 654.04 | 153.31 | 464.58 | 36.13 | 426.00 | 291.65 | 79.65 | 4.58 |
Min | 270.90 | 55.20 | 214.30 | 0.05 | 162.70 | 120.00 | 51.81 | 1.88 |
Max | 763.50 | 274.60 | 591.90 | 130.70 | 660.60 | 779.00 | 167.40 | 12.23 |
SD | 95.48 | 37.74 | 79.83 | 33.13 | 94.55 | 132.98 | 23.00 | 2.09 |
CV | 14.60 | 24.62 | 17.18 | 91.70 | 22.20 | 45.59 | 28.88 | 45.59 |
CH | ||||||||
Mean | 680.96 | 177.63 | 455.31 | 47.99 | 474.82 | 276.15 | 68.27 | 4.34 |
Min | 564.20 | 86.90 | 363.80 | 1.10 | 286.00 | 110.00 | 50.94 | 1.73 |
Max | 779.20 | 218.20 | 577.00 | 157.90 | 635.00 | 460.00 | 86.81 | 7.22 |
SD | 65.74 | 26.87 | 54.12 | 42.72 | 95.38 | 99.24 | 10.09 | 1.56 |
CV | 9.65 | 15.13 | 11.89 | 89.02 | 20.09 | 35.94 | 14.78 | 35.94 |
STAND | Tree Density (trees/ha) | Tree Canopy Cover (%) | Tree Main Species | Shrubs * (%) | Grasses (%) | Forbs (%) | |
---|---|---|---|---|---|---|---|
0-Bl | |||||||
(1) Boimorto | 1000 | 90 | Quercus robur | L1: 60 | L2: 10 | 30 | 20 |
(2) Cerceda | 1000 | 80 | Quercus robur, Castanea sativa | L1: 30 | L2: 25 | 35 | 10 |
(3) Cerqueiras | 400 | 70 | Quercus robur | L1: 65 | L2: 20 | 70 | 30 |
(4) Fragavella | 500 | 70 | Quercus robur | L1: 25 | L2: 40 | 50 | 40 |
(5) Monfero | 500 | 70 | Quercus robur | L1: 20 | L2: 30 | 60 | 20 |
O-My | |||||||
(6) A Lama | 500 | 80 | Quercus robur | L1: 85 | L2: 75 | 75 | 15 |
(7) Lovios | 600 | 80 | Quercus roburFagus sylvatica | L1: 50 | L2: 80 | 10 | 20 |
(8) Xestoso | 900 | 70 | Quercus robur | L1: 75 | L2: 60 | 10 | 5 |
O-Ru | |||||||
(9) A Rua | 700 | 80 | Quercus robur | L1: 50 | L2: 75 | 5 | 15 |
(10) Lourizán | 200 | 70 | Quercus robur | L1: 30 | L2: 60 | 35 | 25 |
AH | |||||||
(11) Arnuide | ----- | ----- | ----- | L1: 25 | L2: 70 | 40 | ----- |
(12) Cerqueiras | 1100 | 60 | Pinus pinaster | L1: 25 | L2: 60 | 60 | 20 |
(13) Cerponzóns | 300 | 30 | Pinus pinaster | L1: 45 | L2: 60 | 35 | ----- |
(14) Coto do Muiño | 1200 | 40 | Eucalyptus globulus | L1: 60 | L2: 75 | 25 | 35 |
(15) Fragavella | ----- | ----- | ----- | L1: 70 | L2: 50 | 35 | 20 |
(16) Oia | 300 | 60 | Pinus pinaster | L1: 20 | L2: 60 | 50 | 10 |
(17) Pouzos | ----- | ----- | ----- | L1: 60 | L2: 35 | 35 | 35 |
(18) Silleda | ----- | ----- | ----- | L1: 70 | L2: 35 | 65 | ----- |
CH | |||||||
(19) Eirexa | ----- | ----- | ----- | L1: 30 | L2: 50 | 25 | 10 |
(20) Rubiás | ----- | ----- | ----- | L1: 30 | L2: 60 | 35 | 15 |
References
- Silva-Pando, F.J.; Rozados Lorenzo, M.J.; González-Hernández, M.P. Grasslands and Scrublands in the Northwest of the Iberian Peninsula: Silvopastoral Systems and Nature Conservation. In Pasture Landscapes and Nature Conservation; Redecker, B., Finck, P., Härdtle, W., Riecken, U., Schröder, E., Eds.; Springer: New York, NY, USA; Berlin/Heidelberg, Germany, 2002; pp. 271–283. [Google Scholar]
- Celaya, R.; García, R.R.; Benavides, R.; García, U.; Osoro, K.; Jáuregui, B.M.; García, R.R. Changes in heathland vegetation under goat grazing: Effects of breed and stocking rate. Appl. Veg. Sci. 2010, 13, 125–134. [Google Scholar] [CrossRef]
