KE I33o. 06
Land evaluation in Kakamega District.
A qualitative and quantitative land evaluation
: for Soy and Lumakanda Locations,
West Kenya.
Part 1: Text.
Rene PM van Dongen & Joris Frenke!
University of Utrecht Faculty of Geographical Sciences April 1390 •* : ;' "
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32? 'Listen Ocol my old friend. The ways of your ancestors are good, their customs are solid and not hollow. They are not thin, not easily breakable. They can not be blown away by the winds because their roots reach deep into the soil.'
Okot p'Bitek 'Song of Lawino1 ? ! CONTENTS.
pag.
ACKNOWLEX3DEMENTS , 5
IJST OF FIGURES AND TABLES 6
LIST OF MAPS AND APPENDICES IN PART 2 8
CHAPTER I: LAND EVALUATION IN KAKAMEGA DISTRICT. 1. 1 Introduction to Land Evaluation in Kakamega District 11
CHAPTER 2: GENERAL DESCRIPTION OF THE RESEARCH AREA. 2. 1 Physical Geographical Land Unit Mapping. 17 2. 1. 1 Introduction. 17 2. 1. 2 Climate and Agro-Ecological Zones 18 2. 1. 3 Geology 22 2. 1. 4 Land use ,. 22 2. 1. 5 The physical-geographical landunit map 23 2. 1. 5. 1 Source maps 23 2. 1. 5. 2 Description of the physical-geographical landunits 23
CHAPTER 3: METHODS. 3. 1 The Nested Analysis of variance (N. A. V) 25 3. 1. 1 Introduction. 25 3. 1. 2 Sampling method. 25 3. 1. 3 Method of calculation. 25 3. 2 Methods of Soil Mapping 27 3. 2. 1 Introduction. 27 3. 2. 2 Describing the soil profiles 28 3. 3 Methods of Land Evaluation: 32 3. 3. 1 Introduction. 32 3. 3. 2 The Qualitative KSS model 33 3. 3. 3 Quantitative land evaluation: the WOFOST model 39 3. 3. 3. 1 Introduction. 39 3. 3. 3. 2 The model structure 39 4 pag. CHAPTER 4 RESULTS. 4. 1 Results from Nested Analysis 48 4. 1. 1 Analysis of variance results 48 4. 1. 2 The amount of variance 50 4. 1. 3 Conclusions 51 4. 2 Results of infiltration experiments 51 4. 2. 1 Description of the results 51 4. 2. 2 Effect of Land Management 52 4. 2. 3 Adaptations of the results for use in WDFOST. 52 4. 3 Results from pF-experiments 54 4. 4 Soil Profile Descriptions 56 4. 4. 1 Introduction. 56 4. 4. 2 Classifications 56 4. 4. 3 Major Classification Units 58 4. 4. 4 Soil Genesis 60 4. 4. 5 Legend of the DIDC-Soil Map 63 4. 4. 6 Comparison between LBDA (1985) and DIDC (1989) maps 66
CHAPTER 5 RESULTS OF THE LAND EVALUATION. 5. 1 Introduction. 70 5. 2 Results of the KSS Model 70 5. 3 Input in the WDFOST Model 73 5. 4 Results of the WOFOST Model 78 5. 5 Some conclusions » 86 5. 6 Differences between the Land Evaluation systems 88
CHAPTER 6 CONCLUSIONS 89
REFERENCES 94 PART 2: CONTENTS.
PART 2 pag. MAPS 1-45
APPENDICES 46-165 Acknowledcrements.
For all the help during our stay and fieldwork in Kenya and during the preparations and the computation in Holland, we highly appreciate the help of the following persons: In Kenya: Mr T.R Okuku, Mr E. O. Nyanja, Daniel, drs. G. Franck, drs. W. Hoenderdos (all from the D. I. D. C. -Rakamega). Mr J.N. Qureshi, Mr P. O. S. Oduor (National Agricultural Laboratories), Mr Dürr (F. U. R. P. -Nairobi ) and the people of the L. B. D. A. in Kisumu; In Holland: Dr H. Th. Riezebos, Dr J. J. Sterkenburg, Prof. Dr J. Hinderink, Prof. Dr. P.A Burrough (all University of Utrecht), Dr J. Wolf (S.O. W.-Wageningen), Dr B. H. Janssen (L.U. W. ), Dr E. M.A. Smaling- and Dr W. Andriesse (Stiboka-Wageningen) and Dr W. G. Sombroek and Dr. V. W. P. van Engelen (I.S.RI.C -Wageningen). And last but not least all the friends in Soy and environs: Mr Gerrit Noordam, Mr Philip Wakaba, John Mburu, Grace Kalekye Masalai and her children, Barnabas Koech, Paul Kiprotich, Francis Juma, Eluid Karanja, Zakayo Mwachi, Mr M. Wamachali and all the people of the extension program in Liquyani and all the people of Soy Country Club for so kindly giving us the ' laboratory' and other facilities!
Zeist, April 1990 Joris Frenkel René van Dongen LIST OF FIGURES AND TABLES
FIGURES:
Fig. 1. 1 Position of Kakamega District with South-West Kenya. Fig. 1. 2 Position of Western Province within Kenya. Fig. 1. 3 Position of Soy and Lumakanda Locations within Kakamega District. Fig. 1. 4 Agricultural land per capita (ha. ). Fig. 1. 5 Population density per location in Kakamega District in 1988. Fig. 2. 1 Length of growing season in Soy and Lumakanda Locations. Fig. 2. 2 Rainfall in Kakamega District. Fig. 2. 3 Agro-Ecological zones in Kakamega District. Fig. 3. 1 Hierarchy of nested sampling scheme. Fig. 3. 2 Summary of a land evaluation procedure. Fig. 3. 3 Production levels within WOFOST. Fig. 3. 4 A summary of the model structure of WOFOST. Fig. 3. 5 Soil water balance in WOFOST. Fig. 3. 6 Stream model of the original program block controlling infiltration. Fig. 3. 7 Adjusted stream model of the program block controlling infiltration. Fig. 4. 1 Percentage of cumulative variance for N, K, pH-^O, P, CEC, Base Saturation and organic C percentage. Fig. 4. 2 Percentage of cumulative variance for theta (pF2), bulkdensity, percentage of clay, sand and pores. Fig. 4. 3 The determination of moisture content at field capacity. Fig. 4. 4 Classification of soils with an argic horizon in FAO (1989). Fig. 4. 5 Soil genesis along a slope. Fig. 5. 1 Supply of N and P along a slope in Sango. Fig. 5. 2 Increase in Base Saturation in topsoil in BLA transect. Fig. 5. 3 Monthly rainfall data for 1962 at Turbo Forest Station. Fig. 5. 4 Monthly rainfall data for 1983 at Turbo Forest Station. TABLES: Table 2. 1 Monthly rain and temperature data for meteorological stations in Soy and Lumakanda Locations. Table 3. 1 Terms in the analysis of variance. Table 3. 2 The landqualities in the KSS model. Table 3. 3 The ratings of the land qualities. Table 3. 4 Conversion tables for some Kenyan crops. Table 4. 1 Standard deviations for different parameters for different distance levels. Table 4. 2 Results from infiltration measurements. Table 4. 2a Fraction of rain not infiltrating, as depending on relative moisture content of the soil. Table 4. 3 Soil units in Soy and Lumakanda Locations: two methods compared. Table 4. 4 Summary of the most important soil chemical data. Table 4. 5 The Legend Units of the soil map in Soy and Lumakanda Locations. Adapted after Andriesse and van der Pouw, 1985. Table 4. 6 Comparison of LBDA map and DIDC map. Table 5. 1 Ratings of land qualities in Lumakanda location. Table 5. 2 Ratings of land qualities in Soy location. Table 5. 3 Suitability for crops in Lumakanda Location. Table 5. 4 Suitability for crops in Soy Location. Table 5. 5 Climatic input for WOFOST. Table 5. 6 The soil physical data used as input for WDFOST in Soy and Lumakanda Locations. Table 5. 7 Some general information about the origin of the soil chemical data in Soy and Lumakanda Locations. Table 5. 8 The supply of nutrients N, P and K in Soy and Lumakanda Locations. Table 5. 9 Amount of rainfall used by the different crops. Table 5. 10 Start of the growing season plus crop development indications for the evaluated cro p. Table 5. 11 The quantitative evaluation results for maize. Table 5. 12 Fertilizer requirements for maize production (as calculated by WDFOST) together with actual amounts of fertilizer given. Tables. 13 Nutrient Limited Yields for crops in Soy and Lumakanda Locations in wet season. Table 5.14 Nutrient Limited Yields for crops in Soy and Lumakanda Locations in the dry season. Table 5. 15 Fertilizer recommendations by W. A.R.C (1988). Table 5. 16 Fertilizer gift classes for beans and sunflower in Soy and Lumakanda Locations. Table 5. 17 Relative need for N, P and K for cassavA and sweet potato in Soy and Lumakanda Locations. Table 5. 18 'Fertilizer requirements for sorghum and millet in the dry season in Soy and Lumakanda Locations. Table 5. 19 The nutrient content of the most common fertilizers. LIST OF MAPS AND APPENDICES IN PART 2:
LIST OF MAPS IN PART 2. FART 2: pag.
Map 1. Geology of Soy and Lumakanda Locations 1 Map 2a. Land use in Soy Locations 2 Map 2b. Land use in Lumakanda Locations 3 Map 3. Physical Geographical Landunits in Soy and Lumakanda Locations 4 Map 4a. Position of soil profiles and gaugings in Forest Land Estate (BLA transect) 5 Map 4b. Position of soil profiles and gaugings in Selbourne Estate (BLB transect) 6 Map 4c. Position of soil profiles and gaugings in Sergoit Settlement scheme (BLC transect) 7 Map 4d. Position of soil profiles and gaugings in Sango Settlement scheme (VSE transect) 8 Map 4e. Position of soil profiles and gaugings in Mautuma Settlement scheme (VSN transect) 9 Map 4f. Position of soil profiles and gaugings in Lumakanda Settlement scheme (VSS transect) 10 Map 4g. Position of soil profiles and gaugings in Kongoni Settlement scheme (PLG transect) 11 Map 4h. Position of soil profiles and gaugings in Sergoit Settlement scheme (VAL transect) 12 Map 5. Position of nested cluster and sample areas in Lumakanda Location. 14 Map 6. Position of nested cluster and sample areas in Soy Location. 15 Map 7a. Physiographic Soil Map of Lumakanda Location (hlack and white) 16 Map 7b. Physiographic Soil Map of Lumakanda Location (colour map) 17 Map 8a. Physiographic Soil Map of Soy Location (black and white) 18 Map 8b. Physiographic Soil Map of Soy Location (colour map) 19 PART2: pag. Map 9. Qualitative Landevaluation Results Situation for Maize in Lumakanda Location. 20 Map 10. Qualitative Landevaluation Results Situation for Beans in Lumakanda Location. 21 Map 11. Qualitative Landevaluation Results Situation for Sunflower in Lumakanda Location. 22 Map 12. Qualitative Landevaluation Results Situation for Sweet Potato in Lumakanda Location. .... 23 Map 13. Qualitative Landevaluation Results Situation for Cassave in Lumakanda Location. 24 Map 14. Qualitative Landevaluation Results Situation for Sorghum and Millet in dry season in Lumakanda Location. 25
Map 15. Quantitative Landevaluation Results Situation for Maize in Soy Location. 26 Map 16. Quantitative Landevaluation Results Situation for Beans in Soy Location. 27 Map 17. Quantitative Landevaluation Results Situation for Sunflower in Soy Location. 28 Map 18. Quantitative Landevaluation Results Situation for Sweet Potato in Soy Location. 29 Map 19. Quantitative Landevaluation Results Situation for Cassave in Soy Location. 30 Map 20. Quantitative Landevaluation Results Situation for Sorghum and Millet in dry season in Soy Location. 31
Map 21. Landevaluation Results for Maize. Situation in Lumakanda Location 32 Map 22. Landevaluation Results for Beans. Situation in Lumakanda Location. 33 Map 23. Landevaluation Results for Sunflower. Situation in Lumakanda Location. 34 Map 24. Landevaluation Results for Sweet Potato. Situation in Lumakanda Location 35 Map 25. Landevaluation Results for Cassava. Situation in Lumakanda Location 36 Map 26. Landevaluation Results for Millet. Situation in Lumakanda Location in dry season 37 Map 27. Landevaluation Results for Sorghum Situation in Lumakanda Location in dry season 38
Map 28. Landevaluation Results for Maize. Situation in Soy Location 39 Map 29. Landevaluation Results for Beans. Situation in Soy Location 40 Map 30. Landevaluation Results for Sunflower. Situation in Soy Location. 41 Map 31. Landevaluation Results for Sweet Potato. Situation in Soy Location 42 Map 32. Landevaluation Results for Cassava. Situation in Soy Location 43 Kap 33 Landevaluation Results for Millet. Situation in Soy Location in dry season 44 Map 34. Landevaluation Results for Sorghum. Situation in Soy Location in dry season 45 10
LIST OF APPENDICES IN PART 2.
PART 2: pag. Appendix I Legend Units in the D. I. D. C -Soil Map (1: 100000) of Soy and Lumakanda Locations 46
Appendix II Soil profile description forms for Soy and Lumakanda Locations: BLA1-3 53 BLB1-6 59 BLC1-4 71 VSE1-5 79 VSNl-5 ; 89 VSS1-4 99 PLG1-4 107 VALl 115
Appendix III Soil gauge description forms for Soy and Lumakanda Locations: BLAbl-5 117 BLBbl-7 122 BLCbl-4 129 VSEbl-7 133 VSNbl-7 140 VSSbl-8 147 PLGbl-8 -. 155 VALbl-2 163
Appendix IV Day numbers in Julian Calendar. 165 11
Chanter 1 Land Evaluation in Kakamecra District.
1. 1 LSiDfl EVSlW*tii On 1iD As a part of our study Physical Geography at the State University of Utrecht, research was done in the North Eastern part of Kakamega District (West Kenya). Fieldwork took place from July 1968 till January 1989. Data analysis and reporting followed from March 1989 till March 1990.
The area under concern was situated in the north-eastern part of Kakamega District, Western Province. Ihe position of Western Province in Kenya is indicated in the figure 1. 1 and 1. 2.
MAP OF CENTRAL KENYA AND THE WHITE HIGHLANDS
UGANDA ISIOLO
Central in. URANG
airobi Ml MACHAKOS'
African Reserves Crown Lands Provincial boundaries District boundaries Sources: Heyer. 1981. p.91: van Zwanenberg. 1975. p.32
Fig. l. 1 Position of Kakamega District within South-West Kenya, (source: Jungheim, 1988). 12
1
Si * . ^^ '• ItKl :(K)km > . • • /\ ETHIOPIE v Mandera
L1 ( 32 /' V lodwar \ / 1 * 31 / f """S >- '"> t8 Lk Marsabit \ 33 V OEGANDA'• ir : SOMALIË ; •\ / xtpenquriti S ^i T MaraU 19 WEstMcf ff L bac X KaKSréjaËHorH \ 1K*t •iW 20 ^ Vj iy #\ M -. — L M* 29 \_^1 /S RASTERN .'NJÖ'%tA^i Kenclvo /»" (•Garitsa 1> a •V ^ \ 34 '-J a \ Kitui ^ \ \ f NAIR0BÎ\ \ "T "\ Mâcha kos\ *' î5°"*\ 26 S LU ^Z ^ Kajiado \ 1 V\< com M Lamu •v'; /) 1 Wundanyi ƒ 37 ƒ \ *«> y' «JKilifi ~\ 7 39 V^38 * Kwal« 7
Kenya r
Administratieve indeling /
w •aern Province Rift \ ilky Pnxten CeMnl Prance Nonk-Eolen Pnnten 1 Bun gom j 8 N'arok 2 Nyandarua 32 Mandera BUSIÜ 9 Kajiado -- Nveri 33 Wajir }• Kakamcga 10 Nakuru 2.' Kinnyapa 34 Garissa II Kencho -4 Muranga Embu Provinciehoofdsiad Nys«za Prtmacr i: Nandi 25 Kiambu Co»Pro*£. 4 Siava LasinGishu 35 Tana River Mrru Diïtnktshootdsiad 5 Kiiumu 14 Elzeyo Marakwel Eutcru Province 36 Lamu 6 Horna BJ> 15 Banngo 26 Machako* 37 Kilifi Trans-Nzoia -" Kilui 38 Mombasa r Wcsl Pokol 28 Embu Ik Turkjna 2"J Mem 41) Tana If Sjmburu :0 :o Mar^abil Nairotà
Fig. 1. 2 Position of Western Province within Kenya, (source: Blankhart, 1978) 13
In figure 1. 3 the position of Soy and Lumakanda Locations is given within Kakamega District.
ADMINISTRATIVE MAP OF KAKAMEGA DISTRICT
A. DIVISIONS B. LOCATION S
HAMISI VIHIGA 0 20km
N.Butsotso C. Wanga ) E.Wanga y /\y^Cr^ E.lsukha Muriiei-TKakamega pality /—-v^ Forest
S. Wang a Manama' "" ~~~ Marama/ [ N. \ W \ ,_ldakho S.Marama S. Idakho w. Kisa JE.Kita yE.Marafloll WMaragoli
N.Bunyore *ySham«khokho ^N- Maragoli W. Bunyoref Banja
Source:DIDC/GIS.1987
Fig. 1. 3 Position of Soy and Lumakanda Locations within Kakamega District. 14
Soy and Lumakanda Locations are a part of the so called 'White-Highlands' (figure 1.1), the areas where white settlers had their estates. After Kenya became independent in 1963, these areas were split up and sold to African fanners. The results of this partition of the land can still be seen nowadays in the so- called ' settlement schemes'. The average farm size in these locations is relatively large, compared to the rest of the District (average land per capita more than 0. 40 ha, see fig 1. 4).
FIGURE 6 AGRICULTURAL LAND PER CAPITA (HA). KAKAMEGA DISTRICT. 1988
A/8 I Town/Forest •• «-.0.09 MM 0.10-0.19 0.20-0.29 0.30-0.39 0.40.->
20km Source: DIDC/GIS.1987
Fig. 1. 4 Agricultural land per capita (ha. ) in Kakamega District, Kenya, (source: DIDC/GIS, 1987 in Jungheim, 1988). 15 Furthermore the population densities are relatively low: less than 249 persons per square kilometer (see figure 1. 5).
FIGURE 4 POPULATION DENSITY PER LOCATION, KAKAMEGA DISTRICT. 1988
Inhabitants per km2
O- 249 250- 499 Uli 500- 749 750-999 1000-1250
y i
20km
Source: DIDC/GIS. 1987
Fig. 1. 5 Population density per location in Kakamega District, 1988. (source: DIDC/GIS, 1987 in Jungheim, 1988) 16 The aim of this study is to make a design for a land evaluation methodology that can support Rural Development Purposes in the Kakamega District, West Kenya. Within the framework of the District Focus Strategy for rural development, the Kakamega District is building up a District Information and Documentation Centre. This D. I. D. C must become a ' data base7 for all Rural Development activities in Kakamega District. The parties involved in this Kakamega District Focus Project are the Lake Basin Development Authority, the Department of Urban and Regional Planning (university of Nairobi), the Kakamega District Development Committee (represented by the District Development Officer) and the Department of Geography of Developing Countries (University of Utrecht). The task of Physical Geographers in this study is to evaluate the agricultural potentials in the area and to forecast the effects of certain changes in agricultural management, concerning fertilizer supply. We tried to find out what kind of methodology could be followed best to determine different production levels. An important sub-aim was to give an indication of the statistical variance of the most important soil properties that determine agricultural production, so that spatial dimensions of these soil properties could be identified. The chronological order of the different steps followed in this study are: 1) Making a Physical Geographical Land Unit Map by combining information from the field and from literature. 2) Nested analysis on the phonolithic plateau to estimate the variance of the most important variables. 3) Sampling in the different land units to fill the data base needed for land evaluation. With the information, a final Physiographical Soil Map was produced. 4) Quantitative and qualitative land evaluation on the basis of the Physiographic Soil Map.
The most important questions that had to be answered were: - How accurate is the existing 1: 250. 000 soil map (Andriesse and •. van der Pouw, 1985)? - Which alternative crops can be grown in the wet and dry season and what kind of yields can be esqoected? - What are the most important factors limiting growth? - Can some fertilizer recommendation be done on basis of this study? This study is only the first step in a more elaborate process of agricultural research! A complete agricultural research embraces the following steps (Röling, 1984). 1) Formulating recommendations 2) Adapted field tests 3) Adapted recommendations 4) Formulating message The only thing we did was step no. 1. We formulated some recommendations for change in the agricultural behaviour pattern of farmers. The next step should be to do sor.ie adaptive field tests, to check these recommendations. The results will lead to adaptive recommendations and then, finally the message can be formulated.
L 17 Chapter 2 General Description of the Research Area.
2. 1 PhysiC-fll Ctepcrraphicyi T.wrid Unit Mapping. 2. 1. 1 Introduction The natural environment is composed of at least five different factors, which are: 1. Climate 2. Geology 3. Geomorphology 4. Soils 5. Vegetation Together and in relation to each other these factors• constitute the conditions under which the land is used. For this part of the research, literature, maps and air photographs have been studied. This information was completed by field examination. A good indication of the geomorphology and the soils is given in the Reconnaissance Soil Map of the Lake Basin Development Authority area (scale 1:250 000) by Andriesse and van der Pouw (1985). The information about the geology was broadened by examining the geological maps of the area (Gibson, 1954 and Sanders, 1963). Supplementary information was gathered by simultaneously examining the air photographs. A vegetation map was made during a two week period in which the whole fieldwork area was explored on bicycle. During these two weeks the existing information about soils, geology and geomorphology was checked and corrected. This first part of the fieldwork resulted in a map with six physical- geographical landunits, which are described in section 2. 1. 5. 18 2. 1. 2 Climate and Agro-Ecological zones. The growing season is defined here (according to F. A. 0. 1979) as the period in which there is a surplus of precipitation over evapotranspiration, plus the period required to evapotranspire an assumed 100 mm of water from excess precipitation, stored in the soil. If temperature becomes too low it will affect the growing period as well. The beginning of the growing period is when precipitation equals half the evaporation. A humid period occurs when precipitation exceeds the evapotranspiration. Such a period is required to replenish the soil moisture. The end of the rain period falls in when the precipitation becomes equal or less than half the evapotranspiration. The end of the rainy season will be at the same time the end of the growing period, unless a humid period existed. With 100 mm or less stored in the soil profile the growing period will extend somewhat into the dry period. When stored soil moisture is evapotranspirated the end of the growing period is determined. The rainy season in the area extends from March to August/September (see fig 2.1). There is no intermediate dry season between the long rains (March-May) and the short rains (August-September), in contrast with the main part of the Kakamega District.
