agricultural technology economic viability and poverty alleviation

AGRICULTURAL TECHNOLOGY, ECONOMIC VIABILITY

AND POVERTY ALLEVIATION IN KENYA

By

W. Oluoch-Kosura1, E.S. Ariga1, A.M. Okeyo1, M.M. Waithaka2 and A.M.

Kyalo3

1University of Nairobi, College of Agriculture and Veterinary Sciences, P.O. Box29053, Nairobi, Kenya.2 Kenya Agricultural Research Institute Headquarters, P. O. Box 57811 Nairobi,Kenya3 Ministry of Agriculture Headquarters, P. O. Box 30028 Nairobi, Kenya.

A paper for the Agricultural Transformation Policy in Sub-Saharan AfricaWorkshop held at Serena Hotel, Nairobi, Kenya. on 27-30 June, 1999

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Table of contents

INTRODUCTION ............................................................................................................................................ 4

1.1 BACKGROUND........................................................................................................................................... 41.2 THE PROBLEM ........................................................................................................................................... 51.3 THE OBJECTIVES........................................................................................................................................ 8

2. APPROACHES AND METHODS ADOPTED FOR THE STUDY ........................................................... 9

2.1 FORMATION OF STUDY TEAM...................................................................................................................... 92.2 LITERATURE REVIEW AND ANALYSIS........................................................................................................... 9

3. ANALYSIS OF TECHNOLOGIES ........................................................................................................... 10

3.1 MAIZE PRODUCTION TECHNOLOGY AND TECHNOLOGY PACKAGE. ............................................................. 103.1.1 Maize Seed and Fertilizer Technology ............................................................................................ 103.1.2 Policy implications for increased fertilizer utilization and maize production .................................. 16

3.5 IMPROVED LIVESTOCK SEED-STOCK TECHNOLOGIES. ................................................................................. 193.5.1 Artificial Insemination.................................................................................................................... 193.5.2 Role of AI in elevating poor peasant livestock keepers to commercial small scale dairy farmers level213.5.3 Some policy Implications for Livestock Improvement Research. ..................................................... 26

3.6 IMPROVED SMALL SCALE DAIRY TECHNOLOGY PACKAGE ........................................................................... 273.6.1 The zero grazing system.................................................................................................................. 273.6.1.1 Advantages of zero grazing.......................................................................................................... 273.6.1.2 Problems associated with zero grazing......................................................................................... 283.6.1.3 The zero grazing technology package components ....................................................................... 283.6.2 Gross margin analysis of Zero-grazing as technology package ....................................................... 293.6.3 Policy implications ......................................................................................................................... 52

4. RESEARCH-EXTENSION LINKAGES AND OTHER FACTORS IN TECHNOLOGY ADOPTION 54

4.1 RESEARCH-EXTENSION LINKAGE ............................................................................................................. 544.2 OTHER FACTORS AFFECTING TECHNOLOGY APPLICATION ON FARMS......................................................... 55

5. GENERAL CONCLUSION AND RECOMMENDATIONS .................................................................... 56

6. REFERENCES ........................................................................................................................................... 57

List of Tables

Table 1. Average growth rates in maize area, yield, and adoption of improved seed and fertilizerin Kenya, 1963-91..............................................11

Table 2. Economic analysis for growing maize using farmer's practice and recommendedpackages during short rains 1994/5, long rains 1995 and short rains 1995/6 in Upper-Midland 2 of Central Kenya .......................................12

Table 3. The value cost ratio and profit due to fertilizer application in maize in West Pokot(Kaibos - LH 2) in 1997. .........................................13

Table 4. The value cost ratio and profit due to fertilizer application in maize in Trans Nzoia(Tulwet) in 1997...............................................14

Table 5. Profitability of fertilizer use in maize according to agroecological zones, 1998.....15Table 6 Characteristics of Ruiru 11 compared to that of SL 28 coffee variety. ..........18Table 7 Profitability of SL and Ruiru 11 varieties in Ksh/ha during their fifth year and peak of

production after establishment on new land .............................18Table 8. Number of artificial inseminations carried out by KNAIS, 1984-97............21Table 9 Estimated gross margin analysis of AI service when inseminator uses motorbike and

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moped .....................................................25Table 10. Reference small-scale dairy farming situation .........................30Table 11. Gross margin of milk production under zero grazing conditions.............31Table 12. Research expenditures as percentage of AGDP and contribution of GoK and donors.54

List of FiguresFigure 1. Percentage distribution of absolute poverty by percent number of people in Kenya..7Figure 2. Number of artificial inseminations (‘000) carried out by the Kenya National Artificial

Insemination Services (KNAIS) and private firms in Kenya: 1971-1997 ...........22

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INTRODUCTION

1.1 Background.The major challenges facing Kenya today are poverty and unemployment. About 50% of the

rural population and 30% of the urban population live below the poverty line. With 80% of the

population being rural the poverty problem is overwhelming. The country has been unable to

generate adequate employment and wage employment has been declining over the recent past.

While in the 1970s the growth rate of employment was about 4% per annum, in the current

decade, the growth rate has been about 1.9% per annum, which is below the population growth

rate estimated at about 3%. The country has also witnessed declining growth in income per

capita. While in the 1960s per capita income grew at 2.6% p.a. this declined to 0.4% in 1980s.

Between 1990 and 95 the decline was even more dramatic at negative 0.3% (Kenya, 1997).

The poverty line is defined here as the value of consumption of food and non-food items below

which individuals cannot afford the recommended energy intake plus a minimum allowance for

non-food consumption. The poverty line has been estimated at about US$ 200 and 300 for

rural and urban areas respectively (GoK, 1998). This translates to less than one US$ per day.

Of Kenya’s total land area of 57.6 million hectares, 9.4 million or about 16% is classified as

high and medium potential land for agriculture. The remaining area estimated at 84% makes up

the arid and semi arid lands (ASALs). Out of the ASALs 48 million hectares, about 9 million

hectares can support crop production, 15 million hectares is adequate for livestock production

while the rest is dry and only useful for nomadic pastoralism. The ASAL supports about 20%

of the population, 50% of livestock and 3% of current agricultural output and 7% of

commercial output. ASALs have low natural fertility which are prone to compaction and

vulnerable to erosion.

The agriculture sector dominates the economy and contributes virtually to all the stated national

goals including achievement of national and household food security, industrialization by year

2020 as well as provision of employment opportunities. Currently, agriculture accounts for

about one-third of the gross domestic product, employs more than two-thirds of the labour

force, accounts for almost 70% of the export earnings (excluding refined petroleum), generates

the bulk of the country's food requirements and provides significant proportion of raw materials

for the agricultural based industrial sector. Overall, the smallholder sub-sector contributes about

75% of the total value of agricultural output, 55% of the marketed agricultural output and

provides just over 85% of the total employment in agriculture.

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The sector’s ability to contribute effectively to the national goals hinges on identifying and

implementing measures which promote high and sustainable growth rate. Mellor (1990)

asserted that agricultural productivity growth is normally the major source of sustained

improvements in rural welfare. Three sources of agricultural growth can be identified in Kenya.

One is the expansion of cultivated area. The second is substitution or switching towards higher

valued commodities. The third is intensification. The first source of agricultural growth is

currently extremely limited. The cultivable land available to open up has diminished over the

years with rapidly rising population estimated at about 3% per annum to the extent that the land

holdings are becoming sub-optimal economic units and there is ever increasing temptation to

migrate to the marginal and fragile zone. Moreover, irrigation development which could help in

increasing cultivable land has been very slow due to the seemingly high cost associated with it.

Commodity substitution will contribute significantly to growth only if the input and output

markets function in a way to allow the producers and the private sectors respond appropriately

to the market signals. This is expected to occur if the on-going structural adjustment

programmes succeed in limiting government intervention to its core functions (of public good

nature) and allowing the private sector to take up the production, marketing and distribution

role. Most agricultural growth will therefore come from the third source: increased output per

unit land area. The realization of this growth potential will hinge on shifting rapidly from

resource based to science and knowledge-based agriculture. The objective of this paper is to

analyze the potential for agricultural technology to solve one of Kenya’s biggest challenges,

alleviation of poverty.

1.2 The problemDespite many years of capital investment in agricultural research and technology development in

Kenya, poverty and hunger still threaten human survival and livelihood. About 50% of rural

household live below the poverty line. Information from the Kenya Agricultural Research

Institute (KARI) indicates that many viable technologies that have been developed are currently

not being applied by the the farmers. This has led to farmers achieving as low as 6% of what is

potentially possible (Salasya et. al., 1998). For instance, in Kakamega district, while the

potential yield of maize was 50-60 bags per hectare by research station standards, the average

yield on farms was a meager 3 bags per hectare. This is one of the districts where between 50-

60% of the population live below the poverty line (Kenya, 1998). This particular example

shows the magnitude of the problem facing poor households in accessing available technologies

which can substantially alleviate poverty. The same situation of lack of adoption in livestock

and processing technologies which can alleviate poverty abound in Kenya.

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Kenya has developed a poverty atlas, mapping out the incidence and depth of poverty (Error!

Reference source not found.). The country has subsequently launched a National Poverty

Eradication Plan spanning 1999-2015. The highest incidence of poverty is found in the Arid

and Semi-Arid Land (ASAL) districts of Northern Kenya. Few of the ASAL districts have

more than one percent of the country’s total poor households despite their high percentage

incidence of poverty. The poor in ASAL areas tend to be physically isolated, have inferior

access to basic goods, services and infrastructure and rely on an uncertain resource base.

Failure to identify development options in these areas will increase the pressure for large sums

for relief. The sustainable opportunities for the ASAL areas arise from using domesticated

livestock, wild animals and trees to improve rural livelihoods. Their economies are vulnerable

to major climatic changes. Floods and drought alternate to destroy many of the assets created

by development investments. Relief is needed on a recurrent basis to prevent hunger and

suffering.

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Figure 1. Percentage Distribution of Absolute Poverty by Percent Number of People in Kenya

Source:; Government of Kenya (1998)

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The areas of high and medium potential contain most of the rural population and so many of the

rural poor are concentrated here as well. In Machakos and Kakamega together reside 10% of

the nation’s poor. If a further four district - Makueni, Siaya, Kitui and Bungoma - are added to

these first two, then 25% of all poor households have been identified. Even though the highland

districts, including those in the coffee zone are counted as well-favoured in national terms each

of them also has a large number of poor people and households. Rapidly increasing population

has intensified the pressure on land-based resources and left poorer groups without adequate

access to the basic means of production. There is also some evidence that traditional norms of

reciprocity and community safety-nets have largely broken down in a number of these areas,

leaving the resources and income poor especially vulnerable to weather, disease and economic

variations.

