Qualitative land suitability assessment for pyrethrum cultivation in west Kenya based upon computer-captured expert knowledge and GIS

Agriculture Ecosystems & Enwonment Agriculture, Ecosystem and Environment 56 (1996) 187-202

Qualitative land suitability assessment for pyrethrum cultivation in west Kenya based upon computer-captured expert knowledge and GIS P. Wandahwa, E. van Ranst

*

University of Gent, Department of Geology and Soil Science, Laboratory of Soil Science, Krijgslaan 281/ S8, 9000 Gent, Belgium

Accepted 25 August 1995

Abstract Selection of the best land for pyrethrum cultivation and determination of the production limiting factors are done through a qualitative process of matching land characteristics with the crop requirements using a model PYCULT built in the Automated Land Evaluation System (ALES). Climatic, soil and landfotm requirements for pyrethrum cultivation are provided. Climatic and land suitability maps are presented. About 42% of the land under study was found to be suitable for growing pyrethrum. Five percent of the area is highly suitable, the rest has limitations of some kind. Land with very severe limitations owing to soil erosion hazard and soil wetness make up 5% and 3%, respectively. Moderate and severe climatic limitations affect about 7% and 11% of the land, respectively. The small scale maps and the land attributes used render PYCULT useful to land-use planners and researchers at the national level. The results can be employed by land-use planners to select areas suitable for pyrethrum cultivation and by researchers to focus on more detailed research in areas of varying suitabilities. At the farmers’ level, PYCULT can be used, provided more detailed local information on climate and soils is available. Keywords: Pyrethrum requirements; Land suitability; ALES; IDRISI; Kenya

1. Introduction Pyrethrum (Chrysanthemum cinerariaefolium) is a small perennial plant cultivated for extraction of pyrethrins from the dried flower achenes. Pyrethrins are six active ingredients of acids and alcohols used in the manufacture of insecticides (Chandler, 1951; Head, 1966; Head, 1969). Natural pyrethrins have rapid toxic action against a wide range of insect

* Corresponding author. Tel.: 32-9/264 46 26; fax: 32-9/264 49 97.

species but do not harm man or mammals and do not leave toxic and oily residues (Elliot et al., 1969; Purseglove, 1982). Introduced into Kenya in 1929, pyrethrum has grown to become the third most important industrial crop after coffee and tea in terms of domestic exports. In 1990, export earnings rose to 153 million Kenya pounds from 14.9 million the previous year. The increased export earnings are a reflection of improved world market prices and increased demand for Kenyan pyrethrum. Since 1987, the Kenya government has on several occasions increased producer prices to encourage

0167.8809/96/$15.00 0 1996 Elsevier Science B.V. All rights reserved SSDI 0167.8809(95)00641-9

188

P. Wandahwa,

E. van Ranst/Agricul:ure,

Ecosystem and Environment 56 (1996)

greater production. The demand among smallholder farmers, who almost exclusively grow the crop, increased putting pressure on land-use planners to select suitable areas for plant multiplication and for flower (generative) production. Land evaluators, assisting land-use planners in the selection of areas suitable for agricultural purposes, use techniques that range in degree of detail from farmers’ experience and expert judgement to integrated computer models simulating soil-water flow, nutrient uptake, associated crop growth and environmental effects (Bouma, 1989; Van Diepen et al., 1991; Van Lanen et al., 1992). Recently developed qualitative methods that capture expert knowledge (Maes et al., 1987) are particularly attractive when quick results are required or when the data available are not sufficient for quantitative methods based on computer simulation models (Van Lanen and Woperies, 1992).

’

BARING0

187-202

A widely used qualitative physical land evaluation method based on expert knowledge is the land suitability method developed by FAO (1976) for assessing suitability of land for a specific use. Suitability is expressed in descriptive terms: highly suitable (S 1); moderately suitable (S2); marginally suitable (S3); unsuitable with (Nl) or without (N2) possibilities for land improvement. The Automated Land Evaluation System (ALES) developed by Rossiter (1990) is based on this framework (FAO, 1976) for land evaluation and offers the possibility of capturing local expert knowledge in decision trees (DTs). In contrast to methods developed for specific crops in specific environments (Wood and Dent, 1983; Batjes et al., 1987; Batjes and Bouwman, 1989; Batjes, 1994), ALES can be used to construct models for a wide range of applications in any environment. Linkage of ALES with IDRISI (a geographical information system; Eastman, 1992)

-L-/Y-

NAROK

Fig. 1.Location of the study area.

P. Wandahwa, E. van Ranst/Agriculture.

Ecosystem and Environment

through the module ALIDRISI (Rossiter and Van Wambeke, 19941, reduces the problems of mathematical inflexibility and lack of spatial representation within ALES. The objective of this study was to present climatic, soil and landform requirements for generative pyrethrum cultivation and demonstrate their potential in qualitative land evaluation through the combined use of ALES and IDRISI. The land suitability evaluation is classified as qualitative because of the descriptive nature of the results which are based upon expert knowledge. Quantitative, socio-economic land suitability evaluation as described by FAO (1983) is not undertaken. However, the concept of land use and associated crop requirements are formulated against a socioeconomic background as one of the driving forces in the evaluation. Crop yields are introduced as a means of checking and to some extent calibrating the suitability assessment derived from ratings of land qualities.

