Livestock Research for Rural Development 18 (3) 2006 Guidelines to authors LRRD News

Citation of this paper

Determinants of adoption of poultry technology: a double-hurdle approach

H Teklewold, L Dadi, A Yami* and N Dana

Debre Zeit Agricultural Research Center, PO Box 32, Debre Zeit, Ethiopia
alemuyami@yahoo.com

Abstract

The objective of this study was to determine factors influencing the rate and intensity of adoption of poultry technology, assuming the two decisions process were separate. The double-hurdle class of model has been applied in this paper with this important distinction in mind. The model was fitted to a sample of 200 smallholder farmers from east Shewa and Welayeta zones in Ethiopia.

Results indicate that 41.5% of the study farmers reported adoption of exotic poultry with a mean proportion of 0.54. As expected, there were different sets of factors behind the decision to adopt and the decision about to which extent to do so. Farmers' decision on adoption of poultry technology was positively affected by sex of the household head, family size, availability of supplementary feed, credit and extension service and extent of expected benefit from poultry, and negatively affected by market problem. On the other hand, farmers' decision on the extent of adoption of exotic poultry breed was positively influenced by age of the household head, experience in adoption of poultry technology, expected benefit from poultry and negatively influenced by market problem.

Keywords: adoption, double-hurdle, Ethiopia, poultry technology, rate and intensity


Introduction

Poultry, in one form or another, is kept in most areas of the world. In Ethiopia, rural poultry represents a significant part of the rural economy in particular and of the national economy as a whole. Besides the provision of employment and easily disposable income for small-scale farmers, particularly in the off-season from cropping, rural poultry integrates very well into other farming activities as it requires relatively little time and investment. However, rural poultry is considered invisible as it is rarely counted in wealth ranking as cattle, sheep and goats are. There are about 56.5 million poultry of all species in Ethiopia. Local chicken constitutes about 99% of the total poultry population in the small-scale rural farms (Alemu and Tadelle 1997). Poultry are important providers of eggs and meet as well as being valued in religious and cultural life. The total poultry egg and meat production in Ethiopia is estimated to be about 78,000 and 72,300 metric tonnes, respectively. Per capita consumption of these products is also very low relative to the world and African standards.

The predominant production systems for rural poultry in Ethiopia can be characterized by low input, scavenging and traditional management system consisting of local breeds. Birds under the traditional system receive some supplementation based on available grains, by-products and food scraps. Rural poultry suffers serious losses from predators and diseases. While the importance of poultry disease is well recognized, conventional treatment techniques are expensive to use and do not provide adequate cover and protection for rural birds. The indigenous birds are small in body size and low producers of meat and egg (EARO 2000). For example, the productivity of scavenging hens is 40-60 small-sized eggs/bird/year (Tadelle 1996; Alemu and Tadelle 1997). The modern poultry production system is very small in size and confined to urban and peri-urban areas and contributes less than 2% of eggs and meat production in the country.

In Ethiopia, poultry research and extension (distribution of exotic breeds) started in the late 1950s. Since then higher learning institutions, research organizations, the Ministry of Agriculture and Non-Governmental Organisations (NGOs) have distributed many exotic breeds of chicken to farmers and urban-based small-scale poultry producers. For example, about 1 million exotic breeds have been distributed to different parts of the country by Debre Zeit Agricultural Research (DZARC) alone in the forms of cockerels, pullets and chicks or in the form of fertile eggs (DZARC poultry research division, personal communication). Alemu and Tadelle (1997) indicated that, previously, the extension system had promoted schemes in which cockerels from selected strains were reared up to 15 to 20 weeks of age and then exchanged for local cockerels owned by rural subsistence farmers. More recently, the regional agricultural bureaux included poultry technology in their new extension program. The poultry extension program promotes mainly exotic breeds proved to perform better than local breeds in terms of meat and eggs production. The scheme involves distributing of five pullets and a cockerel to individual farmers with limited extension follow up such as technical advice on improved poultry feeding, watering, and housing and disease control.