1.3 The objectivesThis paper aims at identifying and analyzing a selected number of crop, livestock and

agricultural processing technologies or technological packages that have been developed in

Kenya in the recent past with respect to the following:

1. Evolution of technology generation

2. Transfer methods and current status, including the level of adoption in targeted areas

3. Identification of technologies

4. Evaluation of their economic viability

5. How the application of the technology can contribute to poverty alleviation in the

targeted areas

6. Policy implications and recommendations for future initiatives

The effectiveness and impact of agricultural technologies therefore depend on:

(a) Policy environment

(b) Capacity of institutions (service providers) to extend the technology to farmers

(c) The complexity of the technological packages, level of education and diversity of the

intended recipient farmer population (farmer characteristics or profiles)

(d) The diversity of the sector or sub sector (cultural homogeneity and market condition)

(e) The Agro-climate condition (ecology)

(f) The status of infrastructure development: roads, telecommunication and energy

(g) The prevailing socio-cultural beliefs

(h) The existing governance style (whether there is commitment to have effective judiciary,

regulatory bodies) to eradicate corruption and poverty

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2. APPROACHES AND METHODS ADOPTED FOR THE STUDY

2.1 Formation of study teamAs specified in the terms of reference and given the nature of agricultural technology generation

and transfer a - person team was formed to analyze the technology generation-viability-poverty

alleviation nexus. The team consisted of a research extension liaison officer from the ministry of

agriculture, an agricultural economist from KARI, a crop scientist and an animal scientist from

the University of Nairobi and the team leader, who is an agricultural economist at the University

of Nairobi. The team reviewed the terms of reference to have a common understanding of the

scope of work and the expected output.

2.2 Literature review and analysisIt was understood that the focused literature review together with the team members

experiences in their respective fields would bring out the issues revolving around the economic

viability of available technologies on farms to increase productivity and consequently alleviate

the persistent poverty in rural areas particularly and the country in general. Attempts at

integrating these viable technologies on research process as well as the barriers to achieve a

coordinated approach to technology generation and transfer were identified.

Based on perceived potential to alleviate poverty in the areas designated to have high incidence

and greater depth of poverty in Kenya (GoK, 1998), the team selected specific crop and

livestock technologies and technology packages as well as processing technology package to

demonstrate the economic viability. The technologies included the following:

(i) Maize production technology package.

(ii) Improved coffee production technology.

(iii) Improved livestock seed-stock technology

(iv) Improved small scale dairy technology package

The recommendations for policy were derived from the analysis of the literature review as well

as on the economic analyses of each of the selected technologies.

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3. ANALYSIS OF TECHNOLOGIES

3.1 Maize Production technology and Technology Package.Maize is the major staple food in Kenya. The consumption pattern has shifted to maize over the

years to the extent that over 90% of the population rely on maize as a food item. This has

caused a situation where famine in Kenya is associated with lack of maize, even if there are

other food items like rice or potatoes. Seventy five per cent of Kenya's maize is produced by

smallholders who cultivate about 85% of total land area under maize.

3.1.1 Maize Seed and Fertilizer TechnologyGiven its importance, the government initiated a maize improvement research program as early

as 1955 at Kitale, the centre of high potential maize production area in Kenya (Lynam and

Hassan, 1998). Research at Kitale focused on developing late maturity hybrids for the highland

areas where typically rainfall is confined to one long season. After 1957, Katumani and Embu

research programs were established to cater for semi-arid mid-altitude and moist mid-altitude

areas while the Mtwapa program concentrated research on lowland, coastal agricultural zones1.

By 1975 ten hybrid maize and three composites had been released and a significant number had

adopted this first generation of improved maize material (Gerhard, 1975). Table 1 shows the

growth rates of adoption of improved seed and fertilizer in Kenya between 1963 and 1991.

Large-scale farmers located in the high-potential areas appeared to be the early adopters of the

package and by 1974 almost half of them used the new maize varieties. Small-scale farmers in

the relatively marginal areas were the slowest to adopt the technology package with only 16%

of the farmers adopting the new varieties by 1984. Although adoption of inorganic fertilizer

followed closely on the adoption of improved seed in the large farm sector, it appears the

smallholder adoption of fertilizer lagged substantially behind their adoption of improved

varieties and remained virtually negligible in marginal areas2.

Small-scale producers seem to prefer a maize variety that is early maturing, high yielding, does

not lodge easily and that yield large quantities of stover for feeding livestock and mulching,

attributes that most of the above hybrids meet.

1Agricultural zones in Kenya are defined mainly by moisture supply but differentiated by altitude,

temperature, rainfall, soil types and fertility and ranges of crops economically grown (Jaetzold, 1982).

2This differs from the green revolution experience in Asia where both technologies were adopted as apackage

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Table 1. Average growth rates in maize area, yield, and adoption of improved seed and fertilizerin Kenya, 1963-91

Growth rate

Parameter 1963-74 1975-84 1985-91 Overall

Area (% yr-1)Yield (% yr-1)Number of new varieties released% of farmers who have adopted Improvedseeds

Large-scale farmers in high-potential zoneSmall-scale farmers in high potential zonesSmall-scale farmers in low-potential zones

% of farmers who have adopted Fertilizera

Large-scale farmers in high-potential zonesSmall-scale farmers in high-potential zonesSmall-scale farmers in low-potential zones

4.610.913

47.516.04.0

42.310.82.3

0.40.62

24.242.212.1

17.324.62.3

(1.1)4.46

22.236.639.7

23.127.86.0

2.37.121

93.994.756.8

82.763.210.6

Source: Lynam and Hassan, 1998.a Represents the percentage of farmers who once bought improved seed or fertilizer, but does

not reflect current adoption rates. Large-scale were defined as farmers having more than 8 ha

of land. High-potential zones consist of the wet highlands and mid-altitude regions. Low-

potential zones are the semiarid and lowland tropical zones.

Maize yields in farmers fields in mid altitude of Eastern and Central Kenya range between 1 and

1.5 tons per ha against research potential of 5 tons per ha (KARI, 1994). Surveys conducted in

these areas have identified low soil fertility, low adoption of recommended varieties and low

plant population to be the main biophysical factors contributing to low maize yields in farmers'

fields. Most of the high and medium potential areas of Kenya have been utilized for maize

production and hence increase of the crop through area expansion is not feasible. Increase in

production will be achieved only by increase in yield per unit area as there is no area for

expansion beyond the current 1.8 million hectares. Therefore wide spread adoption of intensive

production methods using recommended technology packages is required.

Trials conducted for three seasons in the upper midland zone 2 of Central Kenya to quantify

yield gap between research and farmers' fields show that there is considerable room for

improvement in maize yields as shown by the marginal rate of return, which is the percentage

change in net benefits due to a unit increase in the total costs that vary as one moves from one

practice to the other.

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Table 2. Economic analysis for growing maize using farmer's practice and recommendedpackages during short rains 1994/5, long rains 1995 and short rains 1995/6 in Upper-Midland 2of Central Kenya

Season 1Treatments/Technology

Gross benefitKsh/ha

Total variablecost Ksh/ha

Net benefitsKsh/ha

Marginal rate ofreturn forinnovation (%)

Short rains1994/95

Farmer's wayDensityVarietyF X D X V*

16,064.017,132.618,051.021,292.6

6,061.46,683.57,137.28,707.2

10,002.610,449.110,913.212,585.4

71.8102.2106.6

Long rains1995

Farmer's wayF X D

22,271.128,706.4

7,155.412,268.1

15,155.816,438.2 25.9

Short rains1995/6

Farmer's wayDensityF X D X V

18,435.219,869.921,935.0

6,479.37,165.98,820.4

11,955.912,704.013,114.5

109.024.8

Source: Gitari et al, 1996

1F = fertilizer, D = density and V = variety.; farmer's way = growing local seed, plant density of

37,037 plants/ha and fertilizer rate of 20 Kg N plus 20 Kg P2O5/ha.; Recommended technology

package = growing hybrid 512, plant density of 53,333 plants/ha and fertilizer rate of 50 Kg of

N plus 50Kg of P2O5/ha.

A study to establish determinants of fertilizer use and the gap between farmers' maize yields and

potential yields in Kenya, revealed that Kenyan farmers apply lower rates of inorganic fertilizers

in their maize crop than is considered economically optimal (Hassan, et al, 1998). The gap

between farmers' yields under current practices and the yields that could be attained if optimal

levels of nitrogen (N) and phosphorus (P) were applied is on average 30%. About one million

tons of maize could be added to current domestic production (a 33% increase) if farmers

improved their soil fertility management practices.

Fertilizer is a major agricultural input in Kenya. Kenya's annual consumption of mineral fertilizer

nutrient stands at about 100,000 metric tonnes while the potential is estimated to be about four

times as much (Mulagoli, 1999). According to sessional paper of 1994, increased and efficient

use of fertilizer is perceived among ways through which increase crop production can be

achieved. The target agricultural rate is 5.3% per annum with an expected increase in fertilizer

use of 12.2% per annum. However, the current fertilizer use is only 37% of the estimated rate

per year and mainly in plantation and high value crops. This results in continuous declining trend

in food production leading to perennial food shortage, famine and food imports.

On farm verification trials have been conducted through Fertilizer Extension Project of the

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ministry of agriculture. It covered 15 districts countrywide representing medium and high

potential agroecological zones of Kenya. This was a follow up to the Fertilizer Use

Recommendation Project (FURP) earlier implemented by KARI. Results indicated that there

was a good response of maize to fertilizer application in various parts of the country with

variable profit margins. In Kaibos (West Pokot, LH 3) in 1997, Maize was more profitable with

highest return to investment in fertilizer if farmers applied triple super phosphate (TSP) and

calcium ammonium nitrate (CAN) at the rates of 25 Kg per acre each respectively (Table 3).

This gave value cost ratio (VCR3) of 5.6, meaning that for every Ksh 1 invested in fertilizer the

farmer got a return of extra Ksh 4.60. Although it was not risky to double fertilizer rate to

50+50+0 (VCR = 2.6), the profit may be only enough to purchase fertilizers with very little

balance to finance other activities.

Table 3. The value cost ratio and profit due to fertilizer application in maize in West Pokot(Kaibos - LH 2) in 1997.

Nutrient level Agronomic fertilizer effect Economic fertilizer effect

N + P + K Average yieldincreaseKg/ha

Value of yieldincrease Ksh

Fertilizer costKsh

Profit due tofertilizer Ksh

VCR

0+0+0 - - - -

25+25+0 2,226 22,256 4,000 18,256 5.6

50+50+0 2,063 20,634 8,000 12,643 2.6

25+25+0+5t FYM1

1,812 18,124 6,000 12,124 3.0

25+60+0 (FP2) 1,798 17,982 3,375 14,607 5.3

FYM1 is farm yard manure and FP2 is farmer's practice

In zones represented by Tulwet in Trans Nzoia, it would be more profitable if farmers applied

CAN (75+0+0) in two equal splits in maize to obtain a VCR of 21.7 (Table 4). In this region

there was no demand for phosphorus when enough nitrogen was applied. Even doubling the

rate of application to (150+0+0) would still be economical (VCR of 10). Thus farmers would

earn enough money from sale of maize and retain balance to finance other activities. This would

alleviate their poverty and improve the living standards.

3Value cost ratio (VCR)is an indicator that shows the amount of money generated for every shilling invested

in fertilizer use in a maize crop.