2. Materials

and methods

The study area is situated in the western part of Kenya between latitudes l”30’N and 2% and longi-

56 (1996) 187-202

tudes 34”30’E and 38”30’E. It comprises 16 administrative districts covering approximately 97 300 km2 (Fig. 1). Fig. 2 1s . a schematic presentation of the research approach, integrating IDRISI (GIS), ALES and expert knowledge in the land suitability assessment. Land resources database consisting of maps of soils, landform, rainfall, elevation and administrative regions at the scale of 1: 1000 000 were digit&d and stored in IDRISI. Meteorological station records and altitude data, that together with rainfall data were used to make thermal and moisture digital maps, were introduced in IDRISI as values files. The information was used to prepare evaluation basemaps. Expert knowledge was applied in ALES by defining the land utilisation type (LUT) and crop requirements, selecting the relevant land characteristics and constructing the decision trees (paths) used by the program (PYCULT) to rate the land qualities and award the physical suitability subclasses. Files for land characteristics of the evaluation basemaps were prepared in ASCII format, read into ALES (Fig. 2, arrow number 1) and stored as ALES database. After evaluation, the results were assigned to the basemaps through an interface program ALIDRISI (Fig. 2, arrow number 2). Spatial analysis in IDRISI resulted in production of maps and tables.

Database -digitized maps Expert knowledge

Digital thermal and moisture data 1

Agro-climatic zones (ACZs)

LUT Crop requirements Decision trees

Expert knowledge module

Storing land characteristics

ALES database

1

-_)

Agro-ecological units (AEUs) Evaluation results Spatial analysis

47 Land suitability maps, tables I = transfer of land characteristics from

189

Evaluation module

+ Reports IDRIM to ALES, 2 = transfer

Fig. 2. Relation between land suitability,

of evaluation results to IDRISI

expert knowledge

and GE.

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2.1. Soils and landfomt The Exploratory Soil Map of Kenya published by the Kenya Soil Survey (Sombroek et al., 1982) provides information on soils and landform at the national level and incorporates all the soil information available by 1980. The Fertiliser Use Recommendation Project (FURP), started in 1985 under the auspices of the Kenya Agricultural Research Institute (KARI), executed an exhaustive review on available natural resource data in order to facilitate decisions on where to establish crop research trial sites. Soil-related land factors available in the legend of the Exploratory Soil Map of Kenya were selected and rated (FURP, 1988; Smaling and Van de Weg, 1990) based on representative soil profiles at the trial sites. The representative soil profiles were characterised and classified according to FAO/UNESCO (1974), with adjustments where applicable following ‘the Kenya concepts’ (Siderius and Van der Pouw, 1980). The FURP (1987) profiles do not represent single soil mapping units but ‘groupings of soils’. A ‘grouping of soils’ consists of Exploratory Soil Map units which meet the following requirements as the FURP representative profile: the chosen soil related land factors such as effective depth, drainage conditions, inherent nutrient availability (parent material), top soil properties (organic matter, base saturation) and moisture storage capacity have the same or similar ratings; and soil classification is the same or similar. The FURP therefore utilised the Exploratory Soil Map as the background database to transform existing soil map units, using properties that are strongly related to crop production and compiled new soil maps (FURP, 1987) for each of the districts involved. The small scale of the Exploratory Soil Map does not make it the most suitable data set to be drawn upon. However, the multitude of existing reconnaissance and semi-detailed maps (e.g. Andriesse and Van der Pouw, 1985; Van Wijngaarden and Van Engelen, 1985; Michieka et al., 1986; Boxem et al., 1987) proved useful. Thirty-four soil profiles with their analytical data representing 20 different ‘groupings of soils’ were used for this study. Some data such as drainage, flooding and soil depth were used as indicated in the soil profile descriptions, others had to be recalculated

Ecosystem and Environment 56 (1996) 187-202

Table 1 Values of the land characteristics of soils’ Land characteristics

Flooding Drainage Texture/structure Coarse fragments (%) Soil depth (cm) Calcium carbonate (%) Apparent CEC (cmol( + > kg- ’clay) Sum of basic cations (cmol( + ) kg _ ’ soil) pH water 1:2.5 Organic carbon (%) Electrical conductivity of saturation extract (dS m- ’) Exchangeable sodium percentage (%)

used for some of the ‘groupings

‘Grouping of soils’ Mollic Nitisol

Verto-eutric Planosol

Ando-luvic Phaeozem

None Well C > 60s 0 125 0 22.6

Seasonal Imperfect CL 0 123 0.9 54.0

None Moderate c<6Os 3 78 0 56.8

14.8

21.5

23.1

6.8 2.7 1 0

6.7 4.26 0

6.1 2.17 0

0.6

6.0

0.9

C > 60s clay (over 60%) and blocky structure;

