Theoretical Framework
The basic concept of SEM is a powerful, multivariate technique found increasingly in scientific investigations to test and evaluate multivariate causal relationships. The key in the regression analysis is to determine how much of the change in the dependent variable is explained by the independent variable or variables. Although multiple regression analysis can only be applied to observed variables, the basic principles can be applied to structural equation modeling (Kline, 2011).
As a new statistical analysis technique, allows testing research hypotheses in a single process by modeling complex relationships among many observed and latent variables. However, in the method of structural equation modeling, direct and indirect effects are put together. The researcher develops hypotheses about the relationships among variables that are based on theory, previous empirical findings, or both. These relationships are direct or indirect whereby intervening variables mediate the effect of one variable on another. This researcher also determined the relationships are unidirectional or bidirectional, by using previous research and theoretical predictions as a guide. The researcher outlines the model by determining the number and relationships of measured and latent variables (Beran and Violato, 2010).
1. Supposed flock size about the likelihood of getting a MD disease, reduced egg production or increased the death rate,
2. Perceived number staff in ability of the advised action to reduce MD threat i.e. susceptibility and severity, and
3. Patterns of correlation among a set of litter management of the recommended action for the control of MD.
4. Confirms the correspondence of the data of the relations in the theoretical model.
Thinking of SEM as a combination of factor analysis and path analysis sets the researcher up for thinking about SEM’s two primary components: the measurement model and the structural model. The measurement model of SEM allows the researcher to evaluate how well his or her observed (measured) variables combine to identify underlying hypothesized constructs. Confirmatory factor analysis is used in testing the measurement model, and the hypothesized factors are relationships between flock size, litter management (LM), number of staff and between numbers of sick, drop in egg production (DEP), number of death.
As shown in figure 15, the first observed, endogenous variables (Number of sick, drop in the egg of production, Number of death) is linked to the observed, exogenous variables (flock size, litter management, number of staff), and unobserved, exogenous variables (e1, e2 and e3) (Question 1, Question 2, and Question 3) which are composed of the questions answered in the research questionnaire. In the figure, the number of variables in this model: Umber of observed variables (9), number of unobserved variables (3), number of exogenous variables (6), and number of endogenous variables (3) in figure (1).
Questionnaire Design
A questionnaire was designed based on the framework described above. Each model construct was measured using a set of rating scale items (questions). The supposed flock size about the likelihood of getting MD disease, reduced egg production, or increased the death rate, was elicited by three rating questions. The first question assessed the frequency of MD outbreaks as experienced in different farms' flock sizes. The second question referred to the experienced frequency in the small flock size (<50). Both questions were answered on a four point rating scale, which indicated the occurrence of having an outbreak every <50, 51-1000, 1000-10,000, or >10001 chicken flock. The third question measured the chicken flock size associate with MD occurrence by a three points rating scale (high, medium, low). The perceived number of staff or ability was measured using two rating questions on a three points scale (large, medium, and small):
One the question was about the impact of MD relative to general chicken egg production problems and the other question about the impact of MD relative to the impact of other poultry diseases. The patterns of correlation among a set of litter management of the advised action for the control of MD was measured by two rating scale questions on a three points scale (high, medium, low) for the proposed control measures related to litter management, and influence chicken performance and important for the welfare chicken. To confirms the correspondence of the data of the relations in the theoretical model for the control of MD. The objectives were weighed for three suggested specific MD control measures, including 1) management of chicken at farm level, 2) MD vaccine address, 3) biosecurity of chicken farms.
Data Collection
Chicken Production systems
Poultry production has a major role in the economy of developing countries, including an important role in poverty alleviation by means of income generation and household food security. More than half of Ethiopian households both in rural and urban areas keep chickens, although there is considerable variation in the distribution of chicken keeping, with most households in highland areas keeping chickens, and far fewer doing so in lowland pastoral areas (Sambo et al., 2015). Whilst village chickens are widely accessible and require few inputs, productivity is low and constrained by, among other things, disease, predation and scarcity of feed. Interventions to improve production include vaccination; bird distribution; management interventions; cross-breeding programs; and combined programs, but few interventions have been demonstrably sustainable in village chicken production systems (Bettridge et al., 2018).