- Fuhlendorf, S.D.; Engle, D.M.; Elmore, R.D.; Limb, R.F.; Bidwell, T.G. Conservation of Pattern and Process: Developing an Alternative Paradigm of Rangeland Management. Rangel. Ecol. Manag. 2012, 65, 579–589. [Google Scholar] [CrossRef] [Green Version]
- González-Hernández, M.; Mouronte, V.; Romero, R.; Rigueiro-Rodríguez, A.; Mosquera-Losada, M.R.O. Plant diversity and botanical composition in an Atlantic heather-gorse dominated understory after horse grazing suspension: Comparison of a continuous and rotational management. Glob. Ecol. Conserv. 2020, 23, e01134. [Google Scholar] [CrossRef]
- López, C.L.; García, R.R.; Ferreira, L.; García, U.; Osoro, K.; Celaya, R. Impacts of horse grazing on botanical composition and diversity in different types of heathland. Rangel. J. 2017, 39, 375–385. [Google Scholar] [CrossRef] [Green Version]
- Rigueiro-Rodríguez, A.; Mouhbi, R.; Santiago-Freijanes, J.J.; Hernández, M.D.P.G.; Mosquera-Losada, M.R. Horse grazing systems: Understory biomass and plant biodiversity of a Pinus radiata stand. Sci. Agricola 2012, 69, 38–46. [Google Scholar] [CrossRef]
- González-Hernández, M.P.; Silva-Pando, F.J. Grazing effects of ungulates in a Galician oakwood (NW Spain). For. Ecol. Manag. 1996, 88, 65–70. [Google Scholar] [CrossRef]
- Castro, M. Silvopastoral Systems in Portugal: Current Status and Future Prospects. In Agroforestry in Europe; Rigueiro-Rodríguez, A., McAdam, J., Mosquera-Losada, M.R., Eds.; Springer: Dordrecht, Germany, 2008; Volume 6, pp. 111–126. [Google Scholar] [CrossRef] [Green Version]
- Pantera, A.; Papadopoulos, A.; Papanastasis, V.P. Valonia oak agroforestry systems in Greece: An overview. Agrofor. Syst. 2018, 92, 921–931. [Google Scholar] [CrossRef]
- Pardini, A.; Nori, M. Agro-silvo-pastoral systems in Italy: Integration and diversification. Pastoralism 2011, 1, 26. [Google Scholar] [CrossRef] [Green Version]
- Varga, A.; Demeter, L.; Ulicsni, V.; Öllerer, K.; Biró, M.; Babai, D.; Molnár, Z. Prohibited, but still present: Local and traditional knowledge about the practice and impact of forest grazing by domestic livestock in Hungary. J. Ethnobiol. Ethnomed. 2020, 16, 1–12. [Google Scholar] [CrossRef]
- Castro, M.; Teixeira, A.; Fernández-Núñez, E. The nutritive value of different Mediterranean browse species used as animal feeds under oak silvopastoral systems in Northern Portugal. Agrofor. Syst. 2021, 95, 269–278. [Google Scholar] [CrossRef]
- Osoro, K.; Vassallo, L.M.; Celaya, R.; Martínez, A. Livestock production systems and the vegetation dynamics of Less Favoured Areas (LFAs): Developing viable systems to manage semi-natural vegetation in temperate LFAs in Spain. In Livestock Production in the European less Favoured Areas: Meeting Future Economic, Environmental and Policy Objectives through Integrated Research; Laker, J.P., Milne, J.A., Eds.; Macaulay Land Use Research Institute: Aberdeen, UK, 1999; pp. 133–143. [Google Scholar]
- García, R.R.; Fraser, M.D.; Celaya, R.; Ferreira, L.M.M.; García, U.; Osoro, K. Grazing land management and biodiversity in the Atlantic European heathlands: A review. Agrofor. Syst. 2012, 87, 19–43. [Google Scholar] [CrossRef]
