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African Crop Science Journal
African Crop Science Society
ISSN: 1021-9730 EISSN: 2072-6589
Vol. 7, Num. 4, 1999, pp. 549-558
African Crop Science Journal, Vol. 7. No. 4, 1999

African Crop Science Journal, Vol. 7. No. 4,  pp. 549-558, 1999                                                          

Farmers’ perceptions and adoption of soil management technologies in western Kenya

M. Makokha, H. Odera,  H.K. Maritim, J.R. Okalebo and D. M. Iruria
Moi University, Faculty of Agriculture, P.O. Box 1125, Eldoret, Kenya

Code Number: CS99046


A study was conducted to test two hypotheses: that farming conditions significantly influence farmers’ perceptions of new agricultural technologies and probability of adoption, and that  farmers’ perceptions of technology-specific attributes associated with use of new technologies significantly influence adoption decisions. A tobit model analysis of a  random sample of sixty farmers revealed farmers’ participation in agricultural field days and on-farm trials to be significant at 0.05 level in explaining adoption decisions. Other farm  variables that were significant in explaining adoption were farmers’ participation in agricultural seminars and workshops (P< 0.01), contact with extension (P<0.05 level) and decision to reduce use level for inorganic fertilisers (P< 0.01). The social status of the farmers was not significant in explaining adoption behaviour. Among the technology-specific attributes, reliability in supply and availability of technologies was significant at 0.05 level in explaining adoption. The impacts of technologies on plants’ growth vigour and yield were significant at  (P< 0.05). Convenience in use of technologies and labour requirements was not significant in explaining adoption decisions. The results indicate that early technology adopters are likely to be those who participate in local activities that introduce and explain new approaches to soil fertility management.

Key Words:  Agricultural technologies, East African Highlands, nutrient replenishment, organic resources, tobit model


Une étude a été conduite pour tester deux hypothèses: que les conditions de culture influencent significativement les perceptions des agriculteurs pour les nouvelles tecnologies agricoles et la probabité d’adoption et que les perceptions des agriculteurs pour les caractéristiques de technologies spécifiques associées à l’utilisation des nouvelles technologies influencent significativement les décisions d’adoption. Une analyse du modèle tobit d’un échantillon aléatoire de 60 agriculteurs a révélé que leur participation dans les journées champêtres et dans les essais en milieu réel êtait significative à un niveau de 5% dans l’explication des décisions d’adoption. Les autres variables liés à la ferme qui étaient significatifs dans l’explication d’adoption étaient: la participation des agriculteurs dans les seminaires et ateliers (P<0.01), contact avec vulgarisateurs 5%, et la décision de réduire le niveau d’utilisation des engrais  inorganiques (P<0.001).  La situation sociale des agriculteurs n’a pas été significative dans l’explication du mode d’adoption.  Parmi les caractéristiques de technologies spécifiques, la fiabilité dans l’approvisionnement et la disponibilité des technologies étaient significatives à 5% dans l’explication d’adoption.  Les impacts de technologies sur la vigueur de la croissance de la plante et du rendement étaient significatifs à 5%.  La commodité d’utilisation des technologies et des exigences de la main d’oeuvre n’étaient pas significatives dans l’explication de la décision d’adoption.  Les résultats indiquent que les adopteurs en premier des technologies sont ceux qui participent dans les activités locales qui introduisent et expliquent les nouvelles approaches de la gestion de la fertilité du sol.

Mots Clés: Technologies agricoles, Hautes terres de l’Afrique de l’Est, restauration des nutritifs, resources organiques, modèle tobite


A number of studies have been conducted on factors influencing perceptions and adoption of new technologies both within and outside the sphere of agricultural production. Current literature on adoption studies reveal three major approaches to farm technology dissemination.

The innovation-diffusion model. This model considers access to information about an innovation to be the key factor in determining adoption decisions. This school of thought assumes that appropriateness of innovation is as given and thus reduces the problem of technology adoption to communication of information on a given technology to potential end-users.  The model places emphasis on extension contact, use of mass media  and opinion leaders  as a means of influencing adoption for new technologies. Classical examples of works in this line of thought include Rogers (1962), Agrarwal (1983), and Benor et al. (1984).

