Livestock Research for Rural Development 25 (2) 2013 Guide for preparation of papers LRRD Newsletter

Citation of this paper

Evaluation of nutritive value of leaves of tropical tanniferous trees and shrubs

Kechero Yisehak and Geert P J Janssens*

Department of Animal Sciences, College of Agriculture and Veterinary Medicine, Jimma University, P. O. Box 307, Jimma, Ethiopia
yisehakkechero.kebede@ugent.be
* Laboratory of Animal Nutrition, Faculty of Veterinary Medicine, Ghent University, Heidestraat 19, 9820 Merelbeke, Belgium

Abstract

This study was conducted to compare the nutritive value of indigenous fodder trees and shrubs (IFTS) and assess the relationship between farmers' IFTS preference, the perception of their characteristics, and analyzed nutritional value at two distinct altitudes within the same area ("high altitude" and "low altitude"). Results were based on laboratory analyses of plant samples and a diagnostic survey of randomly selected 360 livestock farmers. Fifty IFTS were identified and examined for proximate and fibre components, in vitro digestibility, digestible nutrients, energy and condensed tannins (CT). Farmers scored the identified IFTS on a scale of 1 to 4 on nutritive value, growth rate, biomass, compatibility and multifunctionality.

Nutritive value ranged widely among IFTS from 66 to 242 g CP/kg dry matter (DM), 185 to 502 g neutral detergent fibre (NDF)/kg DM, 0.1 to 228 g CT/kg DM, 478 to 745 g total carbohydrate (CHO)/kg DM, 332 to 963 g total digestible nutrients (TDN)/kg DM and 5 to 15 MJ ME/kg DM. Trees showed higher CP contents than shrubs though CHO was higher for shrubs, especially at high altitude (P<0.05). Farmers' scores for nutritive value were positively correlated with CP content of IFTS (r = 0.36; P<0.05). Even though the association was negative for CHO content (P<0.01; r = -0.32), these scores were higher at high altitude (P<0.05). A negative relationship was observed between CT and TA, CP, DMD, OMD, ME and TDN (P<0.05).

It was concluded that  although variation within shrubs and within trees was high – CP was higher in trees than in shrubs and lower CHO in trees than shrubs, therefore warranting further research in the added value for ranging ruminants' nutritional status of providing fodder tree material instead of only access to pasture and shrubs. Farmers' perception of nutritive value of IFTS was partly associated with protein content, but other unidentified factors were contributing to their preference. Geographical differences exert shifts in the perceived and analyzed nutritive value of IFTS, thus care should be taken when developing recommendations for the use of IFTS in an entire region.

Keywords: tannin, fodder trees and shrubs, in vitro digestibility, nutritive value, total digestible nutrients


Introduction

Inadequate feed supply, both in quality and quantity, is a major constraint of ruminant livestock production in many southern parts of the world (De Leeuw 1995; Adugna et al. 2000; IFAD, 2008). In the Gilgel Gibe catchments of Southwest Ethiopia, this is incredibly prevalent and acute in the dry season. Crop by-products, available during dry season, have a low nutritive value due to low protein and fermentable energy, despite their rapid growth during the period of heavy rainfall; in addition, high temperature leads to grass maturing early before the dry season. This obviously adds to the poor performance of ruminant livestock. Improvement of the productive and reproductive performance of smallholders' ruminants warrants methods of extending the availability and quality of local feedstuffs produced on smallholder farms. One potential way (Solomon 2004; Mekoya et al. 2008) for increasing the quality and availability of feeds for smallholder ruminant animals in the dry season may be through the use of various locally available fodder trees and shrubs (IFTS). Fodder trees and shrubs of tropical origin are important in livestock production because they can supply significant amounts of protein. Unfortunately, their content of anti-nutrients like condensed tannins (CTs) vary widely and unpredictably (Babayemi et al. 2004b). Their effect on animals ranges from beneficial to toxicity and death (Makkar et al. 2003). A first step in the targeted use of shrubs and trees as feed resource is the analysis of their nutritive value, and identification of environmental factors that may affect their nutritive value. 

As it is described in many studies that plant species, farming systems, soil types, feed quality and availability, and many more characteristics vary between geography (ILCA 1990; Ayana and Barrs, 2000; Holecheck et al. 2005) and feeding season (Zarazaga et al. 2009; Kim et al. 2006). Studying combined effects of geography, season and species diversity could give a clue for farmers that can design a feeding strategy based on locally available feed resources. 

