Biomass measurement and monitoring of trees and shrubs in a semi-arid region of central Kenya

Journal of Arid Environments (1999) 42: 97–116 Article No. jare.1999.0509 Available online at http://www.idealibrary.com on

Biomass measurement and monitoring of trees and shrubs in a semi-arid region of central Kenya

A. Rosenschein*, T. Tietema- & D. O. Hall*? *Division of Life Sciences, Kings College London, Campden Hill Road, London W8 7AH, U.K. -Environmental Options B.V., Meerndijk 52, 3454 IIS De Meern, The Netherlands (Received 4 September 1998, accepted 2 March 1999) Tree and shrub biomass productivities were measured over a 5-year period at the Rehabilitation of Arid Environments project in Baringo, Kenya. Tree stem basal areas were measured along with destructive tree data. Inside two fields individual species productivities between 0·60 and 11·57 (oven-dry) t ha\year\ were estimated, compared to 0·48 t ha\year\ outside one field. In 1993/4 drought reduced productivities. The average annual increment was 10·4% and 35·6% inside the two fields compared to only 0·8% outside the one field. The methods used in this study are suitable for extensive monitoring of trees in savannas, especially if combined with remote sensing such as aerial digital photography.  1999 Academic Press Keywords: Acacia; Cordia; Prosopis; Baringo; Kenya; land rehabilitation; tree and shrub biomass

Introduction The crucial role of woody plants in semi-arid regions has long been recognized (Scholes & Walker, 1993; Breman & Kessler, 1995) but there is a lack of multiple-year data on the productivities of tree species in such environments. Most tree data only includes standing biomass at a point in time and does not present continuous monitoring of annual productivities. Tietema (1993a, b) further developed a method for estimating the biomass of semi-arid tree and shrub species using the simple measurement of stem basal area and crown diameters; he showed that it was widely applicable to semi-arid species (and this has been confirmed in the present work). In 1982 a project was started in the Baringo district of Central Kenya [under the auspices of the Rehabilitation of Arid Environments Trust (formally Baringo Fuel and Fodder Project)] to rehabilitate severely degraded areas around the lake and on the surrounding hills which were subject to heavy grazing pressure (de Groot et al., 1992). The aim was to work with agropastoralist communities to achieve sustainable land management systems in arid and semi-arid areas. Rehabilitation was started by enclosing ?Corresponding author. 0140}1963/99/060097#20 $30.00/0

 1999 Academic Press

98

A. ROSENSCHEIN ET AL.

areas of various sizes from 6 to 400 ha and planting a variety of indigenous and exotic tree species along with re-seeding with indigenous grasses. The management of these fields provided us with the opportunity to monitor the productivities of trees and shrubs in the enclosed fields on a continuous basis. This monitoring provided data of ecological importance besides providing the project and the local community with information on the productivities of the trees, shrubs and grasses. This would complement the data from ongoing collection of fuel wood, grazing with cattle, sheep and goats, and the removal of thatching grass which was occurring in a number of fields. Detailed monitoring began in 1992 and stopped in 1996; it may continue in the future. In this paper we provide productivity information over 5 years for trees and shrubs in two fields, one close to the lake and the other 5 km away in the hills. We also made monitored productivities outside the field in the hills to compare the effects of continuous grazing pressure and fuel wood collection with the field sites, and where daily rainfall data was available back to 1985. An additional aim of this research was to demonstrate the use of simple stem basal diameter measurements as a reliable tool for monitoring the productivities of semi-arid tree species. Our experience over the last decade has been that this technique can be easily learnt and used by students with a minimum degree of supervision. Because of the extensive areas of semi-arid ecosystems we also are attempting to use these tree measurements as a means of correlating biomass weight with remote sensing based on digital videography and digital photographs; this research is still underway. Thus, ultimately we expect that the methodology used here will be useful in combining ground measurements and remote sensing in a practical method for estimation of annual tree productivities over extensive areas (House et al., (1999)). Project site and background information The RAE Trust Project, located around Lake Baringo (13 N, 363 E) in the Baringo district of central Kenya, was initiated in 1982 to address the urgent ecological and social problems caused by the severe degradation of the semi-arid Baringo area. Methods were developed for rehabilitating degraded land on a large scale and establishing communitybased, sustainable land use systems among agropastoralists (Meyerhoff, 1991; de Groot et al., 1992). On request from the community, revegetation was achieved by planting suitable drought-resistant trees and grasses in the project fields after clearing much of the existing undesirable vegetation, mostly Acacia reficiens and Acacia nilotica shrubs and trees which had become the predominant species following years of overgrazing and increased land pressure from human and livestock populations. The land was first prepared for planting by the construction of microcatchments in the flat lowlands and in the rocky foothills by ripping with the aid of heavy machinery. The fields were then enclosed with solar-powered electric fencing, which prevented field incursions by domestic and wild herbivores such as cattle, goats, sheep, camels, zebra and other indigenous ungulates. This was especially necessary in the early years of tree and shrub establishment and also allowed for the management of controlled livestock grazing by the local people. Involvement of the three ethnic groups inhabiting the Baringo area was essential at all stages in the planning and operation of the project. This was recognized, at the outset, to be of vital importance in the sustainability and ultimate replication of the project. The species of trees and grasses planted were selected from a mixture of locally-found indigenous and exotic species during discussions and consultations with the local people. Amongst the first plants selected were fast growing exotic Prosopis and Leucaena tree species (the latter died off rapidly in the lowland site) and the indigenous grasses Cenchrus ciliaris, Enteropogon macrostachyus and Eragrostis superba. The trees were known to produce rapid and high biomass increments (Felker et al., 1983; Verma,

BIOMASS MEASUREMENT IN A SEMI-ARID REGION

99

Figure 1. Location of project fields and administrative sub-locations (Meyerhoff, 1991).