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Table 4. The value cost ratio and profit due to fertilizer application in maize in Trans Nzoia(Tulwet) in 1997

Parameter Nutrient level N+P+K

0+0+0 75+0+0 150+0+0 60+60+0

Average yield (Kg/ha) 3,518 11,470 11,085 9,261

Average yield increase (Kg/ha) - 7,952 7,567 5,743

Value of yield (Ksh) 46,907 152,933 147,800 123,480

Value of yield increase (Ksh) - 106,027 100,893 76,573

Fertilizer cost (Ksh) - 4,896 9,792 7,567

Profit due to fertilizer (Ksh) - 101,131 91,101 69,006

Value Cost Ratio (VCR) - 21.7 10.3 10.1

Following similar on-farm verification trials in other regions, economic fertilizer rates and

profitability in maize, as indicated by VCR, are indicated (Table 5). Farmers used certified seeds

of the recommended maize variety in each agroecological zone. Economic rate indicated the

amount of fertilizer that yields maximum profit.

The higher the VCR value the more it is worthwhile to invest in fertilizer rates indicated to

produce maize. A VCR less than 1.0 indicates that farmers in the zone should not apply

fertilizer rates indicated to produce maize. For example in Kakamega Municipality (0.6), Lower

Nyakach in Kisumu (0.4) and Gachoka in Embu (0.9). High returns on fertilizer investment in

maize were realized in Noigam in Transzoia (21), Kabuchai in Bungoma (13), Kimondo in

Trans Nzoia (11) and Kiminini in Bungoma (10).

Farm yard manure application is a national extension recommendation and in some regions

where it was applied at 5 tons per acre, in addition to fertilizer, VCR significantly increased.

Positive response of maize yields to application of farm yard manure was recorded in

Muhoroni/Koru and Maseno, both, in Kisumu, Njukiri in Embu and Kabatini in Nakuru. Farm

yard manure was applied only in farms where it was available at no significant extra cost.

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Table 5. Profitability of fertilizer use in maize according to agroecological zones, 1998.

AEZ Region (District) Cropping pattern Economic rate N+P+K VCR

LM 1 Khuisero (Kakamega)Sang'alo (Bungoma)Bumala (Bungoma)Butere (Kakamega)Nyahera (Kisumu)

Maize/BeansMaize/BeansMaize/BeansMaizeMaize/Beans

25+25+025+25+025+25+025+25+075+0+0

2.83.44.43.66.3

LM 2 Muhoroni/Koru (Kisumu)

Kabras (Kakamega)Municipality (Kakamega)Maseno (Kisumu)

Maize/BeansMaize/BeansMaize/BeansMaizeMaize/BeansMaize/Beans

25+0+025+0+0+5tFYM150+75+0150+25+075+0+075+0+0+5tFYM

3.74.32.4.631.82.8

LM 3 Lower Nyakach (Kisumu)Sirisia (Bungoma)Gachoka (Embu)Rwika (Embu)

Maize/BeansMaize/BeansMaizeMaize

150+75+060+60+025+25+025+25+0

.434.5.931.9

LM 4 Itiira/Siakago (Meru) Maize 25+25+0 2.8

UM 1 Shinyalu (Kakamega)Kimilili (Bungoma)

Maize/BeansMaize/Beans

0+50+075+0+0

4.72.6

UM 2 Igoji (Meru)Gikuuri (Embu)

Njukiri (Embu)

MaizeMaize/BeansMaize/BeansMaize/BeansMaize/Beans

75+0+075+75+075+75+0+5tFYM75+75+075+75+0+5tFYM

5.82.33.32.95.7

UM 3 Murkwijit (West Pokot)Kabuchai (Bungoma)


25+25+00+30+0

2.113

UM 4 Ririmpoi (West Pokot)Tartar (West Pokot)Noigam (Transzoia)Biribiriet (Transzoia)Kaplamai (Transzoia)Lunyu (Transzoia)Kipsaina (Transzoia)Makhele (Transzoia)Kibomet (Transzoia)Kiminini (Bungoma)Likuyani (Kakamega)

Maize/BeansMaize/BeansMaizeMaizeMaizeMaizeMaizeMaizeMaizeMaizeMaize

75+0+075+0+060+60+0150+0+075+0+075+0+0150+0+0150+0+075+0+00+30+075+0+0

2.44.4212.39.31.82.01.76.8103.7

LH 2 Chemundu (Nandi)Talau (West Pokot)Kaibos (West Pokot)

Maize/BeansMaizeMaize

60+90+025+25+025+25+0

2.93.75.6

LH 3 Kabatini (Nakuru)

Ngwataniro (Nakuru)Kimondo (Transzoia)

Maize/BeansMaize/BeansMaize/BeansMaize

75+0+075+0+0+5tFYM75+0+075+0+0

1.42.24.311

LH 4 Mia Moja (Laikipia)Kalalu (Laikipia)


25+25+025+0+0

6.69.1

Source: Mulagoli, 1996 and 1997

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3.1.2 Policy implications for increased fertilizer utilization and maize productionThe central objective of the Kenya government and fertilizer policy is to ensure that adequate

and quality fertilizers are made available to the farmers at affordable prices and that they are

used at the right time and in the correct proportions.

Promotion of high N products such as urea will reduce the relative cost of the N component,

lower the nutrient-grain price ratio, and improve the returns to fertilizer use for maize farmers.

Public investment in rural roads, removal of restrictions on fertilizer imports, and the provision

of credits and tax incentives to private traders to invest in private storage and distribution

facilities would have a significant impact on diffusion of fertilizer. The marketing of fertilizers in

small packages (10 to 25 kg instead of 50 kg), which are easier to transport, would also help

increase in fertilizer use, especially by small scale farmers in rural areas. The demand for this

packaging has led to traders opening the 50 kg bag and weighing fertilizer quantities as desired

by farmers there by cheating them on quantity and at times quality when adulteration occurs.

This calls for self regulating mechanism on fertilizer quality control.

Another means of increasing the profitability of fertilizer use would be to achieve higher yield

gains (biological response) from fertilizer application, both through breeding research (better

adapted hybrids for marginal zones) and crop management research and extension efforts (to

disseminate fertilizer recommendations conditioned by soil type and nutrient analysis). The

development of alternative low-cost sources of N, such as green and animal manures for

efficient nutrient recycling, and methods of soil conservation, will reduce soil nutrient depletion

and enhance maize productivity in the long run (Qureshi, 1987). Hassan et al, 1998 emphasize

that the strategy for ensuring wider diffusion and adoption of improved technologies for maize

production in Kenya is to develop a more efficient extension service (characterized by regular

visits and increased use of contact farmers and farmer groups) and more input supply

mechanisms. More emphasis needs to be placed on marginal environments and female headed

households. Channels of disseminating fertilizer use information such as radio programmes,

short subject specific extension publications should be strengthened.

Focused effort should be made at systematic and comprehensive characterization of farming

systems using participatory approaches, targeting specific types of households with specific

types of technologies to alleviate their poverty.

Research structure should be organized along agro-ecological zones rather than provincial

stations to tackle development of crop technologies to alleviate poverty in specific farming

systems. Crop and location specific fertilizers should be readily available in each region.

17}}

The government, donors and NGOs should target poorer households with relevant development

assistance. These households are characterized by the following features (Sutherland et al,

1997); (a) limited access to cash income including salaried employment, and sales of fruits,

crops and livestock products, (b) few convertible assets such as livestock, timber trees and land,

(c) few labour saving devices such as ox-drawn equipment, draught animals, wheelbarrows,

spray pumps, rainwater harvesting tanks, and larger hand tools, (d) food insecurity which tends

to divert labour and time away from production activities on own land to survival activities such

as food for work programmes, bartering chickens and handicrafts, and working for others, (e) in

some cases insecure access to land.

To increase farmers' demand for fertilizer, high yield gains from fertilizer use and lower

nutrient-grain price ratios have to be achieved. Supply factors that influence the price of

fertilizer are (a) transportation and transaction costs and (b) dominance of low N fertilizers,

such as DAP, and 20 20 0 among the commercial products traded in Kenya.

Transportation and transaction costs lower marketing margins and hence form a major

disincentive to private traders to enter and engage in fertilizer delivery, to small markets in

remote areas. Donovan (1995) showed that in 1992, marketing cost per ton per kilometre of

maize imported from the US to western Kenya were almost five times greater than they were in

the US. This is despite the fact that the distance traveled by maize from farms to the US Gulf

ports was about 1.5 times the distance between Mombasa and western Kenya.

3.2 Coffee production technology

Smallholder coffee producers account for 65% of the total coffee production in Kenya. The

average yields of 345 kg per hectare are, however, low compared with those of estates, 1,013

kg per hectare. Yields of 10-15 kg per tree per year have been reported in Central Kenya under

smallholder production. The difference in production yields is due to variation in technologies

used and the levels of management. While the estates usually apply recommended levels of

fertilizers, pesticides and herbicides, the smallholder producers apply less than the recommended

rates due to financial constraints or due to lack of know-how.

Several varieties of coffee (SL.28, SL 34, K7 etc) have been released during the last 30-40

years of research, but most outstanding is the Ruiru 11, whose characteristics are outlined in

Table 6.

18}}

Table 6 Characteristics of Ruiru 11 compared to that of SL 28 coffee variety.

Parameter Variety

Ruiru 11 SL 28

Plant population (trees/ha)Yield (tons/ha)Quality indexCBD indexLeaf rust index

2,500 2.70 3.60 0.53 0.01

1,329 2.17 3.56 7.11 2.11

Ruiru 11 is resistant to coffee berry disease (CBD) and leaf rust (LR), two major diseases that

affect coffee production worldwide. This results into saving on fungicides as indicated in Table

7. Ruiru 11 comes into production in the second year while traditional varieties do so in the

third year, hence early flow of benefits (net revenue during the second year is Ksh.79,380 per

ha) which is half of the total establishment on one hectare of new land which is Ksh 131,207.53

(Coffee Research Foundation, personal comm.).

Table 7 Profitability of SL and Ruiru 11 varieties in Ksh/ha during their fifth year and peak ofproduction after establishment on new land

Cost Category

Ground fertilizersFoliar fertilizersFungicidesInsecticidesHerbicidesCasual LabourTotal Variable Costs Ksh/haGross Margin Ksh/haYield Achieved ton clean coffee/haYield Achieved Kg of cherry/haYield Achieved Kg of cherry/tree

SL Variety

17,849640

47,5332,9883,345

47,749120,144268,656

2.014,00010.5

Ruiru 11

13,1806350

2,9883,345

78,29698,444329,237

2.215,400.00

6.2

Since the release of this variety supply has never met demand for seedlings. The reduction in

cost of inputs is substantial at 30% according to Coffee Board of Kenya estimates. This is

particularly important for smallholders who have very limited resources. The spacing of 2m by

2m (2,500 trees per hectare) is also ideal for smallholder producers who can now grow extra

trees as compared to the conventional spacing of 2.74m by 2.74m (1,329 trees per hectare) for

most varieties of arabica coffee. Ruiru 11 is also much shorter than the other varieties making it

easy to harvest. The gross margin of Ruiru 11 is 22% higher than that of SL 28 at the peak of

production.

Coffee production under smallholder systems is essentially labour intensive: planting, weeding,

19}}

pruning, and picking. Profit margins are generally small and that explains why farmers abandon

management of coffee trees when world prices experience a significant fall. Estimates by

Coffee Board of Kenya in 1997 under three scenarios of low, medium and high yields indicate

gross margins ranging from Ksh 4,256 to Ksh 62,114 per hectare under smallholder production.

