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

Agriculture Ecosystems & Enwonment

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 Since 1987, the Kenya government has on several 49 97. occasions increased producer prices to encourage

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 ex- ports. 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.

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) 187-202

greater production. The demand among smallholder farmers, who almost exclusively grow the crop, in- creased 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 inte- grated computer models simulating soil-water flow, nutrient uptake, associated crop growth and environ- mental 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).

A widely used qualitative physical land evaluation method based on expert knowledge is the land suit- ability method developed by FAO (1976) for assess- ing 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 eval- uation 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 geo- graphical information system; Eastman, 1992)

’ BARING0 -L-/Y-

NAROK

Fig. 1. Location of the study area.

P. Wandahwa, E. van Ranst/Agriculture. Ecosystem and Environment 56 (1996) 187-202 189

through the module ALIDRISI (Rossiter and Van Wambeke, 19941, reduces the problems of mathe- matical 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 evalua- tion as described by FAO (1983) is not undertaken. However, the concept of land use and associated crop requirements are formulated against a socio- economic 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 suit- ability assessment derived from ratings of land quali- ties.

2. Materials and methods

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

Database -digitized maps

tudes 34”30’E and 38”30’E. It comprises 16 adminis- trative 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 assess- ment. 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 infor- mation was used to prepare evaluation basemaps.

Expert knowledge was applied in ALES by defin- ing the land utilisation type (LUT) and crop require- ments, 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.

Expert knowledge

Digital thermal and LUT moisture data

Expert 1 Crop requirements knowledge

1 Decision trees module Agro-climatic zones -_) (ACZs) Storing land ALES

characteristics database Agro-ecological units (AEUs)

Spatial analysis Evaluation results Evaluation

module

47 Land suitability maps, tables

+ Reports

I = transfer of land characteristics from IDRIM to ALES, 2 = transfer of evaluation results to IDRISI

Fig. 2. Relation between land suitability, expert knowledge and GE.

190 P. Wandahwa, E. uan Ransr/Agriculrure. Ecosystem and Environment 56 (1996) 187-202

2.1. Soils and landfomt Table 1

The Exploratory Soil Map of Kenya published by the Kenya Soil Survey (Sombroek et al., 1982) pro- vides information on soils and landform at the na- tional level and incorporates all the soil information available by 1980. The Fertiliser Use Recommenda- tion Project (FURP), started in 1985 under the aus- pices 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.

Values of the land characteristics used for some of the ‘groupings of soils’

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 charac- terised 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 condi- tions, 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.

Land characteristics ‘Grouping of soils’

Mollic Verto-eutric Ando-luvic Nitisol Planosol Phaeozem

Flooding None Seasonal None Drainage Well Imperfect Moderate Texture/structure C > 60s CL c<6Os Coarse fragments (%) 0 0 3 Soil depth (cm) 125 123 78 Calcium carbonate (%) 0 0.9 0 Apparent CEC (cmol( + > 22.6 54.0 56.8 kg- ’ clay) Sum of basic cations 14.8 21.5 23.1 (cmol( + ) kg _ ’ soil) pH water 1:2.5 6.8 6.7 6.1 Organic carbon (%) 2.7 1 4.26 2.17 Electrical conductivity of 0 0 0 saturation extract (dS m- ’ ) Exchangeable sodium 0.6 6.0 0.9 percentage (%)

C > 60s clay (over 60%) and blocky structure; CL, clay loam.

The FURP therefore utilised the Exploratory Soil Map as the background database to transform exist- ing soil map units, using properties that are strongly related to crop production and compiled new soil maps (FURP, 1987) for each of the districts in- volved. 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. An- driesse and Van der Pouw, 1985; Van Wijngaarden and Van Engelen, 1985; Michieka et al., 1986; Boxem et al., 1987) proved useful.

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%.

