GRIPS Development Database No. 1

The 2004 REPEAT Survey in Kenya (First Wave): Results February 2005

Takashi Yamano, Keijiro Otsuka, Frank Place, Yoko Kijima, and James Nyoro

National Graduate Institute for Policy Studies (GRIPS)

Takashi Yamano, Associate Professor/Faculty Fellow, GRIPS/FASID IDS Joint Program

Keijiro Otsuka, Professor/Program Director, GRIPS/FASID IDS Joint Program

Frank Place, World Agroforestry Center, Kenya

Yoko Kijima, Assistant Professor/Faculty Fellow, FASID/GRIPS IDS Joint Program, FASID

James Nyoro, Director, Tegemeo Institute of Agricultural Policy and Development, Kenya

Acknowledgements Support for this research project has been provided by a 21st Century Center of Excellency (COE) Project, titled “Asian Development Experience and Its Transferability,” at GRIPS. In Kenya, the project was carried out in collaboration with GRIPS, World Agroforestry Center, Tegemeo Institute. GRIPS 21st Century COE Project Address: 7-22-1 Roppongi, Minato-ku, Tokyo, Japan E-mail: gakujyutsu@grips.ac.jp

Executive Summary

The National Graduate Institute for Policy Studies (GRIPS), in collaboration with Tegemeo Institute of Egerton University, the World Agroforestry Centre (ICRAF), and the International Livestock Research Institute (ILRI), undertook research on poverty, environment, and agricultural technologies (REPEAT) with the overall goal of identifying agricultural technologies and farming systems that will contribute to increased agricultural productivity, the sustainable use of natural resources, and reduced poverty in Kenya. The research findings will be used to inform policy makers, development practitioners, and other stakeholders in formulating and implementing policies and strategies in Kenya. This report summarizes findings from the first wave of the 2004 REPEAT Survey in Kenya. The survey is based on data collected from 894 households in 99 sublocations in western and central Kenya. The report identifies seven major findings:

First, the report finds that the average per capita expenditure among the sampled household is $181 for six months. To obtain the annual per capita expenditure, we will add the six months per capita expenditure from the second wave of the REPEAT survey to this figure. Out of the per capita expenditure of $181, cash expenditure is $133, while the rest, $48, is consumption of commodities produced on the farm. Second, the report also finds that the average per capita income is about $199 for six months. Again this figure will be combined with the per capita income from the second wave of the survey to construct the annual income. Out of the per capita income, crop income (including amount consumed at home) takes the largest share of 43 percent, followed by wage income, which provides constant monthly wage, of 24 percent, livestock income of 21 percent, and non-farm business activities of 12 percent. Although shares from different sources differ across provinces, crop income provides the largest income share in all provinces surveyed (Nyanza, Western, Rift Valley, Central, and Eastern). When stratified by income quartiles, wage income provides the largest share of income among the highest income quartile, while among the lowest income quartile the crop income provides the largest share. Third, as expected, the report finds maize (96% of sampled households produced maize) and beans (82%) as the most common crops. Banana is the third most common crop (61%), and napier grass is the fourth (58%). Tea provides the largest income, $356, to its producers, and sugarcane provides the second largest income, $218. Although it is difficult to evaluate the value of napier grass production because only a small amount of it is traded in limited areas, it seems to provide a significant value. A rough evaluation shows its value of production as $130, which makes napier grass third in terms of production value. Maize and beans combined provide about $170 to their producers. Horticulture crops, such as sukumawiki (kale) and cabbage, provide large per acre production values, but are grown mainly on small plots and therefore do not contribute large amounts at the farm level.

Fourth, the report finds that 80 percent of sampled households applied chemical fertilizer on crops. An important source of financing chemical fertilizer is credit from cooperatives of high-value crops such as tea and coffee. About 84 percent of tea producers received credit from Kenya Tea Development Authority, while about 22 percent of coffee producers received credit

from coffee cooperatives. Fifth, about 54 percent of sampled households own at least one dairy cow, while 21 of them own at least one local cow. Among 890 milking cows, about 77 percent of them are either cross breeds of exotic and local cattle or pure exotic breeds. Pure Holstein cows produce 8.5 liters of milk per day on average, while other crossbred cows produce 4 to 5.4 liters per day. Local cows produce only 1.9 liters of milk per day. The report also finds that about 34 percent of households that sold milk during the last six months sold some milk to private traders, while only 19 percent of them sold milk to dairy cooperatives or the Kenya Cooperatives Creamery (KCC). The largest share, 47 percent, however, is sold to neighbors. Compared with earlier reports on milk marketing from International Livestock Research Institute, the market share of private traders seems to have increased. Sixth, the report finds a highly integrated crop-dairy production system (which we refer to as the Organic Green Revolution technology) promising in increasing not only dairy productivity but also providing much needed nutrients and organic matter to crop production. For instance, in a period of six months, the zero-grazing dairy households produced 1,055 liters of milk, applied about 2,712 kilograms of manure on crops, and produced and fed 5,757 kilograms of napier grass to their cattle on average. In contrast, the exclusively grazing and semi-grazing dairy households produced less milk, applied less manure on crops, and produced and fed less napier grass to their animals than the zero-grazing dairy households. Although labor activities required for the zero-grazing dairy production are physically demanding and knowledge intensive, the labor hours spent on livestock production is about the same across different dairy production systems. The results in this report suggest positive impacts of manure application on crop production. Seventh, the report finds that high-income households earn a large proportion of income from non-farm activities. The results from the determinants of the non-farm activity participation indicate that education help individuals to participate in both self-employed businesses and regular wage activities. In sum, the findings in this report suggest some promising agricultural technologies in Kenya. Over the next several years, the REPEAT project will re-visit the sampled households and communities so that we can evaluate how the adoption and use of some technologies affect productivity, poverty, and natural resources.

Table of Contexts:

1. Introduction 2. Data 3. Expenditure Profile and Poverty

3.1. Per capita “Cash” Expenditure 3.2. Total Expenditure 3.3. Income Profile

4. Farm Production 4.1. Land Tenure Systems 4.2. Major Crops in Kenya 4.3. Input Use 4.4. Export Crop Production after the Liberalization: Tea vs. Coffee

5. Livestock Production 5.1. Livestock Holdings 5.2. Adoption of Dairy Cattle 5.3. Dairy Production 5.4. Milk Production 5.5. Milk Marketing

6. Organic Green Revolution in Uganda 6.1. Organic Green Revolution: Mechanism 6.2. Evidence from Kenya

7. Non-farm Income 7.1. Major Non-farm Activities: List 7.2. Participation in Non-farm Activities

8. Conclusion Reference

Tables

Table 1. Sampled communities and households in central and western Kenya Table 2. Sampled households and sub-locations in the original ILRI surveys and the 2004 REPEAT survey Table 3. Six-month per capita expenditure and expenditure share by Province Table 4. Six-month per capita expenditure share by quartile Table 5. Six-month per capita home and cash expenditure by Province Table 6. Six-month per capita income and income share by Province Table 7. Six-month per capita income share by quartile Table 8. Land tenure at the plot level Table 9. Land acquisition and the ownership of the title deed Table 10. Crop production –value production at the household level Table 11. Percentage of producer households of major crops Table 12. Inputs application by Province Table 13. Maize yield Table 14. Sources of interlinked credit by Province Table 15. All credit sources to purchase inputs Table 16. Livestock ownership – the household level Table 17. Adoption of improved cows – household models Table 18. Dairy production systems in central and western Kenya Table 19. Dairy production systems by District Table 20. Percentage of households using various feeding stuff Table 21. Milk production at the cow level in February 2004 by cow breed Table 22. Milk marketing by distance to Nairobi Table 23. Milk marketing in 1998/2000 by District Table 24. Table 25. Integration of dairy and crop production Table 26. Adoption of manure use – the plot level analysis Table 27. Maize yield – the plot level analysis Table 28. Non-farm self-employment and wage activities Table 29. Determinants of participation in business and labor activities

Figures

Figure 1. Sampled sub-locations in central and western Kenya Figure 2. Distribution of per capita expenditure for six months in US$ Figure 3. Proportion of land acquisition by source in central and western Kenya Figure 4. Export crop production in central and western Kenya Figure 5. Value Production and fertilizer application per hectare: aggregated value Figure 6. Daily milk production (liters/cow/day) by cattle breeds in February 2004 Figure 7. Proportion of manure applied on crops in Kenya and Uganda

1. Introduction

The National Graduate Institute for Policy Studies (GRIPS), the Foundation for Advanced Studies on International Development (FASID), Tegemeo Institute of Egerton University, the World Agroforestry Center (ICRAF), and the International Livestock Research Institute (ILRI) undertook Research on Poverty, Environment, and Agricultural Technologies (REPEAT) with the overall goal of identifying agricultural technologies and farming systems that will contribute to increased agricultural productivity, sustainable use of natural resources and reduced poverty in Kenya. This study is also taking place in Uganda and Ethiopia. The survey in Kenya was financed by the Center of Excellency Grant, which is provided from the ministry of Education and Science of Japan, through National Graduate Institute for Policy Studies (GRIPS). It is hoped that the knowledge obtained form the REPEAT Project will be used to guide policy makers, development practitioners, and other stakeholders in formulating and implementing policies and strategies for sustainable natural resource use, increased agricultural productivity and reduced poverty.

A principle reason for this new data collection effort is that existing studies of impacts are weakened by low numbers of observations and by lack of temporal data. Sub-saharan African agriculture is complex with a great deal of heterogeneity of climate, topography, markets, and social systems. Studies involving small samples invariably become unable to identify the importance of many of these factors. Impact studies have largely been based on cross-sectional data and face many difficulties related to endogeneity (i.e. among many dynamic patterns, what is causing what?). Understanding sequencing of behaviors and the timing of indicators is critical for sorting out this complexity.

The main focus of this project is to examine the links between recent agricultural innovations on short and long term productivity change and poverty reduction in smallholder communities and households. Agricultural innovations considered will include crop, livestock, and natural resource management technologies and practices as well as the underlying methods that are used to develop and disseminate them. Productivity change is taken to be improvements in productivity of existing resources and enterprises (e.g. adoption of input packages leading to higher yields of crops) as well as the shifts in the composition of resources or enterprises (e.g. adoption of higher value added crops). We will measure such changes in the short-term and the long-term (e.g. assessing yields and changes in soil conditions). Poverty will be assessed using a variety of measures including conventional income, expenditure, and asset measures as well as more qualitative concepts of ability to reduce exposure or effects of risks, and enhancing human capital.

The major research questions we aim to address are:

1. How do agricultural innovations contribute to pathways out of poverty?

2. How are community level adoption patterns of innovations affected by agro-ecology, population pressure, market access, and methods of development and diffusion?