1987) and were seen as essential in demonstrating enhanced biomass productivities and thereby helping to attract interest and involvement by the local people (de Groot et al., 1992). In order to assess the survivability and productivities of the tree and shrub species, two of the project’s 19 fields were selected for study (Fig. 1). The selection criteria reflected the variation in field sites between a lake-side low altitude (900 m a.m.s.l.) deep soil area and a higher altitude (1050 m a.m.s.l.) rocky escarpment site. The tree and shrub species found inside and outside the fields are shown in Table 1 which includes both planted and regenerated trees. Materials and methods Fields and survey methods A comprehensive site survey of the area was undertaken (dimensions of field or study area, access paths, etc.). This was complemented with aerial photographs at a scale of 1 : 1000, followed by detailed scale drawings of the site topography.

A. ROSENSCHEIN ET AL.

100

Table 1. Tree species inside and outside Field 1B South and Field 5 identified in quadrats* (includes both indigenous and exotic species-)

Species

Inside Field 1B South

Acacia aneura Acacia coriacea Acacia holosericea Acacia mellifera Acacia nilotica Acacia nubica Acacia pendula Acacia reficiens Acacia senegal Acacia tortilis Acacia victoriae Balanites aegyptiaca Berchemia discolor Boscia angustifolia/coriacea? Cadaba farinosa Capparis sp. Combretum aculeatum Combretum hereroense Cordia sinensis Diospyros scabra Ehretia obtusifolia Gardenia volkensii Grewia similis/villosa? Hibiscus sp. Maerua crassifolia Ormocarpum keniense Parkinsonia aculeata Prosopis chilensis/pallida? Salvadora persica Solanum incanum Ximenia americana Ziziphus mauritiana Ziziphus mucronata

y y y y y y y y y y y y

y y y y y y y y

Inside Field 5 Outside Field 5 y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y

y y y y y y y y

y y

* Other species in Field 1B South and Field 5 include: planted trees: Cassia sturtii, Enterolobium cyclocarpum, Leucaena leucocephala, Tamarindus indica, Terminalia spinosa; and regenerated trees: Acacia brevispica/elatior/seyal, Commiphora species, Dichrostachys cinerea, Grewia bicolor/tenax, Maerua crassifolia/oblongifolia, Premna resinosa, Sesamothamnus rivae, Terminalia spinosa, Vepris glomerata, Zanthoxylum chalybeum. --Exotic species include Acacia aneura, A. coriacea, A. holosericea, A. pendula, A. victoriae, Parkinsonia aculeata and Prosopis chilensis/pallida. ?More than one species has been identified in the Baringo project fields, but our field work did not distinguish between them.

Sampling sites, positioning of transects and quadrats Trees and shrubs were measured in two fenced fields, Field 1B South on level ground by the Lake (15·4 ha) and Field 5 on gently sloping rocky ground (57·4 ha) on the escarpment 5 km west of Lake Baringo. A field adjacent to Field 1B South (namely Field 1) was analysed for soil properties by Rowntree (1985) and shown to be a predominantly

BIOMASS MEASUREMENT IN A SEMI-ARID REGION

101

Figure 2. Outline map of field, transect and quadrat layout in (a) Field 1B South and (b) Field 5. T"transect.