In the same period, the estates had profits ranging from Ksh 17,618 to Ksh 85,256 per hectare

under low, medium and high yield scenarios. Adoption of Ruiru 11 and other appropriate crop

management practices by smallholders will pull those currently below poverty line to a higher

income and standard of living.

3.5 Improved livestock seed-stock technologies.The economic viability and of a livestock enterprise depends on among other factors the genetic

potential of that given germplasm to respond to improved husbandry (good feeding, housing

and health care). The germplasm, and particularly its genetic potential in itself and when

considered as a resources can impede increases in productivity. For example, in cases where

land is limited and labour is constraining, keeping a cow that has low genetic potential for milk

production would be uneconomical. The well being of the keeper of such a cow would not be

guaranteed. In such instances, development and wide adoption of germplasm that have higher

genetic potential for say milk, meat, egg production has been undertaken in Kenya with the aim

of achieving higher productivity and consequently alleviation of poverty among the livestock

farmers. Upgrading of indigenous cattle breeds towards the exotic dairy breeds has been done

through the use of artificial insemination (AI) technology.

3.5.1 Artificial InseminationTemperate dairy cattle perform better than the indigenous Zebu cattle in the tropical highlands.

As the human population increased in the highlands and land size decreases, it becomes prudent

to adopt technologies that would enable efficient utilization of the scarce resources.

AI technology involves the identification of the very top genetic potential bulls. These are kept

in central stations and semen continually collected, assessed, diluted, packed in straws and

preserved in liquid nitrogen at low temperatures. When needed, each straw of semen can be

used to artificially inseminate a cow that is on heat. One superior bull once identified can

potentially sire thousands of offspring in different herds each year. The genetic superiority of

one individual is therefore quickly spread become beneficial to many farmers than would

otherwise be possible with natural mating.

20}}

When a cow is on heat and needs a bull, the farmer through training is able to detect this by

observing the cows behavior as well as physical changes in external reproductive tract. The

farmers then notifies the inseminators who within preferably 18 hours of onset of heat artificially

inseminate the cow using the semen from a superior bull.

Upgrading of local cattle to the temperate dairy breeds and concurrent use of highly selected

animals through AI has been the technological packages of choice in Kenya and has been largely

responsible for the great strides so far made in the country's dairy sub-sector.

AI is without doubt economically advantageous compared to natural service because:

1. The need to keep a bull and the costs associated with it are avoided.

2. It is cheaper, than natural service. Currently one insemination costs between KShs.200

and Ksh 2,500 depending on the genetic superiority of the semen and the type of service

provided.

3. It makes it possible for farmers to have access to high quality germplasm and thus make

permanent improvement of their stock faster and more efficiently.

4. The spread of venereal diseases is easily controlled.

However, the level and quality of AI service in Kenya has of late declined seriously. Previous

AI service was provided by the government run Kenya National Artificial Insemination Service

(KNAIS), provided at a highly subsidized rate to the dairy farmers. Israelsson and Oscarsson

(1994) indicate that the most low cost model of providing AI service to the farmer was daily

run model, as long as such inseminations are made per day by one inseminator, covering

between 100 to 120 km along a prescribed route. An alternative model would be where the

inseminator waits for information from farmers on when to provide the service.

In situations such as those that characterize the Kenya's smallholder dairy farmers, both models

have proved to be unsustainable for different reasons: First, the mobile system is too expensive

on vehicle maintenance and fuel expenses due to the poor state of roads. The static system is

unsustainable and expensive as AI service on its own is not a viable business undertaking, and

has therefore not attracted private investors following the liberalization of provision of

agricultural services structural adjustments beginning in the mid 1980's.

Although AI as a technology is technically beneficial, lack of appropriate delivery system, its

adoption and effectiveness in Kenya is rapidly declining (Table 8 and Figure 1). The

consequences of such decline is disastrous because the genetic potential and productivity of the

21}}

dairy herd is bound to decline very rapidly4. The low productivity arise from both poor genetic

potential as well as longer calving intervals among cow's in production.

Table 8. Number of artificial inseminations carried out by KNAIS, 1984-97

Number of inseminations (‘000) Total ‘000 Percentage change

Grade cows Zebu cows

19841985198619871988198919901991199219931994199519961997

462465388370348398385246192134105103 68 24

23211715111211422

386486405385359410396250194136

-17+26-17-5-7

+14-3-37-22-30

Source: Government of Kenya, Department of Veterinary Services, Ministry of Agriculture.

Secondly, the AI service fees which currently average Ksh. 450 per insemination although,

much higher than what the KNAIS used to charge, are in reality reasonable given the gross

margins realized by zero grazing units as evidenced in the discussions later in this section and

Section 3.6. It is recommended that the cost of one insemination should not exceed 10% of the

value of annual milk production per cow (Israelsson and Oscarsson, 1994). Given the current

AI charges of Ksh. 450 per insemination, milk price of Ksh.20 per litre and average yield of

3,000 litres per lactation, AI charges forms only 0.1% of the total revenue from milk sales alone

which is far much less than 10%. At lactation yields greater than 4,500 litres, this cost

component becomes negligible. The trends of the number of inseminations on Zebu cattle,

Table 8 show continuous decline. This reflects the successful growth of grade cattle population

through AI.

3.5.2 Role of AI in elevating poor peasant livestock keepers to commercial smallscale dairy farmers level

The bulk of the Zebu cattle owners are poorer members of the farming community who can not 4Available reports currently reveal that only 10% of available semen at the Central Artificial InseminationStation is used annually.

22}}

afford to purchase grade or pure dairy cattle breeds in order to start off commercial dairy

enterprises in the high agricultural potential areas. On average the Zebu cow produces only 900

litres per year half of which is consumed by the calf while the half bred produces 1,500 litres per

year. AI technology gives such farmers opportunity to almost double their milk production per

cow per year in less than three years. Given that the extra overhead costs of maintaining a

crossbred cow is less than 10% above that of the Zebu and that the improvement in milk

production potential is permanent, and can be further built on in the subsequent generations, this

technology offers cheaper avenue for poverty alleviation to aspiring dairy farmers who

otherwise can not afford between Ksh.25,000 and 80,000 to purchase a purebred dairy cow.

Figure 1. Number of artificial inseminations (‘000) carried out by the Kenya National ArtificialInsemination Services (KNAIS) and private firms in Kenya: 1971-1997

Source: Government of Kenya, Department of Veterinary Services, Ministry of Agriculture

Figure 1 shows the trend of number of artificial inseminations carried out over the years (1971-

97). It is clear that between the periods of 1971 - 75 and 1981 - 84 when the government fully

supported the service by adequately funding and equipment, the number of inseminations

steadily increased, while the periods of 1985 - 88 and to date when such support as withdrawn

have been characterized by rapid decline of inseminations carried out by KNAIS. During the

period (1992 - 97) the private inseminators' role in delivering the AI service has been increasing

although there still remains a big gap between the potential demand and what private service

providers offer today.

050

100150200250300350400450500550600

71 73 75 77 79 81 83 85 87 89 91 93 95 97

Year

Nu

mb

er o

f In

sem

inat

ion

s

AI by KNAIS AI by Private firms

23}}

The additional 400 litres per cow is potentially available for sale three years later and thereafter

annually for the next five or so years. Without AI technology it would be nearly impossible for

the resource poor small-scale livestock farmers to join the dairy enterprise. The adoption of AI

technology, especially through the upgrading programme, has enabled approximately 80% of

the current suppliers of milk in Kenya to enter into commercial dairying within reasonably short

period than would have been if direct importation of dairy cows had been adopted.

AI enables farmers to achieve between 0.75% and 1% genetic potential improvement in their

herd's milk production annually. Use of bulls (natural mating) can only achieve much less and if

not well executed can most likely lead to retrogression. One bull can only serve up to 50 cows

each year and even if such services are charged at half of Ksh 350 that of the current average AI

charges, this can only realize an annual income of Ksh.15,000 and yet the annual maintenance

cost of such a bull if kept to serve only 80 cows would be equivalent to less than that of one

dairy cow at Ksh.25,000.

According to the gross margin analyses in Section 3.6 of this report the estimated current

commercial cost of semen plus its delivery at Ksh.700 forms 43% of the total annual veterinary

and AI costs per cow and only 1.5% of the total annual cost per cow per year. This turns out to

be only a negligible 1.4% proportion of the total value of milk sales per cow per year.

Currently AI charges are on average Ksh 150 per cow per year higher than those by bull

service. However, whereas the bull service has no guarantee of providing heifers that are

between 0.75 and 1% better than their parents’ production levels annually, with AI such

improvement rates are guaranteed. This means that with an extra Ksh 150 per cow per year the

AI user would tie a total (principal plus interest) of Ksh. 258 over a three-year period when

heifers born would begin milk production. Because such heifers will have 1% higher potential

they would each year for the next five years produce 30 litres more milk than those born of

natural mating. At the milk price of Ksh 20 per litre this turns out to be an extra Ksh.3,000 per

cow’s lifetime. Besides such a cow would on average, have higher salvage value than that born

from natural mating. Therefore, for an extra Ksh 260 cost an extra revenue of Ksh 1,200 would

accrue assuming that only 40% of calves born are females that reach milk production age.

The appropriateness and viability of any technology can be judged by how much demand it

attracts whenever it is offered in a competitive free market situation. From the trends of the

private artificial inseminations one could argue that AI as a tool for genetic improvement is

indeed the main reason behind the improvement and success of dairy sub-sector in Kenya as is

the case world over. If the potential is realized, then incomes of the poor can be raised and this

24}}

will alleviate poverty.

The failure of AI under the government supported programme was largely due to the wrong

choice of mode of delivery of the service. The KNAIS opted for motor vehicle as the mode of

transporting the service delivery which if compared to motorbike or moped costs much more to

buy and maintain. Besides, a motor vehicle requires more developed road network system and

would inadequately serve most of the rural communities today. The success of the private AI

service providers today is due to their choice of motorbikes instead of motor cars.

Table 9 shows that positive gross margins are realizable by private AI service providers under

different levels of operations and two modes of transport. This shows that AI as a technology is

viable at both the user (dairy farmer) and service provider levels. Results obtained from private

veterinarians indicate that when AI is combined with clinical practice and agro-veterinary retail

practice the gross margin from the AI activity is even larger. Similarly, cooperative member

small scale dairy farmers could lower their AI service costs substantially by training one of their

technicians to provide AI service besides other extension or marketing services.

The purchase and maintenance cost of operating the smallest second hand four-wheel drive car

would be twice that of a motorbike and would therefore beyond the reach of a beginning private

inseminator.

Currently the population of dairy cattle in Kenya is 12 million head. Assuming that 60% of these

are cows and heifers of breeding ages and that 10% of these need to be inseminated monthly,

the approximately 720,000 inseminations need to be carried out. Of these, if 70% are through

AI then it is apparent that a huge potential exists for the privates AI service.

