8. Modalidades procesales
8.2. Procesos de carácter colectivo
Conservation Agriculture is not a single activity, but a behaviour category consisting of several agricultural practices, that could be studied as ‘behaviours’ in their own right. Seven distinguishing agricultural practices have been identified through key informant interviews that are relevant to understand the adoption of CA in Kenya and Madagascar, using the Reasoned Action Approach:
o Ploughing
o Direct planting (planting without ploughing the soil first) o Spraying herbicides (with a knapsack sprayer)
o Shallow weeding (scraping the weeds from the soil surface)
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o Leaving crop residues on the land (including imported mulches from other fields) o Planting cover crops
o Rotating crops
This research looked both at these constituent practices and at the constructed behaviour category of CA which was defined as adopting both direct planting and mulching. Spraying herbicides and shallow weeding were not included in this definition because they are not always strictly necessary for implementing the CA principles as disseminated to and practiced by the farmers. Normally cover crops would be included in this definition, but because non-FFS farmers were not familiar with the concept, their responses regarding the practice of cover crops were not considered to be reliable enough to include. Crop rotation was also excluded, because it proved very difficult to define. Moreover, a basic form of crop rotation was already practiced by most farmers, and relatively little attention was given to this aspect of CA in the trainings at the CA groups or FFSs in either country. Only in Kenya was Shallow weeding considered an important distinct CA practice, as in Madagascar there was no ‘conventional’ practice of deep weeding that involved turning the soil.
For each practice, intentions were assessed in 2013/2014 as likelihood (‘very unlikely’ to ‘very likely’ as measured on a 5-point single Likert item from -2 to 2) of adopting the practice in the rain season of 2014. An average of 70% of the responses for the intention were found to be either ‘very likely’ or ‘very unlikely’. Therefore the intention variable was transformed from a 5-scale ordinal into a dichotomous variable in which the outcomes ‘likely’ and ‘very likely’ were labelled as intenders, and the other outcomes from ‘very unlikely’ to ‘maybe’ were labelled as non-intenders.
Adoption of the CA practices was directly assessed in 2014/2015 as a dichotomous variable (yes-no), independent of the surface area of the farm where it was adopted.
Figure 4-5 Simplified model of the Reasoned Action Approach (Source: Fishbein and Ajzen, 2010) Behaviour Intention
Attitude Outcome
beliefs
Social Norms Social beliefs
Perceived Behavioural
Control
Control beliefs Actual
Behavioural Control
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In the Reasoned Action Approach (Figure 4-5), the constructs of attitude (A), perceived norms (PN) and perceived behavioural control (PBC) are assessed by means of different items to cover different dimensions of the behaviour. Attitude was assessed through three items to incorporate both evaluative (good-bad, foolish-wise) and experiential aspects (unpleasant-pleasant). Injunctive norms were assessed through the perception of whether ‘important others’ approve of the respondent doing the practice (they think I should not-I should…). The descriptive norms are the perception of how many among the people that are respected and admired by the respondent actually adopt this practice (almost all-almost none). Perceived behavioural control was assessed with two items that are conceptually slightly different (see section 2.5.1). The first element in PBC is perceived ease, referring to internal factors (very difficult-very easy). The second element in PBC is perceived control which refers more to the external factors that may influence the PBC (not at all up to me-up to me). Where internal consistency of these multi-item scales was sufficient, the scales were used; in other cases the single items were used (see section 7.3).
To evaluate the internal consistency and reliability of such multi-item scales, it is most common to use Cronbach’s Alpha coefficient (Cronbach, 1951). However, in psychometrics the limits of Cronbach’s Alpha are regularly debated (e.g. Ten Berge and Sočan, 2004) and the Greatest Lower Bound (GLB) is seen as a more accurate way to assess internal consistency and reliability (Sijtsma, 2009). For two-item scales, better reliability estimates are given by Spearman-Brown coefficient as suggested by Eisinga et al.(2013). In this study, these coefficients are presented together with Cronbach’s Alpha.
The respective outcome-, normative-, and control beliefs were assessed for spraying herbicides, direct planting and mulching, as shown in Appendix X and XI. A complete list of beliefs was made through key informants and focus group discussions. This included collecting all possible out-comes (good and bad) of the different CA practices, identifying the social referents, that is the people and groups that potentially influence respondents’ decision making, and listing the control factors that possibly influence perceived behavioural control with respect to adopting the CA practices. The questionnaire was then tested with three individual farmers.
To assess the outcome beliefs the expectancy-value model (Fishbein, 1963; Fishbein and Ajzen, 1975) was applied, relying on the product of belief strength b that a certain behavioural outcome i will occur (very unlikely – very likely), and the evaluation e of the importance of these outcomes.
If outcome i would both be very important to the farmer and be considered very likely to occur, the product bi.ei would be high and the belief would contribute relatively much to a positive attitude.
The correlation of bi.ei with intention gives a direct indication of how important that belief was for predicting the intentions. This method, although common in the literature, is not without potential problems because the scaling method (unipolar or bipolar likert items) in multiplicative models like
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the expectancy-value model influences the found correlation with external criterion like intentions (Bagozzi, 1984; Ajzen, 1991; Gagné and Godin, 2000). It can nevertheless give an extra perspective on the relative importance of the respective beliefs.
According to the same principle, the perceived norms were examined by listing injunctive normative beliefs n for social referents j, and the farmers’ motivation to comply m with the opinion of these referents. If the farmer thinks that a social referent e.g. the neighbours do not approve of him practicing direct planting (n is negative), but the farmer does