Agroforestry Systems

, Volume 87, Issue 4, pp 729–746

Productivity of Jatropha curcas under smallholder farm conditions in Kenya

Authors

    • World Agroforestry Centre (ICRAF)
  • David Newman
    • Endelevu Energy
  • Cristel Munster
    • Norwegian University of Life Sciences
  • Meshack Nyabenge
    • World Agroforestry Centre (ICRAF)
  • Gudeta W. Sileshi
    • World Agroforestry Centre (ICRAF)
  • Violet Moraa
    • World Agroforestry Centre (ICRAF)
  • James Onchieku
    • Kenya Forestry Research Institute (KEFRI)
  • Jeremias Gasper Mowo
    • World Agroforestry Centre (ICRAF)
  • Ramni Jamnadass
    • World Agroforestry Centre (ICRAF)
Article

DOI: 10.1007/s10457-012-9592-7

Cite this article as:
Iiyama, M., Newman, D., Munster, C. et al. Agroforest Syst (2013) 87: 729. doi:10.1007/s10457-012-9592-7

Abstract

With the global bioenergy boom, the planting of jatropha (Jatropha curcas) was widely promoted by the private sector and non-government organizations as one of the candidate tree species for bioenergy in Kenya. This was motivated by the belief that it grows easily with minimal management requirements. The present study attempts to determine whether management practices by smallholder farmers, which are heterogeneous, are optimal for jatropha yields in Kenya. A survey conducted in different agro-ecological zones showed that yields are very low under Kenyan farm conditions. Regardless of the age and management condition, 41 % of the farmers obtained no seed yield, while 79 % obtained up to 0.1 kg/tree. This is dismal in comparison with the figures (up to 2.0 kg/tree) reported from elsewhere for 1–5 year old trees grown under similar conditions. Examination of farmer management practices indicated that irrigation, manuring and weeding, in order to maximize yields, could be offset by misapplication of other components especially, selection of planting materials, timing of planting and choice of intercrops during the establishment phases. This indicates that the anticipated high yields have not been achieved partly because growers are still using unimproved germplasm, management practices are sub-optimal, and the biophysical boundaries of high jatropha yield are poorly defined. Thus at the current stage, jatropha should not be grown by smallholder farmers in Kenya because of low or dismal productivity. If jatropha is to play a role in the pro-poor bioenergy development, future projects need to identify management recommendations that optimize yields. This also needs to take into consideration the preferences and constraints of farm households on labor and land allocation to other farm and livelihood activities.

Keywords

JatrophacurcasSub-optimal managementAgroforestryDomestication

Introduction

While being indigenous to Central America, jatropha (Jatropha curcas) is widely distributed across sub-Saharan Africa. Its present distribution covers the western (e.g., Mali, Burkina Faso), central (e.g., DRC, Cameroon), eastern (e.g., Kenya, Tanzania, Ethiopia, Uganda) and southern (e.g., Zambia, Mozambique) regions of Africa (Heller 1996) where it is often grown as fence, hedge or wind break around homesteads.

While jatropha is not indigenous to Kenya, it has been naturalized in many parts of the country for many decades for reasons other than biofuels (GTZ 2009). Since the year 2000, farmers in western Kenya along the Ugandan border and later in Coast Province have been planting jatropha to serve as a support for the lucrative vanilla vines. As a result no effort was made to nurture jatropha to produce seeds (GTZ 2009).

When the hype over jatropha as a potential bioenergy crop swept throughout the world, even semi-wild shrubs suddenly became useful for generating cash (Renner 2007). It is only within the past few years that jatropha has become widely known as a potential biofuel feedstock among Kenyans. In Kenya the initial enthusiasm came from a handful of non-governmental organizations (NGOs) and private companies working with outgrowers or with small trial plots. However, compared to neighboring Tanzania and Ethiopia where foreign investors rushed to (Habib-Mintz 2010), there have been no large plantations in Kenya at the time this survey was conducted in early 2009. Still stories of large-scale plantations involving foreign investors to plant thousands of hectares on semi-arid land owned by the government or large private ranches continued to be reported (Gathura 2010; Ndurya and Kihara 2010). The initial impression was that jatropha would produce prolifically with little or no inputs, even in marginal semi-arid areas.

Higher and stable yields are essential conditions for economic feasibility of any plantations. Unfortunately, the basic agronomy of jatropha as a plantation crop is not well understood (Openshaw 2000; Gressel 2008; Jongschaap et al. 2007; Achten et al. 2008). It is critical to determine appropriate genetic material, management practices, agro-climatic conditions, or some combination of these factors that will lead to higher yields. One of the critical factors is the agro-climatic condition to determine suitable growing zones. However, despite the lack of documentary evidence, there is widespread belief that jatropha has wide environmental adaptability and can grow well even in marginal land. This belief may have been turned into a myth that jatropha is a wonder crop, which grows easily with minimum management requirements and inputs (Anonymous 2009).

Relying on such a claim, many government institutions and NGOs in developing countries promoted jatropha as a strategic bioenergy feedstock. Some farmers planted the crop with minimum input and labor. However, within a few years, an increasing number of reports are questioning the economic viability of jatropha (Anonymous 2009, 2010; GTZ 2009; Carlisle 2010; Ariza-Montobbio and Lele 2010; Wahl et al. 2010). There is also some evidence that jatropha requires good management like other high value crops (GTZ 2009; Divakara et al. 2010). Overall, at the current level of knowledge, technology and value chain development, not only has the expectation that jatropha can substitute for oil imports significantly turned out to be unrealistic (FAO 2010) but also significant jatropha planting (with the exception of fence) is regarded too risky, with high costs and low returns, to be promoted among subsistence smallholder farmers (GTZ 2009).