A variety of FTS are growing in the Gilgel Ghibe basins of Ethiopia, mainly due to the suitability of the environment and the need to use them as fuel wood, construction, mulch, and shade for cash crops like coffee and spices. They replenish soil fertility, are sources of human and veterinary medicine, and also serve as environmental conservation. There is limited information regarding the effect of species diversity, sites of growing, and their interaction effect on farmers feed preference traits, chemical composition, digestibility and energy densities of IFTS with variable CT contents.  

Objectives

The present study was undertaken to


Material and Methods

Location and duration

The survey was carried out in two distinct locations in the Omo-Ghibe river basin of Ethiopia. The climate of the area is characterized from arid to humid tropical with bimodal rainfall. Farmers in the area carry out mixed crop-livestock agriculture. As a consequence of the high population density, as much as 90% of the land is cultivated. Livestock production is characterized by traditional smallholders that are kept mainly in severely overgrazed private and communal rangelands throughout the year. Multipurpose trees and shrubs local to the region as well as many tropical regions are becoming valuable feed supplements to livestock species.  

Data collection and analytical techniques

The data were collected between January and February 2011 through a cross-sectional field survey following a series of sampling procedures. A reconnaissance survey was conducted to have a notion of understanding about the study area and to select the representative study sites before getting to participatory rural appraisal (PRA) and structured questionnaire. Thereafter, the study area was systematically stratified into two regions based on altitude variations: low altitude region (LAR, 1600-1800 metres above sea level (m.a.s.l)) and high (HAR, 2001-2200 m.a.s.l). Measurement of boundaries of each altitude stratum was done by geographic positioning system (GPS). A total of 360 knowledgeable elder farmers (324 men and 36 women, 180 farmers from each strata), aging between 40 and 69 were included from 12 selected peasant associations (PA) were interviewed. The knowledgeable elders were selected with the help of workers in the Agricultural Development office (DA) at each PA. Feed value preference scoring was done for all identified IFTS on a point scale from 1 (not preferred) to 4 (highly preferred) (Kuntashula and Mafongoya 2005). In view of these authors, feed value (nutritive value) preference score was based on palatability by animals, improvement of body condition, growth and milk production, improvement of intake of straw diets, improved health of animals whilst  preference score for growth and re-growth potential used criteria like growth rate after establishment, re-growth potential after frequent cutting or looping. Farmer’s preferences on compatibility mainly focused on absence of competition with crops on available soil nutrients and moisture improve soil fertility; improve growth of below canopy of annual and perennial crops. Multifunctionality indices were used for timber, poles and other local constructions, fuel wood, fence, medicinal value, shade tree, source of honey, soil stabilization, and farm implements.  

Main questions were related to names of IFTS, their season of use, plant parts given to animals, species of animals fed, features of their availability, feeding system and calendar, farmers’ indigenous knowledge of feed value preference, and ecological functions. For ethical purposes, data were collected with permission of the informants and knowledge of the local administration.   

Three individual plants per species and location were sampled. Leaf samples of browse species were ground and analyzed for dry matter (DM,105c for 16 hours), organic matter (% OM,100-% crude ash), crude protein (CP, N6.25), crude ash (CA, 550oc for 8 hours) and ether extract (EE) according to the standard procedures of AOAC (2005). Neutral detergent fiber (NDF) and acid detergent fiber (ADF) were determined sequentially by the method of Van Soest et al. (1991). NDF and ADF values expressed inclusive of residual ash. Lignin (ADL) was determined by solubilization of cellulose with H2SO4 (Van Soest and Robertson 1985). Hemicellulose (% HC) was calculated from the difference between % NDF and % ADF. Determination of total condensed tannins (CT) was based on oxidative depolymerization of CTs in butanol-HCl reagent using 2% ferric ammonium sulfate in 2N HCl catalyst (Porter et al.1986).  

Metabolizable energy (ME, MJ/kg) value was estimated from the in vitro organic matter digestibility: ME = 0.16 % OMD according to McDonald et al. (2002). The two stage in vitro technique developed by Tilley and Terry (1963) was used to determine in vitro dry matter digestibility (DMD) and OMD of the feeds with some slight modifications. About 0.5 g of milled sample (1 mm sieve) was weighed into a test tube. A 10 ml rumen liquor and 50 ml buffer solution was added to the sample in the tube. The mixture was incubated at 39oC for 48 h, ensuring that it was carefully shaken from time to time. Finally, the tubes were centrifuged and the supernatant decanted. The residue was again incubated with 60 ml pepsin-hydrochloric acid solution (to digest protein) for another 48h at 39oC. This was followed by centrifugation, filtering, drying the residues and ashing. Two blanks (rumen liquor mixed with buffer only) and two standards with known digestibility were included to correct for the indigestible DM from the rumen liquor and to check whether the system was working perfectly. 