Although the poultry sector holds an important position for economic development and food security in Ethiopia, systematic studies have not been conducted to assess the rate and intensity of adoption of exotic poultry breeds and farmers' response to improved poultry technologies as a whole. Studies on factors influencing farmers' decision to invest on poultry production technologies are non-existent. Information regarding use of exotic poultry breeds and associated improved management practices (feeding, housing, health etc.) is very limited. Adoption behaviour may be depicted by more than one variable. It may be depicted by a discrete choice, whether or not to utilize an innovation, or by a continuous variable, that indicates to what extent an innovation is used. The purpose of this study is, therefore, to find out whether it is important to separately analyse the decision whether to reveal potential adopters of improved poultry breed and, if so, how strongly. Such studies are important and lead to improved understanding of the factors influencing the rate and intensity of adoption, helping institutions involved in poultry technology development and transfer to ensure their efficiency and effectiveness in attaining their objectives. This type of research should also help rural development planners in setting priorities for investment resource allocation and the formulation of rural development programs aimed at increasing farmers' income.

The objective of this paper is to examine the rate and extent of adoption of exotic poultry breeds; and to identify and quantify factors that influence adoption of poultry production technologies in rural small-scale poultry production systems. This study makes a distinction between factors affecting the decision to adopt and the decision how much to adopt exotic poultry breeds. The rate of adoption in this study refers to the percentage of farmers who have adopted exotic poultry breeds. The intensity of adoption is defined as proportion of exotic poultry breeds that a given household possess. The paper is organized as follows. The next section discusses theoretical model and empirical specification. Section 3 outlines the source of data and empirical results. Finally, concluding remarks are offered in the last section.


Material and methods

Theoretical model and empirical specifications

Farmers are assumed to maximize expected utility according to a von Neuman Morgenstern utility function defined over wealth (W). When confronted with a choice between two alternative practices, the ith farmer compares the expected utility with the modern technology, EUmi(W) to the expected utility with the traditional technology, EUti(W). While direct measurement of farmers' perceptions and risk attitudes on farming technology are not available, inferences can be made for variables that influence the distribution and expected utility evaluation of the technology. These variables are used as a vector 'X' of attributes of the choices made by farmer 'i' and εi is a random disturbance that arises from unobserved variation in preferences, attributes of the alternatives, and errors in optimisation. Given the usual discrete choice analysis and limiting the amount of non-linearity in the likelihood function, EUmi(W) and EUti(W) may be written as:

EUmi(W) = αmXi + εmi . . . . . . . . . . . . . . . . . . . . . (1)
EUti(W) = αtXi + εti . . . . . . . . . . . . . . . . . . . . . (2)

The difference in expected utility may then be written

EUmi(W) - EUti(W) = (αmXi + εmi) - (αtXi + εti)
= (αm - αt) Xi + (εmi - εti)
= αXi + εi . . . . . . . . . . . . . . . . . . . . . (3)

A preference for the modern technology will result if EUmi(W) - EUti(W) > 0; where as, a preference for the traditional technology will be revealed if EUmi(W) - EUti(W) < 0.

The observed adoption choice of an agricultural technology (for example poultry technology) is hypothesized to be the end result of socio-economic characteristics of farmers and a complex set of inter-technology preference comparisons made by farmers (Adesina and Forson 1995). The empirical analysis permits the investigation of the decision whether or not to adopt exotic breed of poultry and the conditional level of the technology if the initial adoption decision was made. Several hypotheses can be derived on these two sets of decision - factors that affect adoption and factors that affect intensity of exotic poultry breeds.