3. What impacts do innovations in agricultural technology have on rural households in general

and the poor in particular?

4. What are the key priority areas for agricultural research and development strategies and investments in order to impact more profoundly on the rural poor?

5. Which programs and policies have been most effective in enhancing agricultural productivity under varying conditions (e.g. agro-ecology)?

2. Data

The data used in this paper come from 894 households interviewed in a survey conducted in 2004. This survey was a part of REPEAT Project, which is a collaborative research project of Foundation of Advanced Studies on International Development (FASID) and its collaborators in three East African countries (Kenya, Uganda, and Ethiopia). The REPEAT survey in Kenya was financed by the Center of Excellency (COE) Grant, which was provided from the ministry of Education and Science of Japan, through National Graduate Institute for Policy Studies (GRIPS). The sampled households were selected from 2,966 households that were previously interviewed by ILRI in 1998 and 2000.

In 1998, ILRI and its collaborators interviewed 1,390 households chosen from eight districts, representing a wide range of levels of dairy productivity potential and market access within the Nairobi milk-shed, the central Kenya region (Staal et al., 2001). In 2000, ILRI and its collaborators visited 1,576 households from seven districts, representing dairy production and milk marketing characteristics in western Kenya region (Waithaka et al., 2002). In February 2004, 100 sublocations were randomly selected for the REPEAT survey from sub-locations where the 2,966 ILRI households were found in 1998 and 2000. From the 100 sub-locations, 10 previously interviewed households per sublocation were randomly selected and targeted for re-interview in 2004. In 2004, two waves of surveys were planned over a period of six months. The first wave was conducted in February 2004, asking respondents about the last six months (from August 2003 to January 2004). In October 2004, the second wave of the surveys took place to cover the following six months, starting from February 2004.1 By visiting the same households twice over a period of six months, respondents were asked about the last crop season only. Thus we hope that we can obtain more accurate information and, at the same time, keep the length of interviews within a reasonable length.

During the first wave of the surveys, 934 households were located out of the 1,000 targeted households who were interviewed in 1998 and 2000, and 894 households were interviewed successfully.2 Table 1 presents the sampled sub-locations and households in five provinces. Central province has the largest number of sampled sub-locations and households (35 sub-locations and 310 households), followed by Rift Valley (25 sub-locations and 226

1 At the time of writing this report, the second wave of the survey has been completed, and data cleaning is underway in Nairobi.

2 The rest 40 households were located but not available for interview due to several reasons such as non-contact (27 cases), moved away (six cases), refusal (five cases), non-available (one case), and households dissolved (one case).

households). Nyanza, Western, and Eastern provinces have 18, 13, and 8 sample sub-locations, respectively, and 175, 112, and 71 sample households, respectively.

As discussed earlier, all of the sampled households were previously included in the ILRI surveys. Table 2 shows the numbers of sampled households in the original ILRI surveys in 1998 and 2000 and the REPEAT Survey in 2004. Column B presents the numbers of the original ILRI households in each district. Column D presents the numbers of the IRLI households that were interviewed again in February 2004. In the survey, households were asked about their agricultural activities from the short rains of 2003, non-agricultural income generating activities, asset holdings, cash expenditure, and demographic characteristics. Although the REPEAT survey is expected to be merged with the previous ILRI surveys to create panel data, the data are not yet merged. Also the Second Wave of the surveys is not yet available. Thus, in the present report we only use information from the First Wave of the REPEAT survey in 2004, except where some general information from the 1998 and 2000 ILRI surveys is specifically noted.

3. Expenditure Profile and Poverty

3.1. Per Capita “Cash” Expenditure

We start this report with describing the expenditure profile in rural Kenya. In the First Wave of the 2004 REPEAT Survey, we have asked about expenditure and all other production information on the last six months, covering August 2003 to January 2004. In the Second Wave of the REPEAT Survey, we will cover the next six months, from February 2004 to July 2004. Thus, when we combine both surveys, we will be able to calculate annual expenditure and income. Please note, therefore, that the following discussion relates to the expenditure for six months, not twelve months, and therefore the annual per capita figures are likely to be somewhat near half of what might be expected.

First, we present “cash” expenditure profiles for the last six months in each province in Table 3. In the survey, we asked about cash expenditure on 39 items over the last six months. We asked for frequencies of purchase, such as once a week or three times in the last six months, and average spending per purchase. Later, we evaluate home consumption by taking a difference between the total food production minus sales of crop, livestock, and livestock products production. By using such information, we calculate total expenditure in the last six months.

Note, that because “cash” expenditure does not capture self-consumption, which takes higher proportions for poor households, it systematically exacerbates the gap between the rich and poor because poor households consume relatively more from self-production than non-poor households. Despite this drawback, however, the cash expenditure information provides useful information. The average per capita cash expenditure is $133. Eastern province has the highest average per capita cash expenditure at $158.

We divide the 39 expenditure items into five categories: staple food items, fresh food items (such as meats), non-fresh food items (such as sugar), non-food items (such education and medical

expenditure), and social activities (such as contributions to churches or local organizations). The share of non-food items is about 28 percent on average and takes the largest share among the five categories. Shares of other categories are similar across zones.

In Table 4, we present shares of cash expenditure in five categories by the per capita cash expenditure quartile. We find an expected pattern that poor households spend a high proportion of expenditure on food items. For instance, households in the lowest quartile spend about 59 percent of the total cash expenditure on items in food categories (staple food, fresh food items, and non-fresh food items), while households in the highest quartile spend only 37 percent. The households in the highest quartile spend more than half of their cash expenditure on non-food items.

3.2. Total Expenditure

Next, we evaluate self-consumption of crops, livestock, and livestock products. The results are presented in Table 5 (column C). The per capita self-consumption value ranges from $29 to $70. We find the highest home consumption in Rift Valley province. Because the households in Rift Valley tend to be large farmers, it is likely that the home consumption, especially food consumption, is over-estimated. This is because of the way it is calculated. Since we estimate the home consumption as the difference between the home production and sales, it also captures amounts of crops that are being stored for future sale. If this is true and the income earned is spent on non-food items, we over-estimate food consumption..

By combining the per capita cash and home product expenditure, we obtain per capita total expenditure (column A). The average per capita total expenditure for six months is $181 among the sampled households. In Figure 3, we present the distribution of per capita total expenditure in the last six months. The peak of the distribution is at about $90, and the distribution has a long tail on the right-hand side, indicating that there are a few households with significantly larger expenditure profiles. For simplicity, we have truncated the figure at $630 or top one percent of the distribution.

3.3. Income Profile

The previous sub-section describes per capita expenditure for the last six months but does not provide information on how households generate income. Although, we examine each income generating activity later in the following sections, it is useful to take a look at income profile first. The income profile is summarized in Table 6, which shows average per capita income and income share by different income sources and by province.

The average per capita income for the entire sample is US$199.3 Income sources include crop income (including trees), livestock income, off-farm self-employment activities (including farm

3 It is encouraging that the distributions of income and expenditure are similar because other studies in Africa have found that income is often under-reported in comparison to expenditure.

labor income), and regular wage income. Crop income is calculated as production value of farm products minus paid-out costs, which include costs on seeds, fertilizer, hired labor, and rental oxen and machines. Livestock income is also calculated as production value minus paid-out costs. Production value includes animal sales and value of livestock products, such as milk and eggs. Paid-out costs for livestock production include purchased feeds, expenditure on artificial insemination (A.I.) service, bull service, and animal health care service. Non-farm activities include non-farm micro enterprises such as trading various goods and seasonal labor activities. To obtain reasonable profit estimates for micro enterprises, we asked for numbers of low- and high-business months in the past six months and earnings and costs per month during the low- and high-business months. Wage income only includes salaries from jobs that provide a constant monthly salary.

Out of the total income, farm income accounts for 43 percent on average. Among the five provinces, the average per capita income for the last six months is the highest at $327 in Eastern Province (Machakos District), while it is at the lowest level in Western Province at $98. In Eastern Province, the share of the wage income occupies 39 percent of the total income. This seems to differentiate the Eastern province from the other provinces. We will investigate the access to wage income later in this report.

Next, we stratify the sample into quartiles by per capita income (Table 7). As expected, we find that households in lower income quartiles have larger income shares coming from crop production than households in higher income quartiles. The proportion of crop income is about 57 percent among households in the lowest quartile. In contrast, households in higher income quartiles have larger income shares coming from wage income. For instance, households in the highest income quartile have earned 35 percents of income from wage income.

From this table, we can draw two common findings in studies on sources of income in rural Africa (Reardon, 1997; Jayne et al., 2003). First, crop income is the most important income source for poor households. Thus, increasing crop productivity is a crucial issue to secure and increase income for them. In the following sections, we examine crop and livestock production and explore possible ways to increase their productivity. Especially, we examine the impacts of an intensive farming system that integrates improved dairy production system, organic fertilizer, and crop production. We call this system an “Organic Green Revolution” technology and describe this technology in Section 6.

Second, crop and livestock (farm) income is not enough to enable households to move up income quartiles. The non-farm income share is high among the highest income quartile. Although the causality could be in both directions (non-farm income increases total income or high income households have means going into non-farm activities), it is clear that the access to non-farm income plays a critical role in escaping from the poverty. In Section 7, we examine determinants of having non-farm jobs at the individual level.

Farm Production

4.1. Land Tenure Systems

Kenya is one of the few African countries where an individual titling program has been systematically applied to rural lands since the 1950s (Pinckney and Kimuyu, 1994; Migot- Adholla et al., 1994). The results in Table 8 are consistent with our expectation. The sampled households own title deeds on more than 70 percent of all plots that they own or use. In this report, we define a parcel as a field with a continuous boundary which was obtained as one piece of land. They do not own title deed on 14 percent of land that they own. About 13 percent of parcels were rented from others via fixed payments. The rest, 2 percent, were obtained by the sampled households through other channels such as “borrowing from relative,” “share-cropping,” and “just walked-in.” The land rental market is active in Rift Valley province where the average land holding is larger than in other provinces. About 19 percent of parcels were rented-in via fixed payments.

More than half of the parcels were inherited (Table 9). When parcels are inherited, household heads own title deeds on about one third of them. The household heads’ parents own title deeds on another one third of the inherited parcels. Thus, those parcels were inherited by children, but the ownership of the title deeds were not yet transferred from parents to children. In some cases, parents die before they transfer the titles to children. About five percent of inherited parcels are titles under the name of a deceased parent. Because of the AIDS epidemic, there are many cases where male household heads die of AIDS at a young age. Often children are too young to inherit land, and wives do not own title deeds according to local customs. As a result, husbands die without transferring title deeds. In Table 9, we find that the names of deceased husbands are still on about 10 percent of title deeds. It is much more common for household heads to hold title deeds of purchased parcels in their names: this occurs for about 67 percents of purchased parcels. Title deeds of about 7 percent of purchased parcels are still owned by sellers. This is categorized as “others” in Table 9.