The gross energy (GE) and digestible energy (DE) of feeds were calculated using equations from Hveplund et al. (1995) as follows:

 

GE (MJ/kg DM) = CP 24.237 + EE 34.116 + CHO 17.300

DE (MJ/kg DM) = dCP 24.237 + dEE 34.116 + dCHO (kg/kg DM) 17.300

 

where

 

dCP = digestible CP (kg/kg DM) = (0.93 % crude protein in DM -3)/100

dEE = digestible EE (kg/kg DM) = (0.96 % crude fat in DM -1)/100

dCHO = digestible CHO (kg/kg DM) = (digestible OM/100 100-% crude ash in DM)/100. 

The total carbohydrate (% CHO) was calculated as: % total CHO = 100-(% CP + % EE + % Ash + % lignin).  

The content of total digestible nutrient (%TDN) per kg and per kg DM of a feedstuff was calculated as follows (Ranjhnan 2001): TDN, kg = kg digestible crude protein (DCP) + 2.25kg digestible ether extract (DEE) + kg digestible carbohydrates (DCHO).

Statistical analysis

A two-way variance analysis was carried out to see mean differences between two altitudes, between fodder trees and shrubs and their interaction through a 22 factorial arrangement for farmers feed preference score values, chemical analyses, OMD, DMD, digestible nutrients and energy contents. These analyses were performed using GLM procedures of SAS (SAS 2010 version 9.3) computer software following the model Yij = + Li + Pj + Mij + ij, with Yij: response variable (feed preference and nutritional quality); : overall mean effect; Li: ith altitude effect; Pi: jth effect of plant nature (trees and shrubs); Mij: interaction effect between altitude and plant nature and ij is the random error. Duncan’s multiple range tests were used for mean separation. Mean differences were considered significant at P<0.05. To establish the magnitude of relationships that exist, if any, between farmers’ assessment of IFTS feed value scores with the relative assessments derived from laboratory-based indications of feed quality, Spearman’s rank correlation analysis was carried out independently for each district.


Results

Fifty IFTS species were identified by the interviewed farmers: A majority of the IFTS was used in the dry season, especially the leaves (Table 1a and 1b). There was a wide spread in the number of respondents that acknowledged the use of a particular species as feed resource: for example, the shrub Vernonia amegdalina was reported by 85.8% whereas another shrub in the same high altitude region, Tagetes minuta, was only listed by 15.6% of the respondents.  In addition to leaves, farmers collected fruits of Ficus species and pods of legume trees and shrubs as a fodder source. This was witnessed by 40.2% and 43.2% of respondents, respectively. However, no grinding or any other physical treatment was reported to be practiced for the purpose of improving the feeding value of the leaves, fruits and pods in both of the farming locations. Reasons given to the question as to why they did not process (not wilt/dry/grind) the plant parts varied. None of the respondents knew if this could be of value in feeding practices. The highest feed preference value (score 4) was given for 12 IFTS species whereby 63.3% of respondents gave the highest mean score, i. e. 63.3% of all the respondents well knew and utilized the species in ruminant feeding. Accordingly, these species were highly preferred and their perceived nutritional value was considerably different from the rest of the 38 IFTS. However, the lowest preference for feed/nutritive value (score 1) was given to 29 IFTS species out of 50. Little or no overlap was observed between IFTS highly ranked for their multiple functions (in addition to fodder source, e.g. for shade of cash crops, soil erosion control, construction, timber, fuel wood, live fence) on the one hand, and compatibility on the other hand.

 

Table 1a. Fodder trees and shrubs (N pooled to 360; feed preference score 1 to 4) with their characteristics as identified and perceived by farmers in the high altitude region

 

Plant species

Parts used

Feeding season

% Respondents

Nutritive value score

Growth rate score

Biomass score

Compatibility score

Multifunctionality score

Shrubs

 

 

 

 

 

 

 

 

 

 

Calpurnia subdecandra

Leaf

Dry

54.2

2.81

3.83

3.13

2.81

3.53

 

Clausena anisata

Leaf

Dry

24.2

2.10

4.00

2.30

2.55

1.80

 

Erythrina brucei

Leaf

Dry

33.3

2.10

3.90

3.78

3.84

2.73

 

Ficus sur

Leaf

Dry

26.4

2.42

3.11

3.02

3.20

2.05

 

Lippia adoensis

Leaf

Dry

16.7

2.11

2.84

2.41

2.11

2.51

 

Myrsine Africana

Leaf

Dry

33.3

2.86

3.87

2.20

3.75

2.45

 