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

2.2. Agro-climatic zoning

Long-term average annual rainfall maps were available from the Kenya Farm Management Hand- book (Jaetzold and Schmidt, 1982). Other climatic

P. Wandahwa, E. van Ranst/Agriculture, Ecosystem and Environment 56 (1996) 187-202 191

data were available from FAO (1984). Monthly ref- MADT and ETo were mapped by applying the re- erence evapotranspiration (ETo) was calculated us- sults of regression analyses (Eqs. (l)-(4)) of meteo- ing the Penman-Monteith formula (Smith, 1991). rological stations’ records and altitude to digital ele- The length of a dry season was determined as the vation data. Subsequently, LDS was mapped by number of months in which rainfall is less than half applying results of a regression analysis of digital ETo. ETo and MAR data (Eq. (5))

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 altema- tives. 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 long- term mean annual rainfall and the length of a dry season as the balance between rainfall and evapo- transpiration. Though annual variations have not been accounted for, this simpler approach provides a basic tool for national planning.

MAT = 29.41 - 0.006195X, r2 = 0.92 (1)

MANT = 26.15 - 0.005849X, r* = 0.86 (2)

MADT = 32.46 - 0.006485X, r* = 0.93 (3)

ETo=2182.11 -0.4377X, 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>.

The agro-climatic zone map used for the assess- ment of climatic suitability required overlays of indi- vidual 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 long- term air temperature and elevation (Braun, 1980) and between evapotranspiration (ETo) and elevation (Woodhead, 1968; Kalders, 1988). MAT, MANT,

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 land- form 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 evalua- tion, respectively.

Table 2 Characteristics of the air temperature and moisture availability classes

Class Air temperature (“C)

MAT MANT

Moisture availability

MADT MAR (mm) LDS (months)

I > 21 2 19-21 3 17-19 4 15-17 5 12-15 6 IO-12 7 7-10 8 <7

> 17 > 25 >1600 >7 15-.17 22.5-25 1400-1600 6-7 13-15 20-22.5 1200-1400 4-6 11-13 15-20 1100-1200 3-4 < 13 13-15 1000-I 100 l-3

< 13 950- 1000 <I _ W-950 _

<900 _

MAT, mean average temperature; MANT, mean nighttime temperature; MADT, mean daytime temperature; MAR, mean annual rainfall; LDS, length of a dry season.

192 P. Wandahwa, E. onn Ranst/Agriculture. Ecosystem and Environment 56 (1996) 187-202

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 ‘frame- work’ 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 mod- ule 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 character- istics and/or land qualities of the land areas being evaluated. The evaluation module is used for match- ing 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 Jan- uary and March 1993. This was followed by a thorough literature review on the ecological require- ments (Glover, 1955; Kroll, 1962; Kroll, 1963; Mu- turi 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 suit- ability 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-sup- porting for planting material or buy poor quality planting material from other farmers.

Fertilisers, pesticides and insecticides are not ap- plied. 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). Ex- cept 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, mod- erate 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, Ecosystem and Environment 56 11996) 187-202 193

Table 3 List of land characteristics used in PYCULT

Code 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 % %

months dS m-’ %

Sd SI Text

PH

Soil depth (5) Slope (5) , .

“C “C “C mm % cmoh +) kg-’ soil cm %

Texture/structure t 161 _

Soil reaction (7) PH

Numbers in parentheses indicate classes (see Table 2).

The third branch is followed when a value of F2 or 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 (exces- sive 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

> > R (Floodine fexcess surface waterl) - 1 [FO] > > D (Drainape) - - 1 PI......*3 (moderate) - - 2 [SED] . . . . .*2 (slight) - -3mm . . . . ..*I (none) - -4Wl . . . . ..*4 (severe) - - 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, Ecosystem and Environment 56 (1996) 187202

(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

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, E. uan Ranst/Agriculture. Ecosystem and Enuironment 56 (1996) 187-202 195

Table 4 Climatic requirements for pyrethrum (Chrysanthemum cinerariaejdium)

Climatic characteristics Ratings for the climatic characteristic limits

Mean annual rainfall (mm)

LDS a (months)

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

Mean temp. PC) average

1 2 3 4 5

1100-1200 1200-1400 1400-1600 > 1600 1000-l 100 950- 1000 900-950 <9CG

l-2 3 4-6 7 >7 <l _ _ _ _

< 11 1 l-13 13-15 15-17 > 17 15-20 20-22.5 22.5-25 > 25 _

_ 13-15 < 13 _

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

a Length of a dry season.

able; S2 is moderately suitable; and S3 is marginally suitable. Lower-case letters suffixing the class sym- bol denote the kind(s) of limitation(s).