- Lasanta, T.; Khorchani, M.; Pérez-Cabello, F.; Errea, P.; Sáenz-Blanco, R.; Nadal-Romero, E. Clearing shrubland and extensive livestock farming: Active prevention to control wildfires in the Mediterranean mountains. J. Environ. Manag. 2018, 227, 256–266. [Google Scholar] [CrossRef] [PubMed]
- Gavazov, K.S.; Peringer, A.; Buttler, A.; Gillet, F.; Spiegelberger, T. Dynamics of Forage Production in Pasture-woodlands of the Swiss Jura Mountains under Projected Climate Change Scenarios. Ecol. Soc. 2013, 18, 38. [Google Scholar] [CrossRef] [Green Version]
- González-Hernández, M.P.; Silva-Pando, F.J. Nutritional Attributes of Understory Plants Known as Components of Deer Diets. J. Range Manag. 1999, 52, 132. [Google Scholar] [CrossRef]
- Jáuregui, B.M.; Celaya, R.; García, U.; Osoro, K. Vegetation dynamics in burnt heather-gorse shrublands under different grazing management with sheep and goats. Agrofor. Syst. 2007, 70, 103–111. [Google Scholar] [CrossRef]
- Osoro, K.; Ferreira, L.M.M.; García, U.; Jáuregui, B.M.; Rosa García, R.; Celaya, R. Diet selection and performance of sheep and goats grazing on different heathland vegetation types. Small Rumin. Res. 2013, 109, 119–127. [Google Scholar] [CrossRef]
- Osoro, K.; Mateos-Sanz, A.; Frutos, P.; García, U.; Ortega-Mora, L.M.; Ferreira, L.M.M.; Celaya, R.; Ferre, I. Anthelmintic and nutritional effects of heather supplementation on Cashmere goats grazing perennial ryegrass-white clover pastures. J. Anim. Sci. 2007, 85, 861–870. [Google Scholar] [CrossRef] [PubMed]
- Hiemstra, S.J.; de Haas, Y.; Mäki-Tanila, A.; Gandini, G. Local Cattle Breeds in Europe Development of Policies and Strategies for Self-Sustaining Breeds; Wageningen Academic Publishers: Wageningen, The Netherlands, 2010. [Google Scholar]
- Heitschmidt, R.K.; Stuth, J.W. Grazing Management. An Ecological Perspective; Timber Press Inc.: Portland, Oregon, 1991. [Google Scholar]
- Caughley, G.; Sinclair, A.R.E. Wildlife Ecology and Management; Blackwell Science, Inc.: Oxford, UK, 1994. [Google Scholar]
- Holechek, J.L.; Pieper, R.D.; Herbel, C.H. Range Management: Principles and Practices, 5th ed.; Prentice Hall: Upper Saddle River, NJ, USA, 2004. [Google Scholar]
- Yang, Y.; Li, Q.; Garber, P.A.; Grueter, C.C.; Ren, G.; Wang, X.; Huang, Z.; Xiang, Z.; Xiao, W.; Behie, A. Cafeteria-style feeding trials provide new insights into the diet and nutritional strategies of the black snub-nosed monkey ( Rhinopithecus strykeri ): Implications for conservation. Am. J. Primatol. 2020, 82, e23108. [Google Scholar] [CrossRef] [PubMed]
- Tassone, S.; Fortina, R.; Valle, E.; Cavallarin, L.; Raspa, F.; Boggero, S.; Bergero, D.; Giammarino, M.; Renna, M. Comparison of In Vivo and In Vitro Digestibility in Donkeys. Animals 2020, 10, 2100. [Google Scholar] [CrossRef] [PubMed]
- Weiss, W.P.; Tebbe, A.W. Estimating digestible energy values of feeds and diets and integrating those values into net energy systems. Transl. Anim. Sci. 2018, 3, 953–961. [Google Scholar] [CrossRef] [PubMed]