Phytolacca dodecandra

Leaf

Dry

39.7

1.80

3.87

3.78

3.94

3.01

 

Rungia grandis

Leaf

Dry

27.8

2.30

3.31

3.45

4.00

3.95

 

Satureja calmintha/spp

Leaf

Rainy

77.2

3.71

3.33

1.33

2.11

1.50

 

Tagetes minuta

Leaf

Rainy

15.6

1.13

2.51

3.55

2.04

3.65

 

Vernonia amegdalina

Leaf

Dry+rainy

85.8

3.89

3.12

2.20

3.95

3.91

Trees

 

 

 

 

 

 

 

 

 

 

Arundinaria alpine

Leaf

Dry+rainy

41.5

3.50

3.20

2.81

2.70

2.60

 

Buddleja polystachya

Leaf

Dry+rainy

52.5

3.10

2.17

2.75

2.12

2.05

 

Dodonaea angustifolia

Leaf

Dry

40.3

1.25

2.51

3.13

2.71

2.43

 

Dombeya torrida

Leaf

Dry+rainy

33.3

3.83

3.70

2.70

1.70

2.71

 

Draceana steudneri

Leaf

Dry+rainy

55.6

3.91

3.31

2.88

2.45

2.61

 

Ensete ventricosum

Leaf, stem

Dry+rainy

83.1

3.90

3.83

3.73

3.81

3.55

 

Hagenia abyssinica

Leaf

Dry

55.6

3.88

3.43

3.10

4.00

3.46

 

Maesa lanceolata

Leaf

Dry+rainy

33.3

2.37

1.51

3.30

2.12

1.81

 

Millettia ferruginea

Leaf, pod

Dry+rainy

77.8

3.92

2.11

2.14

2.51

2.82

 

Olinia rochetiana  

Leaf

Dry

27.2

1.28

4.00

3.41

2.10

1.44

 

Table 1b. Fodder trees and shrubs (N pooled to 360; feed preference score 1 to 4) with their characteristics as identified and perceived by farmers in the low altitude region

Parts used Feeding season % Respondents Nutritive value score Growth rate score Biomass score Compatibility score

Shrubs

 

 

 

 

 

 

 

Carica papaya2                            

Leaf

Dry

33.3

1.03

3.30

1.30

2.73

Catha edulis

Leaf

Dry+rainy

79.7

2.42

3.55

3.87

2.16

Celtis africana

Leaf

Dry

19.2

1.30

4.00

1.10

3.35

Coffee arabica                                     

Leaf

Dry

33.3

2.20

4.00

3.80

3.80

Coffee Arabica1                                  

Husk

Dry

33.3

1.13

2.30

3.00

1.30

Ekebergia capensis

Leaf

Dry

24.7

1.13

2.43

2.00

2.05

Maytenus obscura

Leaf

Dry

51.9

1.50

2.42

2.30

2.82

Morus alba                             

Leaf

Dry+rainy

24.7

3.10

2.35

2.40

1.50

Myrica salicifolia      

Leaf

Dry

33.3

1.01

3.31

2.31

2.25

Ocimum lamiifolium

Leaf

Dry

30.3

1.83

3.92

3.33

2.20

Premna schimperi

Leaf

Dry

48.6

2.74

1.51

3.51

3.11

Rhamnus staddo

Leaf

Dry

24.4

1.47

2.30

3.30

2.20

Rhus glutinosa

Leaf

Dry

33.3

2.35

4.00

1.50

1.55

Salix subserrata

Leaf

Dry

54.2

1.53

2.17

3.11

3.25

Sida tenuicarpa                                

Leaf

Dry+rainy

55.6

2.49

3.52

1.52

3.25

Trees

 

 

 

 

 

 

 