There are six levels of discrimination in the physi- cal suitability subclass decision tree with a number of decision branches at each level. The next discrimi- nating entity is introduced when no severe limitation is encountered. The final land suitability subclass is based on the highest LQ rating (maximum limita- tion) 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 suitabil- ity 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 haz- ards (n) results in the same decisions as those of the first branch ( = 1) at the same level of discrimina- tion. 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.

2.5. ALES database and evaluation

Data entry templates were used to specify the LCs for which data were entered. Templates are group- ings 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 tem- plates 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 suitabil- ity 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 tem- perature 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-

196 P. Wandahwa, E. van Ranst/Agriculture, Ecosystem and Environment56 (1996) 187-202

creased flower production, but an average tempera- ture above 21°C could inhibit flower production altogether (Roest, 1976).

Table 5 shows the soil and landform require- ments, 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 car- bonate in the soil, and the effects on pyrethrum

@roll, 1963). Guidelines used elsewhere for other crops (Sys and Riquier, 1980; Sys et al., 1993) were followed in rating these LCs. Pyrethrum under rain- fed 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

Table 5 Soil and landfonn requirements for pyrethrum (Chrysanrhemum cinerariaefilium)

Land-use requirements/ characteristics

Ratings for the land characteristic limits

1 2 3 4 5

Erosion hazard Slope (%I

Wetness Flooding a Drainage b

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

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

Organic carbon (o/o)

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

<8

FO WD

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

> 24 24-16 > 3.2 3.2-2.4 6.0-5.6 5.6-5.2 6.0-6.4 6.4-6.8 > 2.4 2.4- 105

< 2.0 2.0-4.0 4.0-8.0 8-15 < 6.0 6.0-10 10-15 15-40

< 16

_ SED

c > 6os, SCL SL,C<6Ov 5-15 15-35 90-60 60-40 12-24 24-35

< 25

ED

< 16(-j 2.4- 1.6 5.2-4.8 6.8-7.5 I .5-0.8

< 30

Fl- MD

c > 6ov, LfS, LS 35-55 40-20 35-50

< 16(+) < 1.6 < 4.8 > 7.5 < 0.8

> 30

F2, F3 I, P, VP

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

> 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) 187-202 197

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 Nyan- darua 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

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 diffi- cult 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

m Highly suited climate

m Moderately suited clin

100 km Marginally suited climate

0 Unsuited climate

Fig. 5. Climatic suitability map for pyrethrum cultivation.

198 P. Wandahwa, E. uan Ranst/Agriculture, Ecosystem and Environment 56 (1996) 187-202

Table 6 Percent distribution of the land potentially suitable for pyrethrum cultivation

Land suitability subclass

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

Potentially suitable land

High Moderate Marginal

4.89 - _ 0.20 - _ _ 7.00 _ _ 0.18 _ 0.03 0.03 _ 0.18 2.69 _

11.08 0.11 0.35 2.30 1.91 0.81 2.54 1.55 2.60 3.37 8.87 13.66 19.29

Total

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

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, Ny- eri, 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 qualita- tive 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-

N

LEGEND ,f’ ‘“” “,Z I, ,*/

-II’/

Sl

m s2c, s3c, S2f

s3t, S3Vf, s3w 0 NW, Nt, NC

I 0 NR

0 100 km

Fig. 6. Land suitability map for pyrethrum cultivation.