The yield of tree crops is a function of genetic material, climatic and soil conditions, age, management and competition for resources. With regard to jatropha, there are significant knowledge gaps on production practices and substantial uncertainties with regard to which of these factors affect the productivity more critically. The lack of knowledge on pre-domestication has hindered the pace of optimal genetic material development (Achten et al. 2010a; Divakara et al. 2010, Kaushik et al. 2007; Sunil et al. 2008; Tatikonda et al. 2010). It is only recently that studies are showing that management practices such as irrigation, fertilization, intercropping, pruning and spacing, significantly affect jatropha growth and biomass production under controlled conditions (Achten et al. 2010b; Behera et al. 2010; Kheira and Atta 2009; Maes et al. 2009a).

However, smallholder farmers cannot always afford to adopt optimum portfolios of technology to maximize yields unlike under controlled conditions at research stations. Due to lack of agronomic knowledge, there has been little extension service and guidance provided to farmers. Consequently, farmers have no option but to apply sub-optimal management practices. This may significantly affect yield potentials and sustainability of jatropha plantations. A report from Mozambique, in fact, suggested that the failure of jatropha production in a community is more attributed to lack of knowledge on appropriate management (e.g. pruning, pest control) than labor constraints and biophysical conditions (Bos et al. 2010).

Sustainable plantations require the removal of the uncertainty of production on the ground (FAO 2008). Therefore, there is a great scope for lessons to be learnt from smallholder farmers’ experience in order to develop agroforestry practices for jatropha to play a role in the pro-poor bioenergy development.

This paper attempts to unravel the challenges of jatropha management under smallholder farmers’ conditions, and identify gaps in research for its domestication within agroforestry systems. Towards that end, the data from an extensive baseline survey in Kenya, one of the first such attempts across the world commissioned by German Technical Cooperation (GTZ) and carried out by the authors (GTZ 2009), are analyzed. Therefore, the primary purpose of this study was to collect and analyze baseline data on jatropha in Kenya. The secondary purpose was to empirically evaluate the various claims about the productivity of jatropha as a potential biofuel crop that have been generated over the past several years.

The research question for this paper is whether the management practices by smallholder farmers, which are heterogeneous, are sub-optimal for jatropha yields. The study attempts to test the following hypotheses: (i) within portfolios of management practices adopted by Kenyan farmers such as planting materials, frequencies of irrigation, manuring, fertilization, weeding, pruning and planting time, some practices are more associated with particular stand types (i.e., monoculture, intercrop, fence), while others are independent; (ii) intensive management positively contributes to higher yields, but independent factors may still negatively affect yield potentials and offset management effects so the overall effects on yields are mixed.

Materials and methods

Description of the study area

The fieldwork was conducted across six provinces (Coast, Eastern, Central, Rift Valley, Western, and Nyanza) reflecting different agro-ecological zones of Kenya. Topographically, Kenya is one of the most variable countries in the world with dramatic variations in climate and agro-ecological conditions (Camberlin et al. 2009; Mutai and Ward 2000). There are two rainy seasons in Kenya: the long rainy season occurs in March–May, while the short rainy season occurs in September–December. The rainy seasons are separated by two dry seasons: June–September and January–February. Rainfall tends to be more localized, except during the onset phase of the long rainy season (Camberlin et al. 2009) due to interaction of the large scale regional atmospheric forces with the topography (Mutai and Ward 2000). Figure 1 shows the agro-climatic distribution of jatropha stands visited during the field survey while Table 1 (Sombroek et al. 1982) provides the definition of the different agro-climatic zones in Kenya.
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Fig. 1

Agro-ecological zones of Kenya, and the distribution of jatropha farms visited

Table 1

Definitions of agro-climatic zones in Kenya

 

Average rainfall (mm/year)

Vegetation type

Plant growth potentials

Crop failure risk (maize)

Humid

1,100–2,700

Moist forest

Very high

Extremely low (0–1 %)

Sub-humid

1,000–1,600

Moist and dry forest

High

Very low (1–5 %)

Semi-humid

800–1,400

Dry forest–woodland

High to medium

Fairly low (5–10 %)

Semi-humid/Semi-arid

600–1,100

Dry woodland–bushland

Medium

Low (10–25 %)

Semi-arid

450–900

Bushland and scrubland

Medium to low

High (25–75 %)

Arid

300–550

Scrubland

Low

Very high (75–95 %)

Very arid

150–350

Desert scrub

Very low

Extremely high (95–100 %)

Source: Sombroek et al. (1982)

Table 2 summarizes the range of biophysical conditions of the sites where jatropha was recorded in Kenya vis-à-vis the optimal growing conditions recorded in the literature including herbarium specimens and plantations (Maes et al. 2009b). While there are some references on the optimal rainfall including the lower and upper estimates (Ouwens et al. 2007; Maes et al. 2009b) few literatures are available on the optimal altitude for jatropha. However, there is some evidence that altitude influences oil content/seed and seed yields/tree with mixed results on oil yields/tree. For example, in Himachal Pradesh in India, maximum number of branches/tree, number of fruits/branch, and number of fruits/tree was recorded at higher elevations (800–1,000 m), while maximum oil content (%) on kernel weight basis was recorded in non-arable lands in lower altitude range of 400–600 m (Pant et al. 2006). The corresponding minimum values were recorded in arable lands at higher altitude range of 800–1,000 m (Pant et al. 2006). Thus the optimal altitude range is taken from Achten et al. (2008), Azam et al. (2005), Heller (1996) and Tewari (2007).
Table 2

Agronomic parameters of Jatropha curcas growing conditions from the literature and the survey in Kenya

Agronomic parameters

From the literature

Kenya (from survey)

Range

Optimal

Range

Mean

Median

Annual temperature (°C)