Acacia abyssynica

Leaf

Dry

21.1

1.21

1.83

2.20

2.55

Albizia gummifera

Leaf, pods

Dry

46.7

2.10

3.92

3.02

2.10

Carissa edulis

Leaf

Dry+rainy

63.9

1.22

3.06

2.88

2.78

Cordia africana

Leaf

Dry

55.3

2.48

3.21

3.00

2.10

Erythrina abyssyinica

Leaf

Dry

25.8

2.11

2.80

3.78

2.21

Euclea divinorum

Leaf

Dry

24.2

2.51

1.74

3.74

3.74

Ficus ovata

Fruit, leaf

Dry

27.8

2.53

3.41

3.64

3.01

Ficus sycomorus

Leaf

Dry

33.3

1.48

2.81

3.13

4.00

Ficus thonningii                       

Leaf, fruit

Dry+rainy

80.3

3.87

3.46

3.75

3.78

Ficus vasta

Fruit, leaf

Dry

33.3

2.73

1.90

3.81

4.00

Grewia ferruginea

Leaf

Dry+rainy

72.8

3.93

4.00

3.51

3.85

Prunus africana

Leaf

Dry

33.3

1.23

4.00

3.20

1.86

Sapium ellipticum

Leaf

Dry+rainy

76.4

3.75

3.42

3.10

2.81

Syzygium guineense 

Leaf

Dry+rainy

53.3

3.85

3.90

2.90

2.56

The number of farmers identifying a species as IFTS was only associated with its nutritive value score (r = 0.54; P<0.01).  Correlations with the other preference scores were not significant (Table 5). Farmers at high altitude attributed a higher nutritive value score to IFTS than in the low altitude region (Table 2). At both the low and high altitudes, farmers gave a moderately higher appreciation of nutritive value for trees in comparison with shrubs (P<0.05). 

 

Table 2. Subjective scores of utilization traits of fodder trees versus fodder shrubs by interviewed farmers (N pooled to 360) compared at low and high altitude (score 1 = low; score 4 = high).

Low altitude

High altitude

P

Score (1-4):

Shrubs

Trees

Shrubs

Trees

SEM

Low vs. High

Shrub vs. Tree

Interaction

Nutritive value

2.10

2.14

2.38

3.30

0.14

**

NS

**

Growth rate

3.07

2.98

3.12

3.29

0.10

NS

NS

NS

Biomass

2.90

2.97

2.91

2.81

0.10

NS

NS

NS

Compatibility

2.47

2.72

2.97

3.12

0.11

*

NS

NS

Multi-functionality

2.47

2.46

2.57

2.82

0.12

NS

NS

NS

  SEM, standard error of means; P<0.05; ** P<0.01; NS= non significant

No influence of altitude or plant type could be identified on the farmers' scoring of growth rate, biomass and multifunctionality. Yet, scores for compatibility were higher at high altitude and the benefit of trees over shrubs was most pronounced at low altitude (P<0.05). On the other hand, the interaction effect of altitude and plant type was also found to be significant for nutritive value score (P<0.05). 

Table 3a and b displays the wide range in analysed nutritive value, e.g. On DM basis, analytical results ranged between 66 to 242 g CP/kg, 185 to 502 g NDF/kg, 0.1 to 228 g CT/kg DM, 331 to 961 g OMD/kg, 282 to 908 g DMD/kg, 478 to 745 g CHO/kg, 5 to 15 MJ ME/kg and 332 to 963 g TDN/kg. Yet, ADF content also tended to be higher in fodder trees at high altitude. Gross energy (GE) was lowest in shrubs at low altitude, but this difference disappeared when estimating digestible energy. Interaction effects of altitude versus plant types resulted significant variation in GE content (P<0.05).  Influence of plant nature or altitude on other nutrients could not be identified. 

  

Table 3a. Potential nutritive value of the edible parts of fodder trees and shrubs in the Gilgel Gibe catchment, southwest Ethiopia at low altitude region