Figure 3 shows relative importance of four land acquisition modes over time. Although inheritance has been the most important acquisition mode of land purchase is also an important land acquisition mode. In between the 1960s and 1980s, about 30 percents of land parcels were purchased. Inheritance became much more important compared to purchase during the 1990s. Since rented plots are for a fixed term, there are just a few cases of parcels that have been rented for more than a decade. However, in the last few years, fixed payment rentals were the most common type of land acquisition method. Only a small portion of parcels have been rented to the same households over five years, for instance. We will examine the relationship between the land tenure and investment, especially organic fertilizer use, in land.

4.2. Major Crops in Kenya

We examine crop production in rural Kenya in this sub-section. Table 10 shows the percentages of households growing the more important crops (column A), the average area devoted to each crop among all households (column B) and among producers only (column C), the average production value of each crop among producers (column D), and the average gross return to one acre of land (column E).

Beans and maize are the most and second most common crops produced, respectively: about 96

and 81 percents of households produced maize and beans, respectively, among the sampled households in Kenya. The average land devoted to maize and beans are 1.31 and 1.04 acres, respectively. Maize occupies the largest cultivated land among all the crops. Yet because intercropping is a common practice, especially between beans and maize, these numbers overestimate the land devoted to each crop. Thus, in areas where intercropping is common, areas devoted to maize and beans are double counted. Because it is very difficult to allocate intercropped areas into each crop, we present the double counted numbers in Table 10. Maize provides about $112 to its producers and beans about $51. Thus producers with both crops receive substantial income from intercropping both crops.

Banana is the third most popular crop produced by 61 percent of sampled households. As we will see later in Table 11, banana is more popular in western parts of Kenya, where it is consumed as one of staple food crops. Napier grass is the fourth most popular crop and is the most popular feed crop for cattle, especially dairy cattle raised in stalls. Because Napier grass is mainly consumed at home by cattle, its production is not usually evaluated as income; rather, it provides a high production value to its producers. The average production value of Napier grass is about $130 among Napier grass producers, who are about 58 percent of sampled households. Per acre production value is also high at $455 per acre. Thus, it is one of high value crops in Kenya. In some areas, such as Kiambu district, farmers who do not have cattle produce Napier grass and sell it to dairy households. We examine dairy production systems in detail in Section 5.

Although coffee used to be one of the most important high value crops, it currently does not provide high income to its producers, because of very poor world prices. The low prices have persisted for several years which have discouraged farmers from applying inputs on coffee. Thus, yields and prices are both low (there are some exceptions; some farmers producing high quality Arabica still receive relatively good prices). On average coffee producers received $42 from coffee. In fact, many coffee producers actually do not bother to harvest coffee beans any more. Poor management by coffee cooperatives has also plagued the sector for many years (Karanja and Nyoro, 2002).

In contrast, tea provides higher value production ($336) to its producer than any other crop. Because marketing systems for both coffee and tea have to be well coordinated, it is important to study why the coordinated marketing system works for tea but not coffee. The conventional wisdom points to poor management of coffee cooperatives, poor management may not explain the entire difference between tea and coffee production. In Section 4.4, we examine this issue further.

Major Crops by Regions

Although maize and beans are universally popular across regions in Kenya, other crops have regional variations. In Table 11, we present proportions of households growing the major crops for each province. Table 11 shows that banana is produced more in western regions, while Napier grass is produced more in Central Province, where dairy production is more advanced. In Central Province, more than 80 percent of all sampled households produced at least some Napier grass.

Irish potato is mainly produced in Rift Valley and Central Provinces. Better transportation infrastructure in both provinces and proximity to Nairobi and tourist markets provide opportunities for households in these provinces to produce Irish potato and other perishable vegetables. Coffee and tea are produced in central and western highland areas, thus not in Rift Valley, while sugarcane is produced in midland areas in western regions. For these crops, processing is done in rural areas and therefore proximity to Nairobi is less important.

4.3. Input Use

In Table 12, we present proportions of households who applied various crop inputs by province. Unlike other African countries, notably neighboring Uganda, a significant proportion of farmers apply chemical fertilizer on crops. It is more so in central, western, and Rift Valley regions of Kenya, where about 80 percent of sampled households applied at least some chemical fertilizer on crops (column A, Table 12). In Central Province this can be explained by the predominance of cash crops. In the Rift Valley, maize is a highly commercialized enterprise on the relatively larger farms. In Western Province, the explanation is certainly more complicated, partly due to cash crops, but also due to very poor soils that require some nutrient inputs to produce a decent crop. Only in Eastern Province, were there fewer than 70 percent of households who applied chemical fertilizer. This is probably because of high value crops produced in each province as shown in the previous table (Table 11). In Eastern province, coffee is the only high value crop but as discussed later, coffee production has been declining in most parts of Kenya due to poor marketing coordination by coffee cooperatives.

Manure application is also popular across provinces in Kenya. As we will investigate more in Section 6, the quantity of manure application is much higher in areas where dairy cows are kept in zero-grazing stalls than where they are grazed. Purchased seeds are also widely adopted throughout the sites. Over 80 percent of households use at least some purchased seeds, instead of recycled seeds. Hired labor is also used by about 60 percent of households across the provinces.

In Rift Valley, there are some large landholders who hire machines and apply insecticide. They produce maize in large fields and use advanced technologies. In other provinces, less than 20 percent of households applied insecticide. They apply insecticide on high value crops, where expensive inputs are some times supplied to poor households on credit from crop traders or processing firms/cooperatives. In next section, we investigate the importance of contract farming in tea and coffee production.

4.4. Export Crop Production after the Liberalization: Tea vs. Coffee

In Kenya, a number of policy reforms have been implemented gradually in the coffee sector ever since the beginning of the liberalization in 1992 (Karanja and Nyoro, 2002). Although the reform changes have reduced the government involvement in coffee markets and resulted in lower processing costs, the politicization and poor governance of coffee co-operatives have

delayed the liberalization process. In the midst of the slow liberalization, coffee processing factories suffer from under-utilization of facilities and declining world coffee prices in recent years. As a result, the total coffee production in Kenya has been declining in the past ten years (Figure 4).

In contrast, tea production continues to increase in Kenya (Figure 4). Before liberalization, the Kenya Tea Development Agency (KTDA) owned a single vertically integrated marketing system of tea. After the liberalization, however, a parallel system has emerged where farmers can sell green leaf tea directly to private companies or individuals for immediate payments with no other contractual arrangements on other services such as credit and extension (Nyangito and Kimura, 1999). Unlike the other commodity sectors, the KTDA continues to provide fertilizer on credit to farmers even after the liberalization. There are several reasons why the KTDA could avoid strategic failures experienced in other sectors. First, farmers sell tea leaves to the KTDA almost every morning. This helps the KTDA to monitor farmers and keep good records of past deliveries.

Second, tea requires NPK, which is an effective fertilizer on tea but not economical on other crops in Kenya. NPK is not as available as DAP or UREA in local markets and expensive. Thus, farmers prefer to obtain it from the KTDA. In contrast, coffee cooperatives provide DAP and UREA, which are commonly applied on other crops such as maize. Therefore, it is easy for coffee producers to buy and sell DAP or UREA in local markets or to apply them on other crops. Third, since the KTDA determines the maximum level of credit to a farmer based on his or her past deliveries to the KTDA, the farmer has incentives to maintain good delivery levels to keep the future credit limit high. Although coffee cooperatives also determine the interlinked credit limit based on past deliveries of coffee cherries to cooperatives, the rule seems to be more strictly applied by the KTDA than coffee cooperatives (Karanja and Nyoro, 2002).

Management of coffee cooperatives is often criticized as poor and lacking financial accountability. The reform policies were expected to bring efficiency in the coffee marketing system, but after more than ten years since the liberalization, the contrast to the tea sector is obvious. In the next section, we construct a dynamic model based on good practices found in the Kenyan tea sector.

Table 14 shows the numbers of sampled households in five provinces in Kenya. Central province has the largest number of sampled households (310 households). Central province has highland areas that are suitable for both tea and coffee. Out of the 310 households 65 households produced tea, and almost all of them received credit from the KTDA. About one third of the sampled households (114 out of 310) in Central province produced some coffee, but only one third of them received credit from coffee cooperatives. Nyanza province also has both tea and coffee producers mainly in its highland areas. Again in Nyanza province, many tea producers (42 out of 51) received credit from KTDA but only one coffee producer received credit from coffee cooperatives. In total, 84 percent of tea producers in the sample received credit from KTDA, but only 22 percent of coffee producers received credit from coffee cooperatives. In some parts of Kenya, sugarcane cooperatives provide credit to sugarcane farmers to purchase inputs. In our sample, however, only 8 percent of sugarcane producers received credit.

Interlinked credit from KTDA and coffee cooperatives are virtually the only credit sources for purchasing inputs in rural Kenya. Table 15 shows the number of households who received credit from various credit sources to purchase inputs. 4 Other than the KTDA, coffee cooperatives, and sugarcane companies, only fifteen households received credit from other sources such as traders, farmers groups, and other unspecified sources to purchase inputs.

On average, tea producers, who obtained credit from the KTDA, received 242 kilograms of fertilizer. Because the KTDA provides NPK to tea producers, we have evaluated the value of the fertilizer by using province level NPK prices and found that the average value of in-kind credit worth about 5,000 Kenya shillings, which is about US$67 in 2004. Coffee producers received less than half of what the tea producers received. Among those who received credit from coffee cooperatives, the average quantity of fertilizer received is 99 kilograms, which is worth about 2,236 Kenya shillings (US$30). Although there are 22 households who received credit from other sources, we will focus only on the households with interlinked credit from KTDA and coffee cooperatives in the following sections.

Marginal Value Production and Fertilizer Application

Next, we investigate the relationship between the value of production and the fertilizer application per hectare. To compare different crops, we have valued the production by using province level prices. Because inter-cropping is common in Kenya, as in many Sub-Saharan Africa, it is difficult to isolate how much of fertilizer is applied on one crop when the crop is intercropped with other crops. Therefore, we have employed two methods. First, we have aggregated the value of production at the plot level to produce the total value of productions of all inter-cropped crops from one plot. An alternative method was to restrict the sample to four crops (maize, bean, tea, and coffee) and consider that the fertilizer was applied on only the four crops even if the four crops were intercropped with other crops. We include beans in the restricted samples because beans are almost always inter-cropped with maize. We call the latter samples as Restricted Samples and the former as Unrestricted Samples. Although restricting the sample over estimates the amounts of fertilizer applied on four crops, in practice farmers also try to apply expensive fertilizer to commercial crops, such as tea, coffee, and maize, even when they are inter-cropped with other low-value crops.