- AFRC. Energy and protein requirements of ruminants. An Advisory Manual Prepared by the AFRC Technical Committee on Responses to Nutrients; CAB International: Wallingford, UK, 1993. [Google Scholar]
- Maizeret, C.; Sung, T.M. Etude du régime alimentaire et recherche du déterminisme fonctionnel de la sélectivité chez le chevreuil (Capreolus capreolus) des Landes de Gascogne. Gibier Faune Sauvag. 1984, 3, 63–103. [Google Scholar]
- Sineiro, F.; Osoro, K.; Díaz, N. Bases para la producción e intensificación ganadera en el monte gallego: La utilización de la vegetación espontánea y la siembra y mejora del pasto. In Pastos y Forrajes en Alimentación Animal; Tella, D.C., Ed.; Actas de la XXII Reunión Científica de la S.I.N.A.: Santiago de Compostela, Spain, 1984; pp. 195–219. [Google Scholar]
- Putman, R.J.; Pratt, R.M.; Ekins, J.R.; Edwards, P.J. Food and Feeding Behaviour of Cattle and Ponies in the New Forest, Hampshire. J. Appl. Ecol. 1987, 24, 369. [Google Scholar] [CrossRef]
- Alibes, X.; Tisserand, J.L. Tableaux de la Valeur Alimentaire pour les Ruminants des Fourrages et Sous-Produits D’origine Méditerranéenne; CIHEAM: Zaragoza, Spain, 1990. [Google Scholar]
- Yan, T.; Agnew, R.E. Prediction of nutritive values in grass silages: I. Nutrient digestibility and energy concentrations using nutrient compositions and fermentation characteristics. J. Anim. Sci. 2004, 82, 1367–1379. [Google Scholar] [CrossRef]
- Wan, H.F.; Chen, W.; Qi, Z.L.; Peng, P.; Peng, J. Prediction of true metabolizable energy from chemical composition of wheat milling by-products for ducks. Poult. Sci. 2009, 88, 92–97. [Google Scholar] [CrossRef]
- Stergiadis, S.; Allen, M.; Chen, X.; Wills, D.; Yan, T. Prediction of nutrient digestibility and energy concentrations in fresh grass using nutrient composition. J. Dairy Sci. 2015, 98, 3257–3273. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Rivas-Martínez, S.; Penas, Á.; González, T.E.D.; Cantó, P.; Del Río, S.; Costa, J.C.; Herrero, L.; Molero, J. Biogeographic Units of the Iberian Peninsula and Baelaric Islands to District Level. A Concise Synopsis. In The Vegetation of the Iberian Peninsula; Springer: Berlin/Heidelberg, Germany, 2017; pp. 131–188. [Google Scholar] [CrossRef]
- Izco, J.; Amigo, J.; García-San León, D. Análisis y clasificación de la vegetación leñosa de Galicia (España). Lazaroa 1999, 20, 29–47. [Google Scholar]
- Goering, H.K.; Van Soest, P.J. Forage fiber analysis (aparatus, reagents, procedures and some applications). Agr. Handb. Agr. Res. Serv. USDA 1970, 379, 1–19. [Google Scholar]
- Tilley, J.M.A.; Terry, R.A. A TWO-STAGE TECHNIQUE FOR THE IN VITRO DIGESTION OF FORAGE CROPS. Grass Forage Sci. 1963, 18, 104–111. [Google Scholar] [CrossRef]
- Alexander, R.H. The Establishment of a Laboratory Procedure for the "in vitro" Determination of Digestibility; Research Bulletin n° 42; The West of Scotland Agricultural College: Perth, UK, 1969. [Google Scholar]
- SAS Institute. SAS/STAT® 9.1 User’s Guide; SAS Institute, Inc.: Cary, NC, USA, 2004. [Google Scholar]
- Draper, N.R.; Smith, H. Applied Regression Analysis, 3rd ed.; John Wiley & Sons: New York, NY, USA, 1998. [Google Scholar]