Farmer's age may negatively influence both the decision to adopt and extent of adoption of improved poultry breeds. It may be that older farmers are more risk averse and less likely to be flexible than younger farmers and thus have a lesser likelihood of adopting new technologies. However, it could also be that older farmers have more experience in farming and are better able to assess the characteristics of modern technology than younger farmers, and hence a higher probability of adopting the practice. Adesina and Forson (1995) indicated that the expected result of age is an empirical question. There is no agreement in the adoption literature on this as the direction of the effect is generally location or technology specific. Family size, a proxy to labour availability, may influence the adoption of poultry technology positively as its availability reduces the labour constraints faced in poultry production. Education augments one's ability to receive, decode and understand information relevant to making innovative decisions (Wozniak 1984). This creates an incentive to acquire more information. Farmers with more education should be aware of more sources of information, and be more efficient in evaluating and interpreting information about innovations than those with less education. Thus it is hypothesized that producers with more education are more likely to be adopters than farmers with less education. Linear education splines, which allow the effect of education on adoption to vary according to the educational level (i.e, illiterate, read and write, primary and secondary education), are also used. Their coefficients are interpreted in the same way as a coefficient of a continuous schooling variable, that is, approximately the percentage effect that the variable has on adoption. This educational variable was classified so as to have quite a good number of farmers in each group and to make a distinction between the existed educational phases.

The decision to adopt any single innovation depends on the availability of interrelated inputs (Wozniak 1984). This suggests that the decision to adopt a current innovation may be conditional on the utilization of previously available complementary inputs. Provision of supplementary feed in the farm rather than letting to scavenge, is considered as a complementary practice in poultry production, and expected to influence adoption of poultry technology positively. Similarly, the availability of concentrate feed and vaccination for poultry may influence the adoption of exotic poultry breed positively, as both of them are considered as interrelated technological innovations in poultry farming and taken as a package with exotic poultry breed. The availability of credit may also positively influence adoption of exotic poultry technology by relaxing the binding capital constraints that farmers face during initial investments or helps to finance the variable costs associated with production of improved poultry breeds. Agricultural extension may also enhance the efficiency of making adoption decisions. In the world of less than perfect information, the introduction of new technologies creates a demand for information useful in making adoption decisions (Wozniak 1984). Of the many sources of information available to farmers, agricultural extension is the most important for analysing the adoption decision. Based on the innovation-diffusion literature (Adesina and Forson 1995), it is hypothesized that extension visit is positively related to adoption by exposing farmers to new information and technical skills about disease control, housing and equipment and feeding.

Market access factors, which refer to the existence of local markets offering good sales opportunities and adequate transport facilities, are obvious prerequisites for poultry development. As most consumers with greater purchasing power live in cities, intensification of poultry production should be initiated at least in areas having a good road network and transport facilities. The advantage of market opportunities is the incentives for the farmer to ensure that production is improved. Hence it is hypothesized that farmers' problems on market as a proxy for market access factor is negatively affected by the decision to adopt and the decision how much to adopt exotic poultry breeds. It is also hypothesized that number of poultry sold to the market is considered as a proxy measure of the expected benefits an economic agent obtained, and that benefit suggest the probability of adoption is positively related to the number of poultry sold to the market. Farmers that are able to take advantage of the good return from poultry production may find adoption of exotic poultry breed economically very attractive. Therefore, producers with larger number of marketed output derive expected benefit from being aware of technological advancements in inputs or techniques used in production and from adopting those improvements than producers with smaller expected benefits. Number of years since adopting exotic poultry breed is hypothesized in a way that those producers with more years of experience in adoption of poultry technology are more likely to increase the intensity of adoption. Each work activity produces goods or services and provides work related learning opportunities. Like education and other training, learning and perfecting skills on the job through experience makes an incentive for producers to intensify degree of adoption.

Econometric specification: the double hurdle model

A feature of many models of technology adoption, for example straightforward binary or censored data models, is that the process, which results non-adoption, is assumed to be the same as that which determines the intensity of adoption. Thus, for example, if a given farmer's characteristic is known to have a positive effect on the extent of adoption, then a very high value of this characteristic would inevitably lead to the prediction of adoption for such farmer. While such assumptions may turn out to hold, there is no reason to expect this apriori. One reason why such an assumption might fail is that there may exist a proportion of the population of farmers who would out of principle, never adopt under any circumstances.