CL, clay loam.

over a certain depth (upper 25 cm or depth of the rooting system), by using weighting factors for the different profile sections (Sys et al., 1991). Land characteristics (LCs) thought to influence the rooting conditions of the crop were calculated over 100 cm depth or depth to the root restricting layer. Organic carbon, soil reaction and sum of basic cations were calculated over the upper 25 cm using weighted averages. Apparent cation exchange capacity of the clay fraction in the B horizon or at 50 cm depth was calculated without a correction for organic matter. Values of the land characteristics used are given in Table 1 for some of the ‘groupings of soils’. The Exploratory Soil Map of Kenya uses six slope classes in 12 combinations: A, O-2%; AB, O-5%; BC, 2-8%; C, 5-8%; BCD, 2-16%; CD, 5-16%; D, 8-16%; DE, 8-30%; E, 16-30%; EF, 16-56%; F, > 30%. The slope classes inventoried were: < 8%, < 16%, < 30% and > 30%. 2.2. Agro-climatic zoning Long-term average annual rainfall maps were available from the Kenya Farm Management Handbook (Jaetzold and Schmidt, 1982). Other climatic

P. Wandahwa, E. van Ranst/Agriculture,

data were available from FAO (1984). Monthly reference evapotranspiration (ETo) was calculated using the Penman-Monteith formula (Smith, 1991). The length of a dry season was determined as the number of months in which rainfall is less than half ETo. IDRISI, a raster (grid) based GIS consisting of a collection of computer programs (modules) that act upon a geographical database (Eastman, 1992) was used for agro-climatic zoning. Agro-climatic zoning is a basic means of assessing the climatic suitability of geographical areas for various agricultural altematives. The approach illustrated here recognises that the major aspects of climate which affect plant growth are moisture availability and temperature. Moisture availability has been accounted for in terms of longterm mean annual rainfall and the length of a dry season as the balance between rainfall and evapotranspiration. Though annual variations have not been accounted for, this simpler approach provides a basic tool for national planning. The agro-climatic zone map used for the assessment of climatic suitability required overlays of individual climatic characteristic maps. Five climatic characteristics identified as relevant for pyrethrum cultivation were: mean average temperature (MAT), mean nighttime temperature (MANT), mean daytime temperature (MADT), mean annual rainfall (MAR) and the length of a dry season (LDS). In Kenya, significant linear relationships exist between longterm air temperature and elevation (Braun, 1980) and between evapotranspiration (ETo) and elevation (Woodhead, 1968; Kalders, 1988). MAT, MANT, Table 2 Characteristics Class

I 2 3 4 5 6 7 8

of the air temperature Air temperature

and moisture

availability

191

Ecosystem and Environment 56 (1996) 187-202

MADT and ETo were mapped by applying the results of regression analyses (Eqs. (l)-(4)) of meteorological stations’ records and altitude to digital elevation data. Subsequently, LDS was mapped by applying results of a regression analysis of digital ETo and MAR data (Eq. (5)) r2 =

MAT = 29.41 - 0.006195X,

0.92

MANT = 26.15 - 0.005849X,

r* = 0.86

(2)

MADT = 32.46 - 0.006485X,

r* =

(3)

ETo=2182.11

-0.4377X,

0.93

r*=0.71

(4)

LDS = 4.21 - 0.00494ETo - 0.00608MAR r2 =

0.73

(5) where MAT is the mean average temperature CC), MANT is the mean nighttime temperature (“Cl, MADT is the mean daytime temperature (“Cl, ETo is evapotranspiration (mm), LDS is length of a dry season (months), MAR is mean annual rainfall (mm) and X, is elevation cm>. Each climatic characteristic map was then divided into classes according to Table 2 and overlaid to form agro-climatic zones (ACZs). The agro-climatic zone map was then overlaid with the soil and landform maps to form agro-ecological units (AEUs). AEUs are units that characterise areas having similar climate and soils (FURP, 1988; Smaling and Van de Weg, 1990) but disregarding the variations on a microscale or of soil complexes in this study. Data files for the ACZs and AEUs were prepared and read into ALES for climatic and land suitability evaluation, respectively.

classes Moisture availability

(“C)

MAT

MANT

MADT

MAR (mm)

LDS (months)

> 21 19-21 17-19 15-17 12-15 IO-12 7-10 <7

> 17 15-.17 13-15 11-13 < 13

> 25 22.5-25 20-22.5 15-20 13-15 < 13

>1600 1400-1600 1200-1400 1100-1200 1000-I 100 950- 1000 W-950 <900

>7 6-7 4-6 3-4 l-3
MAT, mean average temperature; LDS, length of a dry season.