- Neter, J.; Wasserman, W.; Kutner, M.H. Applied Linear Statistical Models: Regression, Analysis of Variance and Experimental Designs; 3rd ed.; McGraw-Hill: Boston, MA, USA, 1990. [Google Scholar]
- Kozak, A. Effects of multicollinearity and autocorrelation on the variable-exponent taper functions. Can. J. For. Res. 1997, 27, 619–629. [Google Scholar] [CrossRef]
- Schröder, J.; Rodríguez, R.; Vega, G. An age-independent basal area increment model for maritime pine trees in northwestern Spain. For. Ecol. Manag. 2002, 157, 55–64. [Google Scholar] [CrossRef]
- Van Soest, P.J.; Jones, L.H.P. Effect of silica in forages upon digestibility. J. Dairy Sci. 1968, 51, 1644–1648. [Google Scholar] [CrossRef]
- Smith, G.S.; Nelson, A.B.; Boggino, E.J. Digestibility of Forages In Vitro as Affected by Content of “Silica”. J. Anim. Sci. 1971, 33, 466–471. [Google Scholar] [CrossRef] [PubMed]
- Minson, D.J. Effect of chemical composition on feed digestibility and metabolizable energy. Nutr. Abstr. Rev. 1982, 52, 591–615. [Google Scholar]
- Varhegyi, I.; Szentmihalyi, S.; Varhegyi, J.; Simon, Z. Relation of crude fibre content and cell-wall constituents to dry matter digestibility in roughages. Acta Agron. Hung. 1987, 36, 341–350. [Google Scholar]
- Paton, D. Elaboration of a multi-variate model for the determination of the metabolizable energy of Mediterranean bushes based on chemical parameters. J. Arid. Environ. 2003, 53, 271–280. [Google Scholar] [CrossRef]
- Mahipala, M.K.; Krebs, G.; McCafferty, P.; Dods, K.; Suriyagoda, B. Faecal indices predict organic matter digestibility, short chain fatty acid production and metabolizable energy content of browse-containing sheep diets. Anim. Feed. Sci. Technol. 2009, 154, 68–75. [Google Scholar] [CrossRef]
- Nagy, J.G.; Hakonson, T.; Knox, K.L. Effect of quality on food intake in deer. Trans. of the Thirty-Fourth, N. Amer. Wildl. and Nat. Res. Conference; Wildlife Management Institute: Washington, DC, USA, 1969; pp. 146–154. [Google Scholar]
- Drożdż, A.; Osiecki, A. Intake and digestibility of natural feeds by roe-deer. Acta Theriol. 1973, 18, 81–91. [Google Scholar] [CrossRef] [Green Version]
- Crawford, H.S. Seasonal Food Selection and Digestibility by Tame White-Tailed Deer in Central Maine. J. Wildl. Manag. 1982, 46, 974. [Google Scholar] [CrossRef]
- Van Soest, P.J. Rice straw, the role of silica and treatments to improve quality. Anim. Feed. Sci. Technol. 2006, 130, 137–171. [Google Scholar] [CrossRef]
- Goddard, J.; McLean, E. Acid-insoluble ash as an inert reference material for digestibility studies in tilapia, Oreochromis aureus. Aquaculture 2001, 194, 93–98. [Google Scholar] [CrossRef]
- McGeough, E.J.; O’Kiely1, P.; Kenny, D.A. A note on the evaluation of the acid-insoluble ash technique as a method for determining apparent diet digestibility in beef cattle. Ir. J. Agric. Food Res. 2010, 49, 159–164. [Google Scholar]
- Sales, J.; Janssens, G.P.J. Acid-insoluble ash as a marker in digestibility studies: A review. J. Anim. Feed Sci. 2003, 12, 383–401. [Google Scholar] [CrossRef]