In principle, the decisions on whether to adopt and how much to adopt can be made jointly or separately (Berhanu and Swinton 2003). The Tobit model used to analyse under the assumption that the two decisions are affected by the same set of factors (Greene 1993). In the double-hurdle model, on the other hand, both hurdles have equations associated with them, incorporating the effects of farmer's characteristics and circumstances. Such explanatory variables may appear in both equations or in either of one. Most importantly, a variable appearing in both equations may have opposite effects in the two equations. The double-hurdle model, originally due to Cragg (1971), has been extensively applied in several studies such as Burton et al (1996) and Newman et al (2001). However, this model has been rarely used in the area of adoption of agricultural technologies; an exception would be Berhanu and Swinton (2003).

The double-hurdle model is a parametric generalization of the Tobit model, in which two separate stochastic processes determine the decision to adopt and the level of adoption of technology. The double-hurdle model has an adoption (D) equation:

The log-likelihood function for the double-hurdle model is:

Data

This study was conducted in east Shewa zone of the Oromia Regional State and Welaita zone of the Southern Nations, Nationalities and Peoples (SNNP) Regional State in 2002. A multistage random sampling technique was used to select sample of 200 farmers from these two zones. About 100 farmers were randomly sampled and interviewed from each zone. Detailed definitions of all variables in the survey can be found in Table 1. The dependent variable in the first stage probit equation is farmer's adoption of exotic poultry breed. This variable takes a value of 1 if the farmer adopts poultry technology and 0 otherwise. A total of 83 households (or 41.5%) reported adoption of exotic poultry during the study period. Location wise, the difference in adoption of exotic poultry breed among farmers in east Shewa and Welayeta zones is non significant. Proportion of exotic poultry breed in the household is used as another dependent variable in the second stage truncated regression. The mean proportion of exotic poultry breed is 0.22 for the full sample and 0.54 for the adopting households. The explanatory variables comprised both the continuous and binary variables. Summary statistics for all the variables for the full, non-adopter and adopter of exotic poultry breed are provided in the annexes.

Table 1. Variable definitions

Variable

Definition

D (binary variable)

1 = if proportion of exotic poultry>0 and 0 otherwise

Y

Proportion of exotic poultry breed

zone

1=if the study zone is esat Shewa and 0 otherwise

sexhh

1=if sex of the household head is male and 0 otherwise

agehh

Age of the household head, years

agesquar

Age squared

levleduc

Education level of the head (ordered dummies if 0=illiterate; 1=read and write; 2=grade 1-4; 3=grade 5-8; 4= grade 9-12

famlsize

Total family size

farmsize

Total farm size in kert (i.e, 1kert 0.25ha)

suplfeed

1 = if the household provide supplementary feed and 0 otherwise

avalconc

1 = if concentrate is available and 0 otherwise

vaccinat

1 = if vaccination is available and 0 otherwise

totincom

Total household income, Birr

offfarm

1 = if the household participated in off-farm work and 0 otherwise

Credit

1 = if the household get credit and 0 otherwise

extcontc

1 = if the household has access to extension service and 0 otherwise

mktprob

1 = if the household face poultry market problem and 0 otherwise

poulsold

Number of poultry sold

yradopt

Number of years since adopted exotic poultry breed


Empirical results

The results of the double-hurdle model are shown in Table 2  It shows the results of variables to explain separately the decision to adopt (D) of exotic poultry breed and the decision about to which extent to do so (Y). The Tobit model was estimated by maximizing a separate log-likelihood function, with the univariate normal probability, removed from (7). The Tobit model's results are reported in the appendix. The Huber/White/Sandwich estimator of variances is used, instead of using the conventional maximum likelihood estimator of variances in order to avoid the problem of heteroscedasticity.

Table 2. Test statistics of double-hurdle model

 

Probit, D

Truncated Regression, Y(Y>0)

Wald χ2

60.4

52.8

Prob > χ2

0.00***

0.00**

LOG-L

-108

0.64

AIC(-LOG-L+k/N)

0.62

0.09

Number of observation (N)

200

83

Χ2-Test Double Hurdle versus Tobit: Γ = 45.8 > χ2(17) = 33.4

*, ** and *** refers statistically significant at 10%, 5% and 1% respectively; k = number of parameters

The first step of the analysis consisted of testing the Tobit model against the alternative of a probit plus a truncated regression model (Table 2). The results of the formal test, between the Tobit and the two-step modelling (using a probit plus a truncated regression) represent the overwhelming evidence of the superiority of the double hurdle model. Based on the log-likelihood values of the two models estimated, the LR test results suggest the rejection of the Tobit model. That is, the test statistic Γ=45.78 exceeds the critical value of the χ2 distribution. For good measure, Akakie's Information Criterion (AIC) is included as a model selection criterion. The model with the lowest AIC is preferred. This confirms the clear superiority of the double-hurdle specification. This suggests that the decision to state a positive value for the proportion of exotic poultry breed and the decision about how much to state appeared governed by different process.