Length of Growing Season data from Turbo Forest Station PET in Rainfall Half PET in mm/10days in mm/iOdays mm/10 days 15O
12O -
> •o
t i i i i i i i i < i f I l i I I I i I I i r i i i i i i i i i i i i Jan feb mar ape may jun jul aug sep okt nov dec
years of records; 1968-1973
fig. 2. 1 Length of growing season in Soy and Lunakanda locations. 19 The mean annual rainfall ranges from 1100 mm in the eastern part to 1400 mm at the western border, while the minimum annual rainfall ranges from 750 to 1000 mm, and the maximum annual rainfall from 1500 to 2000 mm (fig 2.2).
FIGURE 7 RA/NFALL. KAKAMEGA DISTRICT
Mean annual rainfall (mm)
20km
Sourc«: JMtxold and Schmidt. 1982. p.363
Fig. 2. 2 Rainfall in Kakamega District. The dry season, which extends from December to February, is more pronounced than in the. main part of the district, with a mean monthly rainfall of about 40 mm, and a much lower median monthly rainfall (table 1). The diurnal rainfall pattern is of the tropical inland type, with a clear maximum in the afternoon and the evening, which is most pronounced during the second part of the rainy season, with maximum rainfall in the afternoon. Percentages of monthly rainfall falling during each hour of the day, for each month, are available for Eldoret Meteorological Station (Tomsett, 1968), lying about 20 kilometres southeast of the area. The median rainfall intensity is 30-40 mm/h. About 25 percent of total rainfall is falling in showers of this intensity, mostly in local showers of 15-30 minutes. About 35 percent is falling in showers with higher intensities and 40 percent with lower intensities (data for Eldoret, Thompson and Lawes, 1971). These data are important for assessing the amount of runoff and erosion and for the amount of water available to plants. 20 Table 1: Monthly rain and temperature data for meteorological stations in Soy and Lumakanda locations. MONTH RAIN half TEMP (mm) PET (mm) (dC) JAN 99. 3 95. 8 18.0 FEB 77.4 90. 1 18. 1 MAR 54. 7 116.4 18.7 APR 102. 7 94. 6 19. 4 MAY 146. 2 74. 9 18. 4 JUN 126.8 68. 6 16. 9 JUL 175. 6 75. 9 16. 7 AUG 236. 0 69. 7 16. 6 SEP 100. 4 73. 1 16. 9 (XT 80. 4 85. 2 17. 6 NOV 47. 3 88. 8 17. 8 DEC 28. 6 95. 1 17.2 total 1275. 4 (data based on Turbo Forest Estate 1968-1973, data for January to April are only for 5 years. Source: E. A.M.D. 1975) Mean annual, temperature varies between 16 and 18 degrees centigrade on the plateau and 20 degrees in the large valleys and near the western border. This means that the area can be classified as temperature zones 5 (cool temperate) and 4 (warm temperate) according to Sombroek et al. (1982) and Jaetzold and Schmidt (1982). The soil temperature regime is isothermic, according to the Soil Taxonomy (1975). The purpose of an agro-climatic or agro-ecological zone map is to provide a tool for assessing which areas are climatically suitable for various land use alternatives, with parcticular emphasis on the suitability for crops or crop varieties. The difference between agro-climatic maps and agro-ecological maps is that in the latter, site conditions other than climate (particularly soil moisture storage) have been incorporated. Sombroek et al. (1982) classified the moisture availability zones for their agroclimatological zone-map by the ratio r/EO (rainfall/potential evapotranspiration) during the growing season. The ratio for this period is about 0.7, so the area lies in the subhumid zone. The soil moisture regime of the soil is udic, and the probability of r<2/3EO in the growing season is less than 5%. Jaetzold and Schmidt based their main agro-ecological zones on the probability of meeting the temperature and water requirements of the main leading crops. In their classification the area is lying in the transition zone between the semi-humid and semi-arid zones. The ratio r/EO is 0.4 to 0.5. They situate the area in the UM4 zone (upper midland, transitional), the sunflower-maize or upper sisal-zone (see figure 2.3). The growing period in this zone (with a 60% reliability) is 115 days or more for the long rains and 115 days or less for the short rains. 21
AGRO-ECOLOGICAL ZONES. KAKAMEGA DISTRICT
v;
>t/ Sunf lower- Maize Aone V^^^^^
ial Sugar-Can^ V Vx.^'COzone 1
/ A—*"Lower Midland' y (^^r J Sugar-Cane zone f~
y yvl/ 0 20km Source: Jaetzoid and Schmidt .1982. p.374
Fig. 2. 3 Agro-Ecological Zones in Kakamega District. 22
2. 1. 3 Geology. In the Soy and Lumakanda locations, two main formations can be recognized (MAP 1). These are: 1. Tertiary Lower Uasin Gishu phonolites. lava series; 2. Pre-Cambrium Basement System rocks, consisting of: a) banded quartz-feldspar gneisses b) gneissose flow-foliated granites or aranodiorites (Sanders, 1963) The gneisses (a) and granodiorites (b) are situated in, some kilometres wide, broad zones, which are NNW-SSE orientated. The Basement System rocks are often referred to as forming pre- Miocene erosion levels (Scott et al., 1971). In Miocene times (25 mill. y. B. P. ) these deeply-weathered rocks were covered by, first, a thin layer of volcanic tuff, and then by the ca. 60 metres thick phonolitic Uasin Gishu lavas. The lavas form the very gently westward dipping Uasin Gishu plateau, of which the main part lies in the district with the same name, east of the area. Phonolitic rock outcrops are often encountered at the steep borders of the plateau and in the incised river valley of the Kipsangui river. The Basement System rocks form the substratum of the river valley slopes, around the border of the plateau, and of a small area of the plateau in the northern part. Granodiorite outcrops can be seen at the big "koppies" of Soysambu in the north, and gneisses in some "koppies" and steep slopes in the southwestern part, near Lumakanda. The various rock types are of importance with regard to the fertility and the grain-size distribution of the soils. According to Andriesse and van der Pouw (1985) the phonolites are intermediate rocks, which means they contain more than 54% SiO2. They consist of a blue-black fine grained groundmass with large porphyritic crystals. The metamorphic basement rocks are medium to coarse grained, and consist of acid rocks, with ca. 70% SiO2. The different weathering products of these rocks cause at least part of the major differences between soils in the area. The soils on the phonolites (on the plateau) have a higher pH, higher cation exchange capacity and higher fertility. Soils on the basement system rocks (mainly the valley slopes) have a lower clay content and a higher sand content than the soils on the plateau, which may be enhanced by sorting, caused by erosion and selective deposition on the valley slopes. In bottomlands and small valleys, soils developed on infill from phonolites are encountered. Generally these are fertile, but poorly drained and very heavy-textured soils. In the large valleys, there are some alluvial soils.
2. 1. 4 Land use. The legend for the land use map consists of map units with an agricultural value. The map shows the following units (MAP 2): 1) Arab]e land (mainly maize intercropped with beans; some sorghum, millet, sunflower, sweet potatoes and cereals and some meadows). 2) Forest. All the forest mapped here is production forest 23 for wood, paper or leather tanning. In some parts the forested areas are being vised as extensive grazing areas, particularly when the forest is cut recently. 3) Shrubs. The shrub vegetation mainly exists on steep slopes, where no intensive land use would be possible. The shrub vegetation is sometimes used for extensive grazing. 4) Pasture. The pasture described here is sometimes in use for intensive grazing, but in most cases it was extensive grazing land. 5) Swamps. Some parts of the bottomlands are swampy and do not have any agricultural use.
2. 1. 5 The physical-geographical landunit map. 2. 1. 5. 1 Source maps. A brief description of the soils in Kakamega District is given by Jaetzold and Schmidt (1982). Their description of the soils is based on the study of Sombroek and van der Pouw (1982) who published an exploratory soil map (together with the agroclimatological zone map) of Kenya (scale 1: 1 000 000). A more detailed study, on soils of the Lake Basin Development Area at a scale of 1:250 000 was done in 1985 by the Netherlands Soil Survey Institute, Wageningen, in cooperation with the Kenya Soil Survey (KSS), Nairobi (Andriesse and van der Pouw, 1985). In the legend of this soil map, landform is used at the highest level of subdivision and geology at the second level. At the third level the actual soil is grouped into individual mapping units. The terminology used to describe the soil units is of the KSS. Two important terms that are öfter« used are ' soil complexes' and ' soil associations'. ' Soil associations' are map units where different soil types occur in a recognizable pattern, whereas such a pattern is absent in ' soil complexes'. The soils of the map units are classified according to the legend of the Soil Map of the World (FAOUnesco, 1974). Due to the fact that landforms and geology are very important in the legend of the soil map, it was a very good base for the physical-geographical landunits map, on which homogeneous landunits are indicated, each with its own geology, geomorphology (landform), soil pattern and landuse pattern.
2. 1. 5. 2 Description of the physical-geographical landunits. By combining the four land components geomorphology, geology, soils and vegetation, six preliminary physical- geographical landunits were recognized (MAP 3): I Plateau, well drained, phonolites; II Plateau, well drained, Basement System; III Plateau, poorly drained; IV Steep slopes with rock outcrops; V Valley slopes; VI Valley bottoms. 24 I Plateau, well drained, phonolites. The geology of the main part of the plateau is of phonolithic origin (intermediate igneous rocks). Two third of the plateau is in use as fanning land. The main crop is maize, often intercropped with beans. Other crops are sorghum, millet, cereals and coffee. There is also a considerable amount of pasture land. One third of the plateau is planted with forest. The forest is for wood, paper and leather tanning (wattle trees) production. The largest forest estates are found in Lumakanda Location, in the area north and north west of Turbo. II Plateau, well drained, ^v^pement system. In the northeastern part of the plateau there is a substratum of acid rocks, viz. quartz-feldspar gneisses and granites/granodiorites. All land is fanning land, with the same crops as in landunit I.
III Plateau, pocy-iy j This unit consists of wetlands lying in depressions on the plateau, of which the main parts are flooded during the rainy season. The soils in this unit have been formed on infill from phonolites. The slopes are 0-2%, generally flat. Most of these bottomlands are in use as extensive grazing area. Some parts are so swampy that they cannot be used for any agricultural purpose. Some other, drier parts are in use for maize production. However, this maize is generally doing very badly due to waterlogging, unless enough ditches have been dug to drain the area and the appropriate variety of maize is being used. IV Steep slopes with rock outcrops. The geology consists mainly of phonolites, with in the southwest also quartz-feldspar gneisses and in the northern part granites and granodiorites. The slopes are very steep, with a slope up to 90 degrees, but generally ca 35 degrees and often less. In the southern part of the area, the slopes sometimes have a shrub vegetation and are in use for extensive grazing. Some less steeper parts on these slopes are in use as arable land. V Val 1 ay slopes. The valley slopes have a geology of Basement System rocks, ranging from granites and granodiorites to quartz-feldspar gneisses, which are acid, and medium to coarse grained rocks. Although Andriesse and van der Pouw (1985) indicated phonolites on some valley slopes in the south, gneisses were found instead. There is a very diverse relief with slopes of 0-8% (flat to undulating). Most of these valley slopes are in use as arable land. The main crop is maize intercropped with beans. Other crops are sunflower, tomatoes, sorgum, millet, bananas. On the northern valley slopes there is some production forest. The area under pasture is small. Erosion is often a problem on these slopes. VI Valley bottoms. The slopes of these valley bottoms are flat to undulating (0-8%). The drier parts of the valley bottoms are mainly in use as arable land, the wetter parts as pasture and there are also some very swampy areas. However, the wetter parts are increasingly being cultivated, for the growth of maize and vegetables. 25
Chapter 3 Methods.
3. 1 The Nested Ap^ytHs of Variance (N. A. V. ).
3. 1. 1 Introduction. For a land evaluation, whether qualitative or quantitative, much information on soil physical and soil chemical parameters must be collected. "This information is not of much value if nothing is known about the spatial variability of the parameters. To obtain information on this spatial variability, a nested sampling and nested analysis can be carried out. The nested analysis is a special form of variance analysis which has been designed to assess the amount and percentage of spatial variation found on different scales of hierarchical distance levels, and to estimate the distances over which major increases in variance occur. Within the area where a nested sampling \ri.ll be carried out the parameters should obey the intrinsic hypothesis of regionalized variable theory (Burrough, 1986). This means that the variation of the parameter value with distance is homogeneous in its variation throughout the area so that differences between sites are a function of the distance between them. Therefore the nested sampling has been carried out only on the plateau, merely on the well drained areas, since this was the only area large enough for a nested analysis, where no trends or other structural effects were to be expected.
3. 1. 2 Sampling method.
The nested sampling scheme has been laid out as follows. Five clusters have been chosen, with a distance of 7000 meters between them (see MAP 5 and MAP 6). Each cluster has been subdivided into 2 subclusters, and these again have been subdivided, and so on till the fourth level, with distances between subclusters of respectively 2000, 500 and 100 meters. In each subcluster of the second level on one sampling site 2 samples have been taten of a distance of 5 meters, to assess the very-short-distance variation. At one of these points replicates have been taten, one of them originally meant to be sent to another laboratory for checking the accuracy of the analyses, but this turned out not to be feasible. Thus there were 5x2x2x2 +5x2 sampling points and 5x2 replicates.
3. 1. 3 Method of Calculation. The sampling scheme is an example of multi-level sampling, and it can be represented as a hierarchy (figure 3. 1) (Oliver and Webster, 1986). Each level in the hierarchy makes a contribution to the total variance. There are 49 degrees of freedom (fifty sampling points) and the immediate aim of the analysis of variance is to apportion these properly and to calculate the sums of squares for each level. The number of degrees of freedom between clusters is 4 (since there are 5 clusters). There are 10 sub-clusters and therefore 9 degrees of freedom. Differences 26 between the clusters account for 4 of these and there remain 5 degrees of freedom for differences between sub-clusters within clusters (level 2).
7000 m, Level 1
Level 2 2000 m. Level 3 500 m. Level 4 100 m,
Level 5 5 m.
Fig. 3. 1 Hierarchy of Nested Sampling Scheme. The sums of squares for each level are then calculated. The total sum of squares is the sum of squares of deviations of individual observations from the general mean. Similarly the between-clusters sum of squares is the sum of squares of the deviations of the cluster means from the general mean, each multiplied by the number of observations that mahe up the cluster mean. Entries for the intermediate levels are determined as follows: for each class at any given level g, the difference between its mean and the mean of the class to which it belongs at the level immediately above, g-1, is squared and multiplied by the number of observations in that class. The sum of these values is the appropriate sum of squares. The computations are laid out in table 3. 1. Mean squares are obtained by dividing the sums of squares by the degrees of freedom, and table 3. 1 gives the components of variance they estimate. Notice that the mean square at any level apart from the lowest contains a unique contribution from that level, plus contributions from the components in all levels below. This enables each component to be estimated separately, by subtracting from the mean square of its level the mean square of one level lower, and dividing by a coefficient. When the sampling scheme is balanced, this coefficient is equal to the number of sub-divisions per cluster at that level. However this sampling scheme is not balanced and therefore the coefficient has to be calculated following another procedure, given by Gower (1962). 27
Table 3. 1 Terms in the balanced analysis of variance.
Level df Sums of squares Components of Variance estimated by mean squares
04 + n^Oj + I 1 4 E\ n n ü- x ' ' * n3n4O2 +
nl n2 _ 2 5 E E n3n4(xij-xi)* 04 + n4O3* + n3n4a2* n^ n2 n3 3 10 E E E n4 (xj_-î]ç-x^-j ) 04 + ^4^3 i=l i=l k=l
1 4 20 E E E E (Xijj^-Xijjç) o4' i=l j=i k=l 1=1
j 2 3 Total 39 E E E E
3. 2 Methods of Soil
3. 2. 1 Introduction.
The physical geographical land unit map (as presented in chapter 2) was a good base for a more detailed study of the soils in the area. To study the soils in more detail, some transects were chosen in different land units. We tried to choose transects in all different types of geology and landform. The exact loca- tion of those transects was furthermore determined by the exis- ting socio-economic data (for instance yield results and amounts of fertilizer applied in 1987) for some areas in Soy and Lumakanda locations. This socio-economic information was gathered by social-geographers in 1988. Also the accessibility played an important role in the final choice of the transect areas. The transect areas are: Lumakanda (VSS), Mautuma (VSN), Sango (VSE), Kongoni (PLG), Forest Land Estate near Turbo (BLA), Selbourne Estate (BLB), Sergoit Scheme (BLC) and Sergoit Valley (VAL). The positions of the transects, together with the socio- economic data areas, are indicated in MAP no. 5 and 6. The reason to sample in transects and not in, for instance a raster scheme, was that some catenas could be expected. 28 3. 2. 2 Describing the Soil Profiles. In every transect, the same experiments were being performed: a) pF-experiments b) saturated hydraulic conductivity experiments c) infiltration experiments d) description of soil pits and gaugings About a): The most important pF values relevant to the availability of moisture for plants, are pF 0 (saturation), pF 2. 3 (field capacity), pF 4.2 (wilting point) and pF 6 (air dry). 1 1 The volumetric F"*?" '^tV "? content at sat"''~?*-ç-iiQn (pF 0) was determined by weighing, saturating and again weighing of the pF cylinders. After the soil sample is being dried in the oven at 105° C, the volume of the water that was present at saturation can be calculated. Division by the totale volume of the cylinder this gives the volumetric moisture content at saturation. An indication of the water content at pF-2. 3 values can be acquired in three ways: 1) In at least two points along a transect, a pF-experiment was performed. Some 1 or 2 square meters were isolated from the surroundings, by making small dikes (10-15 cm. high). The soil within the dikes was saturated with water and covered with a piece of plastic. After two days of free draining, the plastic was removed, and two pF-cylinders were taken froir the top soil. In the laboratory the volumetric moisture content could then be determined. 2) In the laboratory the pF cylinders were saturated (at least for 24 hours). They were then taken out of the water and put in a place were they could drain freely. The decrease in weight could be determined and when the weight was almost constant, the situration of field capacity was reached. Mostly this was after 36-48 hours of free drainage. 3) A third approximation of the water content at pF 2. 3 was found in the Kapenguria Study (Gelens et al., 1976). In this study an empirical relationship between volumetric percentage of water at field capacity and clay percentage was established:
Vol. Perc. at pF 2. 3 = 0. 41 * % clay + 6. 5 ( 3. 1 ) The determination of the volumetric moisture content at PF 4. 2 is problematic. An excellent methodological solution (escpecially in rather primitive laboratory circumstances) would be to grow plants in pots and to take a sample in a cylinder the moment the plant starts wilting. This could not be done for this study. Another possiblity was, again, the Kapenguria study. In this study the difference between pF 2. 3 and 4. 2 (A. M. ) was found to be:
AM. = % clay * 0. 12 + 3. 9 + [ (% C - 1) * 1. 1] ( 3. 2 )
Whereby A. M. = available moisture (percentage) % C = organic carbon percentage and pF 4. 2 = pF 2. 3 - A M. ( 3. 3 ) 29 (In the Kapenguria study (Gelenâ et al., 1976) P. A. M. is also used: the Productive Available Moisture. P. A. M. is described as the soil moisture value between pF 2. 3 and pF 3. 7). The volumetric moisture content at pF 6 (air dry) can be determined with the help of the Schofield Formula: = ln ( ] ( 3 4 } ***** |^i îs - Whereby R = gas constant ( 8. 31 J*K~* mol"1) T = absolute temperature in K M = molecular weight of water ( 18*10~3 kg mol"1) g = gravitational acceleration ( 9. 8 ms"^) e/es = relative water pressure of the air The soil cylinder can be placed in a quiet place, under circumstances whereby temperature and relative humidity are can' be measured. After 10 days the decrease in weight stabilizes and the pF 6. 0 situation is reached. A rough indication of the pF 6.0 value can be found by dividing the pF 4. 2 value by 3 (Wolf, personal communication). About b): In every soil pit that was dug, some cylinders with soil were taken (in top- and subsoil). In the laboratory, these samples were saturated and then, by putting a Mariotte-bottle on top of the cylinder, the saturated hydraulic conductivity was determined (Hing et al., 1980). About c): Infiltration measurements were done at each profile site. The rings were 16 cm long and had a diameter of 10 cm. They were hammered into the soil with a wooden buffer plate and a hammer. At first simple tins without bottom were used, later they were replaced by steel rings, because the tins were often deformed when hammered into the soil. In most cases six measurements were done on one site, in two clusters of three each. A major disadvantage of the rings is that they have to be hammered into the soil, which causes a change in the original soil structure and porosity, especially when the soil is dry. Before starting the sampling scheme, some test measurements were done with the double ring method (e. g. Hills, 1970). This method has been designed to prevent lateral seepage from under the measuring cylinder of infiltrated water, by creating a ' buffer zone' by flooding a second, larger ring which is placed around it. However, this method proved to be not very effective, probably because of the enormous short distance variation of the soils' infiltration capacity. The use of a second ring was also very much water consuming and the method had not been used in other soil surveys in Kenya either, so it was abandoned when the field measurements started. In the rings a constant head of ca. 6 cm was maintained by hanging a bottle of Marriotte above each infiltration ring. As such, the measured infiltration can be compared with the measurements in other soil surveys in Kenya (Gelens et al., 1976, in the Kapenguria area, and Michieka et al., 1978), who used a 12 cm falling head, which gives an average head of approximately 6 30 cm. With increasing time increments, the water levels in the bottles were noted, together with the corresponding times. The sorptivity S and the wet conductivity A of the soil were calculated according to the infiltration formula of Philip (1957): i = S. /t + A. t (3. 5) in which: i = cumulative amount of infiltrated water [m] S = sorptivity [cm/min*] A = wet conductivity [cm min"1] S and A were obtained by plotting i//t against /t and fitting a straight line through the points with the least squares method, S being the intercept of the i//t axis and A being the tangentof the line. Also the infiltration time needed for the first 100 mm (dry run) and the second 100 mm (wet run) were interpolated from the data, if so much water had infiltrated, since these values could be compared with the values obtained by Gel ens et al. (1976) and by Michieka et al. (1978).