- Heinrichs, S.; Schmidt, W. Dynamics of Hedera helix L. in Central European beech forests on limestone: Results from long-term monitoring and experimental studies. Plant Ecol. 2014, 216, 1–15. [Google Scholar] [CrossRef]
- Van Uytvanck, J. The Role of Large Herbivores in Woodland Regeneration Patterns, Mechanisms and Processes. Ph.D Thesis, Research Institute for Nature and Forest, Ghent University, Ghent, Belgium, 2009. [Google Scholar]
Data Set 1 | Equation Code | Equations | R2 | MEF | RMSE (MJ/kg DM) |
---|---|---|---|---|---|
O-Bl (n = 69) | 1a | ME = 37.06541 − 5.21387 ln DM | 0.54 | 1.5143 | |
1b | ME = 34.82655 − 0.00418 ADF − 4.41128 ln DM | 0.57 | 1.4637 | ||
1c | ME = 26.75274 − 0.00613 ADF + 1.69057 ln Lignin − 4.31669 ln DM | 0.61 | 0.55 | 1.3919 | |
O-My (n = 54) | 2a | ME = 13.87868 − 0.01348 ADF | 0.44 | 1.8033 | |
2b | ME = 29.98207 − 0.01009 ADF − 3.07073 ln DM | 0.64 | 1.4462 | ||
2c | ME = −14.41271 − 0.02772 ADF + 8.72691 ln ADF − 3.19398 ln DM | 0.66 | 1.4035 | ||
2d | ME = −22.59736 − 0.03427 ADF + 10.22191 ln ADF + 0.32157 ln Silica − 2.93126 ln DM | 0.68 | 1.3614 | ||
2e | ME = 29.63567 − 0.01175 ADF − 0.02373 Silica + 0.68538 ln Silica − 3.02345 ln DM | 0.71 | 0.64 | 1.3047 | |
O-Ru (n = 48) | 3a | ME = 12.86392 − 0.01074 ADF | 0.30 | 1.6879 | |
3b | ME = 36.28577 − 0.01121 ADF − 4.00149 ln DM | 0.59 | 1.2957 | ||
3c | ME = 31.50200 − 0.01309 ADF + 0.01434 Lignin − 3.38594 ln DM | 0.66 | 0.60 | 1.1748 | |
AH (n = 93) | 4a | ME = −0.92486 + 0.06869 CP | 0.60 | 1.3010 | |
4b | ME = 3.17646 − 0.00534 ADF + 0.06102 CP | 0.65 | 1.2087 | ||
4c | ME = 2.82355 − 0.00410 ADF − 0.01075 Silica + 0.06015 CP | 0.68 | 1.1615 | ||
4d | ME = 27.95771 − 0.00281 ADF − 0.01101 Silica + 0.14590 CP − 7.55436 ln CP | 0.70 | 0.66 | 1.1227 | |
CH (n = 33) | 5a | ME = −19.64457 + 5.70339 ln CP | 0.30 | 1.3237 | |
5b | ME = −10.01671 − 0.00888 ADF + 4.84651 ln CP | 0.42 | 1.2007 | ||
5c | ME = −0.01142 ADF +2.87803 ln CP | 0.41 | 0.36 | 1.2117 | |
Pooled Data (n = 297) | 6a | ME = 35.17172 − 4.96649 ln DM | 0.44 | 1.7823 | |
6b | ME = 32.85982 − 0.00929 ADF − 3.62794 ln DM | 0.60 | 1.5008 | ||
6c | ME = 30.69233 − 0.01053 ADF + 0.00988 Lignin − 3.40714 ln DM | 0.63 | 1.4540 | ||
6d | ME = 18.88889 − 0.01008 ADF + 0.00951 Lignin − 2.57947 ln DM + 1.51553 ln CP | 0.64 | 0.63 | 1.4194 |
Season | Equations | R2 | MEF | RMSE (MJ/kg DM) |
---|---|---|---|---|
Spring (n = 73) | ME = 36.78326 − 5.14401 ln DM | 0.61 | 1.7801 | |
ME = 36.25662 − 0.01835 Silica − 4.95451 ln DM | 0.66 | 1.6458 | ||
ME = 35.44320 + 0.00560 Lignin − 0.01853 Silica − 4.96216 ln DM | 0.68 | 1.6188 | ||
ME = 50.51671 − 3.73678 ln ADF + 0.01452 Lignin − 0.00518 Silica − 3.83576 ln DM | 0.72 | 1.5131 | ||
ME = 53.08717 − 4.35111 ln ADF + 0.01594 Lignin − 3.68131 ln DM | 0.71 | 0.68 | 1.5201 | |
Summer (n = 104) | ME = 33.20858 − 4.67851 ln DM | 0.34 | 1.8197 | |
ME = 61.78940 − 5.05363 ln ADF − 4.06425 ln DM | 0.54 | 1.5094 | ||
ME = 49.65541 − 4.81496 ln ADF − 3.38040 ln DM + 1.48910 ln CP | 0.57 | 0.53 | 1.4729 | |