Figure 5 shows the bivariate relationship between the total value production per hectare and the fertilizer application per hectare by using the unrestricted samples. We stratify all plots into four: maize and beans plots, tea plots, coffee plots, and plots of other crops. If one plot is intercropped with tea, the plot is labeled as a tea plot. We take the same approach to coffee and maize & beans plots. If one plot does not have any of the four crops, then it is labeled as “other crops.” The lines in Figure 5 are created by the locally weighted scattered smoothing (Lowess) method. Thus, the slope of the relationship between the total value production and the fertilizer application is steep for tea, suggesting high returns from additional application of fertilizer. Slopes for other crops are not as steep as tea. Most notably, the slope for coffee is flat even at a low level of fertilizer application.

4 We do not have any information about credit sources for other purposes. However, previous studies indicate that

5. Livestock Production

5.1. Livestock Holdings

For farm households in rural Kenya, livestock is an important asset that can provide regular income and be disposed of in hard times to provide a safety net. In terms of livestock ownership, cattle, goats, and chicken are owned by many households (Table 16). In central and western Kenya, dairy cows, whether cross breeds of European and local cattle or pure European cattle, are widely adopted. European cows are bigger and more productive in milk production, while local cows are resistant against local diseases. As a cross breed of the two, improved cows are productive in milk production and at the same time fairly resistant to local diseases. More than half of the sampled households have at least one dairy cow. Among those who own dairy cows, the average number of dairy cows is 2.1. About 19 percent of sampled households also own at least one heifer, and 23 percent of sampled households own at least one dairy calf. On the other hand, only 8 percent of sampled households own at least one local heifer, and less than 10 percent of sampled households own at least one local calf. Thus, it seems that sampled households are adopting more dairy heifers and calves, shifting to more productive dairy production. In the next section, we examine time trend from the previous ILRI surveys in 1998 and 2000 to our survey in 2004.

Before that, turning to the ownership of other animals, local chicken is the most popular livestock animal among the sampled households. About 79 percent of households owned at least one local chicken in 2004. Among the chicken owners, the average number of chickens is about 19. The average number is much larger than the median since some households own large numbers of local chicken. Local sheep is the second most popular livestock animal. About 29 percent of households owned at least one local goat in 2003. Although dairy goats are becoming popular in some areas, only 2 percent of samples households own dairy goats.

5.2. Adoption of Dairy Cattle

Who have adopted the highly integrated crop-dairy production system in Kenya? To answer this question, we start our analysis with the investigation of the determinants of adopting improved cattle. In Table 16, we present the estimation results from Heckman’s two step model on the adoption of improved cattle and the number of improved cattle among those who have adopted them. The results in Kenya indicate that land size, land title, and woman’s education are important determinants as well as the availability of the public veterinary services in the community. Farm size increases the probability of adopting improved cattle, presumably because households need to allocate a part of the farmland to the production of feeds in the absence of public grazing lands in most parts of western and central Kenya. Thus, the availability of farmland to cultivate Napier grass determines how many improved cattle can be raised in this country.

The possession of a current land title also increases the probability of adopting improved cattle in Kenya. The size of the coefficient indicates about an 18 percent increase in probability. This could be because farmers are afraid of making a big investment in improved cattle, especially in

zero-grazing units, if there is any possibility of losing land due to the lack of land title (assurance effects of land title) or because farmers with land title are able to obtain credit by using land as collateral (credit effects of land title). We are not sure which effect is more important in this case.

Interestingly, women’s education, not men’s, increases the probability of adopting improved cattle. This could be due to the fact that women are primary caretakers of improved cattle when they are kept in zero or semi-zero grazing units, and they are usually more likely to belong to local groups that are linked to dairy production. The availability of government veterinary services increases the adoption of improved cattle, whereas the NGO veterinary services increase the number of improved cattle adopted by the households. These results indicate the importance of supporting services to improved cattle for its wide dissemination. Because improved cattle are more susceptible to diseases than local cattle, farmers will hesitate to invest in expensive improved cattle without supporting veterinary service systems.

5.3. Dairy Production

Feeding Systems

When households adopt improved dairy cows, it is more productive to keep dairy cows in stalls. Keeping cows in stalls is a preferred strategy in areas where grazing land is no longer available. In Table 18, we present the proportions of sample households using different dairy production systems. We stratify households into four categories: no cattle owners, grazing, semi-zero grazing, and zero-grazing.5 Dairy production system in Central Province is most advanced: about half of the sampled households have some cattle, mostly dairy cows, in zero-grazing stalls. Only 14 percent of households exclusively use grazing. In Eastern Province, the dairy production system is also advanced: more than 66 percent of households use zero-grazing stalls at least partly. In Rift Valley and Western provinces, the dairy production system still relies on grazing, and the dairy production system in Nyanza province is on the way to a zero-grazing system. In Table 19, we provide the same information by district. Table 19 shows some variations even within the same province.

When dairy farmers keep their cows in stalls, they need to feed their cows in the stalls. Thus, the availability of feeds can be a constraint to adopt the zero-grazing system. In Table 20, we present proportions of households that use different feeds. In Table 20, we use information from households with some cattle only, excluding the 231 households that do not own any cattle. From column A to G, we list feed item according to its popularity. Maize stover is the most popular item: about 88 percent of the cattle owner households feed their animals with maize stover. Mineral/salt is the second most popular feed item followed by Napier grass. More than 80 percent of cattle-owning households in our samples feed napier grass to their cattle. Napier grass is very popular in Central province, where almost all cattle-owning households feed

5 When a household employs two different systems we classify the household according to the most advanced system. For instance, if a household keeps some cattle grazed but a few cattle in zero-grazing stalls, then we classify the household as zero-grazing household. The proportion of households that employ more than one system is not large.

Napier grass to their cattle. In Central province, households with cattle also use other feeds such as concentrates, fodder leaves, cut grass, and other crop residues.

Milk Production

In Table 21, we present milk production characteristics of dairy production in our samples. There are 890 milking cows among the 894 sampled households. We stratify cows into six categories: local cows, cross breeds (<50 percent), cross breeds (50 %), cross breeds (>50 %), pure Holstein, and other pure exotic. Among the five categories, the largest category is cross breeds (>50 %). There are also many cross bred cows that are fifty percent pure or less than fifty percent. Combined, the number of crossbred cows is more than 56 percent of the total number of milking cows. The pure Holstein and other pure exotic breeds share is about 20 percent.

The average daily milk production per cow is about 4.8 liter per day: 2.8 liters in the morning and 2.0 liters in the evening. Pure Holstein breeds have the highest daily production of 8.5 liters per day. Crossbred cows produce about 4 to 5.4 liters per day, while local cows only product just below 2 liters a day. Figure 6 shows average milk production per day per cow in liters by the month since the last calving for pure exotic breeds of milking cows, cross breeds, and local cows. The daily milk production is at the highest right after the calving and declines over time. Pure breeds produce about 10 liters of milk per day per cow, and the production level declines to just over 6 liters a day after fifteen months. For cross-breeds, the production level starts from just over 6 liters and declines over time to just below 4 liters a day after fifteen months. Local cows produce only 2 liters a day on average even just after the calving, and the production declines to below 1 liter after twelve months.

5.4. Dairy Marketing

Table 22 shows major characteristics of sample households that are stratified by the distance from the nearest district center to Nairobi. Among 890 households, 39% of sampled households live in districts which are located less than 150 km from Nairobi, 29% of households live in districts which are 150-300 km from Nairobi, and 32% live in districts that are more than 300 km from Nairobi. The third and fourth columns show different levels of milk production across the three distances from Nairobi. In the districts close to Nairobi, the average milk production per household is high. The last three columns indicate the proportions of households selling to different milk sales outlets. It is clear that people living in districts far from Nairobi tend to sell milk to neighbors and at local markets. The proportion of households selling milk to cooperatives/KCC or traders is minimal. Thus, distance from major consumption areas seems to be a critical factor in determining households’ milk marketing decisions.

Table 23 highlights major characteristics of 15 sampled districts. The fifteen districts are ordered according to the distance to Nairobi. The distance ranges from 16 kilometers (Kiambu District) to 412 kilometers (Bungoma District). The percentage of households selling milk in each district varies from 24 percent (Vihiga District) to 71 percent (Nyandarua District). It

seems that more households are engaged in milk marketing in districts closer to Nairobi. This can be seen from the income share of milk sales. The dairy sector provides an important income source to households living in districts that are close to Nairobi. Milk sales account for 48 percent of income among households living in Nyandarua District, which is only 110 kilometers from Nairobi, but just 7 percent of income among households in Ranchuonyo District which is 350 kilometers from Nairobi.

The last 4 columns indicate the change in milk marketing outlets from 1998/2000 to 2004. Although no households sold milk through cooperatives and private traders in 1998/2000 in districts that which are 300 kilometers or farther away from Nairobi, some households sold milk to traders in 2004. But the majority of milk producers in districts far from Nairobi continue to sell milk to neighbors and/or at local market. In contrast, about half of the households in districts close to Nairobi sell milk through private traders. There is an exception, Narok district, where no households sell milk to trader or cooperatives even though it is relatively close to Nairobi. This is probably because the Maasai who live in this district graze their animals and consume milk by themselves. The milk income share and the proportion of households selling milk, however, clearly indicate that Narok is a milk deficit district. The share of households who sell milk directly to individuals (neighbors) has been declining in most districts. The share of traders as the primary milk outlet largely increased in milk surplus areas that are located relatively close to Nairobi.

6. Organic Green Revolution in Kenya

6.1. Organic Green Revolution: Mechanism

Like the Agricultural Revolution in 18th century England, the OGR in East Africa will depend on the stall-feeding of cattle using cultivated feed (e.g., napier grass and oats, as shown in Figure 6). However, unlike the Asian Green Revolution, the OGR must rely on organic fertilizer, i.e., manure produced by stall-fed cattle (McIntire et al., 1992). The use of fodder leaves from agroforestry trees, which have the capacity to fix nitrogen, may also improve the quality of any manure used as fertilizer. The cattle involved are the so-called crossbred cows discussed earlier, which – in some ways - is reminiscent of the cross between the Taiwanese and Indonesian rice varieties that helped spark the Asian Green Revolution.6 As with the Asian Green Revolution, African farmers must use improved crop varieties, which are more responsive to nutrients than local varieties. We suspect, however, that these varieties are not as high-yielding as the improved varieties developed for Asia, because there has been comparatively little adaptive breeding research undertaken by international agricultural research organizations for the agro-ecological conditions of Sub-Saharan Africa (Evenson and Gollin, 2003).