102

A. ROSENSCHEIN ET AL.

silt loam overlying a clay rich subsoil with an underlying horizon of sand and fine gravel ranging from 4 to 44 cm in thickness. She concluded that ‘the pattern of (plant) growth was random and therefore unlikely to be related to spatially organised soil properties’. Unfortunately no soil analyses have been done for Field 5 which was clearly stony and rocky. Transect lines were used as the reference point for biomass sampling which was then conducted within a number of quadrats (25;25 m) laid at intervals along the transect lines (see Fig. 2). For example, in Field 5, five transect lines were laid running from east to west across the field and extending a further 100 m outside on either side of the field. Each transect line was approximately 700–850 m long and marked with steel rods, positioned every 50 m. Twenty-five by twenty-five metre square quadrats (625 m) were marked out along the transect lines at intervals of 100 m with the first being positioned 100 m from the field boundary to avoid edge effects. The length of the transect lines varied due to field form, from 650 m at the south end of Field 5 to 500 m at the north end allowing six quadrats to be laid along the longer transects and five along the shorter ones. A total of 28 quadrats were laid inside the field. Outside the field, one quadrat was laid at the ends of each transect line to enable a comparison of tree and shrub standing biomass and productivity inside and outside the field enclosure. Whilst these were not ideal control sites, they were the only practical sites available outside the fields because supervision of the quadrats was possible. The quadrats were marked with steel rods at each corner. Quadrats were precisely located on the north side and to the west of each 100 m marker along the transect lines, except where the quadrat fell on a track or some other man-made obstacle, when the quadrat was re-positioned to an unaffected location in the prearranged priority order; initially to the south-west of the transect line, then the north-east, and if required finally the south-east. This method was adhered to throughout the marking out of the study area. An additional smaller 10;10 m (100 m) nested quadrat was laid in the southeastern corner of each quadrat. The layout and numbering of quadrats is shown in Fig. 2(b). The 28 field quadrats covered an area of 1·75 ha (625 m;28), representing a field sampling size of 3·1% for Field 5, whilst the 15 quadrats inside Field 1B South represented a sampling size of 6·1%. Field 1B South Field 1B South lowland west of Lake Baringo was prepared on an extremely degraded plot which was completely overgrazed, without any grass in the dry season. It was cleared by hand between July and December 1986 of vegetation deemed undesirable by the community; large trees were left standing and water catchment ridges (bunds) were dug. The first planting took place in May 1987 with 7703 trees and an additional 4400 seedlings were planted in April 1988. No irrigation or fertilizers were provided. Field 5 Field 5 was cleared of vegetation in 1984 (except for large trees) and the soil ripped with a bulldozer. Tree planting of 28,213 seedlings took place in April–July 1985, and a further 1452 seedlings planted in June 1986 (replacing dead seedlings). Again, no irrigation or fertilizers were provided. Tree and shrub measurements All trees and shrubs present in the 25;25 m plots with stem diameters equal to or greater than 5 cm were measured. Trees and shrubs with stem diameters less than 5 cm

BIOMASS MEASUREMENT IN A SEMI-ARID REGION

103

were measured in the 10;10 m subplots only. Each tree selected was measured for stem basal area, crown area and height. Crown area (ac) Crown diameter measurements were taken at the extremes of the tree canopy (to the tips of the longest branch) with a tape measure, up to the nearest 10 cm, along the axis of the field transect (K ) and at right angles to the transect (K ), providing an average diameter   (K) used for crown area calculation. Crown area (ac) was then calculated using the formula: ac"(n/4)K. Stem basal area (g) Tree stem diameter (d) was measured just above the stem basal swell (ankle height, approximately 5–10 cm above soil level; Tietema, 1993a) using callipers calibrated in millimetres and readings were rounded up to the nearest millimetre. Two measurements were made along the axis of the field transect (d1) and at right angles to the transect (d2), providing an average diameter (d) used for basal area calculation. Where a tree had multiple stems branching at or below ground level the individual stems were measured and summed. Basal area (g) in cm2, was then calculated with the formula: g"g1#g2#g3 2 gn in which g1"(n/4)d2. A diameter tape was also used for stems with diameters greater than 12 cm and where calliper access was restricted, measurements were rounded up to the nearest millimetre. Height (h) Height (h) was determined using a 5 m stave calibrated with 10 cm markings, held vertically against the trunk of the tree with one end at soil level and read to the highest single point at the top of the tree rounded up to the nearest 10 cm. Trees greater than 5 m high were measured using a clinometer positioned at a known distance from the base of the tree and focused on the highest point of the tree. Shrub measurement Shrubs and small multi-stemmed trees (where it was impossible to measure each closely-spaced stem with callipers) were measured by height and crown diameter only (Tietema, 1993b). This category (‘Shrubs’) excluded annual herbs and all types of grasses which were subject to a separate study.

Destructive sampling and regression analysis Up to 25 trees per species were harvested for weight determination. They were selected by size to represent the range within the fields (see Fig. 3). Fresh (green) weight measurement Trees selected for harvesting were first measured as outlined above. The stem (or stems) were then cut at soil level and the whole tree was weighed using a spring balance attached to a three post crane (weights rounded up to the nearest 0·5 kg). All branches below 2 cm in diameter were then removed and the tree re-weighed. This provided three measurements: the total fresh weight of the tree, the weight of all material with stem

104

A. ROSENSCHEIN ET AL.

Figure 3. Relation between tree biomass fresh weight and stem basal area (g) for a combination of four species: Prosopis, Cordia, A. reficiens and A. mellifera. Weight "0·079233;(g"1·359265)

diameters equal to, or greater than, 2 cm (‘main stem and branches’) and all material with stem diameters less than 2 cm (‘twigs and leaves’). Dry weight measurement A short section of main stem (approximately 5 cm long) was sawn off in the field and weighed to the nearest gram using a battery-powered balance. A small sample of the twigs plus leaves was also cut and weighed. Samples were stored in paper bags and left to dry in the sun prior to final oven-drying to constant weight (weighing to the nearest gram). Regression analysis Regressions were obtained for the most common tree species found inside and outside the fields using a destructive tree mensuration method (Brown, 1976; Tietema, 1993a). The species selected were the exotic trees Prosopis chilensis, P. alba and P. pallida, and the indigenous trees Acacia reficiens, Acacia mellifera (Mimosoideae) and Cordia sinensis (Boraginaceae) (Noad & Birnie, 1990; Coates Palgrave, 1991; Beentje, 1994). External tree dimensions were measured in relation to the total above-ground biomass. For each tree species a regression for basal area tree fresh weight was produced and the R2 was

BIOMASS MEASUREMENT IN A SEMI-ARID REGION

105

calculated. A composite regression equation was also calculated using data from all destructively harvested trees. This allowed an estimation of the weight of the woody biomass of all trees in the fields (Tietema, 1993a).