P. Wandahwa, E. uan Ranst/Agriculture, Ecosystem and Environment 56 (1996) 187-202 199

Table 7 Average district farmers’ yields for 1987- 1991 and the average district land indices of ‘groupings’ of soil units on which pyrethrum is grown

District Soil units (FA~/UNESCO, 1974)

Dry achene yields

kg ha- ’ year- ’ cv (%70) LI

Nakuru Kericho

Uasin Gishu Baring0 Nyandarua

Keiyo Marakwet

Narok

West Pokot Kiambu Laikipia Nandi Meru Nyeri Murang’a Embu

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

- _ 628 15.9 0.95

503 34.1 0.86 491 16.2 0.82 411 52.2 0.82

353 14.6 0.86

331 62.4 0.77

326 55.4 0.61 299 40.6 0.69 294 21.6 0.65 289 59.9 0.81 283 52.3 0.73 250 8.1 0.46 237 9.3 0.43 59 51.2 0.31 34 56.1 0.21

LI, land indices; CV, coefficient of variation.

tional yield factors (called land index in this study) but a realistic attainable yield in the context of the to obtain the predicted yield. The optimum yield is LUT, assuming no limitations (Rossiter and Van not meant to be a biological maximum (FAO, 1978), Wambeke, 1994). The choice of the optimum yield

700

600 R2 = 0.88

I

0 0.2 0.4 0.6 0.6 1

Land index (LI)

Fig. 7. Relationship between dry achene yield and land index.

200 P. Wanaahwa, E. van Ranst/Agriculture, Ecosystem and Environment 56 (1996) 187-202

and the proportional yield factors is normally quite subjective.

The reliability of the qualitative assessment for pyrethrum cultivation is based on comparison be- tween average district farmers’ yields of dried ach- enes (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 offi- cers 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, PY- CULT 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 at- tributes 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-

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 as- sessment 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 mod- els that will permit comparisons between alternative LUTs (e.g. low and high capital intensity) and yield levels in terms of inputs and outputs.

References

Acland, J.D., 1971. East African Crops. An Introduction to the hoduction of Field and Plantation Crops in Kenya, Tanzania and Uganda. Longman/FAO, Rome.

Andriesse, W. and van der Pouw, B.J.A., 1985. Reconnaissance Soil Map of the Lake Basin Development Authority, Western Kenya (scale 1:250000). Netherlands Soil Survey Institute, Wageningen/Kenya Soil Survey, Nairobi, 56 pp.

Batjes, N.H., 1994. Agro-climatic zoning and physical land evalu- ation in Jamaica. Soil Use Manage., 10: 9-14.

Batjes, N.H. and Bouwman, A.F., 1989. JAMPLES: A computer- ized land evaluation system for Jamaica. In: J. Bouma and A.K. Bregt (Editors), Land Qualities in Space and Time. Pudoc, Wageningen, pp. 257-260.

Batjes, N.H., Bouwman, A.F. and Sinclair, K.M., 1987. Jamaica physical land evaluation system. In: K.J. Beek, P.A. Burrough and D.E. MacCormack (Editors), Quantified Land Evaluation Procedures. Publ. 6, ITC, Enscbede, Netherlands, pp. 66-7 1.

Bouma, J., 1989. Using soil survey data for quantitative land evaluation. In: B.A. Stewart (Editor), Advances in Soil Sci- ence, 9. Springer, New York, pp. 177-213.

Boxem, H.W., de Meester, T. and Smaling, E.M.A., 1987. Soils of the Kiliti area, Kenya. Agric. Res. Rep. 929, Centre for Publication and Documentation, Wageningen; Reconnaissance Rep. Rl 1, Kenya Soil Survey, Nairobi, 249 pp.

Braun, H.M.H., 1980. Agro-climatic zones of Kenya. Paper pre- sented at the 4th Annual General Meeting of the Soil Science Society of East Africa, Arusha, October 1980. Kenya Soil Survey, Nairobi, 12 pp.

Chandler, E.S., 1951. Botanical aspects of pyrethrum. General considerations: the seat of the active principles. Pyrethrum Post, 2(3): 1-8.