Plant Species

DM

TA

CP

EE

NDF

ADF

ADL

CT

OMD

DMD

    ME

CHO

DCHO

DEE

DCP

       GE

DE

TDN

Acaccia abyssynica

946

90

178

25

315

272

51

2.4

627

613

10

657

627

2

16

16

11

629

Albizia gummnifera

948

82

206

45

276

248

116

72

398

346

6

552

398

4

19

13

8

400

Cordia africana

946

106

242

42

345

330

36

0.5

455

393

7

574

455

4

22

15

9

458

Croton macrostachyus

948

93

231

32

306

290

111

0.7

606

587

10

534

606

3

21

17

11

609

Ekebergia capensis

948

26

143

15

185

88.4

78

80

961

908

15

738

961

1

13

21

17

963

Erythrina abyssyinica

942

97

240

51

332

302

112

3

421

408

7

500

421

5

22

15

8

424

Euclea divinorum

945

92

140

19

336

275

151

81

670

656

11

597

669

2

13

16

12

671

Ficus ovata

914

123

186

27

444

302

99

191

456

414

7

566

456

3

17

13

9

459

Ficus sycomorus

943

114

172

20

399

348

73

110

464

421

7

621

464

2

16

13

8

466

Ficus vasta

946

99

186

14

346

303

99

8

523

437

8

603

523

1

17

14

10

525

Prunus africana

901

96

137

50

352

331

68

76

649

625

10

679

648

5

10

16

12

650

Sapium ellipticum

908

69

130

19

318

269

54

2

653

610

10

728

653

2

12

15

12

654

Syzygium guineense 

911

84

126

38

503

465

94

172

385

350

6

658

385

4

11

11

7

387

Carica papaya2     

832

136

92

29

313

268

56

0.7

788

723

13

688

788

3

8

17

14

790

Calpurnia subdecandra

901

77

205

37

286

194

46

1

366

358

6

636

366

4

19

13

7

369

Carissa edulis

905

92

136

41

340

273

68

164

393

356

6

663

393

4

12

12

7

395

Catha edulis

936

69

102

30

417

259

106

114

705

673

11

693

705

3

9

16

13

706

Coffee arabica

929

87

84

28

452

341

107

13

443

405

7

694

443

3

8

11

8

445

Coffee arabica1   

891

41

66

19

465

401

129

1

337

282

5

745

337

2

6

8

6

338

Dodonaea angustifolia

946

85

140

35

314

251

86

89

638

624

10

654

638

3

13

16

12

640

Maytenus obscura

913

68

151

35

408

345

115

228

453

387

7

631

453

3

14

13

8

455

Morus alba 

912

185

134

36

310

268

87

1

702

674

11

558

702

3

12

17

13

704

Myrica salicifolia      

926

132

138

45

483

362

105

0.9

409

363

7

580

409

4

13

12

8

412

Ocimum lamiifolium

946

104

216

38

352

331

68

0.1

348

339

6

574

348

4

20

13

7

351

Premna schimperi

946

95

183

27

328

285

146

67

672

659

11

549

671

3

17

17

12

674

Rhamnus staddo

915

143

85

35

370

333

76

3

331

325

5

662

331

3

8

9

6

332

Rhus glutinosa

946

83

144

41

313

253

55

188

355

334

6

676

355

4

13

11

7

357

Sida tenuicarpa       

881

120

131

21

327

217

56

1

721

666

12

672

721

2

12

16

13

723

Saccharum officinarum

948

93

231

32

306

290

111

0.7

522

461

9

534

522

3

21

16

10

525

CP, crude protein; EE, ether-extract, NDF, neutral detergent fiber; ADF, acid detergent fiber; ADL, acid detergent lignin; Hemi, hemicellulose; CT, total condensed tannins;

OMD, in vitro organic matter digestibility; DMD, in vitro dry matter digestibility; ME, metabolisable energy

 

Table 3b. Potential nutritive value of the edible parts of fodder trees and shrubs in the Gilgel Gibe catchment, southwest Ethiopia at high altitude region