12.7–33.3 °C

19.3–27.2 °C

16.6–26.7 °C

23.2 °C

23 °C

Annual rainfall (mm)

440–3,121 mm

1,000–2,000 mm

497–1,976 mm

1,113 mm

1,163 mm

Altitude (m)

0–1,800 m

N/a

0–2,133 m

825 m

736 m

Soils

Well drained, sandy soils w/pH < 9a

Loamy, sandy

aSource: Heller (1996), Tewari (2007), Maes et al. (2009b)

Data collection

Data was collected in February and March 2009. Due to the previously undocumented nature of jatropha activities in Kenya, a survey with a representative sample of current activities was conducted rather than a complete census of current activities. At the beginning of the survey, a list of known jatropha activities, including names and locations of farmers, was compiled from interviews with biofuel stakeholders from the government, private sector and among NGOs. Focus group interviews were also held with local government officials, farmers, and others involved with jatropha within each district or division that was visited during the survey.

For data collection, a structured questionnaire was used. It was designed to collect the basic agronomy, management and economics of jatropha stands established by smallholder farmers. There were methodological challenges for collecting quantitative data on a number of production variables depending not on scientific measurements but on farmers’ remembrance. For example, on yield estimates, farmers were asked to report the quantity of seeds they had “harvested” during the past year prior to the survey (i.e., between February–March 2008 and February–March 2009), and in a separate question, they were asked how many kilograms of dried seeds could be obtained from each tree and acre planted. Additionally, the field enumerators randomly selected six trees on each farm, and counted the number of branches and the number of fruits per branch on each tree. This provided partial estimates of yield potentials of seeds per tree, although with obvious limitations due to the fact that different trees in different parts of the country were flowering and fruiting at different times of the year, not necessarily on the particular day of the interviews. On the other hand, for the questions regarding the input application such as irrigation and fertilizer, the questionnaire was designed to capture the quantity per tree per treatment aside from the frequency of the applications. For, it turned out to be easier for the smallholder farmers to be asked in such a manner as they relied more on labour and simple tools (bucket or tin) to apply inputs to tree by tree planted in small numbers in fairly small plots, whereas large-scale farmers would plan to apply irrigation and mineral fertilizer at plantation level. Still, it is important to acknowledge the errors and bias in the raw data due to the nature of the data collection based on farmers’ remembrance.

Questionnaires were administered by trained enumerators who also carried a global positioning system (GPS) tracking device to collect geographic coordinates of each farm. Data logs were later used to include average rainfall, temperature, and altitude information to the database. In total, 289 farmers growing jatropha were interviewed. Twenty-two of the respondents were found to have returned insufficient data, so these were removed from the database resulting in a sample size of 267.

Data were disaggregated by stand type (i.e. monoculture, intercrop or fence) and management practices. These included choice of planting materials (seeds, seedlings, or cuttings), source of those materials if known [e.g., particular locations in Coast province, Rift Valley Province, Tanzania, Uganda, etc., as well as locally sourced or not (e.g., from neighbors/wildly grown trees, or sourced by agents from elsewhere)], plot size (in hectares: 1 ha is about 2.47 acres), plant spacing (in square meters—e.g., 4 m2 if 2 m by 2 m, 6 m2 if 2 m by 3 m, and 2 m2 if ‘2 m apart’ in case of fence), size of pits (in square centimeters—e.g., 900 cm2 if 30 cm by 30 cm, and 2,025 cm2 if 45 cm by 45 cm, while 0 cm2 if a pit was not made), irrigation [application frequencies applied in the first year, frequencies and volume applied during the past 12 months before the survey, as well as timing in relation to planting (application during planting, or regular application)], fertilization and manuring (frequencies and rate applied), frequencies of pest and disease control, weeding, pruning, the year and month when a stand was established, and types of intercrops, if any.

Meteorological data for each farm visited (annual rainfall, monthly rainfall, monthly temperature, as averages for the 1950–2000 period) were obtained from Global Climate Data (http://www.worldclim.org/methods), whose layers were generated through interpolation of average monthly climate data from weather stations on a 30 arc-second resolution grid (or 1 km2 resolution). Given localized monthly rainfall patterns, which months have more than average rain in average years were estimated. Then the planting timing of each farm interviewed was correlated either with a rainy month or a dry month at particular location to derive a proxy (dummy) variable for planting timing. The planting timing is dummy coded ‘1’ if the planting month of a stand corresponds to a month with more than average rainfall (total annual rainfall divided by 12 months) followed by at least one more rainy month. The dummy variable is taken ‘0’ if the planting month of a farm corresponds to a month, which receives more or less average rain followed by a drier month.

Data processing and statistical analysis

Firstly, descriptive statistics were computed for the management practices to compare heterogeneous management components. Secondly, analysis of variance (ANOVA) was performed to examine the statistical association between particular management components and the stand types (i.e., monoculture, intercrop, and fence). Many farmers in Kenya plant jatropha in small numbers and in small plots of less than 0.4 ha (1 acre). Therefore, it could be difficult to rigorously compare quantities of input application by area, say, per hectare, in the different management type. Consequently, management components were primarily compared on a per tree basis.

Thirdly, in order to present the yield data by stand type over age classes, reported jatropha raw yield data were carefully screened to identify suspected ‘missing cases’. These included cases where some farmers answered, “I did not harvest during the past year” even though it appeared that the trees had produced some seed, which for some reason was left uncollected. This phenomenon could have resulted from the lack of local market for the few seeds produced, lack of labor available to collect them, or the fact that jatropha was planted for reasons other than the production of oilseed. Box-plot figure were used to single out sample values that appear unusually high from the rest of the sample values as extremes/outliers, which have more than one and a half box lengths from the end of the box. Based on the screened data, yield curves were derived for different management types in terms of per tree and per hectare, the latter based on the estimated number of trees per hectare calculated from the spacing and the assumption that jatropha is planted on a plot of 100 m × 100 m and in case of fence on its 400-m perimeter.