Rainfall Rainfall was measured within each field using a standard rain gauge where daily readings were taken over the period from first planting.

Results Rainfall Daily rainfall measurements were made from 1985 by RAE in Field 5 and 1986 in Field 1B South. Such detailed data at a field-specific level in semi-arid areas is unusual to have available when analysing the productivity of trees, shrubs and grasses over a long period of time. However, in this study we only use the aggregated monthly data on a seasonal and calendar basis as shown in Table 2. Further analysis of the daily rainfall data is presently being undertaken by meteorologists at the University of Reading. In Field 1B South the annual rainfall, over a 10-year period (Jan 1986 to Dec 1995) varied between 536 mm and 936 mm with an average of 660 mm. In Field 5 the annual rainfall for an 11-year period (Jan 1985 to Dec 1995) varied between 550 mm and 1076 mm with an average of 754 mm. The rainfall over the 10- to 11-year period was erratic and localized with a period of severe drought from August 1993 to March 1994. The productivity data of Field 5 (Tables 3 & 4) show a decline in productivity during the 1993/4 drought and an increase in productivity when the annual rainfall doubled in 1995.

Regression analysis The relation between tree biomass fresh weight and stem basal area for a combination of four species (N"75) is shown in the regression line of Fig. 3. The line of best fit is shown and is described by using the regression equation: Weight (kg)"0·079233;g 1·359265 where g"basal area in cm2, and an R2"0·90. This is similar to the R2 of 0·92 obtained by Tietema (1993a) for a sample size of 512 trees in Botswana. This regression formula was used to estimate the weights of the trees monitored by stem basal diameter. A tree moisture content of 35·1% was obtained from the laboratory analyses and factored in the final calculations to give oven-dry weight for all the data presented in Tables 3 to 6. The fresh weights of shrubs were estimated according to Tietema (1993b) using canopy diameter and shrub height measurements with the regression equation: Fresh weight (kg)"0·017;S 3·159. where S (m)"height#diameter 1#diameter 2. Tree productivities Field 1B South adjacent to Lake Baringo showed a 3-year average annual productivity increment of 39·4% for large trees (basal stem diameters equal to or greater than 5 cm)

0 41 33 211 167 105 123 61 19 41 70 0

871

}

Calendar year 1 Jan to 31 Dec

Seasonal year 1 Apr to 31 Mar

}

Seasonal year 1 Apr to 31 Mar

January February March April May June July August September October November December

587

Calendar year 1 Jan to 31 Dec

1985

0 0 25 177 40 128 55 87 45 5 18 7

January February March April May June July August September October November December

Field 5 Month

1986

Field 1B South month

842

585

0 0 45 189 60 72 50 70 47 10 21 21

1986

596

577

0 10 24 148 142 146 12 33 0 2 60 1

1987

676

781

10 54 72 144 242 139 7 39 0 20 54 0

1987

586

936

15 5 23 175 98 22 181 191 119 82 11 15

1988

918

0 81 73 139 79 40 234 60 50 76 56 30

822

536

27 126 35 89 87 18 57 22 1 31 38 6

917

664

49 58 46 116 151 22 67 69 3 26 49 8

1990

1990

1989

694 1181

1076

16 0 33 186 129 81 251 132 93 113 7 35

1988

1057

798

4 85 75 126 80 29 217 34 25 48 43 34

1989

625

797

50 5 59 66 46 172 20 290 7 26 40 17

1991

416

665

65 1 2 21 43 154 43 260 6 31 31 8

1991

685

550

0 0 2 82 58 44 95 102 30 75 27 37

1992

617

594

3 12 5 86 52 48 63 154 24 91 28 29

1992

792

633

167 73 3 62 79 95 86 7 8 11 27 16

1993

791

561

155 58 5 15 74 115 64 7 22 11 19 18

1993

480

813

2 6 83 184 117 127 89 116 10 9 65 9

1994

402

788

8 10 42 249 94 124 88 102 6 13 51 3

1994

816

610

0 22 71 39 14 57 139 46 105 41 31 48

1995

813

559

0 11 74 51 33 43 75 60 78 24 69 43

1995

570

}

13 32 9 } } } } } } } } }

1996

519

}

6 34 4 } } } } } } } } }

1996

752

754

27 31 47 129 104 87 105 90 34 41 40 20

Annual average 1985/95

662

660

28 32 31 114 74 82 85 95 33 34 37 16

Annual average 1986/95

Table 2. Monthly rainfall (mm) (based on daily measurement) in two fields. Note highlighted 7-month drought during 5-year measurement period (1992– 1996) 106 A. ROSENSCHEIN ET AL.