About d): In every transect some soil pits were dug, if possible up to 150 cm deep (see MAP no. 4a-h). The soil profiles were described according to FAO (1977). Some general information on the soil and a detailed description of individual soil horizons must be given. The soil colours were described with the help of the Munsell Soil Color Charts (Munsell Color Company, Inc., 1971). In every pit top- and subsoil samples were taken. To get an indication of the nutrient status between the pits, some soil gaugings were done, and samples were taken in top- and subsoil (0-20 cm and 60-80 cm respectively). All soil samples were analyzed at the National Agricultural Laboratories in Nairobi.
- The texture was determined with the hydrometer method; - PH-H2O, pH-KCl, electric conductivity and Ec were all determined in a 1 : 2. 5 soil solution; - Organic carbon was assessed using the method of Walkey-Black; .- the CEC in IN Sodium Acetate (pH 8. 2); - the exchangeable bases in IN ammonium acetate (pH 7. 0); The N. A. L. only measured the C. E. C. for 100 gr. of soil. In classification systems, the C. E. C. in m. e. per 100 gr of clay is often used. To correct the C. E. C. -soil value, 3 m. e. were subtracted for every percent of organic carbon (communication with V. W. P. van Engelen). These m. e. ' s were subtracted from the C. E. C. soil and then the C. E. C. -clay could be calculated simply by division by the clay percentage. So, for instance, when the C. E. C. -soil was 13. 5 me/100 gr soil and the organic C percentage was 1. 5 %, 1.5 * 3 = 4.5 m. e. of the C. E. C. -soil were accounted for by the organic material and therefore subtracted from the C. E. C. -soil. So, corrected C. E. C. -soil becomes: 13. 5 - 4. 5 = 9 m. e./100 gr soil (without organic material). When the clay percentage is 60 %, the C. E. C. -clay would be 9 / 0. 6 = 15 m. e. /100 gr clay. 31 The Base saturation, indicated in Appendix II and Appendix III is calculated as follows: sum of cations B. S. = * 100 (3.6) C. E. C. -soil For almost all samples, P-Olsen vas determined (extraction by Sodium Bicarbonate, pH 8.5). The N. A.L. also gave an indication of the available nutrient content (0. 1 N HC1 + 0.025 N H2SO4 extract). For further details concerning the applied extraction methods, see Hinga et al. (1980). 32 3. 3 Methods of land evaluation.
3. 3. 1 Introduction. The aim of Land Evaluation is to give an indication of the quality of land by comparing the Land Units (LU), defined by Land Qualities and Land Characteristics, with the Land Use Require- ments (see fig. 3. 2) (FAO, 1976 and FAO, 1979).
Land Use System
Land Land Use
Land Mapping Unit I Land Utilization Type
Land Qualities Land Use Requirements
MATCHING 1 I Land Suitability Assessment I
Land Suitability
Fig. 3. 2 Summary of a Land Evaluation procedure. The Land Mapping Units are considered to be homogeneous in their most important qualities and characteristics. In this study, the physiographic Soil Units (see chapter 4 and MAP no 7 and 8) are chosen as Land Mapping Units. The Land Utilization Types are the types of land use one wants to evaluate: in this study maize/ beans, sunflower, cassava, sorghum, millet and sweetpotato. The land qualities give an indication of the most important qualities of the land (e. g. nutrient availability, water availability, erosion risk). The definition of land is very broad: "an area of the earth's surface, the characteristics of which embrace all reaso- nably stable or predictably cyclic attributes of the biosphere vertically above and below this area including those of the 33 atmosphere, the soil and underlying geology, the hydrology, the plant and animal populations, and the results of past and present human activity, to the extent that these attributes exert a significant influence on present and future uses of the land by man" (FAO, 1976). The matching procedure, in which land qualities and land use requirements are compared, gives an indication of the land suit- ability. This indication can be a) qualitative: to grow a certain crop the land is 51 highly suitable 52 moderately suitable 53 poorly suitable N not suitable b) quantitative: which means that an indication of the yield that can be expected can be given, in kg/ha. The height of this calcu- lated yield is then a quantitative measure for the suitability of the land.
The qualitative land suitability can be useful to get a first indication of the suitability of the land at a high level (exploratory (1:1.000.000) and reconnaissance level (1:500.000)). But for planning procedures on a s end-detailed or detailed level (1:100.000 - 1:10.000) a more accurate measure is needed, so that the quantitative land evaluation results can support cost-benefit analyses. In this study both land evaluation models are used. A qualitative model, the KSS-model, as described by Weeda (1987) was used. The WDFOST-model (van Diepen et al., 1988 and van Keulen & Wolf, 1986) was used to do the quantitative Land Evaluation.
3. 3. 2 The qualitative KSS-model. Weeda (1987) ascertained the land quality ratings for the most important land qualities. The land use requirements are not yet defined by the KSS itself, but De Meester and Legger (1988) made a proposal for the most important crops in Kenya, which is also used here (slightly adapted). The landqualities considered here are: Table 3. 2 The landqualities in the KSS-model. Land Qualities Land characteristics used to determine land qualities and information source 1) Temperature - mean annual temp. °C - mean max. temp. °C - mean min. temp. °C - absolute mia temp. °C source: Sombroek et at. (1982) 2) Moisture - average rainfall (P) availability source: LBDA, Kisumu - run-off coefficient (C) source: Weeda (1987) 34 Table 3. 2 Continued. average monthly evaporation (Et) source: Meteorological Department, Dagoretti Corner, Nairobi soil moisture storage capacity (S) source: Weeda (1987) 3) Availability of CEC (me/100 gr soil) nutrients avail P-Qlsen (ppm) organic C (%) exchangeable K, Ca, Mg (me/100 gr soil) pH-H2O 1: 2. 5 source: soil analysis by N.A.L Nairobi 4) Resistance to climate factor erosion source: Sombroek et al. (1985) slope factor source: Andriesse and van der Pouw (1985) and own observations soil factor source: soil analysis by N. A. L. Nairobi 5) Availability of drainage class oxygen for root source: own observations growth and Andriesse and van der Pouw (1985) 6) Ease of cultivation steepness of slope stoniness, rockiness depth of soil to rock workability source: Andriesse and van der Pouw (1985) and own observations 7) Absence of hin- source: own observations drance by vegetation The ratings of these seven land qualities were determined in the following way (Weeda, 1987):
Table 3. 3 The ratings of the land qualities. Ratincr of land oualitv temoerature (Rt) temp, zone mean arm. mean max mean min absolute min temp °C temp °C temp °C temp °C 6 14-16 20-22 • 0-10 0-2 5 16-18 22-24 10-12 2-4 4 18-20 24-26 12-14 4-6 3 20-22 26-28 14-16 6-8 2 22-24 28-30 16-18 8-10 1 24-30 30-36 18-24 10-16 Ratincr of land r^iaiitv moisture availability (Rw) rating month(s) per growing season 1 2 9.5-11 3 6-9 4 4-5.5 5 3-3.5 6 2-2. 5 7 < 2 35
Table 3. 3 Continued.
Ratina for the avai orients (Rn) rating CBC % C avail P exch K me/100gr temp, zone Olsen ppm me/100gr soil soil 4,5,6 1 high > 16 > 2. 5 > 20 > 0. 5 2 moderate 6-16 1. 6-2. 5 11 - 20 0. 21 - 0. 5 3 low 3 - 5. 9 1. 0-1. 5 5-10 0. 10 - 0. 20 4 very low < 3 < 1. 0 < 5 < 0. 10 rating exch Ca exch Mg pH-H2O me/100 gr me/100 gr 1: 2. 5 1 high > 6. 0 > 3. 0 5. 6-6. 8 2 moderate 3. 0-6. 0 1. 1-3. 0 4. 8-5. 5 or 6. 9-7. 5 3 low 1. 0-2. 9 0. 5-1. 0 4. 0-4. 7 or 7. 6-8. 7 4 very low < 1. 0 < 0. 5 <4. 0 or >8. 7
ncr resistance to erosion (Re)
Climate subrating:
rating KE15 > 25 Agro-Climatic Zone (KSS) 1 < 5000 VI, VII 2 5000-10000 II, IV, V 3 > 10000 I, II (whereby KE15 > 25 is the kinetic energy of 15 minute showers with rainfall intensities of over 25 mm/hr)
Slope subrating:
slope % 0-2 2-5 5-8 8-16 16-30 30-45 >45 slope length (m) < 50 1 1 3 3 3 5 7 50-100 1 3 3 5 5 7 9 100-200 1 3 4 4 7 9 9 > 200 1 3 5 5 7 9 11
Soil subrating
rating soil structure stability 1 high 2 medium 3 low
Final ratinq rating (Re) sum of subrating 1 3-7 2 8-11 3 12 - 14 4 15 - 19 36 Table 3. 3 Continued. Ratincr for av?»i 1 ^M litY of oxycren for root crrowth (Ro) rating soil drainage class 1 very high well to excessively drained 2 high moderately well drained 3 low poorly drained 4 very low very poorly drained Ratincr of ease of cultivation (Re) ' subrating steepness of slopes subrating slope % 1 0-8% 2 8-16% 3 16 - 30% 4 30 - 70% 5 > 70% subrating stoniness/rockiness of topsoil subrating stoniness % rockiness % 1 non < 0. 01 non < 2 2 fairly stony 0.01- 1 fairly rocky 2 - 10 3 stony 0. 1 - 3 rocky 10 - 25 4 very stony 3 - 15 very rocky 25 - 50 5 exceed, stony > 15 extr. rocky > 50 Subrating depth of soil to rock (or any other consolidated mate- rial) subrating depth to rock (cm) 1 deep > 50 2 shallow 25-50 3 very shallow < 25 Subrating workability of soil (ease of action), based on the consistency of the tqpsoil (0-25cm). subrating plasticity stickiness wet wet moist dry 1 high non to slight- non to very friable, soft, si. ly plastic slightly friable, hard, sticky loose loose 2 medium plastic sticky firm hard 3 low very plastic very extremely very sticky firm hard Final rating of land quality Ease of Cultivation with power of hand and oxen (Re). 37 Table 3. 3 Continued. Ratina of Re. rating steepness of rockiness/ depth workability slope stoniness of soil of soil 1 1 1 1 1 2 2 2 1 2 3 3 3 1 2 4 4 4 2 3 5 5 5 3 3 Rating of land crualitv pVisence of hin^r^nce by veoetetion (Rv). Rating physiognomic type 1 grassland/cultivated land bushed wooded grassland wooded grassland 2 bushed grassland wooded bushed grassland 3 bushland wooded bushland bushed woodland woodland 4 dense bushland dense wooded bushland dense woodland bushland thicket wooded bushland thicket forest The conversion tables which were used to translate land qualities and crop requirements into land suitability ratings are listed below (De Meester and Legger, 1988). Table 3. 4 Conversion Tables for some Kenyan crops. Maize Rt Rw Rn Re Ro Re Rv SI 1-4 1-5 1+2 1 1 l+2 1 S2 5 5+6 2+3 2 1 3 2 S3 5+6 6 4 3 2 4+5 3 N 7 4 3-5 5 4 Sunflower Rt Rw Rn Re RO Re Rv SI 1-4 1-5 1+2 1 1 1+2 1 S2 5 5 2+3 2 1 3 2 S3 6 6 4 3 2 4+5 3 N 7 4 3-5 5 4 38 Table 3. 4 Continued. Beans Rt Rw Rn Re RO Re Rv SI 1-4 1-5 1+2 1+2 1 1+2 1 S2 4+5 5+6 3 3 2 3 2 S3 5 6 4 4 2 4+5 3 N 6 7 3-5 5 4 Cassava Rt Rw Rn Re Ro Re Rv SI 2+3 1-4 1+2 1+2 1 1 1 S2 1+4 4+5 2+3 2+3 2 2 2 S3 5 6 4 3 2 3 3 N 6 7 4 3-5 4+5 4 Sorcrhum Rt Rw Rn Re Ro Re Rv SI 1+2 1-4 1+2 1 1+2 1+2 1 S2 3+4 5+6 2+3 2 3 3 2 S3 5 7 4 3 3 4+5 3 N 6 7 4 4+5 5 4 Sweet Potato Rt Rw Rn Re Ro Re Rv SI 1+2 1-5 1+2 1+2 1+2 1 1 S2 3+4 5+6 3 3 3 2 2 S3 5+6 6 4 4 4 3 3 N 7 5 4+5 4 Millet Rt Rw Rn Re RO Re Rv SI 1+2 1-5 1+2 1 1 1+2 1 S2 3+4 6 2+3 2 2 3 2 S3 5 7 4 3 2 4+5 3 N 6 7 4 3-5 5 4 The conversion tables for maize, cassava, sunflower and 'millet were slightly adapted here . The adaptations concern the temperature rating. With the original conversion tables of de Meester and Legger, maize and sunflower could maximally key out as a S2 suitability class for temperature zone 4 and 5 (Lumakanda and Soy respectively) whereas in the study of Jaetzold and Schmidt (1982) Soy and Lumakanda locations belong to the so- called maize-sunflower zone, agro-ecologically very suitable for sunflower and maize productioa Probably De Meester and Legger (1988) used other maize and sunflower varieties for their conversion tables. The maize and sunflower conversion tables were therefore adapted in such a way that temperature zone 4 could key out as SI and temperature zone 5 as S2. Sys (1985) also gives a good suitability for maize and sunflower production in temperature zone 4 and 5. For millet and cassava Sys (1985) indicates that temperature is not as limiting as De Meester and Legger showed in their conversion tables. These conversion tables were therefore also adapted. The conversion tables for sorghum, sweet potato and beans were used without any adaptation. 39 3. 3. 3 Quantitative land evaluation: the WOFOST model. 3. 3. 3. 1 Introduction. The crop growth simulation model WOFOST (acronym for WOrld FOod STudies) has been developed during the last decade. The Centre for World Food Studies, a group of Dutch scientists has developed computer models for calculating the potential world food production and the socio-economical and agro-technical factors that were responsible for insufficient food production and malnutrition. The economical relationships were studied by a group of economists at the Free University of Amsterdam. Research on the agro-ecological and technical factors limiting for food production was carried out by scientist from several research institutes in Wageningen. The work of this last group resulted in a model for quantitative land evaluation. In 1988 an elaborate new version nr. 4. 1 of the model was published (van Diepen et al., 1988 and van Keulen and Wolf, 1986). One of the main improvements in this new model compared to previuous ones was the introduction of a new procedure for calculating the nutrient-limited yields. This procedure is based on a system for evaluating the fertility of tropical soils, developed by Janssen et al. (in press). 3. 3. 3. 2. The model structure. Some options for which the WOFOST model can be applied, are: - to analyse growing conditions in present farming systems - to test alternatives with respect to crop cultivar, soil management, growing period etc. - to test the sensitivity of production to changing or variable agro-ecological conditions. - The model can also be used to calculate the yields for the three levels of productions (fig. 3. 3). a) Potential crop production is achieved if all factors influen cing crop production are optimum. In other words: only solar radiation and temperature influence the crop production, and water availability, nutrient supply and crop protection are optimum. b) Water-limited crop production: conditions are optimum, except for water availability. c) Nutrient-limited crop production: crop protection is optimum and crop production depends only on the soil fertility. d) An indication for the actual crop production (for maize) can be obtained by carrying out a survey under farmers. The main inputs required to overcome the gaps between the different production levels, are indicated in fig. 3. 3. 40 Potential Crop Production irriaation Water Limited Crop Production Fertilizer Supply Nutrient Limited Crop Production Management Actuai Crop Production Management Fig. 3. 3 Production levels within WOFOST. The sturcture of the model is presented in fig 3. 4: CLIMATIC CROP REQUIRE- SOIL DATA MENTS DATA DYNAMIC CROP GROWTH SOIL WATER BALANCE PART SIMULATION • MODEL OF THE > M00EL POTENTIAL CROP WATER LIMITED CROP PRODUCTION PRODUCTION BASE >t \ ACTUAL SOIL SOIL SUPPLY NU TRIE SUBROUTINE SUPPLY OF OF NUTRIENTS NUTRIENTS > ' FERTILIZER FERTILIZER NUTRIEKT REOUIREMENT REQUIREMENT FOR POTENTIAL FOR WATER LIMITED LIMITED CROP CROP PRODUCTION CROP PROOUCTiON PRODUCTION Fig. 3. 4 A summary of the model structure of WDFOST. 41 A short description of all input data required: Cling'*"' - minimum air temperature (°C) - maximum air temperature (°C) - shortwave radiation actually received - actual vapour pressure (mbar) - average wind speed (ms~*) - average rainfall per month or per day (mm month"1 or mm day"1) - average number of rainy days per month Soil ^y^T'pulic—data fin ca*?«? of no oroundwater). - conductivity of top soil (cm d"1) - hydraulic conductivity in subsoil (cm d"1) - table with pF-soil moisture relation (at least at pF 0, pF 2. 4, pF 4. 2 and pF 6) Because data on characteristics of local crops were lacking, the standard crop data in data files of the WDFOST model were used. But if sufficient information is available to adapt crop characteristics, the following variables are adviced to be changed (van Diepen et al., 1988): - life span of leaves growing at an average temperature of 35 °C (days). - initial light-use efficiency of CO2 assimilation of single leaves (kg ha"1 h"1 J"1 m2 s) - initial total dry weight (amount of dry seed used for planting) (kg ha"1) - maximum pre-anthesis development rate of the crop (d"1) - maximum post-anthesis development rate of the crop (d"1) - fractions of total dry-matter increase partitioned to the roots, the leaves, the stems and the storage organs. In the WOFOST model the calculations of the potential crop production and the water limited crop production are performed with a time resolution of one day over the growing season. The basis for these calculations is the equation describing the assimilation process of a green canopy: CO2 + H2O > CH2O + O2 (3. 7) Part of the assimilates produced are used by the plant for its maintenance respiration and the remainder is converted to plant dry matter. This dry matter is partitioned to the different plant organs (roots, leaves, stems and storage organs), and this partitioning is a function of the development stage of the plant. In reality potential production can only be reached if during the growing cycle soil moisture conditions are optimal, the crop's nutrient requirements are fully met and weed, pest and disease control is optimum. For calculating the water-limited production also the soil water balance is taken into account. Every day the moisture content in the actual rooting zone is derived from the daily input (rainfall) and the daily losses (transpiration, evaporation) and this value determines if plant growth occurs without any problem, or that growth stops or is reduced because 42 of lack of water or a surplus of water (lack of oxygen), The main elements of the soil water balance are summarized in fig. 3. 5. ground water Fig. 3. 5 Soil Water Balance in WOPOST (Driessen, 1986). Abbrevati ons : P precipitation (cm day"*) T transpiration by plants (cm day"1) E evaporation from soil or water surface (cm day"1) SSt surface storage (cm) I irrigation (cm day"1) SR surface run-off (cm day"1) DP deep percolation (cm day"1) CR cappilary rise (cm day"*) ARD actual rooting depth (cm) MRD maximum rooting depth (cm) Zt depth of groundwater table (cm) 43 Chancres in the soil water model in WOFOST. no SS? RI NPRE= ( 1 -NOU NF ) xRAI N+RI RR+SS yes ÄVAIL=SS+RAIN+RIRR-EL RINPRE = min (SOPE, AVAIL) RIN=min(RINPRE/ (SMO-SM)xRD+T+E+PREC SSPRE=SS+RAIN+RIRR-EL-RIN SS=min(SSPRE/ SSMAX) TSR=TSR+(SSPRE-SS) Fig. 3. 6 Stream model of the original program block controlling infiltration. In fig. 3. 6 and fig. 3. 7 the following terms are used: SS surface storage (cm) RAIN amount of rainfall (cm) RIRR amount of irrigation water (cm) EL amount of evaporation from surface storage (cm) AVAIL amount of water available for infiltration (cm) RINPRE preliminary amount of infiltration (cm) NOTINF not-infiltrated fraction of rainfall SOPE conductivity of the Vet soil, limiting the infiltration rate in case of surface storage of water (cm day"1) RIN amount of infiltration (cm) RD rooting depth in (cm) SMO moisture content at saturation SM actual moisture content T transpiration (cm d"1) E evaporation in (cm d~M PERC percolation in (cm d"1) SSPRE preliminary surface storage (cm) TSR total surface run-off (cm) SSMAX maximum surface storage (cm) afgen (NINFTB, SM/SMO) table relating the not-infiltrated fraction to relative moisture content. 44 no no SS? RAIN>SSMAX? NOTINF=O. 0 yes yes I AVAIL=SS+RAIN+RIRR-EL NOTINF=( (RAIN-SSMAX)x afgen(NINFTB, SM/SMO) )/RAIN RINPRE = min (SOPE, AVAIL) RINPRE= ( l-NÖITNF)xRAIN+RI RR+SS TSR=TSR+NOTTNFxRAIN J RIN=min(RINPRE/ (SMO-SM)xRD+T+E+PREC SSPRE=RINPRE-RIN SS=min(SSPRE, SSMAX) TSR=TSR+(SSPRE-SS) Fig. 3. 7 Adjusted stream model of the program block controlling infiltratioa Figure 3. 6 shows the stream model of the original program block of the WOFOST model which controls infiltration (Van Diepen et al., 1988). If there is surface storage, infiltration won't be determined by the not-infiltrated fraction (NOŒINF), but by the term SOPE, which is equivalent to A, the steady state .infiltration rate (see table 4. 