Then, a multivariate ordinary least-square (OLS) regression analysis was employed to assess the effects of management components, both those associated with particular stand types and those independent, on reported jatropha yield. The seed yield reported for the total 267 cases was used as a dependent variable. The following were used as independent variables: (a) plant age (years); (b) agro-ecological conditions [mean annual rainfall (mm); average annual temperature (°C)]; (c) choices of planting materials (seeds/seedlings dummies, controlling cuttings; locally sourced material dummy); (d) plot size (hectares)/spacing (m2)/pitting (cm2); (e) management parameters, including amount of irrigation water applied per tree (L/tree); timings of irrigation (irrigation during planting dummy and irrigation regular dummy); amounts of manure (g) or fertilizer (g) applied per tree; frequencies of pest or disease control, weeding and pruning (times per year); (f) timing of planting (planting during rainy months dummy); and (g) choice of intercrop (dummy of banana, other tree crops and vanilla, controlling other crops).

Results

Distribution of jatropha stands

Figure 2 presents the geographical distribution of jatropha farms visited. The largest proportion (54 %) of the 267 farms surveyed fell mainly in the semi-humid zone of Coast Province, where the organized out-grower project is occurring in the Shimba Hills. The second most covered region (16 % of farms) corresponded to the semi-humid zone and the semi-arid zone in Eastern Province where jatropha planting has been systematically promoted by Kenyan NGOs. About 13 % of the farms visited were located in humid/sub-humid zones of Nyanza, while 7 % fell in the Rift Valley Province whose agro-ecological conditions greatly vary from humid to semi-arid zones. A very small proportion of the farms visited were in Western (6 %) and Central (4 %) provinces which are agriculturally favorable and hence more populated with less idle land available for jatropha. The smallest number of jatropha farmers was found in the arid zone of low-altitude inland areas of Eastern and Coastal Province.
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Fig. 2

Geographical distribution of jatropha farms visited

Figure 3 shows the proportions of farmers who planted jatropha at different times of the year and average monthly rainfalls of the locations recorded by province. Planting coincided with a rainy month in some locations but with a dry month in other locations. For example, many farmers in Eastern, Nyanza and Western Provinces planted in April. In Nyanza and Western Provinces, it is usually in the middle of the long-rainy season in most sub-regions. In ordinary years, their crops may have at least critical water demand satisfied. Still, those farmers who planted in April in Eastern Province may have missed rain within a month by May, which suddenly turns dry. In contrast, June is the beginning of dry season in many sub-regions/provinces. A large proportion of farmers in Coast Province planted at this time. Still some parts of Rift Valley receive more than average monthly rain in June–August.
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Fig. 3

Distribution of planting months for jatropha against monthly rainfall values (average for 1950–2000) in Kenya

Among the 267 jatropha stands with eligible data, over 75 % were 3 years old or younger by the time of the survey. Only about 13 % of the stands were 7 years or older. Overall, plot sizes dedicated to jatropha were quite small, with those less than 0.1 ha comprising over 55 % of all stands. Figure 4a shows the distribution of plot size by age for the jatropha stands surveyed. The more recent (less than 3 years old) stands were established from seeds or seedlings, while 4 years or older stands were mostly established from cuttings (Fig. 4b). Figure 4c shows the stand types surveyed by age class. Most of the jatropha stands (less than 6 years of age) are either grown in intercrops or monoculture. By contrast, planting as a fence was the most common for stands 7 years or older. This is probably because intercrop and monoculture plantings were adopted mainly as a result of the promotion of jatropha in Kenya over the past few years, while old jatropha stands may have been planted for reasons other than the production of oilseeds, such as fencing or shade. Over 80 % of the monoculture stands and intercrops were established using seeds and seedlings. On the other hand, fences were established using cuttings and seeds (Fig. 4d).
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Fig. 4

Distribution of hectarage (i.e. plot size) planted (a), method of establishment (b), stand type by age of the jatropha (c), and planting materials used by stand type (d)

Overall, intercropping with other food or cash crops was found to be the dominant stand type with 117 stands or 44 % of the total 267 cases, while monoculture and fence were found in similar proportions. Table 3 shows the intercrops adopted by jatropha farmers in Kenya. The crops varied greatly from relatively dwarf maize, beans/peas/groundnuts, and a variety of vegetables (tomatoes, cabbages, potatoes, etc.), to banana and vanilla as well as other tree crops (coconuts, citrus, eucalyptus among others).
Table 3

Plants intercropped with Jatropha curcas by farmers in the study areas in Kenya

Intercrops

No.

%

Maize only

20

17.1

Maize + beans and/or peas

18

15.4

Beans/peas/green gram

14

12.0

Vegetablesa

11

9.4

Banana only

11

9.4

Vanilla

10

8.5

Banana + vanilla

6

5.1

Banana + other crops

6

5.1

Other trees

6

5.1

Cassava

5

4.3

Coconut

3

2.6

Maize + cassava

3

2.6

Maize + other crops

2

1.7

No answer provided

2

1.7

Total

117

100.0

aTomatoes, kale, cabbages, lettuce, chilies

The source of materials varied from local as well as different provinces in Kenya to Tanzania, Uganda and even India (Fig. 5). Some farmers in Central, Coastal, Eastern, Rift Valley and Southern Nyanza Provinces recalled that they had procured materials locally from neighbors or wild grown trees for free. Among those farmers who indicated foreign sources, several referred to Nairobi probably because they had bought seeds or seedlings from nurseries, agents or NGOs there. In contrast most of the respondents recalled that they had purchased materials from agents who had sourced them from far away localities, for example, seeds from hedges grown by Masaai farmers in Rift Valley for Nyanza farmers, or seeds collected in Arusha, Tanzania or in Kampala, Uganda for Coast farmers. Many other farmers (59 out of 267) could not recall from which locations the agents or NGOs had sourced the materials.
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Fig. 5