325 224 101

278 173 106 3742 994 617 378 4737

Calculated field averages Average large trees ha\ Average small trees and shrubs ha\ Average small trees ha\ Average shrubs ha\ Average biomass ha\ 4710 1024 909 116 5734

287 254 32

8242

Mar 94

4934 1282 853 429 6215

359 239 120

8634

Mar 95

5448 1256 792 464 6703

352 222 130

9533

Mar 96

16)5 16)6 29)7 !4)9 16)5

16)6 29)7 !4)9

16)5

92/93

8)0 !11)6 13)6 !67)7 3)9

!11)6 13)6 !67)7

8)0

93/94

4)8 25)1 !6)1 270)0 8)4

25)1 !6)1 270)0

4)8

94/95

Annual % increment

10)4 !2)0 !7)2 8)1 7)8

!2)0 !7)2 8)1

10)4

95/96

11)4 6)6 7)1 5)7 10)4

6)6 7)1 5)7

11)4

Average annual % increment on 4-year basis 1992–1996

*Area of Field 5"57)4 ha. -Biomass measured in 28 quadrats of 625 m each. ?Large trees comprise trees with at least one stem with a basal stem diameter 55 cm; small trees are trees where all stems have a stem basal diameter (5 cm. AAll shrubs were measured by height and crown diameter only. BAverage annual % increment on 4-year basis is calculated as difference between July 1992 and March 1996 biomass, e.g. ((9533!6549)/6549)/4;100"11)4%. Transects 1, 2 and 3 have eight quadrats (six in the field and two outside); Transects 4 and 5 have seven quadrats (five in the field and two outside).

4360 1159 800 359 5519

7630

6549

Mar 93

Measured biomassTotal large trees? 625 m quadrats Total small trees? and shrubs 100 m quadrats Total small trees* 100 m quadrats Total shrubsA in 100 m quadrats

Jul 92

Weights (aggregated) (kg)

Table 3. Annual productivity inside Field 5* over 5 years of measurement 1992– 1996 (oven-dry weight in kg)

BIOMASS MEASUREMENT IN A SEMI-ARID REGION 107

9184 2807 1639 1168 11991

225 131 93

172 147 25 8893 2150 1842 308 11042

4592

4446

8915 2182 2020 162 11097

175 162 13

4457

8713 2864 2320 544 11577

229 186 43

4357

Mar 93 Mar 94 Mar 95

9088 2290 1682 608 11378

183 135 49

4544

Mar 96

!2)9

93/94

3)3 !2)9 30)6 !22)3 !11)0 23)3 278)9 !86)1 8)6 !7)5

30)6 !22)3 !11)0 23)3 278)9 !86)1

3)3

92/93

4)3

95/96

!2)3 4)3 31)2 !20)0 14)9 !27)5 235)0 11)9 4)3 !1)7

31)2 !20)0 14)9 !27)5 235)0 11)9

!2)3

94/95

Annual % increment

0)5 1)6 !2)2 24)3 0)8

1)6 !2)2 24)3

0)5

Average annual % increment on 4-year basisB 1992–1996

*Biomass results were from eight quadrats of 625 m each (two quadrats were destroyed over the measurement period). -Large trees include trees with at least one stem with a basal diameter or 55 cm; small trees include trees where all stems have a stem basal diameter (5 cm. ?All shrubs were measured by height and crown diameter only. ASmall trees and shrubs differ only in terms of the method of measurement and are therefore included together. BAverage annual % increment on 4-year basis is calculated as difference between July 1992 and March 1996 biomass, e.g. ((4544–4446)/4446)/4;100"0·5%.

Calculated averages Average large trees ha\ Average small trees and shrubs ha\ Average small trees ha\ Average shrubs ha\ Average biomass ha\

Measured biomassTotal large trees- 625 m quadrats Total small trees and shrubs 100 m quadratsA Total small trees- 100 m quadrats Total shrubs? in 100 m quadrats

Jul 92

Weights (aggregated) (kg)

Table 4. Annual productivity outside Field 5 over 5 years of measurement, 1992– 1996 (oven-dry weights in kg)

108 A. ROSENSCHEIN ET AL.

135 38 97

136 17 119 4105 907 111 796 5012

Calculated field averages Average large trees ha\ Average small trees and shrubs ha\ Average small trees ha\ Average shrubs ha\ Average biomass ha\ 7890 1101 262 839 8991

165 39 126

7397

Mar 95

8960 1401 247 1153 10360

210 37 173

8400

Mar 96

39)3 !0)4 131)6 !18)8 32)1

!0)4 131)6 !18)8

39)3

93/94

37)9 22)0 2)3 29)8 35)8

22)0 2)3 29)8

37)9

94/95

13)6 27)2 !5)6 37)5 15)2

27)2 !5)6 37)5

13)6

95/96

Annual % increment

39)4 18)2 41)2 14)9 35)6

18)2 41)2 14)9

39)4

Average annual % increment on 3-year basis# 1993-1996

*Area of field 1B South is 15·4 ha. -Biomass measured in 15 quadrats of 625 m each. ?Large trees include trees with at least one stem with a stem basal diameter 55 cm; small trees include trees where all stems have a stem basal diameter (5 cm. AAll shrubs measured by height and crown diameter only. BSmall trees and shrubs: differ only in terms of the method of measurement (see Materials and Method) and are therefore included together. #Average annual % increment on 3-year basis is calculated as difference between March 1996 and June 1993 biomass, e.g. ((8400!3849)/3849)/3;100" 39·4%. Over the period July 92 to March 93 a marked tree method was used for measuring large trees producing a calculated 35·7% increment. This method was replaced in June 1993 by the quadrat method.