2 in chapter 4). This means that, if part of the available rainwater does not infiltrate during showers, some surface storage remains until the next day (as the time step in VTOFGST is one day). In reality this is possible only in the bottomlands and in some very local depressions where clay accumulates and infiltration capacity is low, but in the main part of the area with its moderate to high infiltration rates this situation is very unlikely. Generally, after each shower there will be time enough for surface storage water to infiltrate. This fact can cause serious errors in the water balance simulation if it is ignored by users of the program. This means that the maximum surface storage (SSMAX) should only be set to a value larger than zero, if surface storage remains overnight. Therefore the program block has been adapted in such a way that the value set to SSMAX can be used for determining the amount of rain that infiltrates additionally from surface storage after the end of showers. In another adaptation the not-infiltrated fraction is made dependent on soil moisture content in the root zone, 45 incorporating calculated relations (see chapter 4) in the program. The option of the not-infiltrated fraction being dependent on the amount of daily rain, has not been used, because no relation between the amount and the intensity of rainfall could be derived from the Eldoret-data. For calculating NOTINF, average infiltration rates have been used. The adjusted stream model is depicted in figure 3. 7. The NUTRIE subroutine. This subroutine is used for a) calculating the nutrient limited-yield b) calculating the amounts of fertilizers that must be applied to reach the potential yield and water-limited yield levels. about a) For calculating nutrient-limited yields a system for evaluation of the soil fertility, called QUEFTS (Janssen et al., 1990 in press) is applied. In the WOFOST model the ideas of QUEFTS are incorporated but there are some differences: - the QUEFTS system can be used to calculate yield increases for various applications of the three macronutrients while the WOFOST model can only calculate the fertilizer applications required to reach potential or water-limited yield levels. - the QUEFTS system can only be used for a maize crop, while in the WOFOST model nutrient-limited yields and fertilizer requirements for a large number of crops can be calculated. For both methods, some conditions are important and should be fulfilled as otherwise model results are not reliable. First condition: QUEFTS and the NUTKEE subroutine in the WOFOST model should be applied to soils that are well drained and deeply rootable. Other conditions: QUEFTS and the NUTRIE subroutine should not be applied to soils with one of the following properties: pH H2O < 4. 5 or \ 7. 0 organic C > 80 g/kg P-Olsen > 30 mg/kg exch. K > 30 mmol/kg The calculation of the nutrient-limited yields comprises four steps: STEP I: The potential soil supply of N. P and K is calculated first. Other nutrients are not taken into consideration. Empirical relationships between the chemical properties of the tqpsoil and the potential supply of N, P and K were developed in the course of landevaluation projects in Kenya and Surinam, and are applied for calculating the soil supplies: 46 SN = fN * 6. 8 * org. C ( 3. 8 ) or SN = fN * 68 * org. N ( 3. 9 ) with fN = 0. 25 (pH - 3) (3. 10) in which SN is the potential supply of nitrogen (kg ha"1) and fN is a correction factor related to the pH. SP = fP * 0. 35 * org. C + 0.5 * P-QLsen ( 3. 11 ) or SP = fP * 0.014 * P-total + 0. 5 * P-Qlsen ( 3. 12 ) with fP = 1 - 0. 5 (pH - 6 ) ( 3. 13 ) in which SP is the potential supply of phosphorus (kg ha"1) and fP is the correction factor related to the pH. „„ _ fK * 400 * exch. K . . SK - 2 + 0. 9 * Org. C ( 3. 14 ) with fK = 0. 625 * (3. 4 - 0. 4 pH) ( 3. 15 ) in which SK is the potential supply of potassium (kg ha"1) and fK is the correction factor related to the pH. In the WOFOST model, SN, SP and SK are called Nbase, Phase and Kbase and are required as input data. They can be derived from chemical soil properties with the above-mentioned formulas. The chemical soil data should be established for the topsoil (0- 20 cm) and the chemical analysis procedures should be as follows: PH-H2O : in supernatant liquid of an 1: 2. 5 soil water suspension; shaking time two hours organic C : oxidation by ^0^04, correction factor 1. 03. organic N : digestion with concentrated H2SO4 and salicyclic acid. P-Olsen : 5 gram of soil in 100 ml 0. 5 M NaHCC^, pH adjusted to 8. 5, shaking time 30 minutes. STEP II: The act"^i nutrient uptake by the crop is calculated from the potential nutrient supply. The actual uptake of a nutrient depends on: - the potential nutrient supply - the moisture availability and other factors affecting growth - the supply of other nutrients - the growth duration of a crop 47 The potential supply of nutrients to a crop is first adapted by taking into account differences in growth duration compared to that of the standard crop maize. The actual uptake can now be derived from the potential but the actual uptake of one nutrient, for example nitrogen, is only equal to the potential supply if phosphorus and potassium are sufficiently available. So, limiting supply of phosphorus or potassium reduces the actual uptake of nitrogen. STEP III. Relations between uptake of a certain nutrient and crop yield are established for two situations: 1: When the considered nutrient is fully diluted in plant tissue. That occurs if the nutrient is limiting for crop growth. 2: The nutrient concentration in plant tissue is at its maximum. Another factor such as availability of moisture or another nutrient, limts crop growth. Yields at maximum accumulation of N, P and K for specified N, P and K uptake values are indicated YNA, YPA and YKA. STEP IV: From the actual uptake of N, P and K and their yield-uptake ratios at minimum and maximum nutrient concentration, the ranges in yields can be calculated. From the ranges in yields for the three nutrients the actual yield can be calculated by weighing the effect of the relative availability of the three nutrients. about b) In the subroutine NUTRIE of the WOFOST model, also the fertilizer requirements to reach potential or water-limited production are calculated. First, the nutrient uptake by the crop is calculated by multiplying the amount of leaves, stems and storage organs produced in a potential or a water-limited production situation, with their nutrient concentrations. The nutrient uptake values for the three macro-nutrients are then divided by the recovery fractions (fraction of fertilizer nutrient taken up by the crop) to derive the required applications of fertilizer nutrients. 48 CHAPTER 4: 4. 1 Results of Nestqfj APfllyffifli 4. 1. 1 Analysis of Variance results. During the nested survey it was observed that the soil in the north-eastern part of the area differed substantially from the other soils on the plateau. This was due to a different geological substratum, viz. Basement System rocks instead of phonolites. This difference has been showed clearly by comparing the graphs of cumulative variance of all cluster with the graphs of all clusters except cluster 5, which was situated in the north-east (see MAP 6). It was observed that in calculations with cluster 5, most graphs show a large percentage of variance accounted for on the first distance level (7000 meters), but without it, they don't. Because there is no need of soil sampling to detect the presence of this variation caused by different lithologies, cluster 5 has been omitted in the final calculations. The resulting graphs showing the percentage of cumulative variance with distance level have been depicted in figure 4. 1 and figure 4. 2. Most of the variables which are used in the QUEFTS model, lite N, P, K, pH-^O have a very high percentage of variance accounted for on short distances. Thus all or almost all of the variation which can be found on the largest distance can also be found on a distance of 100 meters. This means that, when carrying out a land evaluation for fields with a minimum size of 1 hectare, there is no need to sample these variables at distances less than 100 meter and it is best to use the means of the whole area. However the percentage of organic matter content C% shows a high percentage being accounted for on 7000 meters. The same is true to a lesser extent for Base Saturation and C. E. C., which are also important for soil classification, but are not used in QUEFTS or VTOFOST. Thus variations in these variables are mappable. The spatial pattern in organic matter content could be caused partly by the position of parts of some clusters in forest, and by differences in cultivation history or period of cultivation. For example, one would expect a higher organic matter content in a soil which has been in use for a relatively short time. Physical and texture parameters all have a percentage of variance of more than 50% accounted for on 100 meter and all of the variation is accounted for on 2000 meter. Mapping these variables would require a large sampling effort (with a sampling distance of 500 meters), while less than 50% of the variation would be solved. Mapping these would hardly be feasible, especially not because the amount of variation, particularly the variation in sand and clay content is fairly small. p. cumulative variance (%) cumulative variance (%) a> (D VO 50 4. 1. 2 The amount of variance. The graphs of cumulative percentage of variance with distance level do not say how large the variation is but only what its relative distribution with distance is. The amount of variation at a certain distance level can be important in land evaluation. It can give an estimation of the range of possible suitability classes or of the range in potential yield which can be expected in a certain area. From the variance at one distance level the standard deviation (which is the root of the variance) can be calculated. With this, confidence limits of the range in which the parameter can vary over this distance with a certain reliability can be determined (Riezebos, 1989). The standard deviation which gives itself the confidence limits with a reliability of 68.4 %, have been summarized in table 4. 1 for a number of parameters and for each distance level. This can be read as follows: for example the whole area mean of organic matter content is 1.15% and the standard deviation is 0. 60%. This means that organic matter can be expected to vary between 0. 55 and 1.75% over the whole area, with a reliability of 68. 4%. But when considering an area of the size of one cluster, the standard deviation is only 0.35%, so in one cluster the expected range will be the sub-area mean ± 0. 35%. In contrast to this, the standard deviation of organic nitrogen content does not depend on distance at all, on 5 meter distance one can expect the same range as on 7000 meter (0.09- 0. 21%). In qualitative land evaluation the parameter ranges will determine the possible suitability classes for each parameter and with this the reliability of a land suitability classification for a certain area can be estimated. With the available data however, this can only be said in the areas of the actual clusters (and of the transects on the plateau). In all other areas of a similar extent, the sub-area mean is not known, so one should use the genereal mean (1. 15 ± 0. 6 %). For example, when assigning land quality ratings with table 3. 3, the rating for the availability of organic C for the whole area of the plateau, can range from very low (< 1.0%) to moderate (1. 6-2. 5%) and the pH rating can range from moderate to high. In one cluster, the organic C rating can range from very low to low, or from low to« moderate. When assigning the suitabilities for a certain crop, it is thus thinkable that more than one suitability class will come out. This sets serious restrictions to the value of this kind of qualitative land evaluation for individual farmers, but it migh clarify the results for, for example, regional planners. In quantitative land evaluation one can calculate the nutrient limited yields with lowest and highest possible parameter values. This will result in an estimation of the range of nutrient limited yields, and will also be clarifying. 51 Table 4. 1 Standard deviations of different parameters for different distance levels. Variable nugget standard deviation over distance: total (a =; 6.4%8 ) mean 5 100 500 2000 7000 meters on plateau Pores % ± 2 ± 3 £ 3 ± 4 ± 4 54 % Sand % ± 3 ± 5 5 ± 7 ± 7 29 % Clay % ± 2 ± 5 ± 5 ± 6 ± 6 59 % pH-H2O ± 0.3 ± 0.4 ± 0.4 0.4 ± 0.4 5. 5 CEC-nt e. 1.7 2.5 ± 2.5 ± 3.1 3. 9 20. 3 m. e. Bas. Sat. % 2.4 ± 5.0 ± 5.0 ± 5.7 ± 8. 8 20.0 % Avail: K-m.e. ± 0. 1 ± 0.3 ± 0. 3 ± 0.3 ± 0.3 0. 71 m. e. Ca-m. e. ± 0.9 ± 1.9 1. 9 ± 1.9 2.0 2. 2 m. e. P p. p. m. 5 ± 6 6 ± 6 + 7 16 p.p. m. N % + 0.06 ± 0.06 ± 0.06 ± 0.06 ± 0.06 0. 15 % C % + 0. 14 ± 0.35 ± 0. 35± 0.35 ± 0. 60 1. 15 % 4. 1. 3 Conclusions. The nested analysis showed that most parameters can not be mapped on the plateau except for organic matter content and possibly C. E. C. and base saturatioa Because most of the variation of other parameters is accounted for on short distances and because this variation is often not very large, one can suffice with the general mean of the whole plateau (for the different parameters) except for the part with Basement System rocks. It is possible to give an estimation of the reliability of land evaluation with the results of the nested analysis. 4. 2 Results from infiltration experiments. 4. 2. 1 Description of the results. The results of the infiltration measurements have been summarized in table 4.2. All reported values are medians, because of the large variation. The time needed for infiltration of the first 100 mm and the second 100 mm has been reported. SQ has been calculated according to the formula: s ( 4. 1 ) = <(esat - e)/esat} * s0 (Driessen, 1986). There is a clear distinction in time needed for the infiltration of 100 mm water between plateau soils and valley slope soils. The median time for 100 mm infiltration on the plateau (including land unit PLG) is 9 minutes, and on the valley 52 slopes 26 minutes, with some very high outlayers. The high infiltration rate of profile BLC3 in the bottomlands has been caused by the enormous cracks in the surface of the heavy clay soil. At VSN3 no infiltration time has been reported, because the infiltration rate was zero, but there were only two measurements done. The times between brackets have been estimated (extrapola- ted) from the infiltration rates, when the amount of infiltration was not reached because of lack of time or lack of water. VSS3 and VSS4, which show high infiltration rates, are located in a transitional area almost at the level of the plateau. 4. 2. 2 Effect of Land management. Almost all experiments have been carried out on maize plots, because these were the most important for WOFOST. Sometimes it was noted that measurements close to the edge of plots, where people and animals are strolling around, gave a lower infiltration rate, especially on the valley slopes. Also on the valley slopes the soils were often very hard, with a dry and hard crust at the surface (most measurements were done when the rainy seaon had ended). On the plateau a hard surface was only to be found under forest cover. On maize plots the surface soil was always very loose, with a less hard and discontinuous crust, which may explain the difference in infiltration rates. This difference might have been even larger when the hard surface crusts would not have been disturbed by hammering the rings into the soil. Apparently, on the plateau the effect of ploughing is large and long lasting, contrary to the valley slopes. 4. 2. 3 Adaptation of the results for use in WOFOST. In the Reconnaissance Soil Surveys in the Kwale-Mombasa- Lungalunga area (Michieka et al. 1978) and in the Kapenguria area (Gelens et al. 1976) not only ring infiltration measurements were done, but also so called "rainfall simulation" experiments. These consisted of irrigating an area of one square meter with water running through a hosepipe in such a way that surface storage was maximal without causing runoff (so in fact these weren' t "rainfall simulation" experiments but irrigation simulation experiments). On a number of sites both ring infiltration and irrigation simulation were carried out. From 22 of these sites (only a few in Kapenguria) Gelens et al. derived a table, correlating t^QO and the irrigation simulation rate ipg. The correlation coefficient between 1/tjoo an<^ *RS was 0.87. It appeared that generally the ring infiltration rate was five times as high as the irrigation simulation rate. This large difference can be attributed to the alteration of the soil structure when the rings are being hammered into the soil, to water running sideways along the rings and to lateral seepage. Obviously the irrigation rates are more realistic, so the ring infiltration rates have been converted to irrigation rates according to the correlation table. With rainfall intensity, duration and frequency data of Eldoret Meteorological Station (Taylor and Lawes, 1971), for each irrigation rate an average fraction of not-infiltrating rain 53 could be estimated, supposing the irrigation infiltration rate to be a reasonable approximation of the real infiltration rate. However, this fraction has been derived from the tioo~value' which had been measured at a certain (measured) soil moisture content. Since the infiltration rate depends on the soil moisture content through the sorptivity value (formula 4. 1), for each site, for relative soil moisture contents (©/©sat) a^ an interval of 0. 1, sorptivity S(0) has been calculated. From the S(0)- and A- values, tioo ^ias been derived1, and the fraction of not- infiltrating rain has been calculated as described above. This resulted in a table with, for each site, the tabulated relation of relative soil moisture content with the average fraction of not-infiltrating rain. Also a table was calculated from median values of SQ and A of all values from the plateau and from the valley slopes has been calculated (table 4.2a)2. The fraction of rain which does not infiltrate on plateau soils is negligible according to these calculations. *At a number of sites, the soil was so dry and hard that it had to be wetted before driving the pF-rings into it, in order to soften the soil and to prevent it from falling out of the rings. Because of this the soil moisture content has not been determi- ned. For these sites a value of 0.15 (ca. wilting point) has been assumed for 6. 2 When deriving t100 from the measured values of S and A, this value was often smaller than the measured value, especially at some sites on the valley slopes and at the bottomland-site BLC3. This has been caused by the use of median values. Since the average infiltration rate was determined by tjoo' tor these cases SQ and A have been corrected by multiplying them with the factor lO/(S/tioo + A-tlOO) (a11 field values). These values have not been reported here. 54 Table 4. 2. Results of infiltration measurements. site No. t100 t2oo. A(Md) S(Mfl) e- iings! (Md) (Md) [cm] cm/min* 1 «sat So VSN1 2 37 (99) 0. 147 1. 11 0. 312 0.426 4. 1 VSN2 2 30 (75) 0. 34 0. 71 0.324 0.408 3.4 VSN3 2 - - 0.000 0.000 0. 326 0. 448 0.0 VSN4 6 33 (75) 0. 25 0. 77 0. 343 0. 411 4.7 VSN5 3 (320) - 0.027 0.052 0. 284 0. 396 0. 18 VSS1 2 28 (67) 0. 67 0.44 0.280 0.432 1.3 VSS2 4 23 (62) 0. 28 1. 19 0.290 0. 503 2.8 VSS3 5 8 19 0. 69 1.65 0. 246 0. 498 3.3 VSS4 5 7 17 0. 91 1. 88 0.211 0. 579 3.0 VSE1 6 11 25 0. 49 1. 60 (0. 188) 0. 457 (2.4) VSE2 6 16 45 0. 28 1.53 0.203 0. 452 2.8 VSE3 6 23 55 0. 71 1.02 0. 174 0. 503 1.6 VSE4 6 5m20 13 1.04 1. 26 0. 172 0. 459 2.0 VSE5 3 73 158 0.065 0. 71 (0.248) 0. 429 1. 1 DAL 6 57 - 0. 144 0. 73 0. 217 0. 599 1. 1 BLA1 3 8 17 0. 88 1. 12 0.263 0. 575 2. 1 BLA2 6 4ml0 (10) 2. 11 0. 65 0.259 0. 612 1. 1 BLA3 6 3m40 7m40 2. 2 1. 10 0.249 0. 597 1.8 BLB1 6 9 22 0. 72 1. 68 (0. 264) 0. 528 (2.3) BLB2 6 15 49 0. 14 2. 4 0. 248 0. 489 4.9 BLB4 6 8m30 23 0. 54 1.97 0. 198 0. 548 3. 1 BLB5 6 13 30 0. 29 1. 34 0. 301 0. 525 3. 1 BLB6 6 48 125 0.085 0.86 0.244 0.535 1.6 BLC1 6 13 28 0. 50 0.96 (0.291) 0.571 (1.3) BLC2 6 9m30 21 0. 78 0. 85 (0. 292) 0. 565 (1.2) BLC3 6 5m20 19 5. 6 2. 9 (0. 366) 0. 591 (3.9) BLC4 6 10 23 0. 93 0.85 (0.271) 0.526 (1.2) PLG1 6 lmSO 4m20 2. 6 3.8 0.249 0.570 6.7 PLG2 6 10 23 0. 64 1.34 (0. 372) 0.510 (1.9) PLG3 6 8m30 20 0. 64 1.54 (0. 199) 0.495 (2.2) PLG4 6 16 34 0.42 0.88 (0. 400) 0. 502 (1.3) Table 4. 2a Fraction of rain not infiltrating, as depending on relative moisture content of the soil. 1. 0 0. 9 0. 8 0. 7 0. 6 0. 5 0. 4 0. 3 0. 2 0. 1 0. 0 plateau 0. 01 0. 01 0. 01 0. 01 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00 valley slopes 0. 20 0. 15 0. 12 0. 08 0. 05 0. 03 0. 02 0. 01 0. 01 0. 01 0. 00 4. 3 Results from pF experiments. The pF data, for the three methods, are listed on the soil profile description forms (appendix no. I). In figure 4.3 the results for the three different methods are plotted in a diagram, against the clay percentage. In all cases, the laboratory method gives the highest volu- metric soil moisture contents at pF 2.3. The Kapenguria study gives a straight line with low values and the field' method gives 55 values that are in between the two other graphs, since in this method 9(pF2. 