Variables which affect farmers’ access to information as determinants of adoption for new technologies have also been widely used in economic models of adoption decisions (Feder  et al., 1985; Kebede et al., 1990; Shakya and Flinn, 1991; Polson and Spencer, 1991; and Stauss et al., 1991). However, this model has variously been criticised as being “top-down” in orientation and thus lacking consideration for farm variables in its “packaging” (Roling, 1988). The model has also been associated with various problems in its implementation, particularly concerning  choice of contact farmers (Moore, 1984), poor research extension linkages  (Chapman, 1988), and weak linkages with farmers at field level (Dejene, 1989). However, quantitative assessment of impacts of the model has revealed improvement in technology adoption and farmers’ knowledge  (Feder et al., 1985; Feder et al., 1986;  Feder and Slade, 1986; Hussain et al., 1994).

Economic constraint model. This model considers economic constraints to distribution patterns of resource endowments as the major contributor to observed adoption decisions (Aikens et al., 1975). Lack of access to land and capital has been demonstrated as being significant   constraints to adoption decisions (Havens and Flinn, 1976; Yapa and Mayfields, 1978). Qualitative effects of factors such as farm size, liquidity  and risk attitudes on decisions to adopt new technologies have been examined (Just and Zilberman, 1983; Harper et al., 1990;  Pitt and Sumodiningrat, 1991). Just and Zilberman (1983) showed the impact of risk attitudes and farm size on adoption.  Theoretical work  has shown that farm size affects adoption decisions through the availability of some “threshold” hectarage where innovations occur. The study demonstrated that given a series of technological components, adoption pattern is a function of profitability, riskiness, initial capital requirement, complexity in use, and availability of each component. Green and Ng’ong’ola (1993) documented factors affecting fertiliser adoption in less developed countries and demonstrated the quantitative impact of policy changes on fertiliser use among  subsistence farmers.

The adopter perception model. This model contends that farmers’ subjective perceptions of new technologies in light of prevailing socio-economic environment  conditions their adoption behaviour. From the seminal works by Kivlin and Fliegel (1966a, b, 1967), the concept of “adopter perception” can now be found in varied agricultural economics literature (Norris and Batie, 1987; Nowak, 1987;  Lynne et al., 1988;  Gould et al., 1989;  Adesina and Zinah, 1993; Adesina and Baidu-Forson,1995). Quantitative studies that have considered farmers’ perceptions in context of adoption decisions have included farmers’ perceptions of a new technology. Farmers are considered to have subjective preferences for specific characteristics inherent in new technologies or innovations ( Ashby and Sperling, 1992). These preferences are assumed to play a significant role in technology adoption. Adesina and Baidu-Forson (1995) contend that adoption of technologies by farmers reflects rational decision-making based upon farmer perceptions of appropriateness of the characteristics of the technologies under investigation.

In this paper, the  adopter perception model is employed in analysis of farmer perceptions and adoption characteristics among subsistence farmers in western Kenya. The paper examines variables that influence perceptions of non-conventional soil nutrient replenishment technologies and their adoption decisions. The paper also looks at technology-specific attributes inherent in such technologies that condition the farmers’ subjective preferences. 

Materials and Methods

The setting. The study was conducted in two administrative districts within the western highlands of Kenya, namely,  Siaya and Bungoma districts.  The districts are characterised by high population density, as high as 1200 persons per square kilometer (Hoekstra and Corbett, 1995), small farm holdings with average farm size of 2.5 ha per farm-farmily; and  low  maize  yields  ranging  between 0.4 t ha-1 - 0.8 t ha-1 compared to estimated potential of 4 t ha-1.  Mean monthly per capita income  is less than the national average  with high disperity between the estimated requirement and actual purchases for inorganic fertilisers  and a high proportion of farmers who have either reduced or abandoned fertiliser use.  A low extension staff to farmer ratio  exists (1:1280)   with  food deficits of up to 64% (Omare, 1998) and low achievement of desired extension goals (32.6%). Average net nutrient losses of nitrogen and phosphorus are 14 and 3 kg ha-1 yr-1, respectively, with overall widespread negative soil nutrient balances (Woomer  et al., 1997).

Due to limited use of conventional inorganic fertilisers in the survey area, combination of organic inputs with relatively inexpensive phosphate rock (PR) is being advocated (Reijntjes et al.,1992). Non-Governmental Organisations (NGO’s) such as International Centre  for Research in Agroforestry (ICRAF) and CARE Kenya; National Research organisations, namely the  Kenya Agricultural Research Institute (KARI) and Kenya Forestry Research Institute (KEFRI); local NGO’s such as Sustainable Community Development Programme (SCODP), Sustainable Agricultural Centre for Research and Development   (SACRED-Africa), and the Organic Matter Management Network (OMMN);  are all promoting use of non-conventional organic resources as supplements to mineral fertilisers in an effort to replenish the nutrient depleted soils of western Kenya (Woomer et al., 1998).