5721 903 256 647 6623

5363

3849

Mar 94

Measured biomass Total large trees? in 625 m quadrats Total small trees and shrubs 100m quadratsB Total small trees- 100 m quadrats Total shrubsA 100 m quadrats

Jun 93

Weights (aggregated) (kg)

Table 5. Annual productivity inside Field 1B South* over 4 years of measurement 1993– 1996 (oven-dry weights in kg)

BIOMASS MEASUREMENT IN A SEMI-ARID REGION 109

89 5

13)0 7)8 12)1 157)9 37)6 18)6

2)9 1)7 2)5 59)2 12)3 1)4

19 13 58 13 13 171

N

110

18)9

2)5

151

N

2·6 27·8

2)4 1)5 2)4 66)2 18)4 1)5

19)8

2)0

1993 Average weight $SE

13)8 9)4 14)6 168)6 48)5 19)6

146

N

19 11 67 13 14 172

N

92 10

3·4 26·8

6)9 2)1 1)9 59)7 15)9 1)6

20)4

2)0

1994 Average weight $SE

22)7 12)2 12)8 184)3 46)0 21)2

1994 Average weight $SE

41·8 75·7

138

N

19 16 68 13 14 157

N

99 10

1994 Average weight $SE N 4·4 39·6

3)1 3)3 1)7 58)5 17)2 2)1

19)7

2)0

1995 Average weight $SE

20)1 14)9 13)6 164)8 47)3 26)0

1995 Average weight $SE

56·1 102·2

144

N

26 21 71 12 16 161

N

34·6 3·0

1993/6 Average annual % increment

3)2 1)7 1)8 64)7 12)5 2)6

17)9 26)5 6)6 5)7 2)1 17)9

20)8

2)1

3)4

1992/6 1996 Average Average annual % weight $SE increment

20)0 14)0 14)5 184)7 40)0 28)6

1992/6 1996 Average Average annual % weight $SE increment

5·3 33·2

1996 Average weight $SE

93 68·1 12 100·8

1995 Average weight $SE N

N: number of trees of given species in quadrats (625 m each). Variation in sample numbers is a result of tree death and/or tree growth into the 5 cm stem basal diameter measurement category.

Acacia reficiens

N

1992 Average weight $SE

33·4 92·4

1993 Average weight $SE

Field 5, outside. Biomass of trees in 8 quadrats

Acacia nilotica 15 Acacia tortilis 12 Balanites aegyptiaca 40 Boscia coriacea 14 Maerua crassifolia 14 Prosopis sp. 157

N

1992 Average weight $SE

Field 5, inside. Biomass of trees in 28 quadrats

Prosopis sp. Salvadora persica

N

1993 Average weight $SE N

Field 1B South, inside. Biomass of trees in 15 quadrats

Table 6. Average oven-dry weight (kg) for trees with a stem basal diameter 55 cm for species with five or more specimens in measured quadrats

110 A. ROSENSCHEIN ET AL.

BIOMASS MEASUREMENT IN A SEMI-ARID REGION

111

and 18·2% for the small trees (basal stem diameters less than 5 cm) and shrubs, with a total annual average biomass increase of 35.6%. The ratio of small to large trees was 6·4 (Table 5). No effect of the 1993–94 drought is seen with the large trees, possibly because of their access to ground water, but the 0·4% decrease in mass of small trees and shrubs during this period is an indication of the lack of surface water. Inside Field 5 the large trees showed an average annual increase of 11·4% and the small trees and shrubs an increase of 6·6%, with a total annual biomass increase of 10·4% (Table 3). The ratio of small to large trees was 6·6. A significant effect of the 1993–94 drought is seen with the large trees decreasing their percentage increment by a half in both 1993–94 and 1994–95 (with some recovery in 1995–96). The drought affect in 1993/4 is also clearly evident in the 11·6% loss for small trees and shrubs and the total annual average increase of only 3·9%. Outside Field 5 the productivities of all trees and shrubs were significantly lower than inside the field with the large trees showing an annual average increment of 0.5%, the small trees and shrubs an increment of 1·6% and the total annual average increment being only 0·8% (Table 4). The ratio of small to large trees was 12·9. Again the 1993–94 drought effect is evident with the annual increment for the large trees being !2·9% in 1993/4, for the small trees and shrubs !22·3%, and the average total being !7·5%. In this case the somewhat lower drought stress of September 1995 – February 1996 is evident from the !20·0% increment of small trees and shrubs and the !1·7% average total biomass. It is evident that this effect outside Field 5 is far greater than inside the field. When the annual increment in productivities of the individual species are examined (Table 6; species were included only where N"5 or greater in all years) it is seen that the total weight increment of the individual species can be high, e.g., 54% for Salvadora persica, 47% for Acacia tortilis and 26% for Balanites aegyptiaca. These increases reflect the recruitment of these species into the 5 cm stem diameter class, whereas the low increment seen with Boscia and Maerua may represent death of trees in the 5 cm class over the 4- to 5-year period of measurement. Unfortunately it was not possible to obtain measurements of individual trees since they were not tagged for identification. The variation in tree species sample size (N) was due to tree death and tree recruitment into the 5 cm stem diameter size limit. Discussion and conclusions The rehabilitation of nearly 1400 ha of severely degraded lands around Lake Baringo since 1982 has provided a valuable opportunity for long-term monitoring of the productivities of trees, shrubs and grasses in this semi-arid environment. Since the fields are closely managed by the community and monitored by RAE for the offtake of fuelwood and thatching grass and for periodic grazing, it has been possible to measure the productivities of trees and shrubs (grass productivities are the subject of a separate study being conducted by the University of Nairobi). Whilst the standing biomass within the fields (Field 1B South, first planted 9 years prior to 1996 and Field 5, 11 years previously) cannot be compared directly with the mature standing biomass outside the fields, it is quite evident that protection of the fields has resulted in significant growth of planted trees and also the regeneration of various indigenous tree species within the fields. Unfortunately it was not possible to make a distinction between the data of planted, regenerated and dead trees, and all are aggregated within the data. The project’s success has resulted from the combination of good biomass productivities, diversity of planted and regenerated species useful for humans and animals, allied to the considerable involvement of local peoples in the management and operation of the project. In Field 1B South (Table 5) the average 3-year increment was 1·78 t ha\ year\. Much of the high average annual increment was due to the large number of Prosopis