3) is directly determined from clay %. Moisture contents at field capacity a comparison of three methods • moist.eont.% + moisi.cont % o moist.cont % lab. meth. Kap. study field meth. 6O 5O at û o 4O a> O O 0 3O ó f o o 1O O A',.' 34 36 38 4O 42 44 46 48 5O 52 54 56 58 60 62 clay percentage (%) Fig. 4. 3 The determination of Moisture Content at Field Capacity Remarkably is the increasing volumetric soil moisture content (by field and laboratory method) when the clay percentage is decreasing (from 40% clay downwards). The reason for this is that the samples with low clay percentages, have high percentages of silt (up to 22 %). The figure can be interpreted as follows: a) The volumetric moisture content, as determined with the labo- ratory method does not give a good indication. Very often, a hard massive pan with a thickness of about 1 cm vas found in the bottom of the cylinder, after they came out of the electric oven. Sometimes there also were problems saturating the samples, probably because of this massive part in the pF cylinder. b) There is a possibility that the values derived by the Kapenguria line are underestimated, when clay percentages are low ( < 40% clay). High clay percentages are more common in the area, so in most cases, the Kapenguria line gives a good indication. The ' Kapenguria values' sometimes are exactly corresponding with the values gathered by the field method and are never overestimating the amount of water available at field capacity. 56 So, as input for WOPQST, the following determination methods were used: Water content at: pF 0 : saturation results from laboratory experiments pF 2. 3 : from Kapenguria-study pF 2. 3 = 0. 41 * % clay + 6. 5 (4. 2) pF 4. 2 : from Kapenguria-study pF 4. 2 = pF 2. 3 - [%clay*0. 12 • 3. 9 + (%C-l)*l. 1] (4. 3) pF 6. 0 : rough indication by pF 6. 0 = pF 4. 2/3 (4. 4) 4. 4 Soil Profile Descriptions. 4. 4. 1 Introduction One of the main subjects of this study is to check the existing soil map (scale 1: 250000) and to make a more detailed map (scale 1: 100000). The most recent soil mapping, including Soy and Lumakanda locations was done by Andriesse and van der Pouw (1985). They made a reconnaissance soil map, scale 1:250000 of the Lake Basin Area. The legend they used was strongly based on the legend made by Sombroek et al. (1980) in their Exploratory Soil Map of Kenya, scale 1: 1000000. The Soil Map that was a result of our study, will from now on be indicated as DIDC-map (scale 1: 100000). 4. 4. 2 Classifications The soils in the area were 'classified with the help of two different classification methods: 1) FAO 1974 legend, with Kenya-concept 2) FAO 1989 revised legend The 1974 legend of the FAO proved in many cases not to be very useful for application in Kenya. Therefore Siderius and van der Pouw (1980) suggested some changes in the application of the FAO terminology, better known as the Kenya-Concept. Escpecially the Ferralsols, Acrisols, Luvisols and Nitosols could not be classified unambiguously with the FAO 1974 legend. There- fore some Intergrades between Great Groups were suggested: Ferralsols intergrading to Acrisols, Luvisols intergrading to Ferralsols, Acrisols intergrading to Ferralsols. Especially the last two groups of intergrades can be found in Soy and Lumakanda Locations. 57 These intergrades have a weakly developed argillic B hori- zon, but the base saturation is less than 50 % in the case of Acrisols and more than 50 % in the case of Luvisols. The CEC varies usually between 16-24 me/100 gr. clay (for Acrisols inter- grading to Ferralsols) and 20-30 me/100 gr. clay (for Luvisols intergrading to Ferralsols). The amount of weatherable minerals range between 4-7 %. The bulk density for intergrades is higher (1. 3 g/cm3) than for proper Ferralsols (1. 1 g/cm3). For the genesis of the Acrisols or Luvisols intergrading to the Ferralsols, Siderius and van der Pouw (1980) suggest that the impoverishment of the Acrisols/Luvisols during the last centuries has been slowed down, due to a decrease of rainfall. In the case of Acrisols intergrading to Ferralsols the impoverishment went somewhat faster due to smaller decrease of rainfall or poorer parent materials. Sombroek et al. (1980) and Andriesse and van der Pouw (1985) applied part of the ideas out of the Kenya-Concept. For instan-- ce: they left out the intergrade idea, but they used the newly defined concept of ferralic properties: a cation exchange capaci- ty (from NH4C1) of less than 24 me/100 gr. clay in at least part of the argillic B horizon. So at unit level in the legends of the Soil Map of Kenya and the Soil Map of the Lake Basin Development Area, there exist units like ferralo-chromic Acrisols and ferra- lo-chromic Luvisols. The classification of these kind of soils has recently been recognized as a problem existing in many tropical and sub-tropi- cal areas (Sombroek and Muchena, 1979 and FAO, 1989). To classify properly, the base saturation alone is not suitable. Therefore it was suggested to make combinations of CEC and base saturation in order to make a good differentiation between high activity clays (Base Saturation > 50 %) and low activity clays (Base Saturation < 50 %). In the new classification of the FAO (1989), these concepts are used. Another major change is that an argillic horizon (now called argic horizon) can be diagnostic without the visual appearance of clay cutans or clay skins. The increase of clay still must be reached within a certain distance within the profile. So, to summarize, the high and low activity clays are clas- sified in the new FAO legend (1989) as follows: NAME CEC me/100 gr clay Base Saturation B-horizon (perc) B-horizon Luvisols > 24 > 50 Lixisols < 24 > 50 Alisols > 24 < 50 Acrisols < 24 < 50 Fig. 4. 4 Classification of soils with an argic horizon in FAO (1989). The actual soil profile description forms, on which the DIDC-soil map of Soy and Lumakanda locations were based, can be found in appendix no. I. A summary with the most important data is given in Table 4. 4. 58 4. 4. 3 Major classification units. The following soil units could be distinguished: Table 4. 3 Soil units in Soy and Lumakanda „Locations: two methods compared. FAO-1974 KEN2A CONCEPT FAO 1989 Acrisols ferraio-chromic Acrisols chromic Acrisols ferralo-humic Acrisols (new) humic Acrisols humic Acrisols humic Alisols Ferrai sols rhodic Ferralsols rhodic Ferralsols Glevsols vertic-humic Gleysols (new) vertic-umbric Gleysols Luvisols chromic Luvisols chromic Luvisols humic Luvisols humic Plinthisols Miscellaneous Ironstone soils dystric Lithosols Acrisols These are strongly weathered and strongly leached soils with an AB horizon sequence. A chromic or humic epipedon overlies an argillic B-horizon of which at least a part has a base saturation of less than 50 %. The subunits that are found in the area are: humic Acrisols, which have an umbric epipedon (rich in acid and humic material), ferralo-humic Acrisols which have a base satura- tion of less than 24 me/100 gr. clay in at least part of the B- horizon together with an umbric epipedon, rich in acid and humic material (this ferralo-humic Acrisols was not yet defined by Som- broek et al. (1980)) and ferrai o-chromic Acrisols which also have horizons with a reddish colour, with hues redder than 5YR (Kenya Concept). The ferralo-chromic Acrisols are the ones standing very close to Ferralsols: they have very few signs of clay cutans or clay skins, their bulk densities are higher (rhofc = 1. 3 g/cm3) than in Ferralsols (rhoj^ = 1. 1 g/cm3) and their structure is less friable than in Ferralsols. In general Acrisols have little weatherable minerals left. The contents of Fe-, Al- and Ti-oxides are comparable to those of Ferralsols or slightly lower; the SiC-2/Al2O3 ratio is 2 or less. The clay fraction consists mainly of well crystallized kaolinite and some gibbsite (Legger in Driessen and Dudal, 1989). The Acrisols in the new FAO legend have an argic B horizon which has a cation exchange capacity (determined by NH4OAC method) of less than 24 me per 100 gr. clay and a base saturation of less than 50 percent in at least some part of the B horizon. For the argic horizon the presence of visible clay skins and cutans is not necessary. The humic Acrisols in the area have an umbric A horizon. Additional to the FAO 89 legend, a chromic Acrisols (ACx)is 59 described: Acrisols having a strong brown to red (rubbed soil has a hue of 7. 5 YR and a chroma of more than 4, or a hue redder rhan 7.5 YR) argic B horizon; lacking a calcic horizon, or concentrations of soft powdery lime within 125 cm of the surface, lacking gleyic and stagnic properties within 100 cm of the surface. This chromic Acrisols can key out after the humic Acrisols (ACu). Alisols (FAO-89 legend). Alisols have an argic B horizon which has a cation exchange capacity (by NH4OAc) of 24 me or more per 100 gr. of clay and a base saturation of less than 50 percent in at least some part of the B horizon. The humic Alisols have an umbric A horizon. In general Alisols are acid soils containing high levels of ' free' aluminium, caused by dissociation and transformation of 2: 1 clay minerals, which is still in process (Legger in Driessen and Dudal, 1989). Luvisols (FAO-74 and FAO-89 leerend). Soils having an argic B horizon which has a cation exchange capacity of 24 me or more per 100 gr clay and a base saturation of 50 percent or more throughout the B-horizon. Chromic Luvisols have a strong brown to red B horizon (rubbed soil has a hue of 7. 5 YR (5 YR Kenya Concept) and a chroma of more than 4, or a hue redder than 7. 5 YR (5 YR Kenya Concept). In a very wet place, near the edge of a bottom land, there was a profile (BLA3, see appendix I) that proved to be a Luvisol, with a humic A and high organic matter content (2.5 %). In the FAO-1974 legend this soil keys out as an orthic Luvisol. In the FAO-1989 legend this soil keys out as an humic Plinthisol. In general Luvisols are mineral soils conditioned by a (sub)humid temperate climate and in warm regions with distinct dry and wet seasons. The processes of mobilization of clay, transport of clay and immobilization of clay are active in these soils and result in distinct agric B- horizons. They have more favourable physical properties than Acrisols and have granular or crumb surface soils that are porous and well aerated (Legger in Driessen and Dudal, 1989). A problem with these soils in warm (sub)humid areas can be that the agric B horizon in the subsoil can dry out in the dry season as a hard layer, very compact or with macropores in it (caused by swelling and shrinking of the clay). Ferralsols fin the FAO-74 and FAO-89 legend). Ferralsols are soils having an oxic B horizon, which means among other things, an apparent CEC of 16 me or less per 100 gr clay (by NH4OAC) and less than 10 percent weatherable minerals in the 50-200H fraction. Rhodic Ferralsols have a red to dusky B horizon (rubbed soil has hues redder than 5 YR with a moist value of less than 4 and a dry value not more than one unit higher than the moist value). In general they are deeply weathered soils with a residual concentration of resistant primary minerals and the formation of kaolinitic clays and iron and aluminium oxides and hydroxides, mainly hematite (reddish) and goethite (yellowish). The most important processes in Ferralsols are hydrolysis and ferralitization (break down and leaching of primary silicate structures which cause release of cations such as Ca, Na, Mg, K and Si). The microstructure of Ferralsols is good, due to the formation at micro-aggregates of clay (pseudo-sand) (Legger in 60 Driessen and Dudal, 1989). Gleysols fin the FAO-74 and FAO89 legend). Soils showing gleyic properties within 50 cm of the surface. In Soy and Lumakanda locations the gleysols exist in the bottom- lands. Here they have high base saturations in top- and subsoil, and high CEC values (me/100 gr clay). The organic C percentage in the topsoil is high, but not high enough to meet the requirements for a mollic A. Because of the vertic properties, existing in top- and subsoil, a new sub-unit was created: vertic-humic Gley- sols (Ghv) (FAO-74) or verti-umbric Gleysols (GLuv) (FAO-89). The exact definition of this group of soils is: other Gleysols having an umbric A horizon or a dystric histic H horizon, showing vertic properties. Plinthisols. These soils are new in the FAO-89 legend. They are soils having 25 percent or more plinthite by volume in a horizon which is at least 15 cm thick within 50 cm of the surface or within a depth of 125 cm underlying an albic E horizon or a horizon which shows stagnic properties within 50 cm of the surface or gleyic properties within 100 cm of the surface. Plinthite is, according to FAO-89, an iron-rich, humus poor mixture of clay with quartz and other elements. It commonly occurs as red mottles, usually in platy, polygonal or reticulate pattern, and it changes irreversibly to a hard pan or to irregular aggregates on exposure to repeated wetting and drying. The processes which are active in Plinthisols are the same as in Ferralsols, with the difference that in a plinthisols there is a segregation of iron mottles, caused by the changing circumstances of reduction and oxidation (caused by a fluctuating ground water table). In the area, one soil was keyed as Plinthisol. According to the FAO-74 legend, it is an orthic Luvisol, with a pisoferric phase. Ironstone soils. This terminology comes from Sombroek (1982) and in these soils the plinthite is hardened irreversibly, due to a lowering of the groundwater level. In detail: soils with a massive ironstone layer (petroferric horizon; also referred to as ' laterite', ' murratn', ' ferricrete' or ' petroplinthite' ) starting within 50 cm of the surface. It is a pragmatic grouping to cover a variety of soil units that all have in common the presence of massive ironstone at shallow depth, although the soils themselves may be genetically different (Sombroek, 1982). To describe these soils with the FAO-74 legend is difficult. The only ironstone soil we found (BLB3, see appendix I) was named dystric Lithosols in the FAO-74 legend, because the murram was there within 25 cm (Kenya Concept) of the surface. 4. 4. 4 Soil genesis. In table 4.4 a short summary is given of the most important data that were collected in Lumakanda and Soy Locaion. 61 Table 4. 4 Summary of most import soil chemical data, (number of observations between brackets) Area geology CEC me/100 gr clay CEC me/100 gr clay topsoil subsoil Lumakanda granites 19.7 ±4.2 (11) 14.8 ±2.2 (11) (VSS) granodiorites Mautuma granites 24.1 ±5.0 (11) 20.6 ±2.4 (11) (VSN) granodiorites Sango gneisses 31.9 ± 11.8 (12) 25.8 ±5.3 (11) (VSE) Kongoni undiff. base. 19.5 ±5.9 (11) 16.0 ±4.6 (11) (PLG) system Forest Land Estate 24. 2 ± 10. 4 (7) 13. 2 ± 7. 3 (6) (BLA) phonolites Selbourne Estate 23.3 ±5.4 (12) 15.8 ±2.2 (8) (BLB) phonolites Sergoit scheme 29. 9 ± 8. 1 (5) 21. 4 ± 4. 1 (4) (BLC) phonolites Valley bottoms 38. 4 ± 13. 7 (5) 30. 8 ± 12. 3 (5) various geol. Bottomslands 30.6 t 5.1 (6) 39.4 ± 11.3 (6) This table makes clear that Acrisols and Ferralsols are to be found on a geology of granitic or grancdioritic origin. Luvi- sols are to be found on gneiss and in the valleys that are filled with colluvium and alluvium. On undifferentiated basement system (granites and granodiorites, but especially gneisses) Luvisols and Acrisols are to be found. Many Ferralsols are to be found in parent material originating from phonolites. There are also some Acrisols and Luvisols found on the phonolites, especially on the slopes towards the bottomlands. The Gleysols are situated in the periodically inundated bottomlands, which have a substratum of fine material infill from phonolithic origin. 62 table 4. 4 continued Area organic C % base sat. % base sat. % topsoil tops oil subsoil Lumakanda 1.4 ± .3 (11) 29.7 ±8.0 (11) 30. 6 ± 8. 9 (11) soils: Ferraisols and Acrisols (UMGA2) Mautuma 1.1 ± . 2 (11) 38. 5 ± 7. 0 (11) 39. 1 ± 5. 8 (11) soils: Acrisols (ULG14) Sango 1. 2 ± . 5 (12) 48.6 ± 13.6 (12) 49. 6 ± 11.0(11) soils: Luvisols (ULR2) Kongoni 1. 3 ± . 4 (11) 62.0 ± 20.3 (11) 57. 6 ± 12. 7(11) soils: Luvisols and Acrisols (UMU6) Forest Land E. 2. 0 ± . 5 (7) 51. 3 ± 20. 6 (7) 49. 7 ± 17. 2 (6) soils: Ferralsols and Luvisols (LIA3) Selbourne Est. 2. 0 ± . 3 (12) 33.3 ± 10.8 (12) 32. 9 ± 11. 5 (8) soils: Ferralsols and Acrisols (LIA3) Sergoit 1. 5 ± . 2 (5) 29.4 ±5.6 (5) 38. 8 ± 18. 2 (4) soils: Ferralsols and Acrisols (LIA3) Valley bottom 2. 1 ± 1. 2 (5) 49. 6 ± 13. 5 (5) 47. 3 + 12.2 (5) soils: Ferralsols and Acrisols (LIA3) Bottomlands 1. 7 ± . 4 (6) 79.3 ± 31.7 (6) 80. 1 ± 32. 3 (6) soils: Gleysols (VXC2 or VIC) Genetically the soil formation in Soy and Lumakanda locati- ons can be described with the help of a very typical catena (see fig 4. 5). On higher parts, the Ferralsols are to be found, with very often, a very hard petroferric horizon in the subsoil. At the lower end of a slope, Acrisols (or Luvisols) can be found. These soils have a well formed argic horizon. In between these Ferral- sols and Acrisols (Luvisols) the intergrades developed (ferralo- chromic Acrisols). In the wet spots near the river or bottomland, Gleysols have formed and sometimes some active plinthite can be found here (due to fluctuating wet and dry conditions). The petroferric horizon, which is rather common in the subsoil on the plateau, gives a clear indication of the genetical circumstances under which these soils were formed. A petroferric horizon (also called murram, laterite, ferricrete, petrpplinthite or ironstone) is formed under changing climatic conditions: the wet climate that is needed to keep the iron in the mobile ferrous form changed into a more drier climate, under which the plinthite hardened irreversibly. Therefore a petroferric horizon is very abundant in the transition zone between rainforest zone (humid) and savanne zone (arid to semi-arid). Also the occurrence of the intergrades is a sign that the weathering of Acrisols/Luvisols towards Ferralsols slowed down, due to a decrease in rainfall. 63 rhodic Ferralsols •petroferric horizon ferralo-chromic Aorisols chromic Acrisols l Gleysols Increase in CEC —* ; Increase in sand %— pli.uhite Increase in bulk density • Fig. 4. 5 Soil genesis along a slope. 4. 4. 5 Legend of the DIDC-Soil Map (scale 1: 100000) Andriesse and van der Pouw (1985) give in their Reconnais- sance soil map of the Late Basin Development Area, a rating for some soil characteristics and qualities. They distinguish the following classes: * Soil drainage Classes (Dra): 1 excessively and somewhat excessively drained 2 well drained 3 moderately well drained 4 imperfectly drained 5 poorly and very poorly drained * Effective soil depth (Dep): Depth to hard or very concretionary, root impeding layers. 1 extremely deep > 180 cm 2 very deep 120 - 180cm 3 deep 80 - 120cm 4 moderately deep 50 - 80cm 5 shallow 25 - 50cm 6 very shallow 0-25 cm * Inherent fertility (Fer): 1 high : three of the following criteria are met: - CEC > 24 me/100 gr clay and fine textural class - BS > 50% - CM > 1. 5% - rich parent rock# 64 2 moderate : two of the following criteria are met: - CEC > 16 me/100 gr clay and either: medium or fine textural class or. : OM > 1. 5% - BS > 50% - moderately rich or rich parent rock^ 3 low : two of the following criteria are met: - CEC <16 me/100 gr clay and medium or fine textural class - BS <50% - CM < 1. 5% 4 very low : all of the following criteria are met: - CEC <16 me/100 gr clay and coarse textural class - BS < 50% - CM < 1. 5% - poor parent rock# # Rich parents rocks: not in Soy or Lumakanda Moderately rich parent rocks: Intermediate igneous rocks (phonolites, basalts, nepheline,etc) and quartz-feldspar gneisses). Poor parent rocks: granites, granodiorites. * Salinity (Sa) 0 non saline : ECe < 4 mmhos/cm throughout profile 1 slightly saline : ECe 4-8 mmhos/cm within 80 cm 2 saline phase : ECe > 8 mmhos/cm within 80 cm * Sodicity (So) 0 non sodic : ESP <6% throughout profile 1 slightly sodic : ESP 6-15% within 80 cm 2 sodic : ESP >15% within 80 cm * Stoniness, Boulders (SB) Loose mineral fragments; stones: 7. 5 - 25 cm diameter; boul- ders: more than 25 cm diameter. 0 non stony non bouldery 0-2% by volume 1 slightly stony slightly bouldery 2-15% 2 stony bouldery 15-50% 3 very stony very bouldery > 50 % * Rockiness (Ro) Outcrops of solid rock at soil surface. 