On-farm-trials conducted in the survey area using some of the non-conventional soil nutrient replenishment technologies have shown promise.  Phosphate rock (PR) at the rate of 100 kg P ha-1 resulted in an incremental yield response of 750 kg of maize.  ICRAF using Tithonia diversifolia at a rate of 5 t ha-1 dry matter realised 1183 kg of maize and an improved fallow agroforestry system resulted in 2228 kg of maize  ha-1 (Niang’ et al., 1996).

The Phosphorus Resource Evaluation Project (PREP) is experimenting with a technology that was specifically developed to amelioriate partches of phosphorus deficiency, a phenomenon that characterises most of the farms in the region. The technology (PREP-PAC) consists of 2 kg of Minjingu phosphate rock together with rhizobium innoculant, legume seed, and urea fertiliser, which is recommended for use on 25 m2 phosphorus deficient patches (Woomer et al., 1998; Nekesa et al., 1999). Through a related study, Moi university is also running on-farm trials that aim at determining farmers’ preferences for phosphate rock fortified compost as a mode of  applying PR. The farmers’ subjective preferences are being viewed from the perspective of convenience in use, impact on crops’ growth vigour, and overall yield levels associated with various technological forms. Preliminary results from this study reveal that technology-specific attributes associated with the mode of use of phosphate rock condition adoption decisions.

Conceptual model.   Following Adesinah and Zinnah (1993), farmers’ adoption decisions regarding any new technology are  assumed to be based upon utility maximisation. For the purpose of this study, we define the use of alternative soil nutrient replenishment technologies by j, where j = 1 for a farmer’s favourable decision, based on subjective perceptions regarding such technologies, and j = 2 for a  farmer’s favourable decision, based on subjective perceptions regarding use of other conventional inorganic fertiliser sources. The non-observable underlying utility function, which ranks the preference of the ith farmer is given by U(MJI, AJI). Thus, the utility derivable from the form of soil nutrient  replenishment strategy depends on M which is a vector of farmer-specific attributes of the adopter and, A which is a vector of technology-specific attributes. Although the utility function is unobserved, the relation between the utility derivable from a jth technology is postulated to be a function of the vector of observed farmer-specific characteristics such as social standing  in society, participation in field-days, agriculture training workshops, and on-farm trials and contact with extension agents. Technology-specific characteristics include the impact of the technology on yield, availability of the technology on the farmer’s  farm or in the immediate neighbourhood, convenience in use, labour requirement and impact of the technologies on crop growth vigour in early  stages of plant growth. Expressed mathematically, this is represented as:

Uji = ejFi(Mi, Ai) + eji         j = 1,2,; i = 1,....,n  [Equation 1]

Equation 1 does not restrict the function F to be linear. As the utilities Uji are random, the ith farmer will select the alternative j = 1 if U1i>U2i or if the non-observable (latent) random variable y*  = U1i - U2i  > 0. The probability that Yi  equals one (i.e., that the farmer adopts use of alternative soil nutrient strategy) is a function of the independent variables, expressed mathematically as follows: 

Pi = Pr (Yi = 1) = Pr(U1i>U2i)

    = Pr[e1Fi(Mi, Ai) + e1i >e2Fi(Mi, Ai) + e2i ]

    = Pr[ e1i - e2i  > Fi(Mi, Ai) (e2 -  e1 ) ]

    = Pr(ei> - Fi (Mi, Ai)e)

    = Fi(Xie)                                             [Equation 2]                                                                                                          

where X is the n x k matrix of the explanatory variables, and e is a k x 1 vector of parameters to be estimated, Pr(e) is a probability function, ei  is a random error term, and Fi(Xie) is the cumulative distribution function for ei evaluated at Xie. The probability that a farmer will adopt the use of alternative soil nutrient replenishment techno-logies is a function of the vector of explanatory variables and of unknown parameters and error term. For all practical purposes, equation 2 cannot be estimated directly without knowing the form of F. It is the distribution of ei that determines the distribution of F. If ei is normal, F will have a cumulative normal distribution.