The regression results (Annex 3) indicate that there is a different set of variables behind the decision to adopt and the decision about how much to state as proportion of exotic poultry breeds. It can be seen now that different sets of variables govern each process. In general, the likelihood of adoption of exotic poultry was good; an average farmer had about 40% predicted probability of adopting the technology. In terms of magnitude, an average household had about 0.51 conditional and 0.22 unconditional proportion of exotic poultry breed.

Table 3. Estimated Marginal Effects of adoption of poultry technology

Variables

Probit, D

Truncated Regression, Y(Y>0)

Coefficient

Robust Std. Err

Coefficient

Robust Std. Err

zone

-0.11

0.09

-0.09

0.08

sexhh

0.27

0.11**

0.02

0.22

agehh

0.016

0.018

-0.022

0.013*

Age Squared

-0.001

0.001

0.001

0.001***

levleduc

0.006

0.030

0.002

0.023

famlsize

0.024

0.014*

0.004

0.011

farmsize

-0.012

0.013

-0.004

0.013

suplfeed

0.26

0.08***

0.04

0.11

avalconc

0.15

0.12

0.04

0.09

vaccinat

0.11

0.24

0.11

0.17

totincom

0.001

0.000

-0.001

0.000

offfarm

0.81

0.09

-0.08

0.09

credit

0.41

0.15**

0.03

0.19

extcontc

0.31

0.09***

-0.07

0.08

mktprob

-0.28

0.09***

-0.41

0.16***

poulsold

0.27

0.014**

0.012

0.005***

yradopt

-

-

0.019

0.011**

*, ** and *** refers statistically significant at 10%, 5% & 1% respectively; Figures in parenthesis are standard error

Focusing on the effects of explanatory variables, as hypothesized, farmers' decision on adoption of poultry technology is significantly affected by sex of the household head (+), family size (+), if supplementary feed is provided (+), credit (+), extension contact (+), market problem (-), and expected benefit from poultry (+). On the other hand, farmers' decision on level of adoption of exotic poultry breed is significantly influenced by age of the household head (-), market problem (-), experience in adoption of poultry technology (+), and expected benefit from poultry.

Estimated changes in the probability of adopting exotic poultry breed and change in intensity of adoption with respect to changes in an explanatory variable are presented in Table 3. The result showed that male household heads are likely to pass the first hurdle (i.e., high likely to be potential adopters of exotic poultry) than female farmers, but conditional on adoption, sex of the head is excluded from the second hurdle since it has non-significant effect on extent of adoption. As compared to female farmers, male farmers have about 27% predicted probability advantage to adopt poultry technologies. Regarding age of the farmer, it can be observed that age indeed has a U-shaped effect on the extent of improved poultry technology adoption with a maximum age 39 years, even though age is excluded from the first hurdle since it has no significant effect on probability of adoption. This implies that farmers aged above 39 are the most likely to have lower level of improved poultry. The result support the hypothesis that with the expectation of risk aversion behaviour of aged farmers for fear of disease and other unexpected events, it is uncertain for these farmers to increase the proportion of exotic poultry as age of the farmers increase. Thus, targeting young farmers for intervention of poultry technology distribution is probably advisable, as young farmers tend to be more flexible in their decisions to adopt new ideas and technologies more rapidly. The coefficient of family size in the adoption equation is positive and statistically significant at the 10% level. The coefficient of family size in the second hurdle is not statistically significant. The result support the hypothesis that, as a good source of labour for poultry production management, households with more family size are more likely to be adopters than families with lower family size.