Planting material sources

Variation in farmer management practices

Planting materials and sources, plot-, spacing- and pit-sizes, frequencies of irrigation, fertilizer, pest–disease control, weeding and pruning applications, as well as volume of irrigation water and planting time were found statistically different across the three stand types (Table 4). Monoculture farms were mostly planted by direct seeding, while those that established fence mostly used cuttings and locally sourced materials. For spacing-size, on an average, intercrop had the largest size, followed by monoculture, while the fence had the closest size. For management practices, intercrop was found to be significantly more intensive than fence in terms of irrigation inputs as well as application frequencies of irrigation, manure or fertilizer in the first year, weeding and pest and disease control. However, the differences were not statistically significant between monoculture and fence types. In contrast, fence was found to be more associated with higher frequency of pruning than intercrop and monoculture stands. Amounts of manure and fertilizer applications were found to be not statistically differentiated among the stand types. Also, mean values of planting month and planting during rainy months were found to be independent of the stand types.
Table 4

Variation in management of Jatropha curcas by stand types

 

Monoculture

Intercrop

Fence

F-value

Planting materials

 Seeds (proportion of households planting)

0.69a

0.50b

0.39b

6.81***

 Seedlings (proportion of households planting)

0.26b

0.36a

0.15b

5.31***

 Cuttings (proportion of households planting)

0.06b

0.15b

0.46a

23.91***

 Locally sourced materials (1 = yes, 0 = no)

0.05b

0.11b

0.49a

24.60***

Plot/spacing/pitting sizes

 Plot size (ha)

0.37a

0.37a

0.14b

3.97**

 Spacing size (m2)

5.16b

7.49a

2.28c

25.27***

 Pitting size (cm2)

1257.86a

1590.24a

681.63b

12.73***

Irrigation application per tree

 Frequency in the first year (times)

12.47a,b

23.90a

2.76b

4.02**

 Frequency after the second year (times)

13.42a,b

35.67a

3.20b

5.25***

 Quantity applied during the past 12 months (L/tree)

59.88a,b

80.17a

5.15b

3.25**

 Application during the time of planting (1 = yes, 0 = no)

0.22a,b

0.40a

0.10b

12.11***

 Application regularly (1 = yes, 0 = no)

0.16a,b

0.22a

0.08b

3.53**

Manure/mineral fertilizer application per tree

 Frequency in the first year (times)

0.83a,b

1.38a

0.23b

6.03***

 Frequency after the second year (times)

0.39

0.59

0.26

1.24

 Manure applied during the past 12 months (g/tree)

822.46

1328.53

436.84

1.91

 Mineral fertilizer applied during the past 12 months (g/tree)

1.46

452.76

0.00

0.70

Frequencies of other management applications

 Pest–disease controls during the past 12 months (times)

1.13a,b

1.97a

0.13b

5.40***

 Weeding during the past 12 months (times)

2.42a,b

2.88a

1.78b

4.84***

 Pruned during the past 12 months (times)

0.54b

0.46b

1.00a

13.48***

Planting timing

 Planting month (1 = January, 2 = February)

6.71

6.73

6.97

0.15

 Planted during rainy months (1 = yes, 0 = no)

0.25

0.29

0.36

1.05

Figures followed by the same superscript letters in a row are not statistically significantly different from other subsets at 5 % level. A subset with a superscript a has the larger value, the one with b has the smaller value, and the one with c has the smallest value. Some clusters may over lap over the two subsets. Figures without any superscripts indicate that the values are not statistically significantly different among monoculture, intercrop and fence types

*** Significant at the 1 % level; ** significant at the 5 % level

Reported raw yield

The yield estimates found under Kenyan farm conditions are very low. Of the total farmers interviewed, 41 % obtained no seed yield, while 79 % obtained up to 0.1 kg seeds per tree, regardless of the age and management condition and 21 % got more than 0.1 kg/tree (Fig. 6a). The distribution of reported raw yields by the age of jatropha stand (Fig. 6b) shows that average seed yields of about 1.0 kg/tree were reported only from farms that were 7 years old or more. Figure 6c shows the estimated seed yield curves by the different management type in terms of per tree for 211 cases out of 267 after excluding missing and outlier cases. The yield curves do not present incremental growth prior to year seven or more, due to the fact that the curves were derived from the data recorded on observations by farmers at one point for each plantation rather than for the entire period of maturation. Up to 4–6 years, the average yields barely reached 0.1 kg/tree or 150 kg/ha for all the management types. Only after 7 years or more, the estimated yield of monoculture reached 0.8 kg/tree, followed by intercrop with 0.64 kg/tree, then fence with 0.38 kg/tree.
https://static-content.springer.com/image/art%3A10.1007%2Fs10457-012-9592-7/MediaObjects/10457_2012_9592_Fig6_HTML.gif
Fig. 6

The cumulative probability of yields (a), reported raw yield (kg/tree) of jatropha by age class (b), and estimated seed yield (kg/tree) of jatropha by management type and by age class for cases without missing and outlier cases (c)

Association of yield with management practices

Table 5 presents the result of a multivariate regression to determine factors affecting seed yield. Among the dependent variables, plant age, amount of manure applied per tree, frequencies of weeding, source of material, time of planting during rainy months, frequency of pruning, and amount of irrigation applied per tree, were found to have significant positive correlation with the yield. Though less significant, irrigation during planting and seeds as planting materials were also positively correlated with yield. In contrast, banana–vanilla intercropping and plot size were negatively correlated with yield. There was a very weak (marginally significant or non significant) but negative relationship between seed yield and plot size, average annual temperature and quantity of mineral fertilizer applied.
Table 5