Following equation (2) the functional form of F is specified with a tobit model, where ei is an independent, normally distributed error term with mean zero and constant variance e2: It can be expressed mathematically as:-

Yi = Xie if  i*  = Xie + ei >T

     =0     if   i*  = Xie + eieT                             [Equation 3]                                

Where Yi is the probability of adopting the alternative soil nutrient strategy; i* is a non-observable latent variable, and T is a non-observable threshold level. Our problem is to estimate e and e2 on the basis of the N observations on Yi and Xi.  If the non-observable latent variable i* is greater than T, the observed qualitative variable yi that indexes adoption becomes a continuous function of the explanatory variables, and 0 otherwise (i.e., no adoption).

The empirical model. The data requirements for this study were obtained from both primary and secondary data sources. The primary data were collected in Bungoma and Siaya districts of western Kenya in 1997  during a 2-month village-level survey which involved 60 small-scale farmers.

The estimated empirical model derived from equations 1 to 3 was developed using farm  variables  and farmers’ subjective preferences for technology-specific attributes associated with the use of alternative soil management  technologies. The dependent variable was the proportion of all available soil nutrient replenishment technologies;  use of conventional inorganic fertilisers, use of compost manure, use of improved fallow agro forestry system, use of green manure (mainly Tithonia diversifolia), and use of rock phosphate.  The definitions, measurements and sample characteristics of the variables are shown in Table 1.

The technology-specific attribute  A in equation  1  was specified in the empirical model to include the following variables: YIELD, SUPPLY, VIGOUR, and CONVENIENCE, while the farm specific factors attribute  M  in the same equation was specified in the model to include; POSITION, WORKSHOP, EXTENSION, ON-FARM, and USE-LEVEL.

Table 1. Definition of variables in the empirical model


The proportion of total soil nutrient replenishment technologies that is constituted by alternative soil nutrient replenishment technologies.


Independent Variables



Contact with extension agents, measured as a binary variable, {1} if the farmer has been in contact with any extension agent and this influenced his/her adoption decisions for alternative soil nutrient replenishment technologies, {0} otherwise.



Participation in on-farm trials, measured as a binary variable, {1} if farmer had participated in on-farm trials, and whatever he/she saw there influenced his/her adoption decisions for alternative soil nutrients replenishment technologies, {0}  otherwise.



Leadership position of the farmer and its influence on adoption decision, measured as a binary variable, {1} if a farmer holding a leadership position has adopted use of alternative soil nutrients replenishment strategies, {0} otherwise.



Participation in agriculture oriented training workshops, {1} if farmer attends such workshops, {0} otherwise.



The impact of alternative soil nutrient replenishment technologies on growth vigour of the crop in early stages of growth.  This was measured as a binary variable; {1} if the farmers’ perception of the impact of alternative technologies influenced his/her adoption decisions, {0} otherwise.



Labour requirements and convenience in use of alternative soil nutrient replenishment technologies and its impact on adoption decisions, measured as a binary variable, {1} if the farmers’ perception of labour requirement and convenience in use of alternative soil nutrients replenishment technologies had an influence on his adoption decisions, {0} otherwise



Impact of a given soil nutrient replenishment strategy on yield, measured as a binary variable, {1} if farmers’ perception of impact of alternative soil nutrient replenishment strategy on yield influenced his/her adoption decision, {0} otherwise.




adoption decisions, {0} otherwise.



Change in the use level of conventional inorganic fertilisers following fertiliser market liberalisation, for those farmers using such inputs, measured as a binary input, {1} if the farmer who reduced his/ her use of such inputs  use alternative soil nutrient technologies, {0} otherwise.

Results and Discussion

Before presenting results of the estimated models,  some background information on the area of study about it is worth noting.  A majority of the farmers in the survey area apply organic nutrient inputs to soil (91.7%). Farmyard manure was the most preferred organic resource having been adopted by 72% of farmers. This was closely followed by green manure in the form of Tithonia diversifolia (52.7%), improved fallow agroforestry systems (47%), and phosphate rock (9.1%). Although a high number of farmers appear to have embraced the use of alternative soil nutrient sources, the application rates for these technologies in the study area fall below what is recommended. The survey results show that only 52% of the farmers using Tithonia apply 1 ton and above of dry matter of the input per ha as opposed to the recommended rate of 5 t ha-1. The remaining 48% apply less than 1 t ha-1 . For those farmers using farmyard manure, 44% apply 1.25 t ha-1 and above opposed to the recommended 5 t  ha-1. The remaining 56% apply less than 1.25 t ha-1 of the input per ha. In the case of Minjingu phosphate rock, all the farmers using the technology reported an application rate of 0.125 t ha-1 (= 16 kg P ha-1). This is half the suggested rate of 0.25 t ha-1 for soil phosphorus recapitalisation with Minjingu phosphate rock  (Woomer et al., 1997).