The positive coefficient of supplementary feed in the adoption equation support the hypothesis that farmers who have already practiced provision of additional supplementary feed are more likely to adopt exotic poultry breed. The positive effect of supplementary feed suggests the utilizations of current innovations and increases the predicted probability of adoption of exotic poultry by about 26%. When feeding is considered as complementary practice to poultry production, the probability of adopting the technology is higher when the practice is employed (adopted) than if it were not. This suggests that the introduction of complementary practices enhances the adoption and diffusion of the introduced technological innovation. Adopting exotic poultry breed and the practice of supplementary feeding together provides synergistic benefits as exotic poultry have larger responses to supplementary feeding.

According to the results of the double-hurdle model, relative to farmers who face credit constraint, the reference group in the present analysis, farmers who take credit are about 41 percent more likely to adopt poultry technology. In many cases, farmers will need to use some of their own equity to finance at least part of their investments. In other case, assets such as land or the crop itself may be used as collateral for financing a new technology. Just and Zilberman (1983) introduced a credit constraint to their static model of adoption under uncertainty. The result therefore suggests that the availability of credit is one of the most important determinants of smallholder farmers' adoption. The variable extension contact shows an effect with expected sign in each model, but is only statistically significant in the case of adoption decision, not in the case of the second hurdle, intensity of adoption decision. The results of the marginal effect analysis indicate that having access to extension service increases the probability of adopting poultry technology by 31%. The larger effects of agricultural extension on the probability of adoption of agricultural technologies may be partially explained by the different role each information source plays in the adoption decision and diffusion process (Wozniak 1984). This nature of adoption decision may dictate the relative impact of information sources on early adoption, signifying its non-significant effect on the extent of the proportion of exotic poultry breeds.

Marketing problem appears to be an important factor in both hurdles. This variable has a significantly negative effect on the probability of adoption decision and extent of adoption of poultry technology. Those farmers who faced a problem on poultry marketing have reduced the likelihood of adoption of exotic poultry breed by about 28%, and conditional on adoption, farmers with marketing problem is being associated with reduction of proportion of exotic poultry breed by 41%. The variable farmer's expected income from poultry shows a significant effect with positive signs in each model. It is clearly evidenced that farmers may decide to adopt and increase the number of exotic poultry breed where expected returns from poultry production were higher. Given the existing experiences of benefits obtained from poultry, changes in farmer's expected returns from poultry tend to have a 3% probability to adopt improved poultry and the expected proportion of exotic poultry breeds also increases by about 1.2%.


Concluding Remarks


References

Adesina A A and Forson J B 1995 Farmers' perceptions and adoption of new agricultural technology: Evidence from analysis in Burkina Faso and Guinea, West Africa. Agricultural Economics 13. p. 1-9.

Alemu Yami and Tadelle Dessie 1997 The Status of Poultry Research and Development. Research Bulletin No. 4. Poultry Commodity Research Program, Debre Zeit Agricultural Research Center, Alemaya University of Agriculture, Ethiopia.

Berhanu G and Swinton S M 2003 Investment in soil conservation in northern Ethiopia: The role of land tenure security and public programme, Agricultural Economics, 29. p. 69-84.

BurtonM Dorsett R and Young T 1996 Changing preferences for meat: Evidence from UK household data. 1973-1993. European Review of Agricultural Economics 23(3). p. 357-370.

Cragg J 1971 Some statistical models for limited dependent variables with application to the demand for durable goods. Econometrica 39. p. 829-844.

EARO (Ethiopian Agricultural Research Organization) 2000 Summary of Livestock Research Strategy, EARO (unpublished).

Greene W 1993 Econometric Analysis. Second edition. Macmillan, New York, p. 791.

Just R E and Zilberman D 1983 Stochastic structure, farm size, and technology adoption in developing agriculture, Oxford Economic Papers. 35(2). p. 307-328.

Newman C Henchion M and Matthews A 2001 Infrequency of purchase and double-hurdle models of Irish households' meat expenditure. European Review of Agricultural Economics. 28(4). p. 393-419.