Potential factors affecting Jatropha curcas seed yield (kg/tree)

 

Standardized coefficients

t Value

Constant

 

−0.606

Plant age (years)

0.248

3.508***

Quantity of manure applied (g/tree)

0.235

3.373***

Frequency of weeding (times)

0.208

3.036***

Locally sourced materials (dummy: 1 = yes, 0 = no)

0.217

2.930***

Planting during rainy months (dummy: 1 = yes, 0 = no)

0.194

2.842***

Frequency of pruning (times)

0.187

2.807***

Quantity of water applied (L/tree)

0.192

2.542**

Irrigation during planting time (dummy: 1 = yes, 0 = no)

0.204

1.796*

Seeds as planting material (dummy: 1 = yes, 0 = no)

0.174

1.688*

Spacing size (m2)

0.09

1.154

Seedlings as planting material (dummy: 1 = yes, 0 = no)

0.073

0.643

Annual rainfall of the location (mm)

0.015

0.188

Frequency of pest–disease control (times)

0.004

0.061

Intercropped with banana/vanilla (dummy: 1 = yes, 0 = no)

−0.153

−2.051*

Plot size (ha)

−0.131

−1.841*

Irrigation regular application (dummy :1 = yes, 0 = no)

−0.161

−1.437

Average annual temperature of the farm location (C)

−0.05

−0.640

Quantity of mineral fertilizer applied (g/tree)

−0.018

−0.292

Pitting size (cm2)

−0.02

−0.263

Adjusted R square

0.354

 

F-value

6.082***

 

*** Significant at the 1 % level, ** significant at the 5 % level, * significant at the 10 % level

Discussion

According to the results from this study, Kenyan farmers got near nil yields of jatropha seed in the first 2–3 years and the yield estimates were very low for 4–6 years (<1.0 kg/tree), while the literatures across different locations of the world exhibit a wide variation in seed yields for 1–5 year old trees (Table 6). Empirical data from East Africa and other regions support this study’s finding as indicated by the following cases. In western Tanzania, a survey of 129 contracted farmers with a German company reported low production with the average yields of 0.0002, 0.0008 and 0.23 kg/tree for the first, second and the third year respectively, against the projected yield of 4–9 kg/tree (Loos 2009). In India, a model farm established in 2005 in Gujarat State produced 0.20 kg/tree in the fourth year. In the more fertile areas of the Assam State, against the projected yield of 5.28 kg/tree from an improved variety, the obtained yield was about 0.76 kg/tree after 2 years (Orange 2009). In Tamil Nadu, India, it is reported that the highest yield in 3-year old plantations in rainfed conditions was only 450 kg/ha compared to 750 kg/ha for irrigated conditions, with significant proportions of non-yielding plots (Ariza-Montobbio and Lele 2010). The authors Ariza-Montobbio and Lele (2010) concluded that the gap between the yields reported by their sample farmers and those reported in the literature (7,500 kg/ha for irrigated plots and 2,500 kg/ha under rainfed conditions after 3–5 years under experimental conditions, as cited from Prajapati and Prajapati 2005; Paramathma et al. 2007) could not be bridged by possible bias of the farmers oral recall.
Table 6

Yields of Jatrophacurcas reported in the literature (dry seed basis)

Rainfall

Location

Age (years)

Yield (kg/tree)

Yield (kg/ha)

References

220

Cape Verde

1,775

Heller (1996)

450

India

1.25

1.7

1,750

Achten et al. (2008)

500–600

>1,000

Jongschaap et al. (2007)

600

Cape Verde

0.7–0.9

Heller (1996)

610

India, Rajasthan

2.5

0.1

318

Achten et al. (2008)

610

India, Rajasthan

2.5

0.5

1,185

Achten et al. (2008)

725

Zimbabwe

0.4

Achten et al. (2008)

800

India

2

1.5

813

Ouwens et al. (2007)

815

Burkina Faso

1.0

Heller (1996)

875

Tanzania, Arusha

3

0.5–2.0

1,708

Messemaker (2008)

1,020

Mali, Digini

2

0.3

340

Achten et al. (2008)

1,020

Mali

0.8

2,675

Heller (1996)

1,200

Nicaragua

5

4.5

5,058

Heller (1996)

1,470

Thailand

1

0.3

805

Heller (1996)

2,000

Indonesia

1

1.6–2.0

4,050–5,050

Ouwens et al. (2007)

4,000

Guatemala

1

0.8

1,263

Ouwens et al. (2007)

Some claim that jatropha can be grown as a plantation crop with low water, nutrient, and other input requirements (Azam et al. 2005; Augustus et al. 2000; Garnayak et al. 2008). Indeed, there is a reported case where an unfertilized 6-year old monoculture plantation (741 trees/ha) in South Africa yielded 1.75 kg/tree with 652 mm annual rainfall (Jongschaap et al. 2009). From this study however it is clear that productivity under smallholder conditions in Kenya is much lower at this moment. This is partly because growers are still using unimproved germplasm (or uncertified seed), management practices are sub-optimal and the biophysical boundaries of high jatropha yield are poorly defined.

The results seemed to affirm our first hypothesis that within portfolios of management practices adopted by Kenyan farmers, some practices, such as frequencies of irrigation, fertilization, weeding, pruning, as well as choice of planting materials, are more associated with particular stand types while some others such as planting timing in relation to rainy months are independent.