Note that stall-feeding of cattle with cultivated feeds does not enhance the total amount of soil nutrients. In fact, the total amount of nutrients will likely decline because of the export of nutrients from the plant-soil-animal system through harvested products and milk. Such a system, however, enhances the internal cycling of nutrients and, hence, the ability to extract and

6 Because of the prevalence of a chronic disease, trypanosomaiasis, it is economical to keep dairy goats in some areas.

use soil nutrients (Buresh 1999). The long-term sustainability of such a system will depend on the inherent amount of soil nutrients available to be extracted, i.e., native soil fertility, and the rate at which nutrients are replenished by exogenous sources, such as nitrogen fixed by agroforestry trees, legume crops, or fertilizer. The sale of milk provides incentives and cash for farmers to purchase fertilizer and many apply it to napier.

In short, the OGR seeks the best possible combination of the most desirable yield-enhancing features of the world’s two, already recognized revolutions in agriculture. Additionally, the use of manure is particularly appropriate for the fragile soils found in Sub-Saharan Africa, which have been depleted in the past by their intensive use without the adequate replenishment of nutrients. In sum, we present a possible development pathway in Figure 7. Figure 7 shows two isoquant curves with two production inputs: chemical and organic fertilizer. In Asia, because the relative price of chemical price was low, the Green Revolution took a path that relied heavily on chemical fertilizer. On the other hand, in Africa, the relative price of chemical fertilizer over organic fertilizer is high, compared with that in Asia. This is especially so in areas where organic fertilizer is available from improved cows, such as highlands in East Africa. Thus, on the isoquant curve, the optimal combination of chemical and organic fertilizer is different from Asia, using relatively more organic fertilizer. Therefore, if the relative price between the two inputs remains high, then the development pathway should be different from that of Asia.

A critical assumption is whether the relative price in Africa will remain high in the future. We think it would remain high for awhile because of high transportation costs and near absence of irrigation in Africa. High transportation costs keep fertilizer prices high, while the near absence of irrigation keeps demand for chemical fertilizer low because of risks associated with unreliable rainfall.

6.2. Evidence from Kenya

Dairy-Crop Integration by Dairy Production Systems

The farming system of the Organic Green Revolution is based on the integration of dairy and crop productions. In Table 24, we show the integration of the two productions for three dairy production systems: exclusively grazing, semi-grazing, and zero grazing. Among 562 households that produced some milk in the last six months, there are 219 households who exclusively graze all of their cows, 181 households who keep at least one milking cow in a semi-zero grazing unit, and 161 households who keep at least one milking cow in a zero grazing unit. The households who exclusively graze all of their cows have more than two milking cows, but the other households have about one and a half cows on average. The zero-grazing households have the highest integration of the dairy and crop productions. They produced about 1,055 liters of milk per day, applied about 2,712 kilograms of manure on crops, and produced and fed 5,757 kilograms of napier grass to their cattle on average in the last six months. The exclusively grazing households, in contrast, produced 663 liters of milk, applied 1,147 kilograms of manure on crops, and produced and fed 1,726 kilograms of napier grass to their animals. The different levels of integration of dairy and crop productions are shown in Figure

7.

The zero-grazing production system is a labor-intensive production system not because it requires longer labor hours than the other systems but because that it requires more physical and knowledge-based activities. Table 25 shows labor hours devoted to livestock production by types of laborers (men, women, children, permanent laborers, and hired laborers) and by activities. We stratify the dairy households in the same way as in the previous table. The total labor hours devoted to livestock production for six months is 1,670, 1,735, and 1,506 hours for the exclusively grazing households, semi-zero grazing households, and zero-grazing households, respectively. These numbers indicate that about 9 hours is devoted to livestock production per day. Men, women, and hired labors work about 2 to 3 hours per day, while children spend just less than one hour per day.

The hours devoted to different activities are presented in Table 25 according to dairy production system. Among the exclusively grazing households, 676 hours are devoted to grazing animals in the past six months or 3.8 hours per day. The semi-zero grazing households spent 491 hours on grazing animals in the past six months, while the zero-grazing households spent only 59 hours. In contrast, the zero-grazing households spent more time on collecting and chopping pastures (470 hours), collecting water for animals (142 hours), and working in stalls (111 hours), while the exclusively grazing and semi-zero grazing households spent significantly less time on these activities. Compared with grazing animals, these activities are physically intensive and require some knowledge about dairy management.

Although the exclusively grazing households did not apply manure on crops as much as the zero-grazing households, they nevertheless spent about the same amounts of time on removing and collecting manure as did the zero-grazing households. This indicates that collecting manure from the zero grazing stalls can be done efficiently because it is easier to collect manure from stalls than from open grazing fields. All three types of dairy households devoted about 100 hours on milking and another 180 hours on spraying and dipping animals. In sum, all three dairy production systems require labor hours at the same level, the zero-grazing production system requires more physically and and knowledge-intensive labor hours than the grazing or semi-grazing livestock production systems.

Impacts on Manure Application on Crops and Maize Yields

Next, let us investigate the factors affecting the organic fertilizer application on crops. Table 26 presents the estimation results of the adoption of organic fertilizer (i.e., manure and compost) and the amount of organic fertilizer application per acre. To investigate the impacts of different dairy/livestock production systems on organic fertilizer application, we separate the numbers of improved cattle fed in stalls and grazed and also the numbers of local cattle fed in stalls and grazed in Kenya.

The results indicate that an increase in the number of stall-fed improved cattle per acre increases the adoption of organic fertilizer by about ten percent. This impact is larger than the impact of the number of grazed improved cattle. Furthermore, the number of stall-fed improved cattle increases the amount of organic fertilizer application significantly by more than 220 kilograms per acre. In contrast, the number of grazed improved cattle does not increase the amounts of

organic fertilizer significantly. It is also interesting to observe that the number of grazed local cattle increases the adoption of organic fertilizer significantly but it does not have any impact on the amounts of organic fertilizer. Thus, both the use of improved cattle and stall-feeding are important determinants of manure application.

Turning to plot characteristics, we find that possession of land title increases the amount of organic fertilizer application in Kenya, and that farmers apply less organic fertilizer on rented-in plots than on other plots. Since organic fertilizer is considered to have long lasting impacts over several cropping seasons, farmers may be reluctant to make such a long-term investment in land that could be taken away by land owners in the near future. We also find that organic fertilizer is more likely to be applied on plots that have been cultivated for a long time. This could be because soil fertility of such plots is less fertile than newly opened up plots and require fertilizer investments to produce crops. We also find that farmers are applying organic fertilizer more frequently on small plots. This could be because it is difficult to apply bulky and heavy organic fertilizer on large plots.

Next, we estimate the impacts of organic fertilizer on maize yield. Because organic fertilizer application is an endogenous variable (e.g., farmers may apply more organic fertilizer where soil fertility is low and yield is low as a result), we employ not only the ordinary least squares (OLS) method but also the two stage least square estimation (2SLS) method. The results are presented in Table 27. The dependent variable is the maize yield (kilograms/hectare) in logs. The OLS estimator of the manure application (1,000 kilograms/acre) is a positive but insignificant in Kenya (column A). When the organic fertilizer application is treated as an endogenous variable in column B, however, the coefficient becomes larger and significant at the 5 percent level. These results strongly indicate that the OLS estimator is biased downward because farmers apply more organic fertilizer on more depleted plots (thus low maize yields without fertilizer). Thus, maize yield would have been much lower, if there had been no application of manure.

Indeed the size of the 2SLS estimator indicates if the organic fertilizer application increases by 1,000 kilograms, then maize yield increases by 30 percent. A simple simulation suggests that the maize yield increases from 757 kilograms per hectare to 1.022 kilograms per hectare when the organic fertilizer application per hectare increases from zero to 1,000 kilograms per hectare in Kenya. The yield increases to 1,380 kilograms per hectare if the organic fertilizer application increases to 2,000 kilograms per hectare, which is close to the average in Kenya. Furthermore, if newly purchased HYV seeds, which are hybrid, are planted, yields seem to increase by 15 to 28 percent. Thus, these results indicate that high levels of maize yield can be achieved, if the OGR takes place.

7. Non-farm Income7

7.1. Non-farm Activities: List

In Section 3.3, we noted that households in the highest income quartiles have a large share of

7 For simplicity, we use the term “non-farm” loosely. We include farm-labor, for instance, in non-farm activities, although this could be categorized as off-farm activity but not non-farm.

income from non-farm activities, either labor or self-employment enterprises. A key question, therefore, is what determines the participation in such activities. In Table 28, we list non-farm activities that household members in the sampled households are engaged in. The list starts with the most common to the least common activity. The total number of individuals who are engaged in non-farm activities is 911 from the 894 households. Thus, on average, there is more than one individual per household who is engaged in non-farm activities.

The most common activity is “wage earner”, which is defined as salaried labor activities with a constant monthly wage. Typical examples are schoolteachers or government officials. There are 289 people who were engaged in this activity. Among them, 30 percent are women. The average number of years of education is 11 years, which is the highest among the all activities. The average number of years of experience is also 11 years, which is the second longest among the activities. Thus, wage earners tend to stay as wage earners for a long time. As expected, the average income among wage earners is the highest among the activities at $971 for the last six months. The second most common activity is trading farm products. In this activity, the proportion of female workers is the second highest at 69 percent. The third most common activity is casual wage earners. They earn on average $198 for the last six months, which is the second lowest income. The lowest income, $136, is earned by farm laborers, which is the fourth most common activity choice.

Some of non-farm activities are important income sources for women. The clothing business has the highest proportion of female workers at 79 percent. Trading farm products has the second largest proportion of female workers, and trading non-food goods has the third largest. In contrast, construction has the lowest proportion of female workers at 3 percent, and the transport sector has the second lowest proportion at 15 percent. Other activities have about 30 percent of female workers. On average the proportion of workers that are female is about 37 percent.

7.2. Participation in Non-farm Activities

To investigate the participation in non-farm activities, we stratify the non-farm activities in two groups: self-employment businesses and the regular wage activity. Then we use a multi-nominal logit model to evaluate the effect of various household and individual factors on obtaining one of these types of employment (a third case is none of these two). The results in Table 29 indicate that education increases the probability of participation in non-farm activities both in self-employment businesses and the regular wage activity. Women are less likely to participate in non-farm activities than men.

Among the household characteristics, the highest education level among male members has a positive impact on the participation in self-employment businesses, but the highest education level of female members has a negative impact on both self-employed businesses and the regular wage activity. This is surprising, but it could be because households with educated women are more likely to focus on farming or on the less formal non-farm jobs.

8. Conclusions

This report summarizes findings from the first wave of the 2004 REPEAT survey in Kenya. The survey is a join collaboration of National Graduate Institute for Policy Studies (GRIPS), Tegemeo Institute of Egerton University, the World Agroforestry Centre (ICRAF), and the International Livestock Research Institute (ILRI). This survey is a part of a research project called the Research on Poverty, Environment, and Agricultural Technologies (REPEAT) with the overall goal of identifying agricultural technologies and farming systems that will contribute to increased agricultural productivity, the sustainable use of natural resources, and reduced poverty in Kenya, Uganda, and Ethiopia. The research findings will be used to inform policy makers, development practitioners, and other stakeholders in formulating and implementing policies and strategies in Kenya.