Chung, B., Salardini, A., Jolly, P., Chapman, K., Holland, R. and Woodberry, W., 1991. Irrigation and nutritional requirements of pyrethrum (Tanacetum cinerariaefiihm L.1 in Tasmania. Paper presented at the 1st National Conference of the Aus-

P. Wanduhwa, E. van Ranst/Agriculfure, Ecosystem and Environment 56 (1996) 187-202 201

tralian Horticultural Society, Macquarie University, Septem- yield of pyrethrins from pyrethrum flowers in Kenya. ber/October 1991. Pyrethrum Post, lO(3): 20-25.

Eastman, J.R., 1992. IDRISI version 4.0. Graduate School of Geography, Clark University, Worcester, MA.

Elliot, M., Kimmel, E.C. and Casida, J.E., 1969. ‘H-Pyrethrin I and -Pyrethrin II: preparation and use in metabolism studies. Pyrethrum Post, lo(2): 3-8.

Purseglove, J.W., 1982. Tropical Crops. Dicotyledons. Longman, UK, 719 pp.

FAO, 1976. A framework for land evaluation. Soils Bull. 32, FAO, Rome, 72 pp.

FAO, 1978. Report on the agro-ecological zones project, Vol. I: Methodology and results for Africa. World Soil Resources Rep. No. 48/l, FAO, Rome, 158 pp.

FAO, 1983. Guidelines: land evaluation for rainfed agriculture. Soils Bull. 52, FAO, Rome, 237 pp.

FAO, 1984. Agrometeorological data: Africa. Plant Prod. Prot. Ser. No. 22, FAO, Rome.

FAO/UNESCO, 1974. Soil Map of the World, 1.5000000, Vol. I Legend. UNESCO, Paris.

FURP, 1987. The Fertilizer Use Recommendation Project. Final Report, Annex Ill: Description of the first priority sites in the various districts, Vols. I-32. National Agricultural Laborato- ries, Ministry of Agriculture, Nairobi.

FURP, 1988. The Fertilizer Use Recommendation Project. Main Report: Methodology and inventory of existing information. National Agricultural Laboratories, Ministry of Agriculture, Nairobi.

Pyrethrum Board of Kenya, 1992. Recommendations arising from Agronomic Research and other Sources in Kenya. Pyrethrum Growers Handbook, Pyrethrum Board of Kenya, Nakuru, Kenya.

Roest, S., 1976. Flowering and vegetative propagation of pyrethrum (Chrysnnthemum cinerariaefolium Vis) in vivo and in vitro. Ph.D. Thesis, Pudoc, Wageningen, 105 pp.

Rossiter, D.G., 1990. ALES: A framework for land evaluation using a microcomputer. Soil Use Manage., 6: 7-20.

Rossiter, D.G. and van Wambeke, A.R., 1994. ALES: Automated Land Evaluation System. Version 4.1. Department of Soil, Crop and Atmospheric Sciences, Cornell University, Ithaca.

Siderius, W. and van der Pouw, B.J.A., 1980. The application of the FAO-UNESCO terminology of the Soil Map of the World Legend for soil classification in Kenya. Misc. Soil Pap. No. M15, Kenya Soil Survey, Nairobi.

Smaling, E.M.A. and van de Weg, R.F., 1990. Using soil and climate maps and associated data sets to select sites for fertilizer trials in Kenya. Agriculture, Ecosyst. Environ., 31: 263-274.

Glover, J., 1955. Chilling and flower bud stimulation in pyrethrum (Chrysanthemum cineruriclefblium). Ann. Bot. (London), 19: 138-148.

Smith, M., 1991. Report on the expert consultation on procedures for revision of FAO guidelines for prediction of crop water requirements. FAO Land and Water Development Division, Rome, 45 pp.

Head, S.W., 1966. A study of the insecticidal constituents in Chrysanthemum cinerurinefolium: (1) their development in the flower head; (2) their distribution in the plant. Pyrethrum Post, 8(4): 32-72.

Sombroek, W.G., Braun, H.M.H. and van der Pauw, B.J.A., 1982. Exploratory Soil Map and Agro-climatic Zone Map of Kenya, Scale 1: I OK1000. Exploratory Soil Survey Report No. El, Kenya Soil Survey, Nairobi.