Fall (n = 80) | ME = 74.90378 − 10.76743 ln ADF | 0.51 | 1.3518 | |
ME = 82.29102 − 8.63380 ln ADF − 3.54878 ln DM | 0.63 | 1.1571 | ||
ME = 73.04114 − 6.48335 ln ADF − 0.40105 ln Silica − 4.10694 ln DM | 0.66 | 0.63 | 1.1152 | |
Winter (n = 40) | ME = 83.41177 − 12.12545 ln ADF | 0.65 | 1.2702 | |
ME = 70.48888 − 10.53676 ln ADF + 0.03610 CP | 0.73 | 0.69 | 1.1154 |
Prediction Equation | Bias | MAE | RMSE | R2 | RMSE% | % RMSE Increment | |
---|---|---|---|---|---|---|---|
General model applied by | O-Bl | 0.2472 | 1.2880 | 1.5467 | 0.52 | 22.8 | 11.1 |
vegetation type | O-My | −0.0849 | 1.1139 | 1.4549 | 0.63 | 23.1 | 11.5 |
O-Ru | 0.1633 | 1.0092 | 1.3032 | 0.58 | 18.5 | 10.9 | |
AH | −0.2480 | 1.2076 | 1.4510 | 0.50 | 32.0 | 29.2 | |
CH | 0.0858 | 0.9542 | 1.2247 | 0.40 | 28.1 | 1.1 | |
Total | 0.0003 | 1.1490 | 1.4194 | 0.64 | 24.6 | 15.3 | |
General model applied by | Spring | 0.0820 | 1.2212 | 1.5474 | 0.70 | 23.0 | 1.8 |
season | Summer | −0.2694 | 1.2489 | 1.5314 | 0.53 | 27.1 | 4.0 |
Fall | 0.1563 | 0.9896 | 1.1624 | 0.64 | 21.7 | 4.2 | |
Winter | 0.2405 | 1.0764 | 1.3929 | 0.58 | 27.4 | 24.9 | |
Total | 0.0003 | 1.1490 | 1.4194 | 0.64 | 24.6 | 5.4 | |
Model by vegetation type | |||||||
O-Bl | 0.0022 | 1.1173 | 1.3919 | 0.61 | 20.5 | ||
O-My | 0.0011 | 1.0137 | 1.3047 | 0.71 | 20.7 | ||
O-Ru | 0.0029 | 0.9203 | 1.1748 | 0.66 | 16.7 | ||
AH | −0.0017 | 0.8768 | 1.1227 | 0.70 | 24.8 | ||
CH | −0.0012 | 0.9393 | 1.2117 | 0.41 | 27.8 | ||
Total | 0.0005 | 0.9713 | 1.2315 | 0.73 | 21.4 | ||
Model by season | |||||||
Spring | 0.0006 | 1.2257 | 1.5201 | 0.71 | 22.6 | ||
Summer | 0.0000 | 1.2400 | 1.4729 | 0.57 | 26.0 | ||
Fall | 0.0000 | 0.9236 | 1.1152 | 0.66 | 20.8 | ||
Winter | 0.0003 | 0.8427 | 1.1154 | 0.73 | 22.0 | ||
Total | 0.0002 | 1.0978 | 1.3464 | 0.68 | 23.4 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 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
González-Hernández, M.P.; Álvarez-González, J.G. Estimating Energy Concentrations in Wooded Pastures of NW Spain Using Empirical Models That Relate Observed Metabolizable Energy to Measured Nutritional Attributes. Sustainability 2021, 13, 13581. https://doi.org/10.3390/su132413581
González-Hernández MP, Álvarez-González JG. Estimating Energy Concentrations in Wooded Pastures of NW Spain Using Empirical Models That Relate Observed Metabolizable Energy to Measured Nutritional Attributes. Sustainability. 2021; 13(24):13581. https://doi.org/10.3390/su132413581
Chicago/Turabian StyleGonzález-Hernández, María Pilar, and Juan Gabriel Álvarez-González. 2021. "Estimating Energy Concentrations in Wooded Pastures of NW Spain Using Empirical Models That Relate Observed Metabolizable Energy to Measured Nutritional Attributes" Sustainability 13, no. 24: 13581. https://doi.org/10.3390/su132413581
APA StyleGonzález-Hernández, M. P., & Álvarez-González, J. G. (2021). Estimating Energy Concentrations in Wooded Pastures of NW Spain Using Empirical Models That Relate Observed Metabolizable Energy to Measured Nutritional Attributes. Sustainability, 13(24), 13581. https://doi.org/10.3390/su132413581