The survey is based on data collected from 894 households in 99 sublocations in western and central Kenya. The sampled households were selected from 2,966 households that were previously interviewed by ILRI in 1998 and 2000 (Staal et al., 2001; Waithaka et al., 2002). In February 2004, 100 sublocations were randomly selected for the REPEAT survey from sub-locations where the 2,966 ILRI households were found in 1998 and 2000. From the 100 sub-locations, 10 previously interviewed households per sublocation were randomly selected and targeted for re-interview in 2004. In 2004, two waves of surveys were planned over a period of six months. The first wave was conducted in February 2004, asking respondents about the last six months (from August 2003 to January 2004). In October 2004, the second wave of the surveys took place to cover the following six months, starting from February 2004.9 By visiting the same households twice over a period of six months, respondents were asked about the last crop season only. Thus we hope that we can obtain more accurate information and, at the same time, keep the length of interviews within a reasonable length. In this report, we have described results from the first wave of the 2004 REPEAT survey. When the data from the second wave of the 2004 REPEAT survey becomes available, the two waves of the survey are to be combined to present the annual expenditure and production information.

The report identifies seven major findings:

First, the report finds that the average per capita expenditure among the sampled household is $181 for six months. To obtain the annual per capita expenditure, we will add the six months per capita expenditure from the second wave of the REPEAT survey to this figure. Out of the per capita expenditure of $181, cash expenditure is $133, while the rest, $48, is consumption of commodities produced on the farm.

Second, the report also finds that the average per capita income is about $199 for six months. Again this figure will be combined with the per capita income from the second wave of the survey to construct the annual income. Out of the per capita income, crop income (including amount consumed at home) takes the largest share of 43 percent, followed by wage income, which provides constant monthly wage, of 24 percent, livestock income of 21 percent, and

9 At the time of writing this report, the second wave of the survey has been completed, and data cleaning is underway in Nairobi.

non-farm business activities of 12 percent. Although shares from different sources differ across provinces, crop income provides the largest income share in all provinces surveyed (Nyanza, Western, Rift Valley, Central, and Eastern). When stratified by income quartiles, wage income provides the largest share of income among the highest income quartile, while among the lowest income quartile the crop income provides the largest share.

Third, as expected, the report finds maize (96% of sampled households produced maize) and beans (82%) as the most common crops. Banana is the third most common crop (61%), and Napier grass is the fourth (58%). Tea provides the largest income, $356, to its producers, and sugarcane provides the second largest income, $218. Although it is difficult to evaluate the value of Napier grass production because only a small amount of it is traded in limited areas, it seems to provide a significant value. A rough evaluation shows its value of production as $130, which makes Napier grass third in terms of production value. Maize and beans combined provide about $170 to their producers. Horticulture crops, such as Sukumawiki (kale) and cabbage, provide large per acre production values, but are grown mainly on small plots and therefore do not contribute large amounts at the farm level.

Fourth, the report finds that 80 percent of sampled households applied chemical fertilizer on crops. An important source of financing chemical fertilizer is credit from cooperatives of high-value crops such as tea and coffee. About 84 percent of tea producers received credit from Kenya Tea Development Authority, while about 22 percent of coffee producers received credit from coffee cooperatives.

Fifth, about 54 percent of sampled households own at least one dairy cow, while 21 of them own at least one local cow. Among 890 milking cows, about 77 percent of them are either cross breeds of exotic and local cattle or pure exotic breeds. Pure Holstein cows produce 8.5 liters of milk per day on average, while other crossbred cows produce 4 to 5.4 liters per day. Local cows produce only 1.9 liters of milk per day. The report also finds that about 34 percent of households that sold milk during the last six months sold some milk to private traders, while only 19 percent of them sold milk to dairy cooperatives or the Kenya Cooperatives Creamery (KCC). The largest share, 47 percent, however, is sold to neighbors. Compared with earlier reports on milk marketing from International Livestock Research Institute, the market share of private traders seems to have increased.

Sixth, the report finds a highly integrated crop-dairy production system (which we refer to as the Organic Green Revolution technology) promising in increasing not only dairy productivity but also providing much needed nutrients and organic matter to crop production. For instance, in a period of six months, the zero-grazing dairy households produced 1,055 liters of milk, applied about 2,712 kilograms of manure on crops, and produced and fed 5,757 kilograms of napier grass to their cattle on average. In contrast, the exclusively grazing and semi-grazing dairy households produced less milk, applied less manure on crops, and produced and fed less napier grass to their animals than the zero-grazing dairy households. Although labor activities required for the zero-grazing dairy production are physically demanding and knowledge intensive, the labor hours spent on livestock production is about the same across different dairy production systems. The results in this report suggest positive impacts of manure application on crop production.

Seventh, the report finds that high-income households earn a large proportion of income from non-farm activities. The results from the determinants of the non-farm activity participation indicate that education help individuals to participate in both self-employed businesses and regular wage activities.

Because the 2004 REPEAT survey is based on the 1998/2000 ILRI surveys, it would be possible to show dynamic changes in farm households over time. We also plan to re-visit the same households in about 3 years. Therefore, by combining these surveys, we should be able to evaluate the impacts of some agricultural technologies on the welfare of households over a long period of time, which will provide us valuable knowledge on how to reduce poverty through agricultural technologies.

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Productivity: The Impact of International Agricultural Research. Wallingford: CABI Publishing.

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Table 1. Sampled Communities and Households

Sampled Sub-Locations Sampled Households

Number of sub- locations

Average number of total households per sub-location

Number of households

Household size Farm size Province

(A) (B) (C) (D) (E)

Number Number Number Number Acres

Nyanza 18 2,518 175 5.99 3.86

Western 13 1,961 112 7.06 2.86

Rift Valley 25 3,428 226 6.28 7.48

Central 35 1,913 310 5.07 3.84

Eastern 8 1,399 71 6.24 4.48

All 99 2,364 894 5.90 4.68

Note:

Table 2. Sampled Households and Sub-location in the original ILRI Survey and the 2004

REPEAT Survey

The Original ILRI Surveys The REPEAT Survey

Year of Survey Sampled households

Sampled Sub-locations

Sampled Households

Province District

(A) (B) (C) (D)

The 2000 ILRI Survey A Year Number Number Number

Nyanza Kisii 2000 269 7 68

Nyamira 2000 250 4 37

Rachuonyo 2000 158 7 70

Western Vihiga 2000 334 4 38

Bungoma 2000 162 5 36

Kakamega 2000 279 4 38

Rift Valley Nandi 2000 123 5 49

The 1998 ILRI Survey B

Rift Valley Nakuru 1998 387 17 157

Narok 1998 67 3 20

Central Kirinyaga 1998 104 6 41

Muranga 1998 284 11 103

Naorobi (Kiambu)

1998 312 12 116

Nyandarua 1998 109 7 50

Eastern Machakos 1998 128 7 71

2,953 99 894

Source:

A Waithaka, et al. (2002);

B Staal, et al. (2001)

Note:

Table 3. Six-month Per Capita Cash Expenditure and Expenditure Share by Province

Expenditure Share Per Capita Cash Expenditure

Staple food Fresh food items

Non-fresh food items

Non-food items

Social activities

Province

(A) (B) (C) (D) (E) (F)

US$

Nyanza 111.0 16.4 18.7 16.3 39.7 8.9

Western 132.0 19.5 15.3 15.1 39.3 10.8

Rift Valley 115.9 13.6 14.7 18.4 39.5 13.8

Central 151.4 21.6 15.3 15.1 36.9 11.1

Eastern 158.2 25.7 14.9 14.0 36.2 9.2

All 132.7 18.6 15.8 16.1 38.4 11.1

Note:

Table 4. Six-month Per Capita Cash Expenditure Share by Quartile

Expenditure Share Per Capita Cash Expenditure

Staple food Fresh food items

Non-fresh food items

Non-food items

Social activities

Province

(A) (B) (C) (D) (E) (F)

US$

Lowest 34.4 19.4 17.8 22.2 31.7 8.9

Second 72.0 22.3 17.6 16.9 32.0 11.2

Third 119.8 18.9 15.4 14.9 38.2 12.6

Highest 304.4 13.8 12.5 10.3 51.6 11.9

All 132.7 18.6 15.8 16.1 38.4 11.1

Note:

Table 5. Six-month Per Capita Home and Cash Expenditure by Province

Per Capita Expenditure

Total Expenditure Cash Expenditure Home Consumption of own

products

Province

(A) (B) (C)

US$ US$ US$

Nyanza 145.2 111.0 34.2

Western 161.7 132.0 29.7

Rift Valley 186.2 115.9 70.3

Central 196.5 151.4 45.1

Eastern 214.3 158.2 56.1

All 180.9 132.7 48.2

Note:

Table 6. Six-month Per Capita Income and Income Share by Province

Income Share Per Capita Income

Crop Income Livestock Income

Non-farm activities

Wage income Province

(A) (B) (C) (D) (E)

US$ percent percent percent percent

Nyanza 125.9 31.0 23.6 16.5 28.9

Western 97.9 65.2 4.4 9.4 21.0

Rift Valley 221.0 39.2 28.8 11.3 20.7

Central 230.2 44.5 21.3 12.7 21.5

Eastern 326.5 41.0 14.2 7.9 36.9

All 198.5 42.8 21.0 12.3 23.9

Note:

Table 7. Six-month Per Capita Income Quartile

Income Share Per Capita Income

Crop Income Livestock Income

Non-farm activities

Wage income Province

(A) (B) (C) (D) (E)

US$ percent percent percent percent

Lowest 13.2 56.7 26.1 7.0 10.3

Second 62.3 46.2 17.4 16.0 20.4

Third 144.1 36.6 20.0 13.6 29.8

Highest 575.1 31.9 20.1 12.6 35.4

All 198.5 42.9 20.9 12.3 23.9

Note:

Table 8. Land Tenure at the Plot Level

Tenure Status Number of plots

Average size in acres Own plots

with title deed

Own plots without title

deed

Rented-in for fixed

payments Others

Province

(A) (B) (C) (D) (E) (F)

Number Acers %

Nyanza 376 1.80 72 12 14 2

Western 172 2.04 66 24 9 1

Rift Valley 378 4.62 58 21 19 10

Central 479 2.53 79 7 12 2

Eastern 133 2.39 79 15 2 4

All 1,538 2.79 71 14 13 2

Note:

Table 9. Land Acquisition and the Ownership of the Title Deed

Who own the title deed?