0 non rocky : 0-2% of area consists of bedrock 1 fairly rocky : 2-10% 2 rocky : 10-25% 3 very rocky : 25-50% 4 extremely rocky : 50-90% * Consistence (Co) Moist consistence of the subsoil; rated according to FAO (1977) 1 half ripe 2 loose 3 very friable 4 friable 5 firm 6 very firm - not rated (shallow and very shallow soils) 65 * Moisture storage capacity (Msc) Available moisture; estimated over effective soil depth 1 very high > 160 mm 2 high 120-160 mm 3 moderate 80-120 mm 4 low > 80 mm * Infiltration capacity (In) Maximum amount of water infiltrating into the dry soil, per unit of time and per unit of area; estimated on basis of soil texture, soil depth, stoniness, boulders and cracking proper ties; ranges according to Israelsen and Hansen (1962) adapted. 1 high > 2.5 cm/hr 2 medium 0.5-2.5 cm/hr 3 low < 0.5 cm/hr * Excess surface water (Ew) 0 none 1 seasonal flooding (flooding refers to superficial passage of water origina- ting from areas outside the terrain concerned; it is usually accompanied by scouring and sedimentation) 2 seasonal ponding (ponding refers to the accumulation of water in and on the terrain concerned due to its relatively low and flat position) 3 permanently waterlogged To complete the rating tables, the slope classes used in the LBDA and DIDC map are given here: A 0 - 2% flat or very gently undulating B 2 - 5% gently undulating C 5 - 8% undulating D 8 - 16% rolling E 16 - 30% hilly F > 30% steep The legend units that haven been distinguished, together with their landquality ratings (revised after Andriesse and van der Pouw, 1985) and can be found in appendix I. In MAP 7a and MAP 8a the soil units for Lumakanda Location are reproduced and in MAP 7b and 8b for Soy Location. 66 4. 4. 6 Comparison between LBDA (1985) and DIDC (1989) Maps. When the DIDC (1989) map (scale 1: 100000) is being compared (Table 4.6) with the LBDA (1985) map (scale 1:250000) the follo- wing differences and similarities become apparent: Table 4. 6 Comparison of LBDA map with DIDC map Area: Lumakanda DIDC-1989 map Geology: granites and granodiorites Soil units: rhodic Ferralsols (50%) ( 1 ) and ferralo-chromic Acrisols (50%) (2) (UMGA2). Dra Dep Fer Sa So SB Ro Co Msc In Ew (1) 2 2-3 3 0 0 0 0 4 2 1 0 (2) 2 4-5 3 0 LBDA-1985 map Geology: granites/granodiorites and phonolites Soil units: rhodic Ferralsols (60%) (1) (LIAI) and ferralo-chro- mic Acrisols (40%) (1) (UMG3). Dra Dep Fer Sa So SB Ro Co Msc In Ew (1) 2 3-4 3 0 0 0 0 3 2-3 10 (2) 2 2-3 3 0 0 0 0 4 1-2 2 0 Area: Mautuma DIDC-1989 map Geology: granites/granodiorites Soil units: ferralo-chromic Acrisols and humic Acrisols (ULG14). Dra Dep Fer Sa So SB Ro Co Msc In Ew 2 3 30000 4-5 320 LBDA-1985 map Geology: quartz-feldspar gneisses Soil units: orthic and rhodic Ferralsols (70%) (1) and humic Cambisols (30%) (2) (ULRA). Dra Dep Fer Sa So SB Ro Co Msc In Ew (.1)2 3 3000042 10 (2) 2 3-4 2 0 0 0 0 4 2-3 1 0 Area: Sango DIDC-1989 map Geology: augen-gneisses Soil units: ferralo-chromic and chromic Luvisols (ULR2) Dra Dep Fer Sa So SB Ro Co Msc In Ew 2 3-4100004-5320 LBDA-1985 map Geology: quartz-feldspar gneisses Soil units: orthic and rhodic Ferralsols (70%) (1) and humic Cambisols (30%) (2) (ULRA). Dra Dep Fer Sa So SB Ro Co Msc In Ew (1)2 3 3000042 10 (2) 2 3-4 2 0 0 0 0 4 2-3 1 0 67 Table 4. 6 continued. Area: Kongoni DIDC-1989 map Geology: Undifferentiated Basement System: predominantly gneis- ses. Soil units: ferraio-chromic and chromic Luvisols and ferralo- chromic Acrisols (UMU6) Dra Dep Fer Sa So SB Ro Co Msc In Ew 2 3 2 0 0 0 0 4-5 2-3 1 0 LBDA-1985 map Geology: Undifferentiated Basement System: predominantly gneis- ses. Soil units: orthic and rhodic-Ferralsols and ferralic Cambisols (UMU2). Dra Dep Fer Sa So SB Bo Co Msc In Ew 2 3-43000042-310 Area: Forest Land Estate, Selbourne Estate and Sergoit Scheme DIDC-1989 map Geology: Phonolites. Soil units: rhodic Ferralsols (70%) (1), mollic Gleysols (10%) (2) and rhodic Ferralsols with Lithosols and Rock Outcrops (10%) (3) and ferralo-chromic and ferralo-humic Acrisols and humic Luvisols and mollic Gleysols (10%) (4) (LIA3). Dra Dep Fer Sa So SB RO Co Msc In Ew (1) 2 3-4 3 0 0 0 0 3 2-3 1 0 (2) 5 3-4 2 0 0 0 0 5 2-3 3 2 (3) 2 4-6 3 0 0 0 0 4 3-4 2 0 (4) 2 3-4 2 0 0 0 0 3-4 2-3 3 0 LBDA-1985 map Geology: Phonolites Soil units: rhodic Ferralsols (60% or 30%) (1) and mollic Gley- sols (30%) (2) and ferralic/gleyic Cambisols and Lithosols (20 or 50%) (3) (LIAI or LIA2). Dra Dep Fer Sa So SB RO Co Msc In Ew (1) 2 3-4 3 0 0 0 0 3 2-3 1 0 (2) 2-4 4-5 3 0 0 0 0 4 4 2 0 (3) 5 3-4 2 0 0 0 0 5 2-3 3 2 68 Table 4. 6 continued. Area: Valleys (in Lumakanda, in Mautuma, Kongoni, Sango and Ser- goit). DIDC-1989 map Geology: Alluvium and colluvium of various parents rocks Soil units: dystric and humic Acrisols and chromic Luvisols (70- 80%) (1) and dystric and vertic Gleysols (20-30%) (2) (VXC2 and VIC). Dra Dep Fer Sa So SB Ro Co Msc In Ew (1) 2 2-3 1 0 0 0 0 3 2-3 2 1 (2) 5 2-3 1 0 0 0 0 5 2 3 2 LBDA-1985 map Geology: various parents rocks Soil units: dystric and humic Camhisols, ferralo-orthic and humic Acrisols (70-80%) (1) and dystric and vertic Gleysols (20-30%) (2) (VXC2 and VIC) Dra Dep Fer Sa So SB Ro Co Msc In Ew (1) 2-3 4-5 2-3 0 0 0 0 4 3-4 2 0 (2) 4-5 3 2-3 0 0 0 0 5 2 2 1 Area: bottomlands DIDC-1989 map Geology: Phonolithic infill Soil units: mollic Gleysols and vertic-humic Gleysols (BI1) Dra Dep Fer Sa So SB Ro Co Msc In Ew 5 2-3 100005232 LBDA-1985 map Geology: Phonolithic infill Soil units: mollic Gleysols (BI1) Dra Dep Fer Sa So SB Ro Co Msc In Ew 5 3-42000052-322 The soils and their characteristics, as they are found on the plateau by the LBDA study .and this DIDC study correspond well: both studies indicate a large amount of Ferralsols and the characterization that is given by both studies for these soils are also corresponding very well (same soil fertility index and same index for consistence and infiltration rate). The main difference between the LBDA study and this DIDC study on the plateau is that the LBDA does not indicate Acrisols or Ferralsols but indicates ferralic/gleyic Cambisols on the slopes towards the bottomland. On the sloping pfT*t in Luma)farv^a. the DIDC—map an^ LBDA—map give the same classification units. On the sloping parts tow?in3s the river Nzoia there are remarkable differences between the LBDA and DIDC map. First, the LBDA map does not indicate Acrisols or Luvisols at all in their soil classification for Soy and Lumakanda Locati- ons. The LBDA map indicates humic Cambisols and rhodic Ferral- sols, whereas the DIDC map indicates ferralo-chromic Acrisols, 69 chromic Luvisols and humic Acrisols. Secondly, the fertility ratings in the LBDA map are underes- timated consequently, especially for soils on undifferentiated Basement System, alluvium and colluvium and phonolithic infill. In the DIDC study higher fertility levels are found, especially higher CEO s (me/100 gr clay) and higher Base Saturations. Thirdly, the consistency rating in the LBDA classifications tends more towards friable to very friable (Ferralsols) whereas in the DI DC-study the consistency tends towards friable to firm (Acrisols and Luvisols). The last correction concerns the geology: in the DI DC-map the geology of some parts was corrected, as a result of examina- tion of the geological map and our own field observations. In Lumakanda, a piece of plateau (phonolites, according to An- driesse and van der Pouw (1985)) was reclassified into Lower- Level Uplands with a geology of Granites and Granodiorites. The steep slopes in Lumakanda (HRC) do not consist of intermediate, igneous rocks (as the LBDA-map indicates) but of gneisses and/or' schists. Close examination of the geological map also learned that the ULRAbc unit in Lumakanda Location (on the LBDA map, near river Nzoia) could be subdivided in another band with granites/ granodiorites (ULG14bc on DIDC-map). In Soy location the upper north-east part of the location was changed from UmUlb (undiffe- rentiated basement System) into UmRlb (gneisses) on basis of the geological map. The soil fertility data for this UmRlb part in Soy location (cluster 5 data, see MAP 6) were also used for the UmRlc part in Lumakanda location (west of HRC unit, see MAP 7a and MAP 7b). So, concluding: in Soy and Lumakanda locations, the rough lines of classification (separation of Plateau from Lower Middle- Level Uplands, Lower-Level Uplands, Minor Valleys and Bottom- lands) are very useful in the LBDA-map and give a clear indicati- on of the geomorphological structure of the landscape. The quantitative data that are attributed to these soil units are less valuable; especially the soil fertility ratings and the consistency ratings (workability! ) do not reproduce the actual situation very well. 70 Chapter 5 RfPuT^fi fff "the Land 5. 1 I ntrodiifitil OIL The crops that have been evaluated with both land evaluation methods are: for the wet season (from inarch till november): maize, beans, sunflower, sweet-potato and cassava and for the dry season (from november till march): sorghum and millet. The choice of these crops was determined by the available conver- sion tables in the KSS model and the available crop data in the data files of the VTOFQST model (the WOFOST model can not evaluate perennial crops like tea, coffee and banana). 5. 2 Results of the KSS model. With the help of the information gathered during the field- and literature study the different land qualities were rated according to the rating system of Weeda (1987, see chapter 4). The ratings of the different landqualities are listed for the different landunits in tab] e 5. 1 and table 5. 2 . Table 5. 1 Ratings of landqualities in Lumakanda location. Landunit Rt Rw Rn Re Ro Re Rv UlR2bc 4 3 3 2 1 1-2 1 U1R2C 4 3 3 2 1 1-2 1 UmRlc 4 3 3 2 1 1-2 1 UlG14bc 4 3 4 2 1 1-2 1 UmGA2ab 4 3 3 1 1 1-2 1 LIA3a 4 3 2-3 1 1 1 1 Blla 4 3 2-3 1 4 4 1 SAla 4 3 2 1 5 4 1 VICac 4 3 2-3 2 2-3 1-2 1 VXC2ac 4 3 2 2 2-3 1-2 1 HRC 4 3 3 2 1 5 3 me 4 3 3 2 1 5 3 Table 5. 2 Rating of landqualities in Soy locatioa Landunit Rt Rw Rn Re Ro Re R UlR2bc 5 3 3 2 1 1-2 1 UmRlb 5 3 2-3 2 1 1-2 1 UmU6bc 5 3 2-3 2 1 1-2 1 LIA3a 5 3 3-4 1 1 1-2 1 Blla 5 3 2-3 1 4 4 1 SAla 5 3 2 1 5 4 1 VICac 5 3 2-3 2 2-3 1-2 1 VXC2ac 5 3 2 2 2-3 1-2 1 HGC 5 3 3-4 2 1 5 3 HIC 5 3 3 2 1 5 3 71 The landsuitabi lity classes for the different crops are listed below (table 5.3 and table 5.4) and in map numbers 9-14 for Lumakanda location and Map numbers 15-20 for Soy Location. For each crop the most limiting landquality rating, determines the final land suitability. In the land suitability classification the most limiting factor is indicated (t = temperature, n = nutrient, o = oxygen, c = cultivation). Table 5. 3 Suitability for crops in Lumakanda. Landunit Maize Beans Sun- Sweet Cas- Millet flower potato sava Sorghum UlR2bc S2n S2n S2n S2t S2tn S2t U1R2C S2n S2n S2n S2t S2tn S2t iftnRlc S2n S2n S2n S2t S2tn S2t UlG14bc S3n S3n S3n S3n S3n S3n UmGA2ab S2n S2n S2n S2t S2tn S2t LIA3a S2t&S2n S2n S2t&S2n S2t S2tn S2t Blla No No No No No No SAla No No No No No No VECac S3o S2t S2o S2t S2t S2t VXC2ac S3o SI S2o S2t S2t S2t HRC Nc Nc Nc Nc Nc Nc HIC Nc Nc Nc Nc Nc Nc Table 5. 4 Suitability for crops in Soy location. Landunit Maize Beans Sun- Sweet Cas- Millet flower potato sava Sorghum UlR2bc S2n S2n S2n S2t S2tn S2t UmRlb S2t S2t S2t S3t S2tn S3t UmU6bc S2t S2t S2t S3t S2tn S3t LIA3a S2n&S2t S2n&S2t S2t&S2n S3t S2tn S3t Blla No No No No No No SAla No No No No No No VICac S3o S2t S2o S3t S2tn S3t VXC2ac S3o Sit S2o S3t S2t S3t HGC Nc Nc Nc Nc Nc Nc HIC Nc Nc Nc Nc Nc Nc These suitability classes mean : 51 Highly suitable (76-100 % of normative yield) 52 Moderately suitable (51-75 % of normative yield) 53 Poorly suitable (26-50 % of normative yield) N Not suitable (less than 25 % of normative yield) The normative yield is the maximum yield that can be attained by a good farmer under actual cicumstances. Normative yield for maize is for instance 6000 kg/ha in Soy and Lumakanda locations (observations the socio-economic research). For beans, the normative yield is 4000 kg/ha, for sweet potato 16000 kg/ha, for cassave 30000 kg/ha, for sunflower 2000 kg/ha (seeds), for millet 2000 kg/ha and for sorghum 4500 kg/ha (GuiJdng et al., 1983). However, the percentages of the normative yields that are indicated as a result of the qualitative land evaluation, are not 72 very reliable. The strength of this qualitative land evaluation is: a) Determination of the main factors limiting crop growth. Often the nutrient availability is a problem, but for Soy Location also the temperature regime may limit growth of some crops. b) Determination of the relative suitability of a land unit for one crops compared to another. As a result, some rough statements can be made: 1: In Lumakanda and Soy, suitability for bean production is highest, and somewhat higher than that for maize and sunflower. 2: Sorghum and Millet have low suitabilities as alternative crops, both in Soy and in Lumakanda, mainly due to the unfavourable climatic conditions. 3: In the bottomlands and swamps, oxygen availability is a problem for all crops. In the valley bottoms, oxygen availabilty is sometimes a problem, especially for maize and sunflower. Growth of these crops is disturbed when oxygen is lacking. The other crops are less sensitive to this degree of oxygen shortage. 4: In Lumakanda location, the nutrient status is often the main factor limiting crop growth whereas in Soy location growth is often disturbed by an unfavourable temperature regime. It should be kept in mind, that such results depend strongly on the crop variety that one wants to evaluate. There are cold- tolerant sorghum varieties (Jaetzhold and Schmidt, 1985) and there are a large number of millet cultivars: Foxtail millet [Setaria italica], Bulrush millet [Pennisetum americanum], Finger millet [Eleusine coracana] and Common millet [Panicum miliaceum]) with their own varieties, all strongly varying in their sensitivity to unfavavourable growing conditions. 73 5 3 Irrmrfc in the WOPOST model First, the input that were used for calcuclation with the WOFOST model shall be described more precisely per landunit. The climatic data used for calculation of the potential production, are listed in tabel 5. 5 . Turbo Forest Station is situated at the boundary of Soy and Lumakanda locations and its information is considered useful for calculations for both locations. Table 5. 5 Climatic input data for the WOFOST model. Name Station: Turbo Forest Station, Kenya. Geographical latitude in degrees: 0. 6°. Elevation above sea level^ 1860 meters. A: empirical constant in Angstrom formula : 0. 25 B: empirical constant in Angstrom formula : 0. 45 a b c d e f g h jan 9.3 26. 5 21. 799 12. 1 1.3 99. 0 8. 0 feb 9. 5 26. 7 22. 552 12.0 1.3 77. 0 6. 0 mar 9. 7 27. 6 23. 849 13. 1 1.5 55. 0 5.0 apr 11.2 27. 6 22. 301 15.3 1.3 103. 0 9. 0 may 11. 9 24. 8 19. 456 16. 1 0.9 146. 0 16. 0 jun 10. 2 23. 5 18. 368 15.3 0.8 127. 0 15. 0 jul 10. 4 22. 9 17. 656 15. 1 0.8 176.0 17.0 aug 10.0 23. 3 18. 284 15. 1 0.8 236. 0 19.0 sep 9. 1 24. 6 20. 794 15.0 0.8 100. 0 13. 0 okt 9. 7 25. 4 22. 092 14. 6 1.2 80.0 10. 0 nov 9. 9 25. 5 22. 301 14. 1 1.4 47. 0 7.0 dec 8.2 26. 3 23. 430 13.3 1.4 29. 0 4.0 with a: month b: minimum air temperature (°C) c: maximum air temperature (°C) d: shortwave radiation actually received e: actual vapour pressure (mbar) f: average windspeed at standard height of 2 m as a function of day number (ms"*) g: average rainfall per month (mm/month) h: average number of rainy days per month The crop data used are the standard data per crop species, as listed the crop data file of the WOFOST model (van Diepen et al., 1988). The sçyi I flat? used are listed in table 5. 6. 74 Table 5. 6 Soil physical data used for calculation with the VTOFOST model for Soy (S) and Lumakanda (L) Locations. Land Unit Location SOPE A. M. VolumetricMoisture Contents at pF KSUB % 0 2.3 4.2 6.0 U1R2 top S + L 70 10. 6 43. 7 24. 1 13.5 4. 5 bot S + L 70 10. 4 46. 9 21. 8 11. 4 3.8 UmRl S + L 111 9. 2 54. 5 29. 7 20. 5 7.0 U1G14 Lum 10 10. 0 42. 5 23.2 13.2 4. 4 UmGA2top Lum 200 12. 5 55. 4 31. 1 18. 6 6.2 bot Lum 200 10. 7 47. 9 23.3 12. 6 4.2 UmU6 top Soy 70 12. 1 49. 9 30.7 18. 6 6.2 bot Soy 70 11. 9 53.4 27.4 15. 5 5. 2 LIA3 top S + L 45 12. 3 57. 0 28.7 16. 4 5. 5 bot S + L 45 10. 6 54. 9 19.6 9. 0 3.0 S + L 184 13. 0 52. 7 30. 1 17. 1 5. 7 S + L 80 12. 8 55. 4 30.5 17. 7 5. 8 S + L 111 13. 0 54. 5 30. 9 17. 9 6.0 S + L 111 12. 6 53. 1 30.9 18. 3 6. 1 S + L 111 12. 5 54. 2 31. 1 18. 6 6.2 S + L 111 11.4 52.0 29. 9 18. 5 6.2 BIla& SAla S + L 10 13. 8 55. 9 31. 9 18. 1 6.0 VI C& VXC2 S + L 111 12. 8 55. 9 24.2 11.4 3.8 S + L 111 10. 9 55. 7 26.2 15. 3 5. 1 S + L 111 9. 0 44. 6 22. 9 13.0 4. 3 S + L 111 10. 3 45. 5 22. 9 12. 6 4.2 A. M means available moisture as defined by Gelens et al., 1976. In this table, SOPE being equal to A mentioned in the soil profile description forms (see Appendix I) is the transmission zone permeability or, in other words, the infiltration rate into a completely wet soil (cm/day). KSUB is the percolation used in calculations without groundwater, from the rooting zone to the subsoil below the potential rooting depth cm/day. The values of KSUB and SOPE are roughly estimated with the help of the values gathered in the infiltration experiments (see appendix II). Before the table with the base soil supply of nutrients is presented (table 5.8), table 5.7 gives some general information about the soil chemical data that were collected. First of all, W is given, specifying the order in which the program WDFOST has been applied for the Soy and Lumakanda areas. So, to evaluate the complete area, 22 runs of W0FOST have to be performed. The land unit concerned is listed and if the transect has been divided in a top and a bottom part of the slope or not. If this was not the case, ' overall' is indicated. Under the heading ' no. ' , the number of observations is given. One can see, that for a number of observations smaller than 3, no standard deviations have been calculated. 75 Table 5. 7 General information on the origin of the soil chemi- cal data from Soy and Lumakanda Location. w Land Unit top/bot. Source of Data no. Organic C% pH-H2O 1 UlR2bc top VSE transect 6 1. 0 ± 0. 3 6.0 2 bottom VSE transect 6 1. 5 ± 0. 5 6.2 1 U1R2C top VSE transect 6 1. 0 ± 0. 3 6.0 2 bottom VSE transect 6 1. 5 ± 0. 5 6.2 3 UmRlc overall cluster 5 10 0. 8 ± 0. 3 6. 1 3 UmRlb overall cluster 5 10 0. 8 ± 0. 3 6. 1 4 UlG14bc overall VSN transect 11 1.1 ± 0. 2 5. 6 5 UmGA2ab top VSS transect 5 1. 3 ± 0. 2 5. 6 6 bottom VSS transect 5 1. 6 ± 0. 3 6.0 7 UmU6bc top PLG transect 6 1.1 ± 0. 3 5. 1 8 bottom PLG transect 5 1. 6 ± 0. 5 6. 1 9 LIA3a overall BLA transect 7 2. 0 ± 0. 5 5.7 10 overall BIiB transect 12 2. 0 ± 0. 3 5.2 11 overall BLC transect 5 1. 5 ± 0. 2 5. 7 12 overall cluster 1 10 1. 7 ± 0. 3 5.6 13 overall cluster 2 10 1. 4 ± 0. 4 5.4 14 overall cluster 3 10 1. 0 ± 0. 6 5.8 15 overall cluster 4 9 0. 6 ± 0. 3 5. 7 BIla& 16 SAla overall bottomland BLA 1 1. 9 5.9 17 bottomland BLB 1 1. 8 5.2 18 bottomland BLC 3 1. 6 6. 5 VlCac 19 VXC2ac overall VAL transect 3 3. 3 6. 3 20 VSS valley 1 1. 1 6.0 21 VSN valley 1 1.4 5.7 22 PLG valley 1 1. 3 6.3 76 Table 5. 8 The Supply of Nutrients N, P and K in Soy and Lumakanda locations. w Landunit top Supply of N Supply of P Supply of F bot- kg/ha kg/ha kg/ha 1 UlR2bc top 49. 0 ± 6. 1 5.4 ± 1. 1 172. 8 ± 50. 6 2 bot 67. 8 ± 22. 6 7.6 ± 2. 4 154. 3 ± 65.0 1 UlR2c top 49. 0 ± 6. 1 5.4 ± 1. 1 172. 8 ± 50. 6 2 bot 67. 8 ± 22. 6 7.6 ±2.4 154. 3 ± 65. 0 3 UmRlc — 26. 8 ± 6. 3 8.5 (est. ) 201. 0 ± 75. 2 3 UmRlb — 26. 8 ± 6. 3 8.5 (est.) 201. 0 ± 75.2 4 UlG14bc — 40. 3 ± 9. 1 5.2 ± 0. 5 102. 2 ± 35. 8 5 UmGA2ab top 56. 1 ± 16. 4 7.8 ± 1. 3 98. 7 ± 27. 9 6 bot 81. 8 ± 18. 4 8. 4 ± 2. 7 102. 2 ± 35. 8 7 UmU6bc top 54. 9 ± 11. 3 6.2 ± 1. 4 170. 6 ± 32.4 8 bot 84. 3 ± 23. 2 8.0 ±1.0 235. 0 ± 136. 8 9 LIA3a — 101. 4 ± 57. 8 10. 5 ± 4. 0 151. 3 ± 34.1 10 — 65. 6 ± 13. 7 7.9 ±2.0 131. 2 ± 58.2 11 — 60. 7 ± 12. 8 9. 9 ± 3. 7 150. 7 ± 26.7 12 — 74. 5 ± 16. 6 10. 1 (est. ) 152. 0 ± 58. 5 13 54. 1 ± 14. 2 7.5 (est.) 175. 4 ± 58.9 14 — 40. 1 ± 15. 5 6. 3 (est.) 212. 6 ± 106. 5 15 — 26. 8 ± 6. 3 8.5 (est.) 201. 0 ± 75.2 16 BIla& SAla — 73. 7 9. 1 69. 8 17 — 61. 2 8.2 103. 1 18 — 50. 1 7. 6 96.0 19 VICac& VXC2ac — 183. 3 13.4 110. 4 20 — 58. 7 6.4 157. 6 21 — 53. 7 6.3 56. 1 22 — 54.4 8.5 134. 6 Although there are clear differences (i. e. variation) in, particularly organic C content between different cluster and transects in unit LIA3, this unit could not be subdivided. The variation has only been shown to exist, by the nested analysis (see section 4. 