While the applications of irrigation and control measures by the surveyed farmers in Kenya were generally suboptimal, it is reasonable to assume that farmers who intercrop jatropha with other crops can have relatively higher incentives to apply irrigation and fertilizers more often than those who plant it as fence to maximize yields of not only jatropha but also the intercropped cash or food crops. While pests and diseases were reported to affect a majority of jatropha farms surveyed (GTZ 2009), they might be most threatening to farmers adopting intercropping. Weeding could be also perceived more important to farmers that intercropped or grow it in monoculture than those that plant it as a fence (GTZ 2009).

In contrast, jatropha planted as fence was pruned more frequently than jatropha planted either as a monoculture stand or as an intercrop. This pruning induces more lateral branches and these produce more fruits. It is an important process to increase the productivity of the plant and to reduce the gestation period (Reddy and Naole 2009). As jatropha bears flowers on terminal buds, pruning is critical to increase the number of branches and terminal shoots capable of producing fruits (Achten et al. 2008). It is most likely that those who planted it as fence may have been motivated to prune it to ensure profuse branching for fencing purpose, without any deliberate intention to maximize seed yields (GTZ 2009).

Kenyan farmers had planted jatropha at different times of the year, independent of the stand types. The timing is critical for maximum crop yields with minimum risks of crop failures, as the availability of moisture in soil greatly affects yield potentials, while the water demand depends on local soil and climatic conditions (Achten et al. 2008). Although no rigorous research has been done on the effects of planting time on jatropha yields, it is generally recommended to plant at the beginning of the rainy season when the rain is assured, especially for direct seeding (Henning 2000; Openshaw 2000), while for cuttings, some studies have indicated the best planting time is 1–2 months before the beginning of the rainy season (Henning 2000). In Kenya, however, only 25–36 % of farmers planted jatropha during rainy months (Table 4). Many farmers simply did not follow any specific planting patterns. It is possible that they may have simply planted jatropha at the least busy time of the year, while during rainy seasons they concentrated their labor and other resources primarily to the cultivation of essential food and cash crops (Iiyama and Zante 2008).

The results of the study also confirmed our second hypothesis that intensive management may positively contribute to higher yields, but independent factors may still negatively affect yield potentials and offset the former effects so the overall effects on yields are mixed.

The regression analysis showed that some conventional management practices, such as irrigation, manure, weeding and pruning applications, significantly affected jatropha seed yields in a positive way along with the age of plants as expected, while agro-ecological variables did not. The significant effects of management on yield potentials concur with the recent findings on the importance of management on biomass growth in research station experiments (Achten et al. 2010b; Behera et al. 2010; Kheira and Atta 2009; Maes et al. 2009a). On the other hand, the non-significant negative association between seed yield and quantity of mineral fertilizer applied is probably due to the small sample size (11 out of 267 farmers) that may have biased the results.

In turn, the impacts of agro-ecological variables were ambiguous. Although not significant, the association between seed yield and average annual temperature was negative. This may be related to the allocation of dry matter over vegetative and generative parts of the trees. High temperatures beyond the optimum range can depress yields and may lead to drought and this may tip the balance towards more allocation of resources for maintenance of the vegetative growth than seed production (Gour 2006; FAO 2010). This should be investigated in future.

Some of the management practices during initial establishment phases such as planting locally sourced materials/seeds, plantation time, supplementary irrigation, and choice of intercrops were found to be significantly affecting the yield potentials.

Locally sourced material and seeds as a planting material were found to be positively correlated to yield. Propagation by seeds is considered ideal for oil production as their ability to develop deep taproots and thus to have more access to nutrients and moisture from deeper soil layers is increased (Achten et al. 2008). On the other hand, improved jatropha germplasm is not yet available. So, the locally sourced materials could be better alternatives for smallholder farmers as they are more likely to be fresh and more adapted to local conditions, while the selection of planting material is recommended from cuttings or seed that have proven, over several seasons, to have high yield and seed oil content under the same irrigation and fertilization conditions that are proposed for the new plantation (FAO 2010).

With some reservations about the parameter estimates, we observe that planting during the rainy season, along with supplementary irrigation at the time of planting, positively affected yields. Conversely, even when farms were located in relatively humid agro-ecological zones, if they did not plant the crop at the beginning or during rainy seasons, jatropha might have missed the opportunity to develop fully for maximum yield. For, vegetative growth and flowering occurs during the rainy season, while there is little growth and the plant will drop its leaves during the dry season. Seeds are produced in the first or second year of growth after the rainy season (Henning 2000; FAO 2010). Although not having been well captured in the survey, timings of fertilizer, weeding and pruning applications could also affect yield potentials.

Intercropping with banana and/or vanilla and other tree crops would negatively affect yield potentials. This was observed more often in humid agro-ecological zones. Jatropha has been anecdotally considered suitable for agroforestry during its early growth (first 5 years). However, when the tree reaches maturity, wider spacing between trees is required. If the right crops are not chosen and proper spacing is not maintained, intercropped jatropha may suffer from competition for light, water and nutrients. In the intercrops, choice of crop had different effect on the jatropha (van Noordwijk et al. 2007).

In general, intercrop and monoculture farmers tended to apply irrigation, manure and weeding, which should positively contribute to yields more frequently than fence farmers. In contrast, farmers using jatropha as fence with comparatively minimum inputs implemented pruning more frequently which incidentally contributed to inducing the production of more lateral branches which produce more fruits, thus inducing more yield (Reddy and Naole 2009). In reality, the on-ground observation indicated that intercropping and monoculture stands with more intensive management did not necessarily result in sufficiently high yields despite significant costs of management incurred (GTZ 2009). The key to understanding the phenomenon may be to look at management practices during establishment phases which, if not applied properly, would have long-term and cumulative negative effects on yield potentials over later years (Achten et al. 2008).