Plant Species

DM

TA

CP

EE

NDF

ADF

ADL

HC

CT

OMD

DMD

    ME

CHO

DCHO

DEE

DCP

       GE

DE

TDN

Dombeya torrida

947

100

238

25

331

321

112

10

6

593

580

10

525

593

2

22

17

11

596

Draceana steudneri

946

104

222

24

353

328

107

26

53

667

649

11

543

667

2

20

18

12

669

Erythrina brucei

947

103

238

61

329

317

120

12

2

455

412

7

478

455

6

22

16

9

459

Ficus sur

945

94

145

28

351

287

94

64

7

461

459

7

639

461

3

13

12

8

462

Ficus thonningii                       

892

125

115

42

484

388

101

95

1

628

603

10

617

628

4

10

15

11

630

Grewia ferruginea

947

96

229

29

317

300

93

17

52

529

491

9

553

529

3

21

16

10

532

Hagenia abyssynica

946

95

175

23

336

286

89

50

37

573

553

9

619

573

2

16

15

10

575

Millettia ferruginea

957

88

241

31

415

380

146

35

73

508

489

8

494

508

3

22

16

9

511

Vernonia amegdalina

946

105

228

45

345

317

97

28

2

585

543

9

526

585

4

210

17

11

588

Arundinaria alpine

900

110

130

55

484

363

143

121

0.1

376

368

6

562

375

5

12

12

7

378

Buddleja polystachya

943

114

161

17

396

342

146

55

5

681

653

11

562

681

2

15

16

12

683

Celtis africana

945

107

194

40

370

333

76

37

82

455

393

7

584

455

4

18

14

8

458

Clausena anisata

934

77

164

28

340

273

68

67

7

653

610

10

663

653

3

15

16

12

655

Ensete ventricosum

944

118

190

15

387

350

57

38

0.1

531

433

9

621

531

1

17

14

10

533

Lippia adoensis

946

104

216

31

352

331

58

21

1

667

630

11

591

666

3

20

18

12

669

Maesa lanceolata

923

91

203

72

346

305

65

41

68

428

396

7

569

428

7

19

15

8

431

Myrsine africana

944

99

129

37

353

297

48

56

3

552

513

9

687

552

4

12

14

10

554

Phytolacca dodecandra

935

92

107

27

354

242

134

119

76

647

611

10

640

647

3

10

15

12

649

Rungia grandis

947

96

237

14

314

303

89

11

54

654

634

11

564

654

1

22

18

12

656

Salix subserrata

920

60

151

64

453

355

116

98

66

436

397

7

609

436

6

14

13

8

439

Satureja calmintha/spp

942

114

122

35

418

334

73

84

0.2

682

632

11

656

682

3

11

16

12

684

Tagetes minuta

945

92

143

29

332

269

64

62

0.1

648

625

10

672

648

3

13

16

12

649


Fodder trees showed a significantly higher protein content than fodder shrubs, with a tendency to be more pronounced at high altitude (P<0.05) (Table 4). 

 

Table 4. Nutritive value analysis of fodder trees versus fodder shrubs in the Gilgel Gibe catchment (southwest Ethiopia) at low and high altitude.
Nutritive value Low altitude High altitude SEM P
Shrubs

Trees

Shrubs

Trees

3 Low vs. High Shrub vs. Tree Interaction

DM, g/kg

923

927

938

938

     NS

NS

Ash, g/kg

94

93

103

98

3

NS

NS

NS

OM, kg

902

907

906

898

3

NS

NS

NS

CP, g/kg

143

170

166

193

7

NS

*

*

EE, g/kg

33

30

35

35

2

NS

NS

NS

NDF, g/kg

365

341

365

376

9

NS

NS

NS

ADF, g/kg

294

292

306

330

8

NS

NS

NS

ADL, g/kg

91

85

77

110

4

NS

NS

*

Hemi, g/kg

71

49

60

46

5

NS

NS

NS

CT, g/kg

58

57

28

30

8

NS

NS

NS

IVOMD, g/kg

493

575

590

543

19

NS

NS

NS

IVDMD, g/kg

460

535

548

516

19

NS

NS

NS

GE, MJ/kg

13

15

14

15

0.3

NS

NS

*

DE, MJ/kg

9.0

10.4

10.7

9.9

0.3

NS

NS

NS

ME, MJ/kg

7.9

9.2

9.4

8.7

0.3

NS

NS

NS

CHO,g/kg

628

590

632

592

9

*

*

**

DCHO,g/kg

564

533

532

560

19

NS

NS

NS

DEE,g/kg

3.3

3

3.2

3

1.7

NS

NS

NS

DCP,g/kg

14.2

16.5

14

16.5

6.4

*

*

*

TDN, g/kg

495

577

592

545

19

NS

NS

NS

 DM, dry matter; TA, total ash; OM, organic matter; CP, crude protein; EE, ether-extract, NDF, neutral detergent fiber; ADF, acid detergent fiber; ADL, acid detergent lignin; HC, hemicellulose; CT, total condensed tannins; IVOMD, in vitro organic matter digestibility; IVDMD, in vitro dry matter digestibility; GE, gross energy; DE, digestible energy; ME, metabolisable energy; TDN, total digestible nutrients; *=P<0.05; **=P<0.01;NS= non significant

The nutritive value score showed a mild positive correlation (Table 5) with the analyzed CP concentration (r = 0.36; P< 0.05), but not with other analyzed and derived nutrient concentrations, except for a weak positive correlation with ADF concentration (r = 0.24; P<0.01). None of the other scores showed significant correlations with the analyzed nutrient concentrations. 

 


Discussion

The fairly high number of reported IFTS and the high percentage of respondents listing several IFTS, indicates that the use of IFTS is widespread in the study region, and agrees with other studies in similar areas by Aucha (2004) and Ntakwendela (2003) in Kenya, Samanya (1996) of Uganda, and Backlund and Bellskong (1991) and Komwihangilo et al. (1995) in Tanzania. The wide range in the measured nutrient and energy content among the identified IFTS presents a challenge to formulate simple recommendations on the use of fodder trees and shrubs. For the most part, farmer perceptions were neither correlated with chemical essay, nor with energy content and digestibility value. This could be due to the fact that the population in the study area has been mainly devoted to crop agriculture, and has had less experience with nutritive value parameters devised based on laboratory analyses. Roothaert and Franzel (2001) and Kuntashula and Matongoya (2005) reported that farmer’s preference and use of local multipurpose fodder trees and shrubs based on roles of fodder trees in improving animal productivity, multiple uses of fodder trees and shrubs, ever increasing trends of soil erosion that affects availability of feed resources and unpredictable climate changes that are reversing the performance of livestock in the region.  