25}}

Table 9 Estimated gross margin analysis of AI service when inseminator uses motorbike andmoped

Purchase price KshInsemination charge per dose Ksh 375 375

RevenueServices per day 4 6 10 4 6 10Semen per dose 450 450 450 450 450 450Revenue from semen sales 604,800 907,200 1,512,000 604,800 907,200 1,512,000Service charges 360,000 540,000 900,000 360,000 540,000 900,000TOTAL REVENUE 964,800 1,447,200 2,412,000 964,800 1,447,200 2,412,000

CostsCost of semen 604,800 907,200 1,512,000 604,800 907,200 1,512,000Stationery 20,700 20,700 20,700 20,700 20,700 20,700Liquid Nitrogen 37,800 37,800 37,800 37,800 37,800 37,800Consumable supplies 45,000 45,000 45,000 45,000 45,000 45,000Fuel 60,000 60,000 60,000 18,000 18,000 18,000Maintenance 2,400 2,400 2,400 1,200 1,200 1,200TOTAL COSTS 770,700 1,073,100 1,677,900 727,500 1,029,900 1,634,700

GROSS MARGIN 194,100 374,100 734,100 237,300 417,300 777,300

Motorbike Moped120,000 60,000

Adapted from American Breeders Service Kenya Ltd., 1998.

Table 9 gives the estimates of gross margin analyses of private AI delivery service based on two

modes of transportation (motorbike and moped) on annual basis. Use of mopeds and

motorbikes compare reasonably well and offer very high gross margins when six and ten

inseminations are offered in a day.

Reasons attributed to the very high AI costs incurred by KNAIS relative to milk prices include:

(i) Wrong choice of AI delivery service (cars instead of motorbikes) given the poor states

of rural roads, the vehicle running costs were too high. Secondly the time taken to

reach cows that are reported to be on heat would be much longer, with a good number

of such cows being inseminated well past the optimum periods.

(ii) Poor infrastructure: the cost of telephone services is too high, while the quality of this

service is dually low.

(iii) Poor market policies: milk is not marketed based on quality (protein and butter fat

content), while semen is priced on quality which, includes anticipated milk quality.

(iv) Poor legal frameworks that govern and regulate the various service providing

institutions. Service providing institution, are commonly poorly run with fraud and

corruption being rampant among cooperative leaders and employees. The legal

machinery is inefficient that justice when applied, it is usually comes too late.

26}}

3.5.3 Some policy Implications for Livestock Improvement Research.

Based on the past experiences, the following are some policy implications for livestock

improvement research in Kenya:

i) For sustainable and effective adoption of agricultural technologies, given the much

reduced government’s roles in the delivery of services, the private sector is increasingly

playing a central roles in service delivery. This takes various forms, but the one that is

already in place and need to be rejuvenated and re-oriented is the farmer cooperative

movement (FCM). The FCM need to adopt a more multi-purpose service provision

roles than has been the case. In the past FCM were restricted to provision of marketing

services for farm produce and inputs (seed, fertilizer and animal feeds). FCM have the

untapped potential of providing wider services to the member and non-member farmers

on a need sensitive basis. It is recommended that FCMs personnel be trained in aspects

such as AI and extension to take over or augmenting government roles.

ii) In the process of technology development considerations must be made of the cultural

implications, technical feasibility and appropriateness as well as its sustainability.

Sustainability depends on the above first two factors as well as the ability of and the

extent to which the existing policies and infrastructure can support such technologies.

iii) Improvement in infrastructure particularly roads, provision of good quality water,

electricity and efficient telephone facilities are by and large essential for agricultural

technologies to effectively and positively impact on the rural based livestock farmers,

who are currently the least beneficiaries of modern agricultural technologies in Kenya.

iv) Despite the almost universal interest of farmers in mixed farming (crop-animal systems),

researchers appear to pursue pure commodity based research without consideration to

the associated interactions between the two which lead to increased productivity. There

is need to recognize such interactions by having a good balance between commodity and

farming systems based research.

27}}

3.6 Improved small scale dairy technology package

3.6.1 The zero grazing systemZero grazing is animal management exclusively under confinement. It started to gain relative

importance from the late 70s mainly due to the rising land pressure. It is the most intensive milk

production system and is implemented by more than 20,000 smallholders all over the country. The

system is characterised by keeping high yielding grade cattle like Ayrshire, Friesians and their crosses.

This system differs from semi-grazing by the absence of pastures, heavy dependence on cultivated

Napier grass and high use of purchased inputs. Milk yields per cow per year, in zero grazing farms

average 3,300 kg, 2,340 kg in semi-grazing farms and 1,800 kg in open grazing systems (Egerton

University, 1990).

The cattle are permanently kept in a cow shed, where they are fed, milked and also sleep. Zero

grazing farmers are predominantly market producers with from 1 to 5 cows. Their main interest

being milk production, the male calves are sold at an early age. Heifer calves are kept in calf pens

from where they are bucket fed with whole milk and some concentrates before they are weaned from

3 to 6 months. After weaning, heifers are kept with the cows. On most farms, cattle are sprayed once

a week to control tick-borne diseases and drenching to control internal parasites is done routinely.

The main feed under zero grazing system is Napier grass (Pennisetum purpureum), a perennial

fodder grass. It is the most popular fodder crop since under normal rainfall conditions, it is ready for

harvest 4 weeks after cutting and on it alone, a cow can produce up to 7 litres of milk per day

(Kariuki and Waithaka, 1992). However, Napier grass is prone to frost damage in the high altitude

areas, cannot withstand very long dry periods as experienced in the low altitude areas and cannot

withstand direct grazing. Napier grass has to be cut from the fields and carried to the cows and is

chopped to reduce wastage through spilling and trampling. Other feeds include farm by-products

which are in season, e.g., maize stover and vegetables as well as commercial concentrates and

mineral supplements.

3.6.1.1 Advantages of zero grazingThe most outstanding advantages of zero grazing (Kariuki and Waithaka, 1992) are:

1. Productivity per unit of land is increased since selective grazing is reduced and one acre

planted with Napier grass can support one cow and her followers (heifer and calf).

2. Animal energy expenditure is reduced as the cows do not have to walk while grazing

or searching for water.

3. Better health and management. Due to the confinement, the incidences of infestation by ticks

are reduced and cows which are sick or on heat can be detected in time.

28}}

4. More manure is available for fodders and crops reducing the use of expensive compound

fertilisers while improving soil fertility. This manure has an added advantage in that it

incorporates urine which is rich in nitrogen.

3.6.1.2 Problems associated with zero grazingThe major problems associated with zero grazing (Kariuki and Waithaka, 1992) are:

i) The cost of constructing a zero grazing unit is high. A unit for two cows and followers costs

more than KSh. 40,000.

ii) The system is labour intensive. Sine a cow eats up to 3% of its body weight in dry matter

basis per day, a 500 kg cow requires 15 kg dry matter equivalent to 100 kg of fresh Napier

grass.

iii) Diseases such as mastitis and foot rot can arise with poor management and low hygiene.

iv) Poor nutrient recycling can occur if manure is not returned to the fodder crops, but is instead put

in the food or cash crop fields.

v) Reduction in selective grazing may lead to poor nutrient intakes. Since cows select only the

green higher quality leaves, cutting and chopping of dead herbage and stems reduces the

quality of the feed offered.

vi) Time of harvesting Napier grass is crucial. The optimal time to harvest is from 4 and not later than

8 weeks after cutting.

3.6.1.3 The zero grazing technology package componentsThe zero grazing technology package as recommended by NDDP has six components:

1. The zero grazing unit consists of resting place and walking area, feed and water trough,

milking place, calf pen, fodder chopping area and manure pit.

2. Calf management includes management of in-calf cow, feeding calf with colostrum, whole

milk and concentrates before weaning, housing, disease control and feeding after weaning.

3. Napier grass management includes variety, area to be planted, planting, weed control,

cutting, fertiliser and manure use and intercropping with legumes.

4. Feeding the dairy cow includes feeding of Napier grass, supplementation with concentrates

and mineral salts.

5. Fertility of the dairy cow involves heat signs and detection, feeding in relation to fertility and

disease prevention.

6. Clean milk production includes hygiene and milking technique.

Unit production costs of milk are lower under zero grazing conditions primarily due to higher milk

yields. Other critical factors are shorter calving interval and reduced animal health costs.

29}}

3.6.2 Gross margin analysis of Zero-grazing as technology packageTo calculate gross margins of milk production under smallholder zero grazing conditions a

spreadsheet model adapted from Waithaka and Nijssen, 1992 is used. Data used are derived from the

experiences and standards developed by the National Dairy Development Programme (NDDP)

which worked directly with small scale dairy farmers and KARI, Naivasha.

The spreadsheet model is used in calculations for a reference farming situation (Table 10). The costs

and revenues of the farming system are split into various components. First the revenue is calculated

from revenue from sales of milk and sales of bull calves, heifers and cull cows. The number of cattle

to be sold is calculated by using the number of cows present, birth rate, calf mortality rate, heifer

mortality rate, adult mortality rate, culling rate, calving interval and age at first calving. The total milk

production per lactation and the calving interval are the two components used for calculation of the

annual milk production per cow and the peak production per day. Only those data together with the

maximum daily milk production from roughage are used to specify the amount of concentrates

required for the desired production. In the calculation, the lactation curve of the cow is estimated.

The costs of feeding the animals are split up in a number of items. The fodder expenses are calculated

as a fixed amount per acre. The number of acres per cow including followers is variable in the model.

A standard of 1.6 bag of CAN and 4 bags of 20 - 10 - 10 fertilizer have been included for production

of Napier grass. Animal health costs are calculated per average present animal. Weekly costs for tick

control are included for all age groups. Costs for AI or bull service are accounted for under animal

health costs.

Other cattle costs include milking pails, miscellaneous cattle costs, adult mortality and interest on

cattle. The adult mortality is included as a cost, because in the reference farming situation the number

of adult cattle is constant. The annual costs for adult mortality can therefore be considered as an

insurance premium. Interest costs for animals present in the farming system are included as

opportunity costs. If the farmer would not invest his money in cattle, but save it in a bank, he would

be earning interest on this money. Now that the money is invested in animals, interest costs on the

total value of the herd must be included in the production cost calculation.

The last group of costs is for use of land, labour, animal housing, a tick control sprayer and, if

applicable, for a chaff cutter. The costs of land have been set as an annual amount per acre. This

amount is the opportunity costs of the land. Also for labour costs, the opportunity costs have been

used. Costs for housing, sprayer and chaff cutter include maintenance, depreciation and interest.

To calculate the gross margin, the total variable cost is deducted from the total revenue. The

resulting amount is divided by the quantity of milk to be sold. This is the average annual milk

30}}

production minus the quantity of milk used to feed the calves. There is no subtraction for household

consumption. A summary of these calculations is shown in Table 11.