Chicken production is an integral part of most rural families’ livelihoods (an estimated two-thirds of Ethiopian villagers keep poultry) and birds are commonly kept at night on perches within the family dwelling, frequently in the kitchen (Brena et al., 2016). Poultry industry in Ethiopia is infant but fast growing sector. The industry faces various challenges such as shortage of feed in terms of quality and quality, poor husbandry practices, prevalence, and wide distribution of infectious and noninfectious diseases. Poor veterinary services and lack of appropriate breeding practices are assumed to be additional challenges. Moreover, the government has given less attention. Disease is resulting in as high as 20–50% estimated mortality rate ranges (Berhe et al., 2019).
Sampling
A sample size of 200 farmers from different production system, was chosen for data collection. This sample size was based on a pragmatic consideration of SEM feasibility and reasonable power of test for the intended statistical analyses, In addition, the minimum sample size for structural equation modeling is suggested as 15 (Bentler and Chou, 1987), and Some researchers suggest that the sample size for SEM should be 200-500, at least 200 (Çelik and Yılmaz, 2013). The farmers were sampled from 11 zones (North Shewa, North Gondar, East Gojjam, Central Gondar, Awi, West Gojjam, West Gondar, South Gondar, Bahir Dar, South Wollo, and North Wollo) in the both intensive and extensive farming system. Selection of zones and cities was based on the authors’ subjective judgment of representativeness of the production systems and on convenience of accessibility. The difference in the number of zones sampled from the different production systems was a reflection of the proportion of zones in the different production systems. This selection procedure was continual in other zones until the required sample size (200) for each sub-city was reached.
The questionnaire was prepared in English and translated into Amharic language for all administration zones. It was directed to the selected farmers and farm owners by face to face interviews. The study proposal was ethically reviewed and approved by the Institutional Review Board of the University Gondar. Oral informed consent was obtained from each participating farmer after reading a written consent form. The use of oral consent was approved by the Institutional Review Board considering the fact that most of the study participants could not read and write to give their consent in writing. The interviewers confirmed the participants’ oral consent by signing on the respective consent form for each interview as per the Board’s guideline.
Model Specification
In the present study, the six basic constructs of the SEM were used to assess farm owner perceptions of MD and its control in North West Ethiopia. In the evaluation of the effect of these factors on the motivation of farm owners to implement control measures against the disease by improving farm management, the intention to participate in hypothetical MD control measures was considered as a proxy of the actual behavior. This is due to absence of any official control in practice to measure the behavior directly. Although intention does always explain to farm management system was always related to disease outbreak. In the analysis, socio-demographics and husbandry variables were used as modifying factors of the perception.
Furthermore, supposed the preliminary study has hypothesized the measurement model described the relationships among these six variables in Figure 15. This has described a model that can be accounted for the observed relationships between flock size, litter management (LM), number of staff and between the number of sick, drop in egg production (DEP), number of death (both were indicators of their respective correlated factors). However, that there was a moderated relationship between all variables. Our model was accounted for the risk factor relationship. In essence, these has created a model that says the relationship between flock size, litter management (LM), and the number of staff and between the numbers of sick, drop in egg production (DEP), a number of death is equal to 1.0 to scale latent variables. Our model determined to the extent that the relationships hypothesized was captured the observed relationships on the parameter path (Kline, 2005).
Data Analysis
The valuations of this model were to endorse appropriate poultry health management to control the emergence of highly pathogenic diseases like MD and measures bio-security for better performance and quality of poultry production in a competitive world. Analyses of the management skills of poultry production to evaluate the proposed risk factor correlation to the occurrence and the reduction of poultry product by MD. By measures, the management risk factor to control the disease was performed in North West Ethiopian situation.
There are 13 correlation coefficients between the variables that used in the SEM, the number of staff, LM, FS, number of sick, number of death, DEP and LM. Likewise, there are correlation coefficient between number of staff, LM, FS, number of sick, number of death, DEP and LM, the commonly applied threshold significant value of 0.05. Each factor was then evaluated using a separate Factor Analysis (FA). From the analysis, Cronbach’s Alpha (coefficient of reliability) based on the average inter-item correlations are evaluated for each parameter. The quantitative data was purified and analyzed using Statistical Package for Social Science (SPSS) version 20 and Analysis of Moments Structures (AMOS) version 18 were used to analyses the confirmatory factor analyze, reliability test, descriptive statistics, Pearson Correlation, and path analysis.