Head, S.W., 1969. The composition of pyrethrum extract. Pyrethrum Post, lo(2): 17-21.

Jaetzold, R. and Schmidt, H., 1982/83. Farm Management Hand- book of Kenya. Vol. II, Parts A. B and C. Ministry of Agriculture, Nairobi.

Kalders, T., 1988. Evapotranspiration in Kenya. Ministry of Water Development. Irrigation Section, Nairobi.

Kroll, U., 1962. The improvement of pyrethrum yields through the application of fertilizers. Pyrethrum Post, 6(3): 32-33.

Kroll, U., 1963. The effect of fertilizers, manures, irrigation and ridging on the yield of pyrethrum. East Afr. Agric. For. J., 28: 139-145.

Maes, J., Vereecken, H. and Darius, P., 1987. Knowledge process- ing in land evaluation. In: K.J. Beck, P.A. Burrough and D.E. MacCormack (Editors), Quantified Land Evaluation Proce- dures. Publ. 6, ITC, Enschede, Netherlands, pp. 66-7 1.

Michieka, D.O., van der Pouw, B.J.A. and Vleeshouwer, J.J., 1986. Soils of the Kwale, Mombasa, Lunga Lunga area. Reconnaissance Rep. R3, Kenya Soil Survey, Nairobi, 300 pp.

Muturi, S.N., Parlevliet, J.E. and Brewer, J.G., 1969. Ecological requirements of pyrethrum. I: A general review. Pyrethrum Post, lo(l): 24-28.

Sys, C. and Riquier, J., 1980. Ratings of FAO/UNESCO soil units for specific crop production. Land resources for popula- tions of the future. Report of the 2nd FAO/UNFPA Expert Consultation, FAO, Rome, pp. 55-95.

Sys, C., van Ran%, E. and Debaveye, J., 1991. Land Evaluation Part II: Methods in land evaluation. Agric. Publ. No. 7, GADC, Brussels, Belgium, 274 pp.

Sys, C., van Ranst, E., Debaveye, J. and Beemaert, F., 1993. Land Evaluation Part Ill: Crop requirements. Agric. Publ. No. 7, GADC, Bussels, Belgium, 197 pp.

Van Diepen, C.A., van Keulen, H., Wolf, J. and Be&out, J.A.A., 1991. Land evaluation: from intuition to quantification. In: B.A. Stewart (Editor), Advances in Soil Science, 15. Springer, New York, pp. 139-204.

Van Lanen, H.A.J. and Woperies, F.A., 1992. Computer-captured expert knowledge to evaluate possibilities for injection of slurry from animal manure in the Netherlands. Gecderma, 54: 107- 124.

Parlevliet, J.E., 1970. The effects of rainfall and altitude on the

Van Lanen, H.A.J., Hack-ten Broeke, M.J.D., Bouma, J. and de Groot, W.J.M., 1992. A mixed qualitative/quantitative physi- cal land evaluation methodology. Geoderma, 55: 37-54.

Van Wijngaarden, W. and van Engelen, V.W.P., 1985. Soils and vegetation of the Tsavo area. Reconnaissance Rep. R7, Kenya Soil Survey, Nairobi, 246 pp.

202 P. Wandahwa, E. van Ranst/Agriculture, Ecosystem and Environment 56 (1996) 187-202

Weiss, E.A.. 1966. Phosphate-lime trials on pyrethrum. Pyrethrum Post, 8(3): 19-21.

Wielemaker, W.G. and Boxem, H.W., 1982. Soils of the Kisii area. Reconnaissance Soil Survey Rep. No. R4, Kenya Soil Survey, Nairobi, 208 pp.

Wood, D.R. and Dent, F.J., 1983. LECS: A land evaluation

computer system. Manual 5: Methodology. Ministry of Agri- culture, Bogor, Indonesia.

Woodhead, T., 1968. Studies of potential evaporation in Kenya. East African Agriculture and Forest Research Organization, Nairobi.