National fsa training Module 18: Impact assessment studies
Table with continuous variables: Fertiliser use by cropping history
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- Adoption history and diversity of adoption patterns
- 18.5 Tools for adoption analysis Measuring adoption
- Index of acceptability
Table with continuous variables: Fertiliser use by cropping history----------------------------------------------------------------------------------------------------- Average years continuous cropping ----------------------------------------------------------------------------------------------------- Without fertiliser (N=50) 3.14 With fertiliser (N=58) 6.40 ----------------------------------------------------------------------------------------------------- t - 3.51 d.f. = 106 p<0.001
Prediction of adoption to occur: probit, logit and tobit models (Lyimo 1997) Many adoption studies deal with adoption as a dependent variable. Both probit and logit models are techniques for estimating the probability of an event to occur, in our case farmers either adopting or rejecting a technology. In analysing adoption, these models use series of farm and farmer characteristics to predict the probability of adoption. The basic difference between the two models is that logit model assumes that the dependent variable follows logistic distribution while the probit model assumes a cumulative normal distribution. Often, there is not only the need to predict adoption, but also to foresee the extent or intensity of adoption. For example, extension needs to know the likelihood that a farmer uses fertiliser and if so, how much. For this kind of studies a commonly used model is the tobit model. Like the probit model, it is also based on the normal distribution. For more information on logit, probit and tobit models, Maddala (1983) can be consulted. Adoption history and diversity of adoption patterns Many adoption studies go beyond an analysis of current practices and attempt to document adoption history. This can be useful for several purposes. It may help project future demand for inputs, determine whether extension needs to be strengthened, or quantify the change in the number of technology users over time to assess impact. It's therefore useful to distinguish between ‘adoption rate’ (which is measured at one point in time) and diffusion process (which is the spread of a new technology across a population over time). In Annex 1, which elaborates on adoption and diffusion, the frequency and cumulative curves of adoption and five categories of adopters were presented. However, the classification of people in five categories of adopters does not necessarily apply to all societies. There are for example strong indications that the dissemination of innovations follows a radically different pattern in Sukumaland. Larsen (1974) found that development in the area was hampered by (i) insufficient incentives to make improvement, (ii) limited aspirations, (iii) lack of resources and knowledge about improvement. In addition, the author considers the inefficiency of public and semi-public organisations particularly regarding dissemination of new knowledge and distribution of new inputs. This state of lethargy is allegedly also the result of the so-called critical mass factor. Different extension services have observed that adoption takes place or not. If opinion leaders and a large part of their followers do not adopt an innovation, then nobody does. If the community does not bless an innovation, then social pressure discourages innovative farmers to continue even if the activity in question is highly beneficial. Disadoption appears to be very frequent. As the Sukumaland example shows, in-depth analysis of adoption patterns and the consequences for the conception of extension programs can be highly relevant. Also the relative importance of other parameters like price ratios, infrastructure, taste, division of labour may not be constant over time. Actual diffusion patterns may not follow the smooth theoretical curves. It is generally expected that farmers will test a new technology on a small part of the farm, and if the results are positive will gradually increase the use of the technology. However, in some cases different elements may be adopted independently, while in other cases there may be a sequential adoption pattern. Sometimes, farmers may have one or more years of experience with the technology only to have subsequently abandoned it or it may be that a significant proportion of farmers have experience with the technology but very few currently use it. It's also important to note the difference between early and late adopters. In the case of technologies that depend on purchased inputs, for instance, the first farmers to adopt a new technology may be larger scale farmers or those with more resources or capacity to experiment with new practices. In most cases, smallholders do not adopt recommendation packages; instead they adopt technological components. In most cases they will start with the less risky components before embarking on risky or costly components (Bisanda and Mwangi, 1997). However, resource-poor farmers have come to adopt practices, such as growing high value cash crops that entail considerable risk. 18.5 Tools for adoption analysis Measuring adoption One of the most important issues in designing an adoption study is to answer the question what is exactly meant by adoption. What are the criteria to consider a certain practice to be the adoption of a new technology?
Farmers may make their own modifications to a new technology (such as a storage technique or a piece of machinery). An adoption study needs to pay careful attention to this type of farmer innovation. The adoption of a new technology can also have implications for the rest of the farming system. An adoption study must be open for these consequences of adoption. For example, researchers may wish to see the widespread adoption of a new variety, but what effects does this have on the use of other crops and other varieties and what about the genetic diversity in farmers' fields? An adoption study is more complicated than it appears at first sight. A survey on the adoption of hybrid maize in three regions of Malawi (Smale et al., 1991) gave the following results: 27% of farmers have adopted hybrid maize, 12% of total maize area is in hybrids, and 25% of total maize production comes from hybrid maize. These results respectively refer to:
Four A’s In Module 10, the four A's for adoptability screening, adoptability analysis and ex-post adoption analysis have been presented in detail. These four A's are: acceptability, affordability, accessibility and attractiveness. These dimensions cover technical and socio-economic issues that determine adoption. It was indicated that several PRA tools (e.g. ranking tools) and semi-structured interviews can address these different aspects of adoption analysis. We limit ourselves here to a short reminder. The following tools may be applied to analyse the 4A’s as defined above:
Index of acceptability This is a basic tool to analyse adoption. It does not give any information regarding the reasons why farmers innovated or did not innovate. It merely quantifies the level of adoption within a certain domain. Farmers are asked if they innovated and if they did for what proportion of the crop under the recommendation. The index reads: (C x A):100. Directory: sed sed -> Tourist Areas Haiti, another discovery sed -> Headmaster’s Prize Giving Speech Summer Term 2015 sed -> Early searches for new physics with the atlas detector E. V. Sedykh sed -> Audition pack sed -> Lovers: Birth of Lilith sed -> Wakko’S 50 state capitals (lyrics) sed -> Acknowledgements sed -> Using Well Logs in the Petroleum Industry Download 236.26 Kb. Share with your friends:
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