Other surveys on fodder trees also observed this heterogeneity in nutrient composition (Kaitho et al. 1996; Mekoya et al. 2008). The magnitude of variation within the group of shrubs or within the group of trees likely overruled many of the potential differences between shrubs and trees. Nevertheless, trees were shown to have a higher protein concentration than shrubs, especially at high altitude. Studies on free-ranging cattle in Africa have previously demonstrated that protein is a crucial limiting nutrient, especially in the dry season (Zinash et al. 1995; Abdulrazak et al. 2000; Solomon 2002). Therefore, trees might have a benefit over shrubs as a feed resource in such situations. Whereas shrubs are available to the animals without human intervention, cattle usually have no access to the edible parts of trees, and their owners need to harvest this material in order to be available to the animals. Without knowing the exact relative contribution of protein and other nutrients of IFTS to the total diet of animals, it is difficult to estimate the nutritional importance of the difference in protein between trees and shrubs. The present data therefore warrant further investigation to quantify the added value of harvesting fodder trees on the nutritional status of ranging cattle in regions where fodder shrubs are present.  Many studies have documented the importance of fodder trees as protein sources for free-ranging ruminants (Osuji and Odenyo 1997; Thorne et al. 1999; Sumberg 2002), but this is the first study to our knowledge that suggests a practical advantage of actively feeding fodder tree parts over fodder shrubs to optimise the protein provision to free-ranging livestock. 

Although many of the seemingly important analysed features, such as energy content, digestibility and tannin load, were not reflected in the perception of the farmers (as determined with the scoring system), their estimation of nutritive value to some extent correlated with the protein content of the IFTS. This suggests that farmers throughout the years have unintendedly identified protein as a limiting factor in the dry season diet of their animals, although it is clear from the fairly low correlation coefficient that other factors still affected their judgment that might be worthwhile being identified. Higher condensed tannin levels (CT<50g/kg DM, total 26 out of 50 IFTS) become highly detrimental (Barry and Manley, 1984) as they reduce digestibility of fiber in the rumen (Reed et al. 1985) by inhibiting the activity of bacteria (Chesson et al. 1982) and anaerobic fungi (Akin and Rigsby 1985), high levels also lead to reduced intake (Merton and Ehle, 1984 cited by Leng 1997). According to Brooker et al. (1999), livestock consuming tannin-rich diets over 50g CT/kg DM usually develop a negative nitrogen balance and lose weight and body condition unless supplemented with non-protein nitrogen, carbohydrate and minerals. It is therefore remarkable that the CT content of the IFTS was not identified as a factor related to the preference of the interviewed farmers (Fig 1 and 2).  Perhaps, the huge variability in CT content among the IFTS within altitude and plant nature did not allow to separate such effects, and there might also have been a trade-off between CP and CT, in a way that IFTS can combine high protein levels with high tannin content, as seen in the present study (e.g. Ficus ovata) and others (Makkar 2003). 

The altitude factor included in the present study was obviously not just a reflection of differences in meters above sea level as such, but implied differences in soil type, erosion status, management system, and other related aspects. Taken this into account, the results still demonstrate that both the perceived and the analysed nutritive values of IFTS can be affected by geographical differences within the same catchment, and that for instance the added value of using fodder trees will depend on these circumstances. For instance, significant interactions or tendency to interactions between plant nature and altitude were observed for CP, ADL and GE; protein and energy are generally considered as the primary aspects of nutrition, and ADL is commonly judged as a negative factor for digestibility (Preston 1995; McDonald et al. 2002). 

Farmers at high altitude not only gave a higher score for nutritive value, but also for compatibility. Although they perceived this difference in compatibility between shrubs and trees within an altitude region, it did not seem to affect their preference, hence apart from nutritive value, other factors not identified in this study will have affected the preference of farmers for particular IFTS. Probably, sociological factors will have been in play as well, such as tradition, but maybe also lack of knowledge on the potential use of certain species, and methods to improve nutritive value through available processing techniques, as has been demonstrated by Thapa et al. (1997) and Thorne et al. (1999). We should also acknowledge that other nutrients than the ones studied here might be of importance as limiting factor for animal performance. Previous work in the Gilgel Gibe catchments demonstrated the wide occurrence of trace element deficiencies in cattle, e.g. copper, that might affect nutrient utilization (Dermauw et al. submitted)


Conclusion


Acknowledgements

The authors gratefully acknowledge the VLIR-IUC Institutional University Cooperation Program for funding the research budget. Animal Nutrition Department of Holeta Agricultural Research Center and Jimma University, Ethiopia are also duly acknowledged for providing different research materials and facilities for the study.


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Received 3 January 2013; Accepted 8 January 2013; Published 5 February 2013

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