All of plots Not owned Head Parents

Deceased parents

Deceased husband

Others

Land Acquisition Mode (A) (B) (C) (D) (E) (F) (G)

Number Percent Percent Percent Percent Percent Percent

Inherited 853 17 33 29 5 10 6

Purchased 391 16 67 2 3 0 12

Rented-in 207 95 1 0 0 0 3

Others 87 77 5 1 1 5 11

Total 1,538 31 36 17 3 6 7

Note:

Table 10. Crop Production– Value Production at the Household Level

Area devoted Production value Percentage of producer households All Producers only Producers only

Production value per acre

Crop

(A) (B) (C) (D) (E)

% acres acres US$ US$/acre

Maize 95.5 1.31 1.41 128.7 112.5

Beans 81.8 1.04 1.29 44.5 51.3

Banana 60.5 0.33 0.54 67.9 281.7

Napier Grass 58.2 0.27 0.46 130.3 454.7

Irish Potato 36.7 0.26 0.70 62.3 152.9

Avocado 32.2 0.18 0.56 14.2 98.8

Sukumawiki 29.6 0.06 0.21 30.0 836.9

Coffee 24.5 0.18 0.83 41.9 60.9

Sweet Potato 23.1 0.07 0.32 21.7 227.8

Mango 20.7 0.16 1.02 11.0 24.9

Tea 17.1 0.14 0.88 335.8 398.6

Sugarcane 14.5 0.07 0.49 217.5 656.7

Cowpea 12.6 0.12 0.97 4.9 33.7

Cassava 13.4 0.07 0.50 17.7 93.7

Pawpaw 12.5 0.06 0.51 21.2 45.8

Green Peas 11.7 0.12 1.03 16.7 50.5

Cabbage 12.0 0.03 0.28 97.1 613.7

Table 11. Percentages of Producer Households of Major Crops

Percentage of producer households

Maize Beans Banana Napier Irish Potato Coffee Tea Sugarcane Province

(A) (B) (C) (D) (E) (F) (G) (H)

Nyanza 95 77 73 58 5 20 33 26

Western 90 85 79 29 0 15 11 20

Rift Valley 99 86 39 38 60 0 6 12

Central 94 76 67 83 59 47 22 11

Eastern 100 100 41 61 0 35 0 1

All 96 82 60 58 37 25 17 14

Note:

Table 12. Inputs Application by Province

Percentages of Households using

Fertilizer Manure Purchased seeds

Hired Labor Hired Oxen or machines

Insecticide Province

(A) (B) (C) (D) (E) (F)

Percent Percent Percent Percent Percent Percent

Nyanza 71 67 91 58 31 14

Western 83 42 77 54 33 12

Rift Valley 82 66 86 66 49 26

Central 88 65 86 66 7 19

Eastern 58 68 68 44 20 13

All 80 63 84 61 27 18

Note:

Table 14. Sources of Interlinked Credit by Sources of Interlinked Credit by Sources of Interlinked Credit by Sources of Interlinked Credit by Province in KenyaProvince in KenyaProvince in KenyaProvince in Kenya

Interlinked Credit (ILC) Crop Producers

Tea Producers Coffee Producers Sugarcane Number of sampled households All

Who received ILC

All Who received ILC

All Who received ILC

Province

(A) (B) (C) (D) (E) (F) (G)

number number number number number number

Nyanza 175 51 42 29 1 23 0

Western 112 10 7 8 0 13 7

Rift Valley 226 9 4 2 0 17 0

Central 310 65 61 114 33 30 0

Eastern 71 0 0 8 2 1 0

Total 894 135

(100%)

114

(84%)

161

(100%)

36

(22%)

84

(100%)

7

(8%)

Note:

Table 15. All Credit Sources to Purchase Inputs

How much did you receive? Number of households that received credit in Fertilizer in Value Credit Source

(A) (B) (C)

Number Kilograms Kenya Shilling

KTDA 114 242.4 5,003

(250.7) (5,035)

Coffee Cooperatives 33 98.8 2,236

(112.3) (2,365)

Sugarcane Companies 7 159.1 3,523

(91.7) (2,108)

Traders (Horticulture etc.) 7 58.9 1,368

(42.4) (875.7)

Farmers Groups 3 84.7 2,100

(26.6) (519.6)

Others 5 481.0 2,520

(586.9) (2,784)

Source: 2004 REPEAT Survey in Kenya

Table 16. Livestock Ownership—Household Level

Proportions of households

Average number owned

Average number owned among owners

Average Price

(A) (B) (C) (D)

% number number US$

Cattle

Local cows 21.0 0.58 2.8 101.5

Local bulls 10.3 0.23 2.2 127.4

Local young bulls 5.3 0.10 1.8 54.9

Local heifers 7.9 0.14 1.7 69.9

Local calves 9.5 0.18 1.9 33.4

Dairy cows 54.4 1.12 2.1 230.9

Dairy bulls 9.1 0.16 1.7 120.1

Dairy young bulls 9.2 0.13 1.5 78.0

Dairy heifers 18.7 0.26 1.4 134.1

Dairy calves 22.8 0.38 1.7 47.5

Other Animals

Local goats 27.4 1.85 6.8 15.2

Dairy goats 2.2 0.08 3.6 40.4

Sheep 29.2 2.37 8.1 19.2

Local chicken 79.1 15.1 19.1 1.9

Local pigs 0.9 0.02 1.9 22.9

Table 17. Adoption of Improved Cows – Household Models

(Heckman’s Two Step Procedure) Prob(y>0) E(y)

(A) (B)

Household Characteristics

Land size 0.037 0.323

(4.76)** (3.73)**

Land size squared -0.0001 -0.005

(3.60)** (2.51)*

Land Title (=1) 0.181 0.816

(3.20)** (1.27)

Ratio of steep plots 0.021 -0.222

(0.42) (0.68)

Female headed household (=1)

-0.014 -0.159

(0.28) (0.48)

Max. years of male schooling 0.007 -0.035

(1.19) (0.81)

Max. years of female schooling 0.025 0.157

(3.96)** (2.41)*

Number of Male adults 0.002 0.172

(0.12) (1.46)

Number of Female adults 0.002 0.035

(0.09) (0.29)

Number of Boys 0.009 0.181

(0.43) (1.29)

Number of Girls -0.020 -0.338

(0.94) (2.43)*

Community Level Characteristics

Dairy Associations available 0.022 -0.136

(0.43) (0.45)

Milk collection points available 0.073 0.174

(1.33) (0.43)

Government veterinary service available

0.097 0.067

(1.82)* (0.16)

NGO veterinary service available 0.084 1.487

(1.46) (3.63)**

Lamda 2.260

(1.16)

Observations 884

Note: 19 district dummies are also included. * and ** indicate 5 and 1 percent significance level, respectively. Estimated coefficients are marginal changes in probability.

Table 18. Dairy Production Systems in Central and Western Kenya

Proportions of different dairy production systems Number of sampled households Not cattle

owner Grazing

Semi-zero grazing

Zero Grazing

Average Land size Province

(A) (B) (C) (D) (E) (F)

Number Percent Percent Percent Percent Acres

Nyanza 175 28 31 32 9 3.86

Western 112 20 55 23 2 2.86

Rift Valley 226 25 44 17 14 6.18

Central 310 21 14 17 48 3.84

Eastern 71 23 11 45 21 4.48

Total 894 23 30 23 24 4.35

Source: 2004 REPEAT Survey in Kenya

Table 19. Sampled Households and Sub-location in the 2004 REPEAT Survey

Dairy Production System Number of sampled households

Not cattle owner

Grazing Semi-zero grazing

Zero Grazing Province District

(A) (B) (C) (D) (E)

Number Percent Percent Percent Percent

Nyanza Kisii 68 21 19 46 15

Nyamira 37 27 27 35 11

Rachuonyo 70 36 44 17 3

Western Vihiga 38 11 79 8 3

Bungoma 36 17 64 17 3

Kakamega 38 32 24 45 0

Rift Valley Nandi 49 16 55 20 8

Nakuru 157 28 36 18 17

Narok 20 20 80 0 0

Central Kirinyaga 41 22 2 20 56

Muranga 103 19 2 20 59

Kiambu 116 22 6 16 57

Nyandarua 50 14 64 16 6

Eastern Machakos 71 23 11 45 21

Total 894 23 30 23 24

Note:

Table 20. Percentages of Households using Various Feed Stuff*

Feed Stuff

Maize Stover

Minerals/ salt

Napier Grass

Concentrates

Other crop

residues Cut grass

Fodder leaves

Province

(A) (B) (C) (D) (E) (F) (G)

% % % % % % %

Nyanza 89 87 70 15 53 19 21

Western 78 73 82 6 22 41 38

Rift Valley 87 97 67 39 28 26 16

Central 92 92 97 53 49 49 33

Eastern 94 43 78 22 33 43 11

Total 88 86 81 34 40 36 25

Note: * 663 households that own some cattle, excluding 231 households.

Table 21. Milk Production at the Cow Level in February 2004 by Cow Breed

Milk Production Number of milking cows

Average number of

calving

Average months since last calving

Morning Milk

Evening Milk

Daily Milk

Breed

(A) (B) (C) (D) (E) (F) (G)

number % number months liters liters Liters

Local cows 206 23.2 7.8 3.0 1.1 0.8 1.9

Cross Breed (<50%) 147 16.5 8.6 2.9 2.3 1.7 4.0

Cross Breed (50%) 123 13.8 8.9 3.1 2.6 1.9 4.5

Cross Breed (>50%) 240 27.0 7.8 3.1 3.2 2.3 5.4

Pure Holstein 146 16.4 7.6 2.8 5.0 3.4 8.5

Other Pure Exotic 28 3.2 8.3 3.5 3.6 2.5 6.1

Total 890 100 8.1 3.0 2.8 2.0 4.8

Source: 2004 REPEAT Survey in Kenya

Table 22. Milk Marketing by Distance to Nairobi

Proportions of household selling milk to Number of

observations % households producing milk

Milk production last 6 months (Lt)

Coop/KCC trader neighbor* Distance to Nairobi

(A) (B) (C) (D) (E) (F)

Number Percent Liters Percent Percent Percent

0-150km 344 55.3 51.3 32.5 39.7 27.8

150-300km 259 59.0 43.1 14.0 45.9 40.1

300km- 287 31.1 17.9 1.0 4.2 94.8

Total 890 48.6 32.8 19.2 34.2 46.6

Note: “Neighbor” includes selling at local market, to restaurants and hotels as well as individuals in neighborhood.