Table 10. Reference small-scale dairy farming situation

Description Input Result

Fertility and mortalityNumber of cowsCalving intervalAge at first calvingCalf mortality rateHeifer mortality rateCulling rateAdult mortality rateFeedsMilk production per lactationMilk production per yearMax production on roughage onlyNapier useMilk intake per bull calfMilk intake per heifer calfVeterinary costsCurative drugs + AI

Cattle pricesBull calvesHeifer 0-1 yearHeifer 1-2 yearsPregnant heifersCowsInterest rateHousing costsInvestmentMaintenanceChaff cutterInvestmentMaintenanceSprayer costsInvestmentMaintenance

3405 30 8 7 25 4

3,0002,7047.5 150

415

1,200/ cow1,000/ pregnant heifer500/ heifer 1-2yrs

500/ heifer 0-1 yearValue1,500

15,00025,00050,00040,000

20

42,0005%

KShs22,000

5%KShs8,0005%

cows days

months % % % %

kg

kg/dayacre/cow

kgkg

Selling2,000

18,00030,000

45,000%

KShs2,100 KShs

KShs1,100 KShs

400

Adapted from Waithaka and Nijssen, 1992.

31}}

Table 11. Gross margin of milk production under zero grazing conditions

Description Quantity Unit Price Unit Amount %

REVENUE

Milk to be marketedBull calvesHeifers 1-2 yearCull cows

Total Revenue

7,4831.350.410.75

litresanimalanimal

202,000

30,00045,000

litreanimalanimalanimal

149,6502,704

12,19833,750

198,302

751617

100

COSTS

Fodder expensesCAN Fertilizer20-10-10 FertilizerEarly weaner pelletsYoung stock pencilsDairy mealMineralsTotal feed costs

AcaricidesPY GreaseDewormerCurative drugs + AIMastriteMilking salveBactergentTotal vet costs

Milking pailsMiscellaneousAdult mortalityInterest on operatingcapitalTotal other costs

LandLabourHousingChaff cutterSprayerTotal other costs

Total costs

3.004.8012.0091363130685

205964981

6.75345

4

69,388

335442,00022,0008,000

acresbagsbagkgkgkgkg

mlgramml

litrekglitre

%

Kshs

acresdaysKShsKShsKShs

900950/50 kg1,300/50 kg1,150/70 kg1,200/70 kg800/70 kg850/70 kg

218/100 80/250185/120

1,200/5120/0.5520/5

6941,600

20

2,000 120 5 5 5

acresbagbagbagbagbagbag

mlgramml

litrekglitre

cowcow

%

acreday%%%

2,7004,56015,6001,4926,22914,9317,186

52,698

4,489 208 1244,8491,620 7204,68016,690

950 2,082 4,800

13,87820,759

6,00042,507 2,100 1,100 40052,107

142,252

2 311 1 410 5

37

3 0 0 3 1 1 312

1 1 3

1015

430 1 1 037

100

Gross margin 56,049


32}}

3.5.2 Role of AI in elevating poor peasant livestock keepers to commercial smallscale dairy farmers level

The bulk of the Zebu cattle owners are poorer members of the farming community who can not

afford to purchase grade or pure dairy cattle breeds in order to start off commercial dairy

enterprises in the high agricultural potential areas. On average the Zebu cow produces only 900

litres per year half of which is consumed by the calf while the half bred produces 1,500 litres per

year. AI technology gives such farmers opportunity to almost double their milk production per

cow per year in less than three years. Given that the extra overhead costs of maintaining a

crossbred cow is less than 10% above that of the Zebu and that the improvement in milk

production potential is permanent, and can be further built on in the subsequent generations, this

technology offers cheaper avenue for poverty alleviation to aspiring dairy farmers who

otherwise can not afford between Ksh.25,000 and 80,000 to purchase a purebred dairy cow.

Figure 2. Number of artificial inseminations (‘000) carried out by the Kenya National ArtificialInsemination Services (KNAIS) and private firms in Kenya: 1971-1997

Source: Government of Kenya, Department of Veterinary Services, Ministry of Agriculture

Figure 1 shows the trend of number of artificial inseminations carried out over the years (1971-

97). It is clear that between the periods of 1971 - 75 and 1981 - 84 when the government fully

supported the service by adequately funding and equipment, the number of inseminations

steadily increased, while the periods of 1985 - 88 and to date when such support as withdrawn

have been characterized by rapid decline of inseminations carried out by KNAIS. During the

period (1992 - 97) the private inseminators' role in delivering the AI service has been increasing

050

100150200250300350400450500550600

71 73 75 77 79 81 83 85 87 89 91 93 95 97

Year

Nu

mb

er o

f In

sem

inat

ion

s

AI by KNAIS AI by Private firms

33}}

although there still remains a big gap between the potential demand and what private service

providers offer today.

The additional 400 litres per cow is potentially available for sale three years later and thereafter

annually for the next five or so years. Without AI technology it would be nearly impossible for

the resource poor small-scale livestock farmers to join the dairy enterprise. The adoption of AI

technology, especially through the upgrading programme, has enabled approximately 80% of

the current suppliers of milk in Kenya to enter into commercial dairying within reasonably short

period than would have been if direct importation of dairy cows had been adopted.

AI enables farmers to achieve between 0.75% and 1% genetic potential improvement in their

herd's milk production annually. Use of bulls (natural mating) can only achieve much less and if

not well executed can most likely lead to retrogression. One bull can only serve up to 50 cows

each year and even if such services are charged at half of Ksh 350 that of the current average AI

charges, this can only realize an annual income of Ksh.15,000 and yet the annual maintenance

cost of such a bull if kept to serve only 80 cows would be equivalent to less than that of one

dairy cow at Ksh.25,000.

According to the gross margin analyses in Section 3.6 of this report the estimated current

commercial cost of semen plus its delivery at Ksh.700 forms 43% of the total annual veterinary

and AI costs per cow and only 1.5% of the total annual cost per cow per year. This turns out to

be only a negligible 1.4% proportion of the total value of milk sales per cow per year.

Currently AI charges are on average Ksh 150 per cow per year higher than those by bull

service. However, whereas the bull service has no guarantee of providing heifers that are

between 0.75 and 1% better than their parents’ production levels annually, with AI such

improvement rates are guaranteed. This means that with an extra Ksh 150 per cow per year the

AI user would tie a total (principal plus interest) of Ksh. 258 over a three-year period when

heifers born would begin milk production. Because such heifers will have 1% higher potential

they would each year for the next five years produce 30 litres more milk than those born of

natural mating. At the milk price of Ksh 20 per litre this turns out to be an extra Ksh.3,000 per

cow’s lifetime. Besides such a cow would on average, have higher salvage value than that born

from natural mating. Therefore, for an extra Ksh 260 cost an extra revenue of Ksh 1,200 would

accrue assuming that only 40% of calves born are females that reach milk production age.

The appropriateness and viability of any technology can be judged by how much demand it

attracts whenever it is offered in a competitive free market situation. From the trends of the

34}}

private artificial inseminations one could argue that AI as a tool for genetic improvement is

indeed the main reason behind the improvement and success of dairy sub-sector in Kenya as is

the case world over. If the potential is realized, then incomes of the poor can be raised and this

will alleviate poverty.

The failure of AI under the government supported programme was largely due to the wrong

choice of mode of delivery of the service. The KNAIS opted for motor vehicle as the mode of

transporting the service delivery which if compared to motorbike or moped costs much more to

buy and maintain. Besides, a motor vehicle requires more developed road network system and

would inadequately serve most of the rural communities today. The success of the private AI

service providers today is due to their choice of motorbikes instead of motor cars.

Table 9 shows that positive gross margins are realizable by private AI service providers under

different levels of operations and two modes of transport. This shows that AI as a technology is

viable at both the user (dairy farmer) and service provider levels. Results obtained from private

veterinarians indicate that when AI is combined with clinical practice and agro-veterinary retail

practice the gross margin from the AI activity is even larger. Similarly, cooperative member

small scale dairy farmers could lower their AI service costs substantially by training one of their

technicians to provide AI service besides other extension or marketing services.

The purchase and maintenance cost of operating the smallest second hand four-wheel drive car

would be twice that of a motorbike and would therefore beyond the reach of a beginning private

inseminator.

Currently the population of dairy cattle in Kenya is 12 million head. Assuming that 60% of these

are cows and heifers of breeding ages and that 10% of these need to be inseminated monthly,

the approximately 720,000 inseminations need to be carried out. Of these, if 70% are through

AI then it is apparent that a huge potential exists for the privates AI service.

35}}

Table 12 Estimated gross margin analysis of AI service when inseminator uses motorbike andmoped

Purchase price KshInsemination charge per dose Ksh 375 375

RevenueServices per day 4 6 10 4 6 10Semen per dose 450 450 450 450 450 450Revenue from semen sales 604,800 907,200 1,512,000 604,800 907,200 1,512,000Service charges 360,000 540,000 900,000 360,000 540,000 900,000TOTAL REVENUE 964,800 1,447,200 2,412,000 964,800 1,447,200 2,412,000

CostsCost of semen 604,800 907,200 1,512,000 604,800 907,200 1,512,000Stationery 20,700 20,700 20,700 20,700 20,700 20,700Liquid Nitrogen 37,800 37,800 37,800 37,800 37,800 37,800Consumable supplies 45,000 45,000 45,000 45,000 45,000 45,000Fuel 60,000 60,000 60,000 18,000 18,000 18,000Maintenance 2,400 2,400 2,400 1,200 1,200 1,200TOTAL COSTS 770,700 1,073,100 1,677,900 727,500 1,029,900 1,634,700

GROSS MARGIN 194,100 374,100 734,100 237,300 417,300 777,300

Motorbike Moped120,000 60,000

Adapted from American Breeders Service Kenya Ltd., 1998.

Table 9 gives the estimates of gross margin analyses of private AI delivery service based on two

modes of transportation (motorbike and moped) on annual basis. Use of mopeds and

motorbikes compare reasonably well and offer very high gross margins when six and ten

inseminations are offered in a day.

Reasons attributed to the very high AI costs incurred by KNAIS relative to milk prices include:

(v) Wrong choice of AI delivery service (cars instead of motorbikes) given the poor states

of rural roads, the vehicle running costs were too high. Secondly the time taken to

reach cows that are reported to be on heat would be much longer, with a good number

of such cows being inseminated well past the optimum periods.

(vi) Poor infrastructure: the cost of telephone services is too high, while the quality of this

service is dually low.

(vii) Poor market policies: milk is not marketed based on quality (protein and butter fat

content), while semen is priced on quality which, includes anticipated milk quality.

(viii) Poor legal frameworks that govern and regulate the various service providing

institutions. Service providing institution, are commonly poorly run with fraud and

corruption being rampant among cooperative leaders and employees. The legal

machinery is inefficient that justice when applied, it is usually comes too late.

36}}

3.5.3 Some policy Implications for Livestock Improvement Research.

Based on the past experiences, the following are some policy implications for livestock

improvement research in Kenya:

v) For sustainable and effective adoption of agricultural technologies, given the much

reduced government’s roles in the delivery of services, the private sector is increasingly

playing a central roles in service delivery. This takes various forms, but the one that is

already in place and need to be rejuvenated and re-oriented is the farmer cooperative

movement (FCM). The FCM need to adopt a more multi-purpose service provision

roles than has been the case. In the past FCM were restricted to provision of marketing

services for farm produce and inputs (seed, fertilizer and animal feeds). FCM have the

untapped potential of providing wider services to the member and non-member farmers

on a need sensitive basis. It is recommended that FCMs personnel be trained in aspects

such as AI and extension to take over or augmenting government roles.

vi) In the process of technology development considerations must be made of the cultural

implications, technical feasibility and appropriateness as well as its sustainability.