During the initial yield estimation exercise it was found that 75 stands from Shimba Hills in Coast Province, whose locations mostly fell in the semi-humid zone, had reported yields significantly lower than the national average. This is despite the fact that many farmers had applied manure and pest–disease controls for their monoculture stands. In fact, most Shimba Hills farmers had planted jatropha seeds sourced from Arusha, Tanzania, in June 2006, when they had been initially introduced to jatropha by a contractor. This coincided with the end of the rainy season and onset of dry weather in the region yet few farmers applied supplementary irrigation during planting. Consequently, it is possible that the inappropriate planting time and use of untested foreign materials may have partly led to “crop failures” among many other unaccounted causes in Shimba Hills.

Farmers intercropping jatropha often apply higher rates of irrigation and manure to maximize food and cash crops that have markets along with jatropha. Yet, if intercropped with banana, vanilla or other tree crops without sufficient spacing, competition for light, water and nutrients as well as little branch development could be detrimental to the yield of jatropha. A farmer in Kadongo, Nyanza Province, had planted about 200 jatropha seeds, procured either from Tanzania or Uganda by an NGO, generally at close spacing of 2 m by 2 m among bananas on his 0.2-ha homestead plot in the dry season of July 2005. The farmer applied supplementary irrigation using water from his borehole and added manure as well. During the past year, he occasionally applied pest and disease control measures, weeding six times a year, and prune once. His jatropha trees grew up to 3 m yet still shorter than bananas; with an average 17 branches and yielded 0.2 kg/tree at fourth year.

The best established jatropha stands with the highest reported yields was a fence in Bungoma, Western Province but its potential value as a bioenergy crop was not known by the owner. The tree has been commonly planted by locals as fence/hedges, a custom probably adopted from neighboring Ugandan communities. The farmer had planted 10–20 seeds as windbreak along his homestead during the rainy season in May 2001. By 2009, 8 trees were surviving with development of over 50 major branches per tree. The farmer occasionally applied composts, weeding and pruning to keep good branch development. However, due to the absence of markets, the farmer never collected seeds, which were left scattered on the ground. Still it could be easily observed that a tree could yield at least a few kg/tree per year.

Therefore, intensifying management at high costs might not guarantee higher yields irrespective of the suitability of the agro-ecological conditions, unless adequate attention is paid to management components during establishment phases. In other words, missing the optimal planting time without supplementary irrigation as well as choosing low quality planting materials and competing intercrops would impair the yield potential of jatropha. This could offset positive effects of subsequent intensive applications of irrigation, manure, and weeding, as well as favorable agro-ecological conditions. Meanwhile sub-optimal planting time and intercrop choices could be attributed to not only external factors but also to preferences and constraints of farm households who have to decide how to allocate their limited labor and other resources to jatropha and other more priority tasks such as cultivation of food and cash crops. For the choice of intercrops, though we could not look into detail, some intercrops may work better than others for smallholder farmers if proper knowledge and extension service are in place, depending on biophysical and socio-economic conditions. This, however, requires intensive research to verify while models need to be developed to examine the competitions for water, light, and nutrients.

Conclusions

This study affirms that management practices by Kenyan smallholder farmers, whose portfolios were heterogeneous, were sub-optimal in view of jatropha yields. Contrary to the belief that jatropha could be easily manageable without much input requirements; it requires fertilizer, water, and good management. It was also observed that missing optimal planting time as well as failure to choose proper intercrops during establishment phases could impair yield potential of jatropha, irrespective of how suitable the agro-ecological and biophysical conditions are and how much inputs farmers might apply. Without proven quality germplasm and appropriate storage, using materials sourced from elsewhere might also add risks of low germination rates and growth potentials. Proper management during the establishment phase could be very critical for survival and might have cumulative effects on growth and potential yields.

When the jatropha boom started some years back, there was a complete lack of knowledge about what factors mostly influenced yields. Currently, there is still little accumulation of knowledge on how to isolate various factors over years to discern the relevant significance of each factor on yield. Thus it is encouraging to see scientists endeavor to accelerate research in developing breeding systems as well as in deducing optimal growing conditions.

However, smallholder farmers are not always privileged to adopt optimum portfolios of technology to maximize yields unlike at research stations. Whenever adopting a newly introduced crop, smallholder farmers should be provided with simple and affordable protocols especially, at establishment phases. Thus, at current stage, jatropha should not be grown by smallholder farmers in Kenya because of low or dismal productivity.

For future research, trials of new germplasm under different management in new areas outside where the jatropha is currently grown are required among others. Especially, domestication strategies and protocols for successful agroforestry need to be tailored for critical needs of smallholder farmers, taking into consideration factors such as their preferences as well as budgetary and time constraints. These factors are rarely accounted for under experimental conditions. Therefore, there is still a great scope for lessons to be learned from empirical truth-revealing studies on experiences of smallholder farmers.

Acknowledgments

This study was commissioned by GTZ as part of the Kenya oilseed baseline study, and we sincerely appreciate the financial and technical support provided for its implementation. We are also grateful to EU, the Japanese and Norwegian Governments for the additional financial support. We thank colleagues at World Agroforestry Centre (ICRAF), many farmers, District Agricultural Officers, Forestry Officers, NGOs and CBO staffs who supported us in one way or another, along with the local enumerators and coordinators involved in the fieldworks. Special thanks go to Dr. Joseph Ogutu (formerly of ILRI) for the data analysis and the estimation of yield. We also greatly benefitted from interactions with Kenyan bioenergy stakeholders, including Ministry of Energy, as well as members of Bioenergy in Africa (BIA) consortium, and some individuals. The two anonymous reviewers provided valuable comments for the manuscript. Finally, the authors are solely responsible for the contents of the study.

Copyright information

© Springer Science+Business Media Dordrecht 2012