Despite the low extension to farmer ratio in the study area, the majority of the farmers have access to extension services (80%), an indication of active frontline extension staff. About 71% of the survey farmers reported that the information they receive from extension agents contains messages on soil nutrient management.  Also, 20.7% of the survey farmers attributed their decision to adopt technologies to extension information. Of those farmers receiving agricultural extension,  21.7% have contact with extension agents more than five times per year, 33.3% four times per year, 28.3% three times per year, while the remainder have contact with extension agents less than three times per year.

A majority of the farmers who participated in this survey  (90%) participate in activities such as agricultural field days and conduct on-farm trials. About 88.5% of such farmers reported that what they see during field days or on-farm trials actually influences their adoption decisions. About 80% of those who attend field days adopt what they see on such occasions, 14.8% tried on smallscale, while 5.8% only considered adopting.

From the farmers’ perspective, most of the organic resources in the study area are readily accessible. For instance, 62.5% of farmers using farmyard manure reported a reliable source. A majority (86%) of those using Tithonia diversiflia as green manure likewise reported sufficient access. All the farmers employing improved fallow agroforestry reported favourable supply of seed. However, only 20% of the farmers using phosphate rock reported a  reliable supply source.

Food security is the major determining factor in farmers’ decisions on whether to invest in nutrient  inputs or not. A majority of the farmers (78%) use nutrient  inputs with the sole aim of improving yields and 31% of these farmers reported an improvement in yield with alternative nutrient replenishment technologies.  Food security rather than profitability was the main factor for decisions regarding investment in farm inputs.

Farm characteristics and adoption decisions. A tobit analysis revealed several farm variables to be positively related to adoption decisions (Table 2). The farmers’ participation in field-days and on-farm trials was significant(P< 0.05) in explaining the farmers’ subjective perceptions of the technologies being demonstrated and the subsequent adoption decisions.

Table 2. Estimated tobit model results for adoption using only farm and farmer related variables (Maximum Likelihood Estimates)

Independent Variables 




Exp (B)b



























Log-likelihood function =-14.802
**, Significant at 1%
*,   Significant at 5%.
bExp (B) = product of equation 3

Conducting on-farm trials and participation in field demonstrations using alternative technologies exposes  farmers to the benefits associated with the use of such technologies. This definitely has a positive influence on the way such farmers perceive the new technologies and hence its significance in influencing their adoption behavior. These results agree with what has been reported by Hussain et al.  (1994) in Pakistan. Dorfman (1996) working in the USA found that a greater number of hours worked off-farm by the farmer lowers the probability of adoption of new technologies.  The results of Dorfman (1996) agree with the findings of our study, which showed that farmers who devote maximum attention to farming through  field-days and involvement in on-farm trials are more likely to be influenced by new technologies. The results differ with the findings reported from Sierra Leone where none of the farm characteristics had any significant influence on the farmers’ perceptions and adoption of new rice varieties (Adesina and Zinnah, 1993).

Farmers’ attendance in workshops and  seminars where new technologies are discussed was significant (P<0.01)  in explaining the farmers’ perceptions of technologies and subsequent adoption behaviour.  Despite the importance of such activities,  participation by farmers was  low as 12.2% had attended only once, 39% twice, 24.4% thrice and 24.4% more than three times. This shows that either such workshops are seldom organised or if organised on a regular basis, participation of farmers is not very high. However, these results concur with the findings of Adesina and Baidu-Forson (1995) in Burkina Faso and Guinea, where they found out that the number of times a farmer participated in training activities influenced their perceptions and adoption of a new sorghum variety. The implication of these results is that involvement by a  majority of the farmers in workshops will have a positive influence on farmers’ perceptions and adoption of alternative soil nutrient technologies.

The results of tobit regression show contact with extension workers to be statistically significant in explaining the farmer’s perception of new technologies and their subsequent adoption behaviour. These results can be explained from innovation diffusion theory, which states that contact with extension agents have a positive effect on adoption (Voh, 1982; Kabede et al., 1990; Polson and Spencer, 1991).

However, the notion that contact with extension agents per se will influence farmers’ perceptions of technologies seems to be misplaced  (Hussain et al., 1994). It is the quality of information being conveyed by extension agents and the intensity of extension effort that are key variables in influencing adoption decisions.