Sustainability depends on the above first two factors as well as the ability of and the

extent to which the existing policies and infrastructure can support such technologies.

vii) Improvement in infrastructure particularly roads, provision of good quality water,

electricity and efficient telephone facilities are by and large essential for agricultural

technologies to effectively and positively impact on the rural based livestock farmers,

who are currently the least beneficiaries of modern agricultural technologies in Kenya.

viii) Despite the almost universal interest of farmers in mixed farming (crop-animal systems),

researchers appear to pursue pure commodity based research without consideration to

the associated interactions between the two which lead to increased productivity. There

is need to recognize such interactions by having a good balance between commodity and

farming systems based research.

37}}









University, 1990).























38}}





vii) The cost of constructing a zero grazing unit is high. A unit for two cows and followers costs


viii) The system is labour intensive. Sine a cow eats up to 3% of its body weight in dry matter


grass.

ix) Diseases such as mastitis and foot rot can arise with poor management and low hygiene.

x) Poor nutrient recycling can occur if manure is not returned to the fodder crops, but is instead put

in the food or cash crop fields.

xi) Reduction in selective grazing may lead to poor nutrient intakes. Since cows select only the



xii) Time of harvesting Napier grass is crucial. The optimal time to harvest is from 4 and not later

than 8 weeks after cutting.









and mineral salts.


disease prevention.




39}}



















health costs.














40}}







3405 30 8 7 25 4

3,0002,7047.5 150

415



15,00025,00050,00040,000

20

42,0005%

KShs22,000

5%KShs8,0005%

cows days

months % % % %

kg

kg/dayacre/cow

kgkg

Selling2,000

18,00030,000

45,000%

KShs2,100 KShs

KShs1,100 KShs

400


41}}



REVENUE


Total Revenue

7,4831.350.410.75

litresanimalanimal

202,000

30,00045,000


149,6502,704

12,19833,750

198,302

751617

100

COSTS





Total costs

3.004.8012.0091363130685

205964981

6.75345

4

69,388

335442,00022,0008,000


mlgramml

litrekglitre

%

Kshs


900950/50 kg1,300/50 kg1,150/70 kg1,200/70 kg800/70 kg850/70 kg

218/100 80/250185/120

1,200/5120/0.5520/5

6941,600

20

2,000 120 5 5 5


mlgramml

litrekglitre

cowcow

%

acreday%%%

2,7004,56015,6001,4926,22914,9317,186

52,698

4,489 208 1244,8491,620 7204,68016,690

950 2,082 4,800

13,87820,759

6,00042,507 2,100 1,100 40052,107

142,252

2 311 1 410 5

37

3 0 0 3 1 1 312

1 1 3

1015

430 1 1 037

100

Gross margin 56,049


42}}

Results

In the reference farming situation with three cows, the gross margin is Ksh 56,049. The main

cost items are labour 30%, other costs, 19%, concentrates 15%, fertilizer 14% animal health at

12% and interest on operating capital at 10%. The major source of revenue is milk at 76%, cull

cows 17%, one to two year old heifers 6% and bull calves 1% (Table 11).

Sensitivity analysis

The reference farming situation is more or less an arbitrary situation. Therefore it is important

to check the gross margin in different situations as well. In every analysis only one factor has

been changed from the reference farming

situation to different levels.

Number of cows

In the reference farming situation the

number of cows is three. The number of

cows is an important factor in the gross

margin calculations. The reason for that is

that a number of costs in the gross margin

calculations are incurred irrespective of the

number of cows although they are not fixed

costs. When the same expenses have to be

divided by a smaller herd size, the gross

margin increases. At two cows the gross

margin is Ksh. 33,759 at six cows it is Ksh.

133,245. For each additional cow, the

gross margin increases by Ksh 23,973.

Milk production per lactation

In the reference farming situation the milk production per lactation is 3,000 litres. Since the

calving interval is 405 days, the annual production per cow is 2,704 litres. At this production

level the gross margin is Ksh.56,049. When milk production is only 2,000 litres per lactation the

gross margin decreases to Ksh. 18,451. At a production level of 4,500 litres the gross margin

increases to Ksh. 103,365. For every 500 liters increase in annual milk production, the gross

margin increases by Ksh 17,000.

Calving interval

The calving interval is 405 days in the

reference farming situation and many

Cows

0

20,000

40,000

60,000

0 2 4 6 8

Number of cows

Gro

ss M

arg

in (

K.S

hs.

)

Lactation yield

0

20,000

40,000

60,000

0 1,000 2,000 3,000 4,000 5,000

Lactation yield (kg)

Gro

ss M

arg

in (

KS

hs)

Calving interval

0

20,000

40,000

60,000

0 200 400 600

Gro

ss M

arg

in (

Ksh

s)

43}}

farmers do not reach this level but border on more than 450 days. The gross margin at the

optimal calving interval of 365 days is Ksh. 70,405. At an interval of 495 days the gross margin

drops to Ksh. 33,395. The main reason for this severe influence of calving interval on the gross

margin is that the annual milk supply

decreases at an unchanged production level

per lactation. For every 30 days increase in

calving interval, the gross margin declines by

Ksh 8,520.

Milk price

Milk price in the reference situation is Ksh

20 per litre. Although milk prices are no

longer controlled, they vary from Ksh 10 in surplus areas such as Nyandarua to Ksh 35 in deficit

areas such as Mombasa. At Ksh 10, the gross margin drops to Ksh -18,777. At Ksh 35, it

shoots to Ksh 168,286. This implies that for every shilling increase in the price of milk, the


Maximum production on roughage

only

The quantity of concentrates needed to

achieve a certain milk production is affected

by both the quality and the quantity of roughage available. Under good Napier management,

like in the reference farming situation, it is possible to produce 7.5 litres of milk per day when

feeding only roughage. At 4.5 litres of milk on roughage only the gross margin is Ksh. 35,740,

at 9.5 litres of milk on roughage only it is Ksh. 65,526. For every litre increase in milk

production the gross margin increases by Ksh 5,954.

Milk Price

0

20,000

40,000

60,000

10 15 20 25 30 35

Max. production on roughage only

0

20,000

40,000

60,000

0 2 4 6 8 10

Roughage (kg/day)

Gro

ss M

arg

in

44}}

Labour costs

The costs for labour differ very much

between different districts. In densely

populated areas, generally the labour cost

is lower. In the reference farming situation

labour costs have been set at Ksh. 120 per

day. When the labour costs are only Ksh.

80 per day, the gross margin decreases to

Ksh. 70,218. At a labour costs level of

Ksh. 160 per day it is Ksh. 41,880 per litre. For every Ksh 10 increase in labour costs, the gross

margin declines by Ksh 3,540.

Interest rate

The interest rate used in the reference

farming situation is 20 %. Interest is

charged on operating capital. The interest

rate changes the production cost of milk by

about Ksh 694 for every 1 % change in interest rate.

Other factors

In addition, the sensitivity of cattle sales prices, fertilizer prices, concentrate prices and

veterinary costs were investigated. These factors have only marginal influence (1%) on the

gross margin of milk. The influence of the milk taken by calves on the gross margin is bigger. In

the reference farming situation bull calves get 50 litres of milk and heifer calves get 415 litres. If

this is reduced to 30 litres and 265 litres respectively, the gross margin increases from Ksh.

56.049 to Ksh.60,645. However, this is only recommended if the calves are provided with milk

replacers which do not compromise calf health and growth.

Labour

0

20,000

40,000

60,000

0 50 100 150 200

Labour per man day (KShs)

Gro

ss M

arg

in (

KS

hs)

Interest rate

0

20,000

40,000

60,000

0 10 20 30 40

45}}









University, 1990).























46}}





xiii) The cost of constructing a zero grazing unit is high. A unit for two cows and followers costs


xiv) The system is labour intensive. Sine a cow eats up to 3% of its body weight in dry matter


grass.

xv) Diseases such as mastitis and foot rot can arise with poor management and low hygiene.

xvi) Poor nutrient recycling can occur if manure is not returned to the fodder crops, but is instead

put in the food or cash crop fields.

xvii) Reduction in selective grazing may lead to poor nutrient intakes. Since cows select only the



xviii) Time of harvesting Napier grass is crucial. The optimal time to harvest is from 4 and not later

than 8 weeks after cutting.









and mineral salts.


disease prevention.




47}}



















health costs.














48}}







3405 30 8 7 25 4

3,0002,7047.5 150

415



15,00025,00050,00040,000

20

42,0005%

KShs22,000

5%KShs8,0005%

cows days

months % % % %

kg

kg/dayacre/cow

kgkg

Selling2,000

18,00030,000

45,000%

KShs2,100 KShs

KShs1,100 KShs

400


49}}



REVENUE


Total Revenue

7,4831.350.410.75

litresanimalanimal

202,000

30,00045,000


149,6502,704

12,19833,750

198,302

751617

100

COSTS





Total costs

3.004.8012.0091363130685

205964981

6.75345

4

69,388

335442,00022,0008,000


mlgramml

litrekglitre

%

Kshs


900950/50 kg1,300/50 kg1,150/70 kg1,200/70 kg800/70 kg850/70 kg

218/100 80/250185/120

1,200/5120/0.5520/5

6941,600

20

2,000 120 5 5 5


mlgramml

litrekglitre

cowcow

%

acreday%%%

2,7004,56015,6001,4926,22914,9317,186

52,698

4,489 208 1244,8491,620 7204,68016,690

950 2,082 4,800

13,87820,759

6,00042,507 2,100 1,100 40052,107

142,252

2 311 1 410 5

37

3 0 0 3 1 1 312

1 1 3

1015

430 1 1 037

100

Gross margin 56,049


50}}

Results

In the reference farming situation with three cows, the gross margin is Ksh 56,049. The main

cost items are labour 30%, other costs, 19%, concentrates 15%, fertilizer 14% animal health at

12% and interest on operating capital at 10%. The major source of revenue is milk at 76%, cull

cows 17%, one to two year old heifers 6% and bull calves 1% (Table 11).

Sensitivity analysis

The reference farming situation is more or less an arbitrary situation. Therefore it is important

to check the gross margin in different situations as well. In every analysis only one factor has

been changed from the reference farming

situation to different levels.

Number of cows

In the reference farming situation the

number of cows is three. The number of

cows is an important factor in the gross

margin calculations. The reason for that is

that a number of costs in the gross margin

calculations are incurred irrespective of the

number of cows although they are not fixed

costs. When the same expenses have to be

divided by a smaller herd size, the gross

margin increases. At two cows the gross

margin is Ksh. 33,759 at six cows it is Ksh.

133,245. For each additional cow, the


Milk production per lactation

In the reference farming situation the milk production per lactation is 3,000 litres. Since the

calving interval is 405 days, the annual production per cow is 2,704 litres. At this production

level the gross margin is Ksh.56,049. When milk production is only 2,000 litres per lactation the

gross margin decreases to Ksh. 18,451. At a production level of 4,500 litres the gross margin

increases to Ksh. 103,365. For every 500 liters increase in annual milk production, the gross

margin increases by Ksh 17,000.

Calving interval

The calving interval is 405 days in the

reference farming situation and many

Cows

0

20,000

40,000

60,000

0 2 4 6 8

Number of cows

Gro

ss M

arg

in (

K.S

hs.