(1)

_

MANT, mean nighttime

temperature;

MADT, mean daytime

temperature;

MAR, mean annual rainfall;

192

P. Wandahwa, E. onn Ranst/Agriculture.

2.3. ALES ALES is a land evaluation computer program based on the FAO (1976) guidelines. In ALES, evaluators build their own ‘expert systems’ taking into account local conditions and objectives. It is not by itself an expert system and does not include any knowledge about land and land use, but is a ‘framework’ within which evaluators can express their own local knowledge (Rossiter and Van Wambeke, 1994). From the point of view of a model builder, the three most important components of ALES are the expert knowledge module, the ALES database and the evaluation module. The expert knowledge module is used for defining the land utilisation types (LUTs) and their land-use requirements (LURs) and allows model builders to construct an inference mechanism called decision trees (DTs) that relate these requirements to the land qualities (LQs). The database is used for the description of land characteristics and/or land qualities of the land areas being evaluated. The evaluation module is used for matching the LURs and LQs and has an explanation facility that enables model builders to understand and fine-tune their models. Expert knowledge on pyrethrum was obtained from farmers and researchers in Kenya between January and March 1993. This was followed by a thorough literature review on the ecological requirements (Glover, 1955; Kroll, 1962; Kroll, 1963; Muturi et al., 1969; Parlevliet, 1970; Acland, 1971; Roest, 1976; FAO, 1978; Wielemaker and Boxem, 1982; Pyrethrum Board of Kenya, 1992). The model ‘PYCULT’ was built in ALES to assess land suitability for generative pyrethrum cultivation using the information acquired. 2.4. Elaborating

PYCULT

2.4.1. The land utilisation

type (code ‘pyc’)

Cultivation of pyrethrum under low management (capital intensity) by small scale farmers producing dried flower achenes for commercial purposes is the land utilisation type (LUT) considered. About 90% of the farmers have less than 1.2 ha of land under pyrethrum. They use local varieties and are self-supporting for planting material or buy poor quality planting material from other farmers.

Ecosystem and Environment 56 (1996) 187-202

Fertilisers, pesticides and insecticides are not applied. Manure may be applied, if available. Tillage of the land is done using either a pair of oxen or a hoe (hand tool> and weeding is done using a hoe. Pruning is done by removing only the dry stems instead of cutting down the old stems, therefore inadequate, and no measures to prevent soil erosion are taken. Yields depend entirely on natural soil fertility and environmental conditions. Farm labour is provided by the farmer and his family and is not costed. 2.4.2. Land-use requirements Land utilisation types are defined within ALES by their land-use requirements, i.e. the conditions that make land more or less suitable for the land uses (Rossiter, 1990). Six LURs considered for the LUT are: climate for generative development (code cl; soil fertility status (code f); salinity and alkalinity hazard (code n>; soil rooting conditions (code s); erosion hazard (code t); soil wetness (code w). Except for soil fertility status, LURs were selected that make the land either physically unsuitable and/or reduce the suitability. Poor soil fertility status only reduces the suitability but does not make the land physically unsuitable for pyrethrum cultivation. Land improvement was not considered for this LUT. The corresponding LQs were put into one of five limitation classes: none (11, slight (21, moderate (3), severe (4) and very severe (5). Land presenting a very severe limitation is physically unsuitable for pyrethrum cultivation. Land presenting slight, moderate or severe limitations reduces suitability in that order. 2.4.3. Decision trees Severity level decision trees were constructed so that the program could infer land quality ratings from subsets of a list of land characteristics (Table 3). Fig. 3 shows a decision tree (path) followed in rating the LQ soil wetness. There are two levels of discrimination in the tree and a number of decision branches at each level. At the first level, the program calls the LC flooding from the list of LCs and checks for its value in the ALES database. There are three possible branches of decisions numbered 1, 2 and 3 to be followed depending on the value encountered in the database.

P. Wundahwa, E. van Ranst/Agriculture,

Table 3 List of land characteristics

used in PYCULT

Name (no. of classes)

Unit of measurement

ACEC

Apparent cation exchange capacity (4)

cc Cfs D LDS ECe ESP Fl MAT MANT MADT MAR oc SBC

Calcium carbonate content (5) Volume of coarse fragments (5) Drainage (7) Length of dry season (6) Electrical conductivity (5) Exchangeable sodium percentage (5) Flooding (excess surface water) (4) Mean average temp. (8) Mean nighttime temp. (5) Mean daytime temp. (6) Mean annual rainfall (8) Organic carbon (4) Sum of basic cation (4)

cmol( +) /Erg clay % %

Sd SI Text

Soil depth (5)

PH

Numbers in parentheses

, .

t 161

months dS m-’ % “C “C “C mm % cmoh +) kg-’ soil cm % _ PH

indicate classes (see Table 2).