4)78 1)46 17)70 9)60 17)70 13)25 1)59 8)61 14)73 15)17 21)70 3)69

Acacia senegal

Acacia nilotica

Prosopis chilensis (0009)

Prosopis glandulosa var torreyana (0001) Prosopis alba (0039) Prosopis alba (0163) Acacia cyanophylla

Albizia lebbeck

Acacia nilotica Acacia tortilis Prosopis juliflora Prosopis glandulosa

t ha\ year\

Sonoran Desert, California

Gujurat, India

North-central and south-central Tunisia

Imperial Valley, California

Sadore, Niger

Location

Reference

70 mm rainfall, 473C July temp, 25-years-old

350 mm rainfall, trees 3·2-yearsold, supplemental irrigation, fertilized Rain-fed conditions and access to ground-water; monsoonal

Sharifi et al. (1982)

Verma (1987)

Tiedeman & Johnson (1992)

Mean annual rainfall 580 mm; N, Lamers et al. (1994) P and K at planting Highest and lowest average monthly temp 40)9/15)13C Mean annual rainfall 650 mm. Felker et al. (1983) 100 days of year with temp. greater than 383C. Irrigated, weed control, no fertilizer after planting.

Notes

Table 7. Productivities of trees from arid and semi-arid areas where annual productivities could be calculated (oven-dry weights)

112 A. ROSENSCHEIN ET AL.

1)55 0)60 6)70 0)60 2)50 0)48

Acacia tortilis* Balanites aegyptiaca*

Boscia coriacea* Maerua crassifolia* Prosopis spp.* Acacia reficiens* Baringo, Kenya, Field 5 outside

Baringo, Kenya, Field 5 inside

*The annual productivity is based upon an assumed spacing of 1000 trees ha\.

2)80 1)75

Salvadora persica* Acacia nilotica*

Baringo, Kenya, Field 1B South

Field 5 outside

0)08 11)57

Field 5 inside

0)49

Prosopis spp.*

Baringo, Kenya, Field 1B South

1)78

Multiple species

Petrolina, Brazil

2)10

Prosopis juliflora

Mean average rainfall 1985–95: 754 mm. No irrigation

Mean average rainfall 1985–95: 754 mm. No irrigation

Mean average rainfall 1986–95: 660 mm. No irrigation Mean average rainfall 1985–95: 754 mm. No irrigation Mean average rainfall 1985–95: 754 mm. No irrigation Mean average rainfall 1986–95: 660 mm. No irrigation

365 m altitude, 500–654 mm annual rainfall. This study

Lima (1986)

BIOMASS MEASUREMENT IN A SEMI-ARID REGION 113

A. ROSENSCHEIN ET AL.

114

species in the field which were planted to provide a rapidly growing tree which in the long run was unlikely to survive pest damage. However, there is no doubt that the Prosopis trees which survived pest damage provided significant quantities of biomass which could be used for both fuel wood and browsing. In the long run it is possible that indigenous species like Salvadora, Acacia and Balanites would provide stable productivities if the Prosopis is unable to withstand pest and environmental stresses.

Table 8. Productivities of trees in arid and semi-arid areas where % annual increments are published

Location and reference

Species/ecosystem

Annual increment (%)

Nylsvley, South Africa Scholes & Walker (1993), table 10)2

Other African savannas Scholes & Walker (1993), table 10)2 Mosdene, South Africa Eastern Botswana Eastern Botswana Zaire Zambia Baringo, Kenya This study, tables 3–6

Burkea africana and Ochra pulchra stems 1977–9 Stems of all species in four transects 50;320 m Litterfall plot; all species in 220;50 m area 1978–83

4)4

Species composition similar to Nylsvley Stems of all species, largely fine leafed 1950 As above, 1963–75 Miombo woodland, stems of all species Miombo woodland, stems of all species

4)7

Field 1B South all large trees all small trees and shrubs Prosopis chilensis/pallida Salvadora persica Field 5 inside all large trees all small trees and shrubs Acacia nilotica Acacia tortilis Balanities aegyptiaca Prosopis chilensis/pallida Field 5 outside all large trees all small trees and shrubs Acacia reficiens

8)7 9)1

4)1 3)8 2)9 2)2

39)4 18)2 34)6 3)0 11)4 6)6 17)9 26)5 6)6 17)9 0)5 1)6 3)4

BIOMASS MEASUREMENT IN A SEMI-ARID REGION

115

Inside Field 5, on the escarpment, lower productivities than the lake-side field were observed; the average annual biomass productivity was 0·49 t ha\ year\ over the 4 years. This compares with an annual average productivity of only 0.08 t ha\ year\ outside the field (Tables 3, 4 & 7). Inside Field 5 the 1993–94 drought severely decreased the average annual biomass increment with similar effects outside the field. Clearly the small trees and shrubs are less able to survive drought stresses. A comparison of the present data with the little which is available in the literature from field monitored studies (Table 7) shows that the annual productivity of Field 1B South incorporating rapidly growing species like Prosopis can result in biomass productivities similar to those observed for a number of sites around the world. The high productivities achieved at Baringo for Prosopis spp. (11·57 t ha\ year\) compare well with those at irrigated sites in California (9·60 to 17·70 t ha\ year\), and high rainfall and ground-water sites in India (8·61 to 21·70 t ha\ year\). Also, the productivities of other species in Field 5 (0·60}6·70 t ha\ year\) are comparable to those for Niger (1·46 to 4·78 t ha\ year\), Tunisia (1·59 t ha\ year\) and Brazil (2·10 t ha\ year\). It is also evident from the data in Table 7 that, as expected, irrigation and fertilization can significantly improve yields, but this is not an option in the Baringo project where all rehabilitation must take place with minimum inputs relying solely on ambient environmental conditions. If a comparison is made with natural ecosystems (Table 8) it is again seen that the annual increments within the fields are considerably higher than those of unmanaged ecosystems. Outside Field 5 shows productivities which are comparable or even lower than those of the ecosystems used for comparison in southern and central Africa. In conclusion, the methodology used in this study should be suitable for extensive monitoring of trees in savanna-type ecosystems by combining simple ground measurements with remote sensing such as digital aerial photography and videography. Aerial monitoring methodology is improving since the resolution of the digital cameras is significantly better every year. Thus it should be possible to use stem basal measurements with the regression coefficients developed in this study and by Tietema (1993a, b), and where possible combined with digital photography (House et al., 1999), as a relatively inexpensive and reliable methodology for tree biomass productivity analyses.

This research was made possible and supported by the Rehabilitation of Arid Environments (RAE) Trust (previously Baringo Fuel and Fodder Project), UNEP, ODA (U.K.) and King’s College London. We especially thank Mr M. Roberts (Executive Director) and Dr E. Meyerhoff (socio-economist) of RAE for their continuous help and making available the detailed RAE annual reports. Numerous colleagues in Baringo, Nairobi, London and Amsterdam assisted with the field work for which we are most grateful. We also thank Peter Marttin for help with collating rainfall data.

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House, J.I., Koh, A., Edwards, E. & Hall., D.O. (1999). Digital photography: revolutionising remote sensing. The Biologist, 46: 123}128. Lamers, J.P.A., Mitchels, K. & Vandenbeldt, R.J. (1994). Trees and windbreaks in the Sahel: establishment, growth, nutritive, and calorific values. Agroforestry Systems, 26: 171–184. Lima, P.C.F. (1986). Tree productivity in the semiarid zone of Brazil. Forest Ecology and Management, 16: 5–13. Meyerhoff, E. (1991). Taking Stock: changing livelihoods in an agropastoral community. Nairobi: ACTS Press. 58 pp. Noad, T. & Birnie, A. (1990). Trees of Kenya. Nairobi, Kenya: Noad and Birnie. Publ. 308 pp. Rowntree, K.M. (1985). Soil survey of field 1, Meisori, Njemps location. Report to the Fuel and Fodder Project, Baringo District, Kenya. Scholes, R.J. & Walker, B.H. (1993). An African savanna: synthesis of the Nylsvley study. Cambridge: Cambridge University Press. 306 pp. Sharifi, M.R., Nilsen, E.T. & Rundel, P.W. (1982). Biomass and net primary production of Prosopis glandulosa (Fabaceae) in the Sonoran desert of California. American Journal of Botany, 69: 760–767. Tiedeman, J.A. & Johnson, D.E. (1992). Acacia cyanophylla for forage and fuelwood in North Africa. Agroforestry Systems, 17: 169–180. Tietema, T. (1993a) Biomass determination of fuelwood trees and bushes of Botswana, Southern Africa. Forest Ecology and Management, 60: 257–269. Tietema, T. (1993b). Possibilities for the management of indigenous woodlands in Southern Africa: a case study from Botswana. In: Pearce, G.D. & Gumbo, D.J. (Eds), The Ecology and Management of Indigenous Forests in Southern Africa, pp 134–142. Harare: Zimbabwe Forestry Commission. Verma, D.P.S. (1987). Wastelands development — a case for Prosopis juliflora. The Indian Forester, 113: 529–540.