)

Lactation yield

0

20,000

40,000

60,000

0 1,000 2,000 3,000 4,000 5,000

Lactation yield (kg)

Gro

ss M

arg

in (

KS

hs)

Calving interval

0

20,000

40,000

60,000

0 200 400 600

Gro

ss M

arg

in (

Ksh

s)

51}}

farmers do not reach this level but border on more than 450 days. The gross margin at the

optimal calving interval of 365 days is Ksh. 70,405. At an interval of 495 days the gross margin

drops to Ksh. 33,395. The main reason for this severe influence of calving interval on the gross

margin is that the annual milk supply

decreases at an unchanged production level

per lactation. For every 30 days increase in

calving interval, the gross margin declines by

Ksh 8,520.

Milk price

Milk price in the reference situation is Ksh

20 per litre. Although milk prices are no

longer controlled, they vary from Ksh 10 in surplus areas such as Nyandarua to Ksh 35 in deficit

areas such as Mombasa. At Ksh 10, the gross margin drops to Ksh -18,777. At Ksh 35, it

shoots to Ksh 168,286. This implies that for every shilling increase in the price of milk, the


Maximum production on roughage

only

The quantity of concentrates needed to

achieve a certain milk production is affected

by both the quality and the quantity of roughage available. Under good Napier management,

like in the reference farming situation, it is possible to produce 7.5 litres of milk per day when

feeding only roughage. At 4.5 litres of milk on roughage only the gross margin is Ksh. 35,740,

at 9.5 litres of milk on roughage only it is Ksh. 65,526. For every litre increase in milk

production the gross margin increases by Ksh 5,954.

Milk Price

0

20,000

40,000

60,000

10 15 20 25 30 35

Max. production on roughage only

0

20,000

40,000

60,000

0 2 4 6 8 10

Roughage (kg/day)

Gro

ss M

arg

in

52}}

Labour costs

The costs for labour differ very much

between different districts. In densely

populated areas, generally the labour cost

is lower. In the reference farming situation

labour costs have been set at Ksh. 120 per

day. When the labour costs are only Ksh.

80 per day, the gross margin decreases to

Ksh. 70,218. At a labour costs level of

Ksh. 160 per day it is Ksh. 41,880 per litre. For every Ksh 10 increase in labour costs, the gross

margin declines by Ksh 3,540.

Interest rate

The interest rate used in the reference

farming situation is 20 %. Interest is

charged on operating capital. The interest

rate changes the production cost of milk by

about Ksh 694 for every 1 % change in interest rate.

Other factors

In addition, the sensitivity of cattle sales prices, fertilizer prices, concentrate prices and

veterinary costs were investigated. These factors have only marginal influence (1%) on the

gross margin of milk. The influence of the milk taken by calves on the gross margin is bigger. In

the reference farming situation bull calves get 50 litres of milk and heifer calves get 415 litres. If

this is reduced to 30 litres and 265 litres respectively, the gross margin increases from Ksh.

56.049 to Ksh.60,645. However, this is only recommended if the calves are provided with milk

replacers which do not compromise calf health and growth.

3.6.3 Policy implicationsThe intensive zero grazing system is predominantly a milk production system. However, even

with such an intensive production system, farmers are not willing to specialize in milk

production but engage in subsistence production where they use minimal inputs. In many areas

in Kenya, the potential for dairy farming is high and specialization in milk production in those

Labour

0

20,000

40,000

60,000

0 50 100 150 200

Labour per man day (KShs)

Gro

ss M

arg

in (

KS

hs)

Interest rate

0

20,000

40,000

60,000

0 10 20 30 40

53}}

areas would increase household milk output enormously, reduce dairy production costs,

increase profitability and subsequently raise the standard of living of the farmers and their

families. Under the current production situation and contrary to popular belief, the costs of

concentrates and fertilizers for fodder do not exert a major influence on the potential returns

from the farm. The most limiting factors to increased productivity are the number of animals on

the farm, milk production levels, calving intervals, milk price, quality of fodder crops and labour

outlay.

It is possible for farmers to maximise returns from their zero grazing systems by increasing their

herd sizes subject to their ability to devote one acre of Napier grass per cow and her followers.

The cows should have high production potential. Napier grass fields should be well maintained

and have fertilizer applied regularly. Adequate labour force must be engaged to feed the cows

and maintain the Napier grass. By observing the recommended calving interval of one calf every

year, farmers can get more returns from their cows.

The main factors limiting increased milk production at the household level are poor

performance of the milk market, poor availability and distribution of major production inputs

and services and the possibility that farmers do not quite appreciate the potential gains of

following research and extension production guidelines. Farmers will not endeavour to increase

production when milk prices do not meet production costs or if they are uncertain of whether

the performance of the existing markets will be efficient and effective. Possible improvements

include increased efficiency and reliability of existing outlets/processors. Infrastructure

improvements would open up distant markets. Provision of storage facilities would reduce

wastage and increase sales volumes while local processing facilities would reduce wastage, add

or increase value and hence returns.

Farmers fail to produce optimally where key inputs and services are not available when they are

required. Farmers groups/organizations should be strengthened to provide inputs and services

while existing input and service providers should be encouraged to provide additional inputs and

services as well.

Farmers also fail to produce optimally when they downplay the importance of research and

extension recommendations. It is surprising that even in a farming system like zero grazing,

where research and extension have had great influence, technical factors like the calving

interval, feeding of cows and calves with concentrates and fodder production are still far from

optimal. To achieve a high household milk output the role of research and extension has to be

intensified. This calls for increased facilitation of research and extension agents so as to perform

54}}

their duties effectively, and to encourage stronger farmer, researcher and extension agent

interactions.

4. RESEARCH-EXTENSION LINKAGES AND OTHER FACTORS IN TECHNOLOGY

ADOPTION

4.1 Research-Extension LinkageIncreased investment in agricultural research is advocated for developing countries to facilitate

growth if the resulting technologies are viable and adopted. Kenya has done relatively well in

investment in research compared with most of the other sub-Saharan countries. Expenditure in

public sector agricultural research in many of the countries are found to be less than 0.5% of the

agricultural gross domestic product (AGDP), while it is recommended that it should be at least

1% given that in high income countries, it is about 2% (Ndiritu, 1994).

Table 17. Research expenditures as percentage of AGDP and contribution of GoK and donors.

Year Research expenditures % of AGDP GOK as % of total

83/8484/8585/8686/8787/8888/8989/9090/9191/9292/93

0.790.890.900.781.20NA

1.74NA

1.992.10

9586899159NA37NA2934

Source: Beynon et al. 1998 and Pardey and Roseboom, 1998:318-9NA = not available.

It is evident from Table 17 that for periods in which data was available, Kenya invested more than

0.5% of AGDP, signifying the importance the government places on this activity. However, it is also

evident that since 1989/90, donor contribution has been proportionately greater than that of the

government creating the impression that the research agenda could be donor driven. That approach

to funding research runs the risk that the evolving research results may not be owned by the potential

beneficiaries or adequately address their problems. However, KARI is considering sustainable

alternative funding sources such as competitive grants as in the agricultural research fund and

commercialization of research activities.

At the same time, currently, KARI is housed in the ministry of research and technology and not in the

55}}

ministry agriculture. Although a Memorandum of Understanding (MOU) has been signed between

the two ministries emphasising joint research and extension activities, in practice staff complain of

persistent lack of funds and long bureaucratic lines of authority which severely constraint the

implementation of planned joint research and extension activities. Kimenye, (1997) has identified four

linkages which could benefit the farmer either directly or indirectly. These are (i) research-extension

(ii) research-extension-farmer (iii) research-farmer and (iv) research-input supplier-farmer. She

found that the first three were the main linkage systems evident in Embu and Mbere districts in

Kenya while the fourth was non-existent, yet it is now acknowledged that the fourth one is very

effective in dissemination and utilization of new technologies (Alders et al). Hassan (1998) also

found a significant relationship between presence of a technology supplier in proximity to adoption of

technology.

The research-extension-farmer type linkage which was provided by the Regional Research Centres

through open days or field days proved effective, because the adaptive research was designed to

incorporate farmers participation in technology evaluation and dissemination. The activities were

decentralized, with venues of open days being closer to many farmers particularly women (Kimenye,

1997). This design of linking research to farmers is desirable and should be encouraged.

4.2 Other Factors Affecting Technology Application on farmsDue to the slow rate of adoption of seemingly viable agricultural technologies on farms, many studies

have been conducted in Kenya to establish the factors determining adoption of technologies (e.g.

Hassan, et al., 1998; Salasya et al., 1998 and Kimenye, 1997).

These studies use logit regression model with the dependent variable being the probability of

adopting a technology and the independent variables being the factors hypothesized to affect that

probability of adoption. The studies report that favourable climatic environment, better access to

extension services, credit, technology supply (e.g., seed or fertilizer) better infrastructure and farmer

characteristics such as gender and membership to a village social or development group significantly

affected adoption. Formal education appeared not to significantly affect adoption seemingly because

the formally educated were in non-farm occupations. Information access by practising farmers was

significant in improving yield because once embodied technology through seed was adopted to

improve yield, crop management methods, involving more complicated processes needed to be

understood and applied.

The greater the complexity of the technology, the slower is the adoption rate. Susceptibility of the

varieties to pests, ease of processing and the product taste also affect adoption of technology. It is

56}}

also worth noting that intra-household transfer of agricultural knowledge, which is usually assumed

to take place was found not to be the norm. Knowledge acquired by men for instance may not

necessarily be transferred to the women unless they stand to be benefit as in cash crop production

(Kimenye, 1997). This implies that extension messages meant to boost yields need to be targeted to

those who actually apply the technology. The poverty map already prepared by the government can

be used to target the beneficiaries.

5. GENERAL CONCLUSION AND RECOMMENDATIONS

The power of technological solutions to solve poverty problems currently witnessed in Kenya will be

limited unless the myriad of policy, institutional and supply constraints are addressed and resolved.

The policy reform process is underway and if this culminates in an environment of predictable, stable

and consistent policies, the policy induced price distortions which have rendered technological

application on farms unprofitable and risky will be eliminated. Moreover, with a better policy

environment, investment in rural infrastructure and transport network can bring down input costs

considerably by reducing one of the major supply constraints to adoption. This will also facilitate

farm produce to be disposed of easily, creating opportunity to increase farm incomes and thereby

alleviating poverty. Reforms meant to eliminate public sector monopolies for input supply and

loosening foreign exchange and licensing restrictions on imports of farm inputs will similarly be

helpful just as widening the input-output market through regional integration.

Considerable adaptive research and stronger and decentralized research-extension farmer linkage is

required to increase the speed with which farmers apply the viable technologies once the policy

environment is right. The best judges of agricultural technology are the end users-the farmers and

involving them in technology evaluation is critical to the success of research investment programs.

Involving the farmers in the research agenda setting will also facilitate research to be demand driven

and problem solving. This will also facilitate establishing a proper balance between commodity and

farming systems research or between commodity, factor and natural resource management research

for increased sustainable production to alleviate the persistent poverty in Kenya.

57}}

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agricultural technology economic viability and poverty alleviation

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