1.2. ), but this has not been mapped. Therefore this unit is considered as one, ' homogeneous' unit, which can be described best by as much data as observations are available. The most important factors influencing nutrient limited yields do not have very large variations at short distances (especially organic C, C. E. C., and available K see figure 4.1). So, the best indication for the nutrient-limited yield in LIA3a can be derived by calculating this yield per observed location. The resulting yield indication will be the mean of all these values (from UA3a), together with its standard deviation. The subdivision of the VSE, VSS and PLG transects into top and bottom parts is based on the assumption that nutrients tend to accumulate on the lower part of a slope, due to downward processes such as eluviation, erosion and downward percolation of water and nutrients. This statement can be illustrated with fig 5. 1. and fig 5. 2, although the trend in fig 5. 1 is not very strong (R2 = 0. 62 for N supply and R2 = 0. 52 for P supply). 77 Supply of N and P along a slope situation from Sango O N supply • P supply kg/ha kg/ha CO I 100 10 Z Top of slope Bottom of slope R2 Nsuppl = 0.62 R2 PsuDpl. = 0.52 Fig. 5. 1 Supply of N and P along a Slope in Sango. In another transect, an increase in base saturation can also be seen, going downwards along the slope (fig 5. 2). O CEC m.e./ • Bas. Sat.in 100 gr soil 100 8 • • - 60 • E • • • - 20 8 I CO 4 aj 40 •• - a> o V) 20 • o o 8 o o o o o • LIA3a sloping to BI 1a Fig. 5. 2 Increase in Base Saturation in topsoil in BLA transect. 78 5 4 Results of the WDFOST model. The daily rainfall data, which has been used for the calculation of the water-limited yields, originate from 1962. Äs Fig. 5. 5 shows, 1962 was a year with an average amount of rainfall (1300 mm). Compared with annual rainfall amounts ranging between 1200 and 1400 mm (see fig 2.2) this year can be considered representative compared with ' mean' rainfall. This year was chosen for all the crops except for millet and sorghum. rainfall in half PET in mm/month mm/month :oo -- o .-'••" \ • \ \ - . ' ' / • -. TOO - \ . -r ' ' \ o 1 ( 1 1 1 I 1 1 1 1 1 jan feb mar apr may iun jul aug sept oct nov dec Fig. 5. 5 Monthly rainfall data for 1962 at Turbo Forest Statioa rainfall in half PET in mm/month mm/month / \ JOO o "'• - ' ' \ c \ a E 10ù ,.. ..••'• " • ...... A \ r\ i i i i i i i i i i i i jan feb mar apr may jun jul aug sep oct nov dec Fig. 5. 6 Monthly rainfall data for 1983 at Turbo Forest Statioa 79 Growth of millet and sorghum has been analyzed for a dry season, using the rainfall data for 1983 (see fig. 5. 6) because of its well distributed and average amount of rainfall in the dry season. For the various crops, only daily rainfall data originating from Turbo Forest Station (see Table. 5.9), have been used. Table. 5. 9 Amount of rainfall used by the different crops. Crop Year of rainfall Amount of Rainfall (mm) during a growing season Maize 1962 926 Beans 1962 607 Sunflower 1962 434 Sweet Potato 1962 1078 Cassava 1962 1242 Sorghum 1983 455 Millet 1983 418 In Table 5. 10 the start date of the simulated growth and the end of the growth are indicated. Maximum LAI and minimum LAI at the end of the growing season are given (in both cases for the potential and the water-limited situation). Table. 5. 10 Start and end of the growing season plus crop development indications for the evaluated crops. Crop Start-End length LAI max LAImax LAI en< d of simu- of simu- Pot. prod. Wat.lim. pot. W. L. lation lation + day no. + day no. Maize 80-248 168 8. 8 (130) 8. 5 (140) 4. 5 4. 5 Beans 80-187 107 5.7 (130) 4.5 (140) 0. 3 0. 3 Sunflower 200-304 104 5.2 (270) 5. 1 (270) 3. 9 3. 9 Sweet Potato 80-326 246 8.4 (170) 8.6 (170) 0.03 0.03 Cassava 80-63 348 11.5(150) 9.0 (180) 6. 5 1.7 Sorghum 280-85 170 9. 6 (330)9. 3 (330) 1. 3 0.5 Millet 280-77 162 5.0 (35) 4.0 (18) 5.0 1.8 * For the day number: see Appendix III (Day numbers according to Julian Calendar). The WOFOST model was usde first for maize, because actual yield for maize were available. With these indications the yields as calculated with the WOFQST model could be cheked. The results of the evaluation for maize are presented in table 5. 11. Potential and water-limited yields are indicated (at the end of the table) and for the nutrient-limited yields also the standard deviations are given, if available. The nutrient-limited yields are classified and this classification is presented in two maps (see MAP no. 21 and 28). The actual yields for maize are given for four sites: two in Soy and two in Lumakanda. 80 Table. 5. 11 The Quantitative Evaluation results for inaize. Landunit Location Nutrient-limited Actual Yield Yield (in 1987")i (* 103 kg/ha) (* 103 kg/ha) UlR2bc S + L top: 2.4 [2] bot: 3.4 [4] UlR2c L top: 2. 4 [2] bot: 3.4 [4] UmRlb S 1. 6 [1] UmRlc L 1. 6 [1] UlG14bc L 2.3 [2] 2. 4 ± 0. 8 (34) UmGA2ab L top: 3.0 [4] 2. 8 ± 1.6 (32) bot: 3. 5 [5] 2. 8 ± 1.6 (32) UmU6bc S top: 2. 8 [3] 3. 0 ± 1.9 (30) bot: 3.8 [5] 3. 0 ± 1.9 (30) LIA3a S + L 3. 1 ±1.0 [4] (7) 2. 8 ± 1.7 (33) Blla S + L 3. 1 ± 0. 2 [3] (4) SAla S + L not arable VICac & VXC2ac S + L 3. 6 ±1.3 [4] (5) HPC L not arable HGC S not arable HIC L + S not arable Potential Yield is in all cases 10. 5 * 103 Ira/ha Water Limited Yield. is also L0'.. 5 * 103 kg/ha Yield classes between [ ] Number of observations between ( ) 2 1987 was a year with normal to high amounts of rain. Table 5. 12 Fertilizer requirements to reach potential maize production (as calculated with WDFOST model) and the actual applications of fertilizer given. Landunit top or N P2O5 K2O Nl Ntop P2O5 K2O bottom (kg/ha) (kg/ha) UlR2bc top 24 60 0 bot 20 60 4 UlR2c top 24 60 0 * bot 20 60 4 UmRlc 24 60 0 UlG14bc 20 60 4 31 47 33 — UmGA2ab top 24 60 12 32 65 65 — bot 24 60 12 32 65 65 — UmU6bc top 24 60 12 27 48 62 bot 24 60 0 27 48 62 — LIA3a 20 60 4 39 63 59 BIAla 24 60 12 SAla — — — VICac& VXC2ac 24 60 12 HRC — — — HGC — — — me — — — 82 Table 5. 13 Nutriënt limited yields (in 103 kg ha"1) for crops in Soy and Lumakanda Location in wet season. Landunit Beans Sunflower Sweet Potato Cassava I UlR2bc top 1.6 [2] 0. 96 [1] 3. 8 [2] 4. 8 [2] bot 2.2 [3] 1.3 [2] 5.0 [5] 6.3 [4] ülR2c top 1. 6 [2] 0.96 11] 3.8 [2] 4.8 [2] I bot 2.2 [3] 1.3 [2] 5.0 [5] 6.3 [4] UmRlb 1. 1 [1] 0.65 [1] 2.6 [1] 3.2 [1] UmRlc 1. 1 [1] 0.65 [1] 2.6 [1] 3.2 [1] UlG14bc 1.5 [2] 0.90 [1] 3.4 [3] 4.4 [2] UmGA2ab top 2.2 [3] 1.3 [2] 4.4 [4] 5.6 [3] bot 2.4 [3] 1. 5 [3] 5.2 [6] 6.5 [4] ümU6bc top 1.8 [2] 1. 1 [2] 4.2 [4] 5.4 [3] bot 2.3 [3] 1.4 [2] 5.6 [7] 7. 1 LIA3a 2. 1 ± 0. 7 1. 1 ± 0. 4 4. 6 ±1.3 5.8 ±1.7 [3] (7) [2] (7) [5] (7) [3] (7) Blla* 2. 3 ± 0. 1 1.4 ± 0.08 4.5 ±0.2 5.8 ± 0. 3 [3] (3) [2] (3) 15] (3) [3] (3) SAla a a. a a. a a. a a. VICac*& VXC2ac* 2.4 ±0.8 1.5 ± 0. 5 5.2 ±1.7 6.6 ±2.2 [3] (4) [3] (4) [6] (4) [4] (4) HRC a a. a a. a a. a a. HGC a a. a a. a a. a a. HIC a a. a a. a a. a a. Potential yield 7. 7 6. 1 24. CI 35. C1 Water limited yield 7.7 6. 1 24. C1 18. C) Yield classes between [ ] Number of observations between ( ] a a. = not arable severe risk of waterlogging I I 81 Table 5. 12 continued NI = first application of nitrogen. Nt s top dressing of nitrogen. 1 kg P2O5 = 0. 437 kg P 1 kg K2O = 0. 830 kg K In table 5. 12 the fertilizer requirements are listed for maize per landunit. For each potential or water-limeted yield calculated, the amounts of N, P and K to reach that yield are calculated by the WOPOST model. From these amount, the ratio between these quantities can be derived. For instance: in landunit UmRl, nutrients N, P and K should be applied in relative amounts of 3.5 : 1 : 0 . This is a nice relationship, but it doesn' t say too much. It is more interesting to know if the fertilizers applied nowadays or advised to apply compensate in an optimum way the nutrient shortages in the soil. To check' this, it is assumed that: P is the most limiting nutrient, because of its relatively low solubility in these rather acid soils. applied fertilizer P has a recovery fraction of 0. 08, and applied fertilizer N and K have a recovery fraction of 0. 30 (CWFS, 1985). the amounts of P2Os advised to apply on the various crops (Western Agricultural Research Centre, Kakamega, 1988) are correct, and can be used as a starting point for calculating the amounts of N and K required additionally. For instance: for maize production in unit UmRl, N, P and K should be applied in a ratio of 3.5 : 1 : 0. The amount of P2O5 applied is 60 kg/ha (W.A.R.G). 60 kg of P2O5 is 60 * 0.437 = 26.2 kg P per ha. The recovery fraction is 0.08, so 26.2 * 0.08 = 2. 1 kg P per ha is taken up by the crop. For an identical yield increase, [ 3. 5 * 2. 1 ] /0. 30 = 24 kg N/ha should be applied. For each land unit (table 5.12), the amounts of fertilizer required are calculated in this way. If these amounts are brought back to their relative relationships, groups with the identical N : P : K relationship can be recognized. For maize these fertilizer-gift classes are (see also table 5. 12) : N J?2^5 K2O a) 24 60 12 b) 20 60 4 c) 24 60 0 83 Table 5. 14 Nutrient limited yield (103 kg ha"1) for crops in Soy and Lumakanda locations in the dry season. Land unit Sorghum Millet yields in 103 kg/ha UlR2bc top 2.0 [3] 2. 1 [2] bot 2. 5 [4] 2.7 [3] UlR2c top 2.0 [3] 2. 1 [2] bot 2. 5 [4] 2.7 [3] UmRlb 1.4 [1] 1.5 [1] UmRlc 1.4 [1] 1.5 [1] UlG14bc 1.8 [2] 1.9 [1] UmGA2ab top 2.2 [2] 2.3 [2] bot 3.0 [4] 2.6 [4] UmU6bc top 2.4 [3] 2.3 [2] bot 2. 5 [4] 3. 1[4] LIA3a 2. 3 ± 0.6 [3] (7) 2.6 ±0.4 [3] (7) Blla 2. 2 ± 0. 1 [3] (3)* 2.2 ±0.2 [2] (3)* SAla n. a. n. a. VICac & VXC2ac 2. 6 ± 0.8 [4] (4)* 2.6 ±0.7 [3] (4)* HRC n. a. n. a. HGC n. a. n. a. me n. a. n. a. potential yield 6.0 4. 2 Water limited yield 2. 3 0. 8-1. 8 Water limited yields can fluctuate in case of groundwater Yield classes between [ ] Number of observations between ( ) Before the fertilizer gifts as calculated for other crops are given (table 5. 16), fertilizer recommendations (table 5. 15), as advised by the Western Agricultural Research Centre in Kakamega, are specified. Table 5.15 Fertilizer recommendations (kg ha"1) by W. A.R.C. (1988). Crop N P2O5 K2O Maize 60 60 0 Beans (pure) 80.5 31.5 0 Beans (mixed) 46 18 0 Sunflower 22. 5 57. 5 0 Sweet potato advised to grow after crop that received fertilizer Cassava advised to grow after crop that received fertilizer Sorghum 20 + 20 20 0 Millet 20 + 20 20 0 In tables 5. 16, 5. 17 and 5. 18 the fertilizer applications calculated for the other crops are given except for sweet potato and cassava. For these crops no fertilizer recommendations are given by the W. A. R. C., and therefore the ratio between the amount of N, P and K is only specified. 84 Table 5. 16 Fertilizer requirements (kg ha"*) for beans and sunflower production in Soy and Lumakanda Locations. For calculation method, see text. Beans (pure) Beans (mixed) Sunflower P N P2O«5K2O N ?2°5> K20 N 2°5 K2O Landunit UlR2bc T 5 32 7 2 18 4 32 60 8 B 5 32 7 2 18 4 32 60 8 UlR2c T 5 32 7 2 18 4 32 60 8 B 5 32 7 2 18 4 32 60 8 UlG14bc 5 32 7 2 18 4 30 60 2 UmU6bc T 5 32 7 2 18 4 30 60 2 B 5 32 1 3 18 1 32 60 8 UmRlb 5 32 1 3 18 1 30 60 2 UmRlc 5 32 1 3 18 1 30 60 2 UmGA2abT 4 32 14 2 18 8 28 60 17 B 4 32 14 2 18 8 28 60 17 LIA3a BLA 3 32 11 2 18 6 30 60 2 BLB 5 32 7 2 18 4 32 60 8 BLC 5 32 7 2 18 4 32 60 8 cll 5 32 7 2 18 4 32 60 8 cl 2 5 32 7 2 18 4 30 60 2 cl 3 5 32 1 3 18 1 30 60 2 cl 4 5 32 1 3 18 1 30 60 2 VXC2ac& VECac Sergoit 3 32 11 2 18 6 32 60 8 Lumakanda 5 32 7 2 18 4 28 60 17 Mautuma 4 32 14 2 18 8 28 60 17 Kongoni 4 32 14 2 18 8 17 60 17 BI la 4 32 14 2 18 8 28 60 17 T = Top part of slope B = Bottom part of slope 85 Table 5. 17 Relative need for N, P and K for cassava and sweet potato production in Soy and Lumakanda locations. For calculation method, see text. Cassava Sweet Potato Landunit: N P K N P K U1R2 top 6 1 6 4 1 3 bot 6 1 6 4 1 3 Ü1G14 6 1 6 4 1 3 UmU6 top 6 1 6 •t v 1 4 bot 6 1 6 4 1 4 UmRl 6 1 6 4 1 3 UmGa top 6 1 8 4 1 3 bot 6 1 8 4 1 3 LIA3 BLA 6 1 8 4 1 3 BLB 6 1 8 4 1 3 BLC 6 1 8 4 1 3 Cll 6 1 6 4 1 3 Cl2 6 1 6 4 1 3 Cl3 6 1 6 4 1 3 Cl4 6 1 6 4 1 1 VXC2 & VIC Sergoit 6 1 8 4 1 3 Lumak. 6 1 6 4 1 4 Mautuma 6 1 6 4 1 3 Kongoni 6 1 8 4 1 4 BI 6 1 8 4 1 4 Table 5. 18 Fertilizer requirements (kg ha"1) for Sorghum and Millet production in the dry season in Soy and Lumakanda locations. For calculation method, see text. Sorghum Millet Landunit: N > *2° N P2O5 K2O U1R2 top 11 20 8 7 20 0 bot 11 20 8 7 20 0 U1G14 11 20 8 7 20 5 UmU6 top 11 20 8 7 20 0 bot 11 20 2 7 20 0 UmRl 11 20 2 2 20 1 UmGA top 11 20 15 6 20 8 bot 11 20 15 6 20 0 LIA3 BLA 11 20 8 7 20 0 BLB 11 20 8 7 20 5 BLC 11 20 8 7 20 0 cll 11 20 9 7 20 0 cl2 11 20 8 7 20 0 cl3 11 20 2 7 20 0 cl4 11 20 2 7 20 0 VXC2 & VIC Sergoit 11 20 16 0 20 8 Lumak. 11 20 8 7 20 0 Mautuma 11 20 15 7 20 5 Kongoni 11 20 8 7 20 5 BI1 11 20 15 7 20 5 86 For calculating the actual fertilizer gifts, the required amounts of nutrient should be divided by the nutrient percentage in the fertilizer. For instance: to apply 20 kg of N in the form of C.A.N. (26 % N), 20/0.26 = 77 kg of C. A. N. is applied per ha. For such conversions, the amounts of N and P2O5 *n t^îe main fertilizers are listed in Table 5. 19. Table 5. 19 The nutrient percentages and contents in common fertilizers. Nutrient percentages: Contents of one 50 kg bag of fertilizer Triple superphosphate 46% P2O5 - no nitrogen and 23 kg of phosphate Single - no nitrogen and 9 kg of phosphate superphosphate 18% P2O5 - 23 kg of nitrogen and no phosphate Urea 46% N - 13 kg of nitrogen and no phosphate C. A. N. 26% N - 13 kg of nitrogen and no phosphate A. S. N. 26% N sulphate - 10. 5 kg of nitrogen and no phosphate of ammonium 21% N - 9 kg of nitrogen and 23 kg of phosphate D.A.P. 18% N+46% P2O5 - 5. 5 kg of nitrogen and 26 kg of phosphate M.A.P. 11% N+52% P2O5 - 10 kg of nitrogen and 10 kg of phosphate 20: 20: 0 20% N+20% P2O5 5. 5 Some Conclusions. In almost all land units, the N gifts recommended by W. A.R.C. are higher than the gifts that are calculated with the WDFOST model on the basis of a prescribed P2C>5-gift. Only in the case of sunflower, the prescribed P2O5~gift indicates nitrogen requirements that are even a bit higher than recommended by the W.Ä.R.C in Kakamega. Rather high requirements of K2O are calculated with the WOFOST model, especially in the case of high organic C percentages. These high amounts are due to an overestimation of the K2O requirement (and to a lesser extent the N requirements) by the WDFOST model, especially when the fertilizer gifts to reach water-limited or potential production are being calculated. In a study on maize production in Kenya (C. W. F. S., 1985) it was found that comparable estimates for required applications of fertilizer nutrients on soils with comparable fertility classes as in Soy and Lumakanda are deroved to get a target yield 87 increase of 900 kg/ha, i. e. 30 kg N/ha, 60 kg of P2O5/ha and 3 kg of K2O/ha (recovery fractions were comparable to those used in this study). This supports the fertilizer requirements per land unit as calculated in this study. Another possible conclusion can be that the recovery fractions of N, P and K as used in this study, are actually lower (see also de Wit, 1986). For the calculation of the fertilizer requirements recovery fractions of 0.3 for N and K and 0.08 for P, but maybe it is more realistic to choose 0.2 or even 0. 1 for N and K and 0.05 for P have been used. An indication for this is the amount of fertilizer that fanners actually supplied on maize in Soy and Lumakanda Locations (table 5.12). This table shows that the amounts of N that the farmers actually applied, exceed the amounts recommended by the W.Ä.R.C., whereas the applied amount of ^2^5 almost exactly harmonizes with the recommended amounts. Considering these actual fertilizer applications, it is remarkable to see that actual yields obtained by the fanners are moderate to low, compared to the nutrient limited yields calculated by the WDFOST model (on basis of the soil fertility 'without fertilizer application). However these actual yields could have been higher if all negative effects of diseases and pests, weed competition and other yield reducing factors plus losses during the harvest could be prevented. As often maize is intercropped with beans, the actual application of fertilizer is shared by the two crops. Fertilizers are an expensive input in crop production, so increasing the recovery fraction of applied nutrients, is extremely important. If the farmer applies the fertilizer at the right place and at the best moment (N and P2O5 in the plant hole but P2O5 not in contact with the roots, larger amounts in split applications), the applied fertilizer nutrients can be used more efficiently and the recovery fractions will rise. In the C. W. F. S. study (1985) for specified maize yield increases on the various soils in Kenya, indications for fertilizer application are given. In this study all soil types are rated according to soil fertility class. On soils in the highest classes (comparable with soil units UmU6, UmGA2, VXC2 and BI1) the required application of P2O5 for a limited yield increase, by far exceeds the required applications of N, for instance N-P2O5-K2O ratios of 0-45-0 or 20-120-0 or 40-145-0, respectively. • On soils with lower fertility, nitrogen availability is much more limitng and the amount of P2O5 that has to be applied is equal or lower than the required N application (for instance 50- 30-0 for maize, or 75-55-0). Thus from the C. W. F. S. study it can be derived that if the soil fertility is less favourable, the N requirements for specified yield increases, increase rapidly. 88 5. 6 Differences between the land ev**i ^aH^n systems. The qualitative KSS system is good for identifying the most limiting landqualities. It can also give a quick identification of the potential land suitability for specified crops. It must be stated, however, that a good rating system for identifying the final land suitability is lacking within the KSS system. The proposal made by De Meester and Legger (1988) is a good start, but their conversion tables depend strongly on the crop variety for which the evaluation is carried out. In many situations the qualitative land evaluation method indicated the nutrient availabiliy as most limiting for crop production. The quantitative QUEFTS system can then be applied beneficially indicating the nutrient mainly limiting the crop prodution and the yields to be expected. Furthermore, the amounts of fertilizer nutrients to be applied for specified yields can be estimated. In this way the quantitative evaluation system can be applied as a supplement to the qualitative system. 89 Chapter 6 Conclusions. Length of the growing season. * In the agro-ecological zones classification, Soy and Lumakanda locations are situated in the transition zone between the semi-humid and semi-arid zones, also called UM4 zone (upper midland, transitional), the sunflower-maize or upper sisal-zone. The growing period in this zone (with a 60% reliability) is 115 days or more for the long rains and 115 days or less for the short rains. Nested »ngi ysis of variance. * Most of the variables which are used in the QUEFTS model, like N, P, K, pH-î^O have a very high percentage of variance accounted for on short distances. This means that, when " carrying out a land evaluation for fields with a minimum size of 1 hectare, there is no need to sample these variables at distances less than 100 meter and it is best to use the means of the whole area. However the percentage of organic matter shows a high percentage being accounted for on 7000 meters. The same is true to a lesser extent for Base Saturation and a E. C., which are also important for soil classification, but are not used in QUEFTS or WDFOST. Thus variations in these variables are mappable. The spatial pattern in organic matter content could be caused partly by the position of parts of some clusters in forest, and by differences in cultivation history or period of cultivation. Physical and texture parameters all have a percentage of variance of more than 50% accounted for on 100 meter and all of the variation is accounted for on 2000 meter. Mapping these variables would require a large sampling effort (with a sampling distance of 500 meters), while less than 50% of the variation would be solved. * The amount of variation at a certain distance level can be important in land evaluation. It can give an estimation of the range of possible suitability classes or of the range in potential yield which can be expected in a certain area. In one cluster, the organic C rating can range from very low to low, or from low to moderate. When assigning the suitabilities for a certain crop, it is thus thinkable that more than one suitability class will come out. This sets serious restrictions to the value of this kind of qualitative land evaluation for individual farmers, but it migh clarify the results for, for example, regional planners. In quantitative land evaluation one can calculate with the help of this information the nutrient limited yields with lowest and highest possible parameter values. This will result in an estimation of the range of nutrient limited yields, and will also be clarifying. 