The Impact of Agriculture Technology Adoption On Farmers' Welfare in Uganda and Tanzania Bethuel Kinyanjui Kinuthia & Edward Mabaya)
The Impact of Agriculture Technology Adoption On Farmers' Welfare in Uganda and Tanzania Bethuel Kinyanjui Kinuthia & Edward Mabaya)
The Impact of Agriculture Technology Adoption On Farmers' Welfare in Uganda and Tanzania Bethuel Kinyanjui Kinuthia & Edward Mabaya)
Relationship between the demographic profile of the respondent and their level
of adoption
Age
Age. Another farmer characteristic that is often examined in adoption studies is age. A farmer's age may influence
adoption in one of several ways. Older farmers may have more experience, resources, or authority that would allow them
more pOSSibilities for trying a new technology. Experience in a particular farming area or with a given crop may not be
strictly correlated with age, however, and it may be worth asking more specifically about experience. On the other hand, it
may be that younger farmers are more likely to adopt a new technology, because they have had more schooling than the
older generation or perhaps have been exposed to new ideas as migrant laborers.
In either case, it is unlikely that the demonstration of a relation between age and adoption per se will be of immediate
utility. It is more important to see if the relationship is due to farmers' experience or education or if the association with
age is more la reflection of characteristics of the farm household, including the distribution of authority, labor
availability, or sources of income. An example of this type of analysis is shown in Box B at the end of this chapter.
---- CIMMYT
The results show that the mean age of adopters in Tanzania is approximately 44 years and that of
non-adopters is close to 45 years. For the case of Uganda, the mean age of adopters is nearly 45
years and approximately 46 years for non-adopters. This shows that in both Tanzania and Uganda,
adopters are relatively younger than non-adopters implying that as the age of the farmer increases,
there are chances of developing resistance to the adoption of new technologies.
----- The Impact of Agriculture Technology Adoption on Farmers’ Welfare in Uganda and
Tanzania ( Bethuel Kinyanjui Kinuthia & Edward Mabaya)
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Technologies: A Case Study of Potato Farmers in Carchi, Ecuador; Selected Paper prepared for
presentation at the American Agricultural Economics Association Annual Meeting, Providence,
Rhode Island, July 24-27, 2005
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Participation Affect Adoption Decisions? Issues for Sustainable Biotechnology
Dissemination
Odoemenem, I.U and Obinne, C.P.O. (2010). Assessing the Factors Influencing Utilization of Improved
Cereal Crop Production Technologies by Small-scale Farmers in Nigeria. Indian Journal of Science and
Technology. 3(1): 180-183
Adeogun, S. O. 2008. Utilization of Cocoa Rehabilitation Techniques Among Cocoa Farmers in Selected States
of Nigeria. Ph.D Thesis of the Department of Agricultural Extension and Rural Development,
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Adetimehin, O., Okunlola, J., Owolabi K. (2018). “Utilization Of Agricultural Information And
Knowledge For Improved Production By Rice Farmers
In Ondo State, Nigeria.” Journal Of Rural Social Sciences, 33(1)
All bivariate correlation analyses express the strength of association between two variables
in a single value between -1 and +1. This value is called the correlation coefficient. A
positive correlation coefficient indicates a positive relationship between the two variables
(as values of one variable increase, values of the other variable also increase) while a
negative correlation coefficient expresses a negative relationship (as values of one
variable increase, values of the other variable decrease).
Demographic Variables C-Value P-Value Interpretation
Age -0.721 0.001 Highly Significant
Sex 1.702 0.636 Not Significant
Educational Attainment 74.447 0.001 Highly Significant
Rice Farming Experience -0.676 0.001 Moderately Significant
Tenurial Status 3.159 0.368 Not Significant
Size of the Farm 14.173 0.512 Not Significant
Agricultural Organization 34.492 0.001 Moderately Significant
Attendance in Trainings/Seminars 72.041 0.001 Highly Significant