Leadership position in the society was found not to be significant in influencing farmers’ perceptions and adoption decisions. These results deviate from expectations of innovation diffusion theory (Voh, 1982; Kabede et al., 1990; Polson and Spencer, 1991). However, these findings agree with those of Adesina and Baidu-Forson  (1995) in West Africa. From an economic and social perspective, these results can be explained by the fact that organic inputs are considered second best alternatives.  Those in leadership positions also tend to be financially secure and would rather continue using mineral fertilisers instead of bulky organic inputs. Farmers  in leadership positions  have other commitments, and as such, do not devote maximum attention to farming. This may have an influence on their perceptions of new technologies and hence adoption decisions (Dorfmann,1996).

Farmers’ decision to reduce application rate  of mineral  fertilisers was significant in explaining their perceptions and adoption of alternative technologies. The results can be explained by factor substitution theory, as the farmers consider the two-soil fertility enhancing inputs to be substitutes. The farmer will substitute the less expensive organic resource for  inorganic sources as a result of change in the relative price of the latter. 

Farmers’ perceptions of technologies  and their influence on adoption. This aspect of the study set out to determine the influence of technology-specific attributes on farmers’ adoption decisions. A tobit analysis revealed all the  attributes under study to be positively related to the probability of adoption. Reliability in supply and availability of alternative resources significantly explained  farmers’ adoption decisions (Table 3). These results agree with the findings of Shakya and Flinn (1985) in eastern Tarai Province of Nepal, Kabede et al. (1990) in Ethiopia and Adesina and Zinnah (1993) working in West Africa.

Table 3. Estimated tobit model results for farmer adoption using only technology-specific attributes, Western Kenya (Maximum Likelihood Estimates)

Independent variables           




Exp (B)b

















Log-likelihood function = -15.46654
*, Significant at 5%.
bExp (B) = product of equation 3

The results imply that for adoption of alternative technologies to be enhanced, the supply of such technologies should be made more reliable. Strategies to ensure achievement of such an objective include planting of Tithonia diversifolia on farm hedges to supplement supplies from public areas, bulking planting seed for  agroforestry species used in improved fallow systems and appointment of stockists in strategic places for retailing of Minjingu rock phosphate.

Influence of technology on crop yields was significant in explaining farmers’ perceptions of soil nutrient technologies. Agriculture in the area of study is both a source of food and income, and as such the higher the yield the better the food security situation for the farmer, and the higher the chances of having surplus for sale to earn income. A rational farmer is likely to adopt technologies, which promise economic returns through improved yields.

Farmers’ perceptions of convenience associated with the use of alternative technologies, and labour requirements for the use of the same technologies were not significant in explaining farmers’ adoption behaviour. This outcome is partly derived from the high population density resulting in  surplus labour. This implies that doubts concerning  acceptability of rock phosphate in its dusty form because of inconvenience involved in its use should be discarded.    

Vigour in plant growth was statistically significant in explaining the farmers’ perceptions of technology  adoption decisions. This outcome can be explained from both physiological and psychological point of view. From a physiological point of view, a healthy crop can survive stress conditions such as pest and disease attack, and drought better than a crop with poor vigour. From a psychological perspective, farmers derive pleasure from successful husbandry practices. The implication  is that the specific technologies that have proved effective in supporting healthy growth should always be included in the “cocktail” of technologies that are recommended to farmers.


The study has demonstrated that farm  variables significantly influence the farmers’ perceptions of new technologies and hence their adoption decisions. The findings of the study have docu-mented that technology-specific attributes inherent in new technologies condition the farmers’ subjective preferences for such technologies and thus, probability of adoption.

The results of this study indicate that though some of the farmers in the region may have adopted the technologies, the use level for such technologies is still sub-optimal relative to the recommended rates. This has resulted in constrained impact of the technologies on the welfare of the target group. Finally, the current study only investigated factors that influence adoption at a single point in time. The study also  did not go further to investigate the influence of the identified variables on intensity of adoption. We recommend that future studies should consider the influence of identified variables on adoption over time.


We acknowledge the financial support from Rockefeller Foundation Forum on Agricultural Resource Husbandry. We also acknowledge financial support from Farm Level Applied Research Management for East and Southern Africa (FARMESA). The study received technical support from scientists and farmers working with the ICRAF field station at Maseno - Kenya.


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  ©1999, African Crop Science Society

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