90 * The nested analysis showed that most parameters can not be mapped on the plateau except for organic matter content and possibly C. E. C. and base saturation. Because most of the variation of other parameters is accounted for on short distances and because this variation is often not very large, one can suffice with the general mean of the whole plateau (for the different parameters) except for the part with Basement System rocks. * It is possible to give an estimation of the reliability of land evaluation with the results of the nested analysis. Infiltration experiments. * There is a clear distinction in time needed for the infiltration of 100 mm water between plateau soils and valley slope soils. * On the basis of the infiltration experiments two changes were made in the original WOFOST 4. 1 version. The first change concerns the amount of water that remains overnight after a shower. In the WOFOSt 4. 1 version, surface storage remains till the next day after a shower. In practice this is often not true. The surface storage will infiltrate in the period immediately after the shower (when the soils are well drained). So the WOFOST model was adapted in such a way that the value set to the maximum surface storage can be used for determining the amount of rain that infiltrates additionally from surface storage after the end of showers. In the WOFOST 4. 1 version ' the fraction of rain not infiltrating* does not depend on the moisture content of the soil. In reality this is not true. So a table was therefore put into the model, specifying the relation between ' the fraction of rain not infiltrating' and the volumetric moisture content of the soil. Determination of the volumetric moisture contents. * The volumetric moisture content, as determined with the laboratory method does not give a good indicatioa This was caused by the fact that often a hard massive pan with a thickness of about 1 cm was found in the bottom of the cylinder. Sometimes there also were problems saturating the samples, probably because of this massive part in the pF cylinder. * There is a possibility that the volumetric moisture contents at pF 2. 3, derived by the formula out of the Kapenguria study, are underestimated, when clay percentages are low (< 40% clay). When clay percentages are high, the formula out of the Kapenguria study . gives values that are very close to what was measured in the field. High clay percentages are more common in the area, so in most cases, the formula out of the Kapenguria study gives a good indication. 91 The P. I. D. C. -soil map. * Acrisols and Ferralsols are to be found on a geology of granitic or granodioritic origin. Luvisols are to be found on gneiss and in the valleys that are filled with colluvium and alluvium. On undifferentiated basement system (granites and granodiorites, but especially gneisses) Luvisols and Acrisols are to be found. Many Ferralsols are to be found in parent material originating from phonolites. ïhere are also some Acrisols and Luvisols found on the phonolites, especially on the slopes to- wards the bottomlands. The Gleysols are situated in the periodi- cally inundated bottomlands, which have a substratum of fine material infill from phonolithic origin. Soil genesis. * On higher parts, the Ferralsols are to be found, with very often, a very hard petroferric horizon in the subsoil. At the lower end of a slope, Acrisols (or Luvisols) can be found. These soils have a well formed argic horizon. In between these Ferralsols and Acrisols (Luvisols) the intergrades developed (ferralo-chromic Acrisols). In the wet spots near the river or bottomland, Gleysols have formed and sometimes some active plinthite can be found here (due to fluctuating wet and dry conditions). The petroferric horizon, which is rather common in the subsoil on the plateau, gives a clear indication of the genetical circumstances under which these soils were formed. A petroferric horizon (also called murrain or ironstone) is formed under changing climatic conditions: the wet climate that is needed to keep the iron in the mobile ferrous form changed into a more drier climate, under which the plinthite hardened irreversibly. Therefore a petroferric horizon is very abundant in the transition zone between rainforest zone (humid) and savanne zone (arid to semi-arid). Also the occurrence of the intergrades is a sign that the weathering of Acrisols/Luvisols towards Ferralsols slowed down, due to a decrease in rainfall. the L. B. D. A-soil map. * In Soy and Lumakanda locations, the rough lines of classification (separation of Plateau from Lower Middle-Level uplands, Lower-Level Uplands, Minor Valleys and Bottomlands) are very useful in the LBDA-map and give a clear indication of the geomorphological structure of the landscape. The quantitative data that are attributed to these soil units are less valuable; especially the soil fertility ratings and the consistency ratings (workability! ) do not reproduce the actual situation very well. T^nfl evaluation in Kakamecra in general. * The crops that have been evaluated with both land evaluation methods are: for the wet season (from march till november): maize, beans, sunflower, sweet-potato and cassava and for the dry season (from november till march): sorghum and millet. 92 of the Qualitativ«? ig^vj ev?1 URUon. * Often the nutrient availability is a problem, but for Soy Location also the temperature regime may limit growth of some crops. * In Lumakanda and Soy locations, suitability for bean production is highest, and somewhat higher than that for maize and sunflower. * Sorghum and Millet have low suitabilities as alternative crops, both in Soy and in Lumakanda, mainly due to the unfavourable climatic conditions. * In the bottomlands and swamps, oxygen availability is a problem for all crops. In the valley bottoms, oxygen availabilty is sometimes a problem, especially for maize and sunflower. Growth of these crops is disturbed when oxygen is lacking. The other crops are less sensitive to this degree of oxygen shortage. * In Lumakanda location, the nutrient status is often the main factor limiting crop growth whereas in Soy location growth is often disturbed by an unfavourable temperature regime. Results of the oiiani-i^ive land When it is assumed that: 1 P is the most limiting nutrient, because of its relatively low solubility in these rather acid soils, 2 applied fertilizer P has a recovery fraction of 0. 08, and applied fertilizer N and K have a recovery fraction of 0. 30 (CWFS, 1985), 3 the amounts of P2O5 advised to apply on the various crops (Western Agricultural Research Centre, Kakamega, 1988) are correct, and can be used as a starting point for calculating the amounts of N and K required additionally, the conclusions are: * In almost all land units, the N* gifts recommended by W. A. R. C are higher than the gifts that are calculated with the WOFOST model on the basis of a prescribed P2O5~ Only in the case of sunflower, the prescribed P2O5~gift indicates nitrogen requirements that are even a bit higher than recommended by the W. A. R. C in Kakamega. * Rather high requirements of K2O are calculated with the WOFOST model, especially in the case of high organic C percentages. These high amounts are due to an overestimation of the K2O requirement (and to a lesser extent the N requirements) by the WOFOST model, especially when the fertilizer gifts to reach potential production are being calculated. * In a study on maize production in Kenya (C. W. F. S., 1985) it was found that comparable estimates for required applications of fertilizer nutrients on soils with 93 comparable fertility classes as in Soy and Lumakanda are derived to get a target yield increase of 900 kg/ha, i. e. 30 kg N/ha, 60 kg of P2O5/ha and 3 kg of K2O/ha. This supports the fertilizer requirements per land unit as calculated in this study. * Considering the actual fertilizer applications, it is remarkable to see that actual yields obtained by the fanners are moderate to low, compared to the nutrient limited yields calculated by the WOFOST model (on basis of the soil fertility without fertilizer application). However, these actual yields could have been higher if all negative effects of diseases and pests, weed competition and other yield reducing factors plus losses during the harvest could be prevented. As often maize is intercropped with beans, the actual application of fertilizer is shared by the two crops. Fertilizers are an expensive input in crop production, so increasing the recovery fraction of applied nutrients, is extremely important. If the farmer applies the fertilizer at the right place and at the best moment (N and P2O5 in the plant hole but P2O5 not in contact with the roots, larger amounts in split applications), the applied fertilizer nutrients can be used more efficiently and the recovery fractions will rise. Differences between the two l^nd evffi^ft'tion systems. * The qualitative KSS system is good for identifying the most limiting landqualities. It can also give a quick identification of the potential land suitability for specified crops. A weakness of the qualitative KSS system is that a good rating system for identifying the final land suitability is lacking. In many situations the qualitative land evaluation method indicated the nutrient availabiliy as most limiting for crop production. The quantitative QUEFTS system can then be applied beneficially indicating the nutrient mainly limiting the crop prodution and the yields to be expected. Furthermore, the amounts of fertilizer nutrients to be applied for specified yields can be estimated. In this way the quantitative evaluation system can be applied as a supplement to the qualitative system. 94 References. Andriesse, W. and B. J. A. van der Pouw (1985) Reconnaissance Soil Map of the Lake Basin Development Authority Area, Western Kenya. Scale 1: 250000. Legend and Rating Tables. Blankhart, S. T. (1978) Kenya. Koninklijk Instituur voor de Tropen, Amsterdam. Staatsuitgeverij Den Haag. Boxern, H.W., T. de Meester and E. M. A. Smaling (eds) (1986) Soils of the Kilifi area, Kenya. Pudoc. Textbook and separate appendices. Burrough, P.A. (1986) Principles of Geographical Information Systems for Land Resources Assessment. Monographs on Soil and Resources Survey No. 12. Davis, J. C. (1986) Statistics and Data Analysis in Geology. Second Edition. Kansas Geological Survey. Diepen, CA. van, C. Rappoldt, J. Wolf and H. van Keulen (1988) Crop Growth Simulation Model WDFOST. Documentation Version 4. 1 Centre for World Food Studies Amsterdam - Wageningen. Diepen, CA. van, J. Wolf, H. van Keulen and C Rappoldt (1989) WOFOST: a simulation model of crop production. Soil Use and Management. Volume 5, Number 1, March 1989. Driessen, P.M. (1986) The water balance of the soil. In: Modelling of agricultural production: weather, soils and crops. H. van Keulen and J. Wolf (Eds. ). Simulation Monographs. Pudoc Wageningen, 1986. - Driessen, P.M. (1988) The Q. L. E.-primer. Department of Soils and Geology, Agricultural University of Wageningen. Driessen, P.M. and R. Duâal (eds.) (1989) Lecture Notes on the geography, formation, properties and use of the Major Soils of the World. Agricultural University of Wageningen; Katholic University of Leuven. East African Meteorological Department (1966) Monthly and annual rainfall in Kenya during the 30 years 1931 to 1960. Nairobi. East African Meteorological Department (1975) Climatological Statistics for East Africa. Part I Kenya. Engelen, V. W. P. van (1984) Soil conditions of the Lelmolk Settlement Scheme (Uasin Gishu District). KSS Site Evaluation Report No. P74. F.-A.O. (1974) Legend to the Soil Map of the World. F.A.O. Rome. F. A.O. (1976) A framework for Land Evaluation. Soil resources development and conservation service land and water development division. Rome. F. A.O. (1977) Guidelines for soil profile description (2nd edition). Soil resources development and conservation service, land and water division, Food and Agricultural Organisation of United Nations, Rome. 66 p. F. A. O. (1979) Report on the Agro-Ecological zones project. Vol. 1 Methodology and Results for Africa. Rome. F. A. O.-Unesco (1989) Soil Map of the World. Revised Legend. Reprint of the World Soil Resources Report 60, FAO, Rome, 1988. Published by International Soil Reference and Information Centre, Wageningen. Gates, CE. and C. Shiue (1962) The analysis of variance of the S-stage hierarchal classification. Biometrics, vol. 18 p. 529-536. Gel ens, H. F. , H. C K. Kinyanjui and R. F. van de Weg (editor), (1976) Soils of the Kapenguria area. Kenya Soil Survey report no. R2. 95 Gibson, A.B. (1954) Geology of the Brotherick Falls Area. Geological Survey of Kenya, Report No. 26, Government Printer, Nairobi. Gower, J. C. (1962) Variance component estimation for unbalanced hierarchical classifications. Biometrics, vol 18 p. 537- 542. Guiking, F. C. T., B. H. Janssen and D. van der ELjk (1983) Soil fertility and nutrient availibility. In: Wielemaker, W. G. and H.W. Boxern (eds. ). Soils of the Kisii area, Kenya. Agricultural Resources Report 922, PUDOC, Wageningen: 36-38, 76-89, 110-111, 194-196. Hills, RC. ,(1970) The determination of the infiltration capacity of field soils, using the cylinder infiltrometer. Britisch Geomorphological Research Group. Technical bulletin no. 3. Hinga, G., F. N. Muchena and CM. Njihia (1980) Physical and chemical methods of soil analysis. National Agricultural Laboratories, Nairobi, Kenya. Houba, V. J. G., J. Ch. van Schouwenburg and J. Walinga (1979) Methods of analysis of soils. Master of Science Course Soil Science and Water Management. Agricultural university Wageningen. Israelsen, O. W. and V. E. Hansen (1962) Irrigation principles and practices. Wiley and sons, New York. 447 p. Huddleston, A. (1954) Geology of the Kakamega District. Degree sheet 33 South East Quarter. Kenya Geological Survey. Report no. 28, 59 pp. Jaetzold, R. and H. Schmidt (1982) Farm Management Handbook of Kenya. Vol. II /A. West Kenya. Ministry of Agriculture of Kenya, German Agricultural Team, Nairobi. Janssen, B. H., F. C. T. Guiking, D. van der Eijk, E. M. A. Smaling and H. van Reuler (1986) A new approach to evaluate the chemical fertility of tropical soils. Trnas. XIII Congress ISSS, Hamburg, p 791-792. Janssen, B. H., F. C. T. Guiking, D. van der Eijk, E. M. A. Smaling, J. Wolf and H. van Reuler (1990) A system for quantitative evaluation of the fertility of tropical soils (QUEFTS). Geoderma, in press. Jennings, D. J. (1964) Geology of the Kapsabet-Plateau Area. Report no. 63. Geological Survey of Kenya, Government Printer Nairobi. Jungheim, K. (1988) Access to land in Kakamega Distrct, Kenya. Department of Geography of the Developing Countries, university of Utrecht. Kenyan Meteorological Department (1984) Climatological Statistics for Kenya. Nairobi. Kenya Soil Survey Staff (19..) Manual for soil survey and land evaluation in Kenya. Miscellaneous Soil Paper No. M24, Kenya Soil Survey, Nairobi (in preparation). Keulen, H. van and J. Wolf (eds.) (1986) Modelling of Agricultural Production: Weather, Soils and Crops. Simulation Monograph, Pudoc Wageningen. Legger, D. (ed. ) (1987) Strongly weathered red and yellow soils in the tropics with emphasis on oxisols and alfisols/ultisols with low activity clays. Agricultural University Wageningen, Department of Seil Science and Geology. 96 Legger, D. (1989) Mineral soils conditioned by a wet (sub)tropical climate. In: Driessen and Dudal (eds. ) (1989). Lecture notes on the geography, formation, properties and use of the major soils of the world. Agricultural University of Wageningen and Katholic University of Leuven. Meester, T. De and D. Legger (eds. ) (1988) Soils of the Chuka- South Area, Kenya. Agricultural University of Wageningen, Department of Soil Science and Geology. Michieka, D.O., B. J. A. van der Pouw and J. J. Vleeshouwer (1978) Soils of the Rwale-Mombasea-Lungalunga area. Kenya Soil Survey, Reconnaissance Soil Survey Report No. R5. Muchena, F. N. and W. G. Sombroek (1981a) The Oxisols of Kenya. Paper presented at the 4th International Soil Classification Workshop, Ruanda, 2-12 June 1981. Muchena, F. N. and W. G. Sombroek (1981b) The Oxisols (Ferralsols) of Kenya. Profile descriptions and analytical data. Miscellaneous Soil Paper No. M23, Kenya Soil Survey, Nairobi. Munsell Color Company, Inc. (1971) Munsell Soil Color Charts. Baltimore, U.S.A. Nyandat, N. N. (1976) Nitisols and rhodic Ferralsols in Kenya, their characteristics, properties and management. In: FAO, 1977, Second meeting of East African sub-committee for soil correlation and land evaluation, Addis Ababa, Ethiopia, 25- 30 October 1976. World Soil Resources Report 47, FAO, Rome. Oliver, M. A. and R. Webster (1986) Combining nested linear sampling for determining the scale and form of spatial variation of regionalized variables. Geographical Analysis 18, 227-242. Philip, J. R. (1975) The theory of infiltration. Soil Science 84, 257-264. Riezebos, H. Th. (1989) Application of nested analysis of variance in mapping procedures for land evaluation. Soil Use and Management. Volume 5, number 1 p. 25-30. Röling, N (1984) Appropriate opportunities as well as approporiate technology. Ceres 97/17, Jan/Feb 1984. pp 15-19 Sanders, L. D. (1963) Geology of the Eldoret Area. Geological Survey of Kenya, Report No. 64, Government Printer, Nairobi. Scott, R. M., R. Webster and C. J. Lawrence (1974) A Land System Atlas for Western Kenya. Christchurch, HPS, England. 363 pp. Searle, D. L. (1952) Geology of the Area Nbrth-West of Kitale Township (Trans Nzoia, Elgon and West Suk). Geological Survey of Kenya, Report No. 19, Government Printer, Nairobi. Siderius, W. and E. B. Njeru (1976) Soils of the Trans Nzoia District. Kenya Soil Survey Report No. 28, July 1976. W.A.R.C., (1988). Handout for the 1988 Western Agricultural Research Centra, Kakamega Field Day. (Friday 22nd July 1988). Siderius, W. and B. J. A. van der Pouw (1980) The application of the F. A. O. /Unesco terminology of the Soil map of the World Legend for Soil classification in Kenya. Miscellaneous soil paper no. Ml5, Kenya Soil Survey, Nairobi. Smaling, E. M. A. and B. H. Janssen (1987) Soil Fertility and Nutrient Availability. In: Boxern, H. W., T. de Meester and E. M. A. Smaling (eds. ) Soils of the Kilifi area, Kenya. Agricultural Resources Report, Pudoc Wageningen. 97 Soil Taxonomy (1975) A Basic System of Soil Classification for Making and Interpreting Soil Surveys. Soil Survey Staff, Soil Conservation Service, USDA, Agriculture Handbook No 436. Sombroek, W. G. and F. N. Muchena (1979) LAC-Alfisols and Ultisols in Eastern Africa. Some problems to their identification and classifications. Proceedings of the Second International Soil Classification Workshop, Part II, Thailand, Soil Survey Division of the land Development Department of Bangkok, Thailand. Sombroek, W. G., H. M H. Braun and B. J. A. van der Pouw (1982) Exploratory Soil Map and Agro-Climatic Zone Map of Kenya. Scale 1: 1000000. Exploratory Soil Survey Report No. El, Kenya Soil Survey, Nairobi. Sys, C. (1985) Land Evaluation. Part I, II and III. State University of Ghent. International Training Centre for Post Graduate Soil Scientists. Taylor, CM. (1968) Rainfall frequency/intensity data for Kenya, Tanzania and Uganda. E. A. M. D., Nairobi, 15 pp. Taylor, CM. and E. F. Lawes (1971) Rainfall intensity, durations and frequency data for stations in East Africa, E. A. M. D., Technical Memorandum, No. 17, Nairobi, 30 pp. Tomsett, J. E. (1968) The diurnal variation of precipitation in East Africa. E. A. M. D. Technical Memory, No. 10, 70 pp. U. N. D. P., World Meteorological Organization (1974) Hydrometeorological Survey of the Catchments of Lakes Victoria, Kijogo and Albert. Vol. I, Meteorology and Hydrology of the Basin, Part I, Geneva. Webster, R. (1977) Quantitative and numberical methods in soil classification and survey. Clarendon Press, Oxford. Wit, C. T. de (1986) Alleen de zon gaat voor niets op. landbouwkundig Tijdschrift 98 (1986) Nr 6/7. Wolf, J., CA. van Diepen and C H. van Immerzeel (1987) Monitoring agro-ecological resources using remote sensing and simulation (MARS-project). A study on the limitations to maize production in Zambia using simulation models and a geographic information system. Centre •Cor World Food Studies, Amsterdam-Wageningen. Vol I and II.