Table 23: Milk Marketing in 1998/2000 and 2004 by District

Share of primary milk market outlet*

Coop/KCC/ FG/processor

Trader

Distance from district center to Nairobi

Proportions of

households selling milk

Income share of milk sales

1998/00 2004 1998/00 2004

District

(A) (B) (C) (D) (E) (F) (G)

km Percent Percent Percent Percent Percent Percent

Bungoma 412 33 0.132 0 0 0 0

Vihiga 375 24 0.118 0 0 0 10

Kisii 369 37 0.119 0 0 0 5

Kakamega 355 29 0.083 0 0 0 0

Ranchuony 350 26 0.072 0 0 0 0

Nyamira 320 39 0.117 0 8 0 15

Nandi 230 64 0.114 6 3 53 43

Nakuru 158 61 0.206 19 16 24 48

Narok 141 30 0.081 0 0 0 0

Nyandarua 110 71 0.480 52 22 18 58

Kirinyaga 100 48 0.169 11 0 16 56

Muranga 87 54 0.187 25 12 21 57

Maragua 67 50 0.245 3 14 44 71

Machakos 63 38 0.107 28 38 3 4

Kiambu 16 66 0.339 58 58 11 27

Note: Primary milk outlet is measured at sublocation level. Figures for 1998 and 2002 are calculated in Waithaka et al. (2002) and Stall et al. (2001). “FG” stands for farmers group. Shares for 2004 are authors’ calculation by using REPEAT data.

Table 24. Integration of Dairy and Crop Production

Dairy Production Systems

Number of Households

Number of milking cows

Per cow milk production in 6-months

Manure application on

crop production

Own-Napier grass feeding

Number

(s.d.)

Liters

(s.d.)

Kgs

(s.d.)

Kgs

(s.d.)

Grazing 219 2.37 663 1,147 1,726

(2.51) (640) (4,501) (4,271)

Semi-Zero Grazing 181 1.57 804 1,192 3,989

(1.06) (674) (2,117) (5,853)

Zero Grazing 161 1.44 1,055 2,712 5,757

(1.26) (763) (4,134) (7,790)

Total 562 1.85

(1.86)

820

(705)

1,609

(3,832)

3,648

(6,209)

Source: 2004 REPEAT Survey in Kenya

Table 25. Labor Hours on Livestock Production (Labor hours for six months)

Grazing Semi-Zero Grazing Zero Grazing

(A) (B) (C)

- Hours - - Hours - - Hours -

Total labor hours for six months 1,670 1,735 1,506

Hired labor 493 574 456

Total family & permanent labor 1,177 1,161 1,050

Men 409 382 332

Women 368 422 386

Children 162 174 127

Permanent labor 238 183 205

Activities (family & permanent labor)

Grazing and watering cattle 676 491 59

Milking 110 103 113

Collecting & Chopping Pastures 139 268 470

Collecting water 95 132 142

Working in stalls 16 39 111

Transporting/selling milk 51 52 100

Spraying & dipping 218 180 161

Removing and collecting manure 59 50 40

Others 2 7 0

Number of households 219 181 161

Table 26. Adoption of Manure Use – The Plot Level Analysis (Heckman’s Model)

Kenya

Adoption of Manure Manure kgs/acres

(A) (B)

Number of cattle / acre

Improved cattle, stall fed 0.099 226.1

(6.33)** (7.11)**

Improved cattle, grazed 0.061 125.6

(1.64) (1.03)

Local cattle, stall fed 0.029 -70.66

(0.84) (0.64)

Local cattle, grazed 0.111 -116.4

(3.38)** (1.03)

Plot Characteristics

Own Land Title (=1) -0.071 334.2

(1.44) (2.27)*

Rented-in Plot (=1) -0.647 -613.6

(6.74)** (1.79)

Steep Plot (=1) -0.180 174.8

(4.50)** (1.31)

ln (Years cultivating) 0.088 76.95

(3.41)** (0.90)

ln (Plot size in acres) -0.156 -1,669

(3.08)** (10.58)**

Household Characteristics

Female headed household (=1)

-0.091 231.9

(2.21)* (1.86)

Max. years of male schooling 0.008 24.43

(1.55) (1.53)

Max. years of female schooling -0.014 15.89

(2.55)* (0.96)

Number of Male adults 0.013 -32.59

(0.80) (0.67)

Number of Female adults 0.012 103.5

(0.74) (2.00)*

Main Crop

Maize 0.480 -274.6

(11.91)** (1.73)

Banana 0.258 1,074

(2.53)* (3.31)**

Napier Grass 0.158 633.4

(2.05)* (2.56)*

Coffee 0.412 1,119

(4.62)** (4.08)**

Community level Fertilizer

Prices

UREA Price -0.004 11.60

(0.63) (0.65)

DAP Price 0.015 -23.73

(4.11)** (1.97)*

Constant -0.904 1,581

(4.00)** (2.04)*

Number of observations 6,450 6,450

Note: 19 district dummies are also included. * and ** indicate 5 and 1 percent significance level, respectively. Estimated coefficients are marginal changes in probability.

Table 27. Maize Yield– The Plot Level Analysis:

Dependent variable = ln(maize yield, kilograms/hectare) OLS 2SLS

(A) (B)

Plot Characteristics

Manure (1,000kgs/hectare) A 0.017 0.300

(0.80) (2.35)*

HYV seeds, new (=1) 0.276 0.145

(3.20)** (1.33)

HYV seeds, recycled (=1) 0.092 0.024

(0.86) (0.19)

# of intercrops =2 (=1) -0.058 -0.154

(0.58) (1.28)

# of intercrops >=3 (=1) -0.263 -0.463

(2.43)* (3.16)**

Own Title (=1) 0.276 0.256

(2.93)** (2.41)*

Plot size in acres -0.448 -0.327

(7.96)** (3.99)**

Plot size squared 0.015 0.006

(2.40)* (0.77)

Rain Shortage (=1) -0.257 -0.252

(3.07)** (2.70)**

Household chara

Female headed HH (=1) -0.106 -0.067

(1.33) (0.75)

Max. years of male schooling -0.001 -0.009

(0.06) (0.77)

Max.years of female schooling 0.019 0.026

(1.78) (2.17)*

Number of Male adults 0.079 0.089

(2.59)** (2.62)**

Number of Female adults 0.051 0.023

(1.65) (0.62)

Constant 6.820 6.727

(42.80)** (37.18)**

Number of observations 1,065 1,065

Note: 96 sublocation dummies are also included in Kenya. * 5 %, ** 1%.

(A) Endogenous variable. Instrumental variables are numbers of stall-fed improved cattle, grazed improved cattle, stall-fed local cattle, and grazed local cattle in Kenya, and numbers of improved and local cattle and the availability of animal houses in Uganda.

Table 28. Non-farm Self-Employment and Wage Activities

Number of individuals

% of female Age Years of education

Years of experience

Income

(A) (B) (C) (D) (E) (F)

number percent years years years US$

Wage earner 289 30 40 11 11 971

Trading farm product 98 69 40 7 9 449

Casual wage earners 92 40 33 8 6 198

Farm labor 53 28 38 6 8 136

General-Kiosk owner 47 40 43 9 7 346

Construction 37 3 37 8 12 634

Clothing Business 24 79 39 8 6 418

Transport Business 20 15 41 9 5 428

Trading non-food goods 19 42 42 9 8 643

Taylor 15 39 37 7 8 294

Others 217 36 40 8 7 423

Total 911 37 39 9 9 717

Note:

Table 29. Determinants of Participation in Business and Labor Activities

Multi-nominal Logit

Business (A)

Regular Wage (B)

Education

1.214 0.822 Some primary (=1)

(3.86)** (3.48)**

1.532 1.336 Primary completed (=1)

(4.57)** (5.04)**

1.450 0.698 Some secondary (=1)

(4.00)** (2.16)*

1.833 1.078 Secondary completed (=1)

(5.42)** (3.71)**

2.597 0.642 Some post secondary (=1)

(7.03)** (1.56)

Individual Characteristics

-0.989 -0.595 Female (=1)

(8.38)** (4.82)**

0.191 0.229 Age

(8.56)** (9.31)**

-0.002 -0.002 Age*Age

(7.80)** (8.60)**

HH Characteristics

-0.044 -0.063 Highest education among female

(2.23)* (3.10)**

0.069 -0.016 Highest education among male

(3.68)** (0.78)

-0.119 -0.061 Number of female

(2.59)** (1.20)

-0.082 0.020 Number of male

(1.76) (0.39)

0.020 -0.015 Land in acres

(2.58)** (1.26)

District Dummies Included Included

Observations 3,174

Note: Dummies for relationships to the head and marital status are also included in the model but not reported here. ** indicates 1% significance level; * indicates 5% significance level.

Figure 1. Sampled Sub-locations in central and western Kenya

Figure 2. Distribution of Per Capita Expenditure for six months in US$

0.0

02

.00

4.0

06

.00

8D

en

sity

0 200 400 600Per Capita Expenditure in US$ (6 months)

Figure 3. Proportion of land acquisition by source in western and central Kenya

0%10%20%30%40%50%60%70%80%90%

100%

1952

1955

1958

1961

1964

1967

1970

1973

1976

1979

1982

1985

1988

1991

1994

1997

2000

2003

Year of acquisition of the plot

Inheritance Purchased Fixed rents Others

Figure 4

. Export C

rop Productio

n in

Centra

l and W

estern K

enya

0

50

100

150

200

250

300

350

1961

1963

1965

1967

1969

1971

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

Coffe

eTea

Sugar C

ane

Figure 5. Value Production and Fertilizer Application per Hectare

Aggregated value productions of inter-cropped crops (Unrestricted Sample)

(Lines are smoothed by using the locally weighted scattered smoothing method.)

20

00

04

00

00

60

00

08

00

00

Va

lue

Pro

du

cti

on

pe

r H

ec

tare

0 200 400 600 800 1000Per hectare fertilizer application

Tea

Coffee

Maize and Beans

Other Crops

Figure 6. Daily Milk Production (liters/cow/day) by Cattle Breeds in February

2004 in Central and Western Kenya

24

68

10

Da

ily M

ilk P

rod

uc

tio

n (

lite

rs/c

ow

/da

y)

0 5 10 15 20Months since Last Calving

Local Cows

Cross Breeds

Pure Breeds

Figure 7. The Integration of Dairy and Crop Productions in Kenya by Dairy

Production System

0

1000

2000

3000

4000

5000

6000

7000

Grazing Semi-Zero Grazing Zero Grazing

Milk Production (liters/cos/6 months)

Manure Application (kgs)

Own-Napier Grass Feeding (kgs)

More Integrated

Figure 8. Proportions of manure applied on main crops in Kenya and Uganda

Note: Maize includes beans, which is often intercropped with Maize.

Maize

48%

Coffee

12%

Banana

6%

Napier Grass

19%

Others

15% Maize

45%

Coffee

20%

Banana

15%

Others

20%

Kenya Uganda

Graphs by Country

Proportions of manure applications on crops