Tadelle Dessie 1996 Studies on Village Poultry Production Systems in the Central Highlands of Ethiopia. M. Sc. Thesis, Swedish University of Agricultural Sciences.

Wozniak G D 1984 The adoption of interrelated innovations: A human capital approach. Review of Economics and Statistics. Number. 66. Volume LXVI. pp. 70-79.





Annexes

Annex 1.  Sample Summary Statistics

Variable

Non-adopting (N=117)

Adopting (N=83)

Full sample (N=200)

Mean

Std Dev

Mean

Std Dev

Mean

Std Dev

Y

-

-

0.54

0.30

0.22

0.33

zone

0.47

-

0.54

-

0.50

-

sexhh

0.90

-

0.96

-

0.93

-

agehh

39.9

12.3

41.8

12.8

40.7

12.5

Age Squared

1743

1092

1913

1263

1813

1166

levleduc

1.85

-

1.78

-

1.82

-

famlsize

6.97

3.10

7.49

3.21

7.19

3.15

farmsize

5.50

3.93

5.41

3.44

5.46

3.73

suplfeed

0.61

-

0.83

-

0.70

-

avalconc

0.09

-

0.16

-

0.12

-

vaccinat

0.03

-

0.05

-

0.04

-

totincom

1085

1406

1755

1962

1363

1688

offfarm

0.26

-

0.33

-

0.29

-

credit

0.01

-

0.07

-

0.04

-

extcontc

0.14

-

0.43

-

0.26

-

mktprob

0.25

-

0.07

-

0.18

-

poulsold

1.39

2.25

2.46

5.35

1.84

3.87

yradopt

-

-

2.46

2.74

1.02

2.14



Annex 2.  Rate and Intensity of Adoption of Exotic Poultry Breed

 

Study zone

Total

East Shewa

Welita

Rate of adoption

Percent of farmers

43

40

42

Sample size (N)

100

100

200

Intensity (Proportion) of adoption

Mean

0.51

0.58

0.54

Standard Deviation

0.29

0.30

0.30

Number of adopters

43

40

83



Annex 3.  Maximum likelihood estimation of double-hurdle Vs Tobit model

Variable

Probit, D

Truncated Regression, Y(Y>0)

Tobit

Coefficient

Robust Std. Err

Coefficient

Robust Std. Err

Coefficient

Robust Std. Err

constant

-3.02

1.19***

0.88

0.43**

-1.01

0.50**

Zone

-0.28

0.23

-0.95

0.08

-0.10

0.09

Sexhh

0.81

0.42**

0.02

0.22

0.28

0.19

Agehh

0.042

0.046

-0.022

0.013*

0.008

0.018

agesquar

-0.0004

0.0005

0.0003

0.0001**

-0.0000

0.0002

levleduc

0.016

0.078

0.002

0.023

0.009

0.029

famlsize

0.061

0.036*

0.004

0.012

0.013

0.014

farmsize

-0.031

0.035

-0.004

0.013

-0.006

0.013

suplfeed

0.78

0.23***

0.04

0.11

0.24

0.10***

avalconc

0.37

0.31

0.04

0.09

0.09

0.13

vaccinat

0.29

0.59

0.11

0.17

0.21

0.20

totincom

0.0001

0.0001

0.0001

0.0001

-0.0001

0.0001

offfarm

0.21

0.24

-0.077

0.087

0.039

0.098

Credit

1.12

0.52**

0.33

0.19

0.19

0.21

extcontc

0.80

0.25***

-0.07

0.08

0.22

0.09***

mktprob

-0.83

0.32***

-0.41

0.16***

-0.33

0.14***

poulsold

0.069

0.037**

0.012

0.005***

0.021

0.009**

yradopt

-

-

0.019

0.011**

0.13

0.018***

Wald χ2 (LR χ2)

60

53

105

Prob > χ2

0.00***

0.00**

0.000***

LOG-L

-108

0.64

-130

AIC(-LOG-L+k/N)

0.62

0.09

0.74

Number of observation, N

200

83

200



Received 17 October 2005; Accepted 11 January 2006; Published 21 March 2006

Go to top