193

The third branch is followed when a value of F2 F3 is encountered in the database and a rating of 5 is awarded. The first branch is followed when a value of FO is encountered. Drainage class is then called from the list of LCs and five possible branches of decisions can be followed at this second level of discrimination. Drainage class value of ED (excessive drainage) in the database means the first branch of decision is followed and a rating of 3 is awarded. Branches 2, 3 and 4 are followed when drainage class values of SED (somewhat excessive drainage), WD (well drained) and MD (moderate drainage) are in the database, respectively. Ratings of 2, 1 and 4 are respectively awarded. The second branch at the first level of discrimination in the tree is followed when a value of Fl for flooding is in the database. Drainage class is called from the list of LCs and two possible branches of decisions can be followed. The first branch is followed when drainage class values of either ED, SED, WD, or MD are in the database resulting in a rating of 4. The second branch is followed when drainage class values of either I

or

Code

Slope (5) Texture/structure Soil reaction (7)

Ecosystem and Environment 56 11996) 187-202

> > R (Floodine fexcess surface waterl) - 1 [FO] > > D (Drainape) - 1 PI......*3 (moderate) - 2 [SED] . . . . .*2 (slight) -3mm . . . . ..*I (none) . . . . ..*4 (severe) -4Wl - 5 [I, P, VP]...*5 (v. severe) - 2 [Fl] > > D (Drainage) - 1 m, SED, WD, MD] . . . . *4 (severe) - 2 [T, P, VP]. . . . . . . . . . .*5 (v. severe) - 3 [F2, F3]. _._.___ ___ ._. . . . . .*5 (v. severe) Discriminating entities are introduced by ’> > ’ and underlined. Values of the entities are bxed]. The level in the tree is indicated by the leader character, ‘-‘. The level in the bmnch is indicated by a numeric value. Result values are introduced by ‘. . . .*‘. Abbreviations: FO = none; Fl = occasional; F2 = seasonal; F3 = permanent; ED = excessive; SED = somewhat excessive; WD = well; MD = moderate; I = imperfect; P = poor; VP = very poor.

Fig. 3. Decision tree to determine land quality ratings for soil wetness.

194

P. Wandahwa, E. van Ranst/Agriculture,

(imperfect drainage), P (poor drainage) or VP (very poor drainage) are in the database resulting in a rating of 5. A physical suitability subclass decision tree was constructed to determine the physical suitability of

Ecosystem and Environment 56 (1996) 187202

the land from the land quality ratings. Land suitable to grow pyrethrum is indicated by the letter S, whereas unsuitable land is indicated by the letter N. Arabic numbers are used to show the sequence of decreasing suitability: class Sl land is highly suit-

> _

> cemtive develom 1 [no limitation] > > 1 ~toooeraohical cog&& - 1 [no limitation] > > ~vsical conditions’) _ - 1 [no limitation] > > w (wetnes~and conditions] . . _ _ - - 1 [no limitation] > > Q&&&V and &&mtv hq&) _ _ - - - 1 [no limitation] > > f Ml fm _ _ - - - - 1 [no limitation]....*Sl _ _ - - - - 2 [slight limitation]. . ..*Sl _ _ - - _ - 3 [moderate limitation].. .*S2f _ _ - - - - 4 [marginal limitation]....*S3f _ _ - - - 2 [slight limitation].... =l _ _ - _ - 3 [moderate limitation] > > f (soil fertilitv status) _ _ - - _ - 1 [no limitation]....*S2n _ _ - - - - 2 [slight limitation]. . . . = 1 _ _ - - - - 3 [moderate limitation].....*S2n/f _ _ - - - - 4 [marginal limitation].....*S3f _ _ _ _ - 4 [marginal limitation] > > f (soil f&itv status] _ _ - - - - 1 [no limitation].. ..*S3n _ _ - - - - 2 [slight, moderate limitation].... =l _ _ - - - - 3 [marginal limitation]....*S3n/f _ _ - - - 5 [severe limitation]....*Nn ?? ?? _ _ - - - 2 [slight, moderate, marginal limitation]. . . . = 1 _ _ - - - 3 [severe limitation]....*Nn _ - - - 2 [slight, moderate, marginal limitation]. . . . = 1 _ _ - - 3 [severe limitation]. . . .*Nw - - - 2 [slight, moderate, marginal limitation].... = 1 - - - 3 [severe limitation]....*Ns - - 2 [slight, mcderate, marginal limitation]. . . . = 1 - - 3 [severe limitation]....*Nt - 5 [severe limitation]....*Nc

Discriminating entities are introduced by ’> > ’and underlined. Values of the entities are [boxed]. The level in the tree is indicated by the leader characters, ‘-‘. The level branch is indicated by a numeric value. Result values are introduced by ‘....*‘. At the same level, ‘=’ indicate the same result as the branch with the numeric value that follows The cut part of the tree is indicated by ‘??‘.

Fig. 4. Extract of the physical suitability

subclass decision tree.

P. Wundahwa,

Table 4 Climatic requirements

for pyrethrum

Climatic characteristics

E. uan Ranst/Agriculture.

(Chrysanthemum

Ecosystem and Enuironment

195

56 (1996) 187-202

cinerariaejdium)

Ratings for the climatic characteristic

limits

1

2

3

4

5

Mean annual rainfall (mm)

1100-1200

LDS a (months)

l-2 _

1200-1400 1000-l 100 3

> 1600 900-950 7 _

<9CG >7 _

Mean nighttime temp. PC) Mean daytime temp. PC)

< 11 15-20


1400-1600 950- 1000 4-6 _ 13-15 22.5-25

15-17 > 25

Mean average temp. PC)

_ 12-15 _

13-15 15-17 10-12

< 13 17-19 7-10

> 17 _ _

19-21 <7

> 21

a Length of a dry season.

S2 is moderately suitable; and S3 is marginally suitable. Lower-case letters suffixing the class symbol denote the kind(s) of limitation(s). There are six levels of discrimination in the physical suitability subclass decision tree with a number of decision branches at each level. The next discriminating entity is introduced when no severe limitation is encountered. The final land suitability subclass is based on the highest LQ rating (maximum limitation) found along the path of decision. Fig. 4 shows parts of the physical suitability decision tree. The program considers the LQ climate (c) as the first discriminating entity. Depending on the rating, there are five branches to follow. The first branch is followed when there is no limitation. When the next LQs have no or slight limitations, a physical suitability Sl is awarded. There are no subclasses to class Sl. Moderate and marginal limitations for fertility status (f) result in subclass S2f and S3f, respectively. A slight limitation for salinity and alkalinity hazards (n) results in the same decisions as those of the first branch ( = 1) at the same level of discrimination. Moderate and marginal limitations for salinity and alkalinity hazards mean that fertility status will be considered. There is no need to consider fertility status when salinity and alkalinity hazards present a severe limitation in which case Nn is awarded. able;

2.5. ALES database and evaluation Data entry templates were used to specify the LCs for which data were entered. Templates are groupings of different sorts of data, e.g. climatic variables

and soil variables. More important, templates are used to specify the order in which data are read into ALES from an external source like GIS. Two templates were defined, one for climatic and another for soil and landform conditions. Data files for the ACZs and AEUs were read into ALES for evaluation. The ‘Why?’ screens were used to fine-tune PYCULT to reflect the ‘real’ situation. Evaluation results were linked to IDRISI through the module ALIDRISI for further analyses and map preparation.

3. Results and discussion Table 4 shows the climatic requirements, limits and the respective ratings used for climatic suitability assessment to identify potentially suitable land for pyrethrum cultivation. Pyrethrum grows well in areas with annual rainfall between 1000 and 1400 mm (Muturi et al., 1969; Acland, 1971; Pyrethrum Board of Kenya, 1992). Annual rainfall greater than 1400 mm increases root rot and bud diseases, whereas a dry period of more than 4 months results in low yields (Parlevliet, 1970). However, a dry period of at least 2 months is necessary to rejuvenate the plants (Acland, 1971). Temperature is the most critical climatic factor affecting the generative development of pyrethrum (FAO, 1978). In order to initiate flowering, a temperature below 17°C is required (Glover, 1955; Roest, 1976). Alternate low (under 13°C) nighttime and warm (15-20°C) daytime temperatures result in in-

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196

Ecosystem and Environment56

creased flower production, but an average temperature above 21°C could inhibit flower production altogether (Roest, 1976). Table 5 shows the soil and landform requirements, limits and the respective ratings used to screen land constraints for the potentially suitable land. Pyrethrum does not effectively shade the ground offering poor soil protection (Wielemaker and Boxem, 1982) and does not tolerate waterlogged soil (Kroll, 1963). The guidelines for evaluation of perennial crops with an open canopy and sensitive to impeded drainage (Sys and Riquier, 1980) are adapted for evaluation of slope and soil wetness. There is a scarcity of information on saline or alkali soils, the amounts of gravel and calcium carbonate in the soil, and the effects on pyrethrum

Table 5 Soil and landfonn

requirements

for pyrethrum

Land-use requirements/ characteristics

(1996) 187-202

@roll, 1963). Guidelines used elsewhere for other crops (Sys and Riquier, 1980; Sys et al., 1993) were followed in rating these LCs. Pyrethrum under rainfed conditions extracts substantial amounts of water between the surface and 60 cm and a smaller fraction between 60 and 100 cm (Chung et al., 1991). These were considered in rating the LC soil depth. With regard to rating of the soil fertility status, available information on the soil pH range and other fertility characteristics &roll, 1962; Kroll, 1963; Weiss, 1966; Acland, 1971; Jaetzold and Schmidt, 1982; Pyrethrum Board of Kenya, 1992) was used. Though grown on soils with a lower pH, the Pyrethrum Board of Kenya recommends soils of pH above 5.6 for pyrethrum. Fig. 5 indicates land potentially suitable (due to

(Chrysanrhemum cinerariaefilium) Ratings for the land characteristic

limits

1

2

3

4

5

Erosion hazard Slope (%I

<8

< 16

< 25

< 30

> 30

Wetness Flooding a Drainage b

FO WD

_ SED

ED

FlMD

F2, F3 I, P, VP

Rooting condirions Texture and structure ’ Coarse fragments (o/o) Soil depth (cm) CaCO, (o/o)

C < 60s to L <5 >90 < 12

c > 6os, SCL

SL,C<6Ov 15-35 60-40 24-35

c > 6ov,

5-15 90-60 12-24

24-16 3.2-2.4 5.6-5.2 6.4-6.8 2.4- 105

< 16(-j

< 16(+)

Organic carbon (o/o)

> 24 > 3.2 6.0-5.6 6.0-6.4 > 2.4

2.4- 1.6 5.2-4.8 6.8-7.5 I .5-0.8

< < > <

Salinity and alkalinity hazard ECe d (dS m- ‘) ESP e (%)

< 2.0 < 6.0

2.0-4.0 6.0-10

4.0-8.0 10-15

8-15 15-40

Fertility sfatus Apparent CEC (cmol( + ) kg- ’clay) Sum of basic cations (cmol( +) kg- ’ soil) pH water (1:2.5)

35-55 40-20 35-50

LfS, LS

SiCm, Cm, S, fS, CS > 55 < 20 > 50

1.6 4.8 7.5 0.8

> 15 >4O

a FO, Fl , F2 and F3 indicate none, occasional, seasonal and permanent excess surface water, respectively. b VP, very poorly drained, P, poorly drained; I, imperfectly drained; MD, moderately drained; ED, excessively drained; SED, somewhat excessively drained; WD, well drained. ’ Cm, massive clay; SiCm, massive silty clay; C > 60~. fme clay, vertical structure; C > 60s. fine clay, blocky structure; C < 60~. clay, vertical structure; C < 6Os, clay, blocky structure; SiCs, silty clay, blocky structure; Co, clay, oxisol structure; SiCL, silty clay loam; CL, clay loam; Si, silt; SiL, silt loam; SC, sandy clay; L, loam; SCL, sandy clay loam; SL, sandy loam; fS, tine sand; S, sand; cS, coarse sand. d Electrical conductivity of saturation extract. e Exchangeable sodium percentage.

P. Wandahwa, E. van Ransr/Agriculrure.

Ecosystem and Environmen? 56 (1996) 187-202

prevail on 3% and 5% of the land, respectively. These limitations preclude the land from the LUT. Land presenting a very severe limitation of soil erosion hazard is mainly situated on ando-humic Nitisols surrounding Mount Kenya and the Abadare range. The suitability of this land can be improved through soil conservation practices only to become marginally suitable because of the severe climatic limitations. Land presenting a very severe limitation of soil wetness is mainly located on verto-eutric Planosols in Narok district with a small percentage on eutric Planosols in Nyandarua district. These soils are in the marginally suitable and highly suitable climates, respectively. Though the limitation is difficult to remove for most farmers, planting the crop on ridges will improve the land to become marginally and highly suitable, respectively. Moderate and severe limitations due to climate (S2c and S3c) affect 7% and 11% of the land, respectively. These limitations cannot be removed. Land presenting moderate limitations due to climate

climate) for pyrethrum cultivation. Application of the qualitative method showed that about 42% of the study area is potentially suitable to grow pyrethrum, whereas 58% is not. The potentially suitable land is distributed as follows: 9% is highly suitable, 14% is moderately suitable and 19% is marginally suitable. An overlay of the climatic suitability map and the soil and landform maps revealed that about 8% (NR) of the study area could not be evaluated based on available soil information. The highly suitable land decreased to about 5%, the rest presents limitations of some kind (Table 6, Fig. 6). Highly suitable land comprises: the humic Niti~01s (FAO/UNESCO, 1974; Siderius and Van der Pouw, 1980) in Uasin Gishu district; nito-chromic Luvisols in Baringo, Keiyo Marakwet and Nyandarua districts; mollic Andosols on the escarpment west of the Rift Valley in Nakuru district; mollic Nitisols in Kericho and ando-luvic Phaeozems in Kericho, Nyandarua and Narok districts. Very severe limitations due to soil wetness and erosion hazard

m

Highly suited climate

m

Moderately suited clin Marginally

suited climate

100 km 0 Fig. 5. Climatic suitability

197

Unsuited climate

map for pyrethrum

cultivation.

198 Table 6 Percent distribution cultivation

P. Wandahwa, E. uan Ranst/Agriculture,

of the land potentially

Land suitability subclass

Potentially

Sl S2f s2c s3w S3t/f s3t s3c NW Nt NR Total

4.89 0.20 _ _ 0.03 0.18

7.00 0.18 0.03 2.69

0.11 1.91 1.55 8.87

0.35 0.81 2.60 13.66

High

Ecosystem and Environment 56 (1996) 187-202

around Nyahururu, 01 Ngarua, Ndindika and west of Rumuruti in Laikipia district; and humic Cambisols south of Kapenguria town in west Pokot district. Land presenting severe limitations due to climate is located mainly on humic Nitisols of Murang’a, Nyeri, Meru and Embu districts. About 2% of the land presenting severe limitations due to erosion hazard (S3t) is found on ferralic Cambisols of the Upland Plateaux in Uasin Gishu district. Farmers practising soil conservation can improve the land such that it becomes moderately suitable for pyrethrum. Validation of models built in ALES is difficult because the results are normally expressed in qualitative terms. However, if available, observed yields can be compared with yields predicted in ALES. Predicting yields in ALES requires knowledge about the optimum yield and the effect (proportional yield factors) of each LQ severity level. The optimum yield is then multiplied by the product of the propor-

suitable for pyrethrum

suitable land Moderate

Marginal

Total

_ _ _ _ _ _ 11.08 2.30 2.54 3.37 19.29

4.89 0.20 7.00 0.18 0.06 2.87 11.08 2.76 5.26 7.52 41.82

mainly comprises: humic Nitisols in Kiambu district between Limuru and Kikuyu towns and west of Nandi hills in Nandi district; nito-chromic Luvisols

N

I,

LEGEND

,f’‘“”“,Z ,*/

Sl m

s2c, s3c, S2f s3t, S3Vf, s3w

-II’/

0

NW, Nt, NC

0

NR

I

0

100 km Fig. 6. Land suitability

map for pyrethrum

cultivation.

P. Wandahwa, E. uan Ranst/Agriculture,

Ecosystem and Environment 56 (1996) 187-202

Table 7 Average district farmers’ yields for 1987- 1991 and the average

district land indices of ‘groupings’

199

of soil units on which pyrethrum

is

grown District

Soil units (FA~/UNESCO,

Dry achene yields 1974)

Nakuru

Mollic Andosols

Kericho

Mollic Nitisols, ando-luvic Phaeozems Humic Nitisols Nito-chromic Luvisols Nito-chromic Luvisols, ando-luvic Phaeozems

Uasin Gishu Baring0 Nyandarua Keiyo Marakwet Narok West Pokot Kiambu Laikipia Nandi Meru Nyeri Murang’a Embu

Nito-chromic Luvisols, humic Nitisols Ando-luvic Phaeozems, mollic Nitisols Humic Cambisols Humic Nitisols Nito-chromic Luvisols Humic Nitisols Humic Nitisols Humic Nitisols Humic Nitisols Humic Nitisols

LI, land indices; CV, coefficient

cv (%70)

LI

628

15.9

0.95

503 491 411

34.1 16.2 52.2

0.86 0.82 0.82

353

14.6

0.86

331

62.4

0.77

326 299 294 289 283 250 237 59 34

55.4 40.6 21.6 59.9 52.3 8.1 9.3 51.2 56.1

0.61 0.69 0.65 0.81 0.73 0.46 0.43 0.31 0.21

kg- ha- ’ year_

’

of variation.

tional yield factors (called land index in this study) to obtain the predicted yield. The optimum yield is not meant to be a biological maximum (FAO, 1978),

but a realistic attainable yield in the context of the LUT, assuming no limitations (Rossiter and Van Wambeke, 1994). The choice of the optimum yield

700 600 R2 = 0.88

I

0

0.2

0.4

0.6

0.6

Land index (LI) Fig. 7. Relationship

between dry achene yield and land index.

1

200

P. Wanaahwa, E. van Ranst/Agriculture,

and the proportional yield factors is normally quite subjective. The reliability of the qualitative assessment for pyrethrum cultivation is based on comparison between average district farmers’ yields of dried achenes (kg ha-’ year-‘) and the average district land indices of ‘groups of soils’ on which pyrethrum is grown (Table 7). Farmers’ total dried achene yields per district for the years 1987- 1991 were available from the Kenya Pyrethrum Board records. The area under pyrethrum is estimated by the extension officers of the Ministry of Agriculture, leading to very variable yields per hectare. Despite the variations, the yields show a general decreasing trend from Nakuru to Embu districts and were found useful as a fast means towards validation of the procedure in this study. LQ severity levels none, slight, moderate and severe were assigned proportional yield factors 1, 0.95, 0.85 and 0.60, respectively. No factor was attributed to a very severe level as such land would already be physically unsuitable. Assuming that LQs affect yield in a multiplicative way, we may say that if an AEU has certain severity levels assigned to the LQs, the optimum yield must be multiplied by a land index (LZ) between 0 and 1 in order to arrive at the predicted yield. By setting the optimum yield equal to 1, PYCULT was used to determine the average district land indices for ‘groups of soils’ on which pyrethrum is grown. Regression analysis between the yields and the land indices gave a high r2 value (0.88) (Fig. 7). The relationship derived revealed an optimum yield of 594 kg ha- ’ year-’ and can be used to predict farmers’ yields for this LUT in the study area.

4. Conclusions Land evaluation results are considered valid if they reflect the land evaluator’s best judgement. Owing to the small scale maps and the land attributes selected, PYCULT can be used for decision making at the national level. The results obtained can be employed by land-use planners to select areas suitable for pyrethrum flower production. Re-

Ecosystem and Environment 56 (1996) 187-202

searchers can also use this information to focus on more detailed and meaningful research options in plant breeding, nutrition, water requirements and soil management within the different suitability areas. The study demonstrates that land suitability assessment for generative pyrethrum cultivation on small scale, low capital intensive farms, can be done successfully provided local information on soils and climate is available. However, there remains a need to develop and validate quantitative production models that will permit comparisons between alternative LUTs (e.g. low and high capital intensity) and yield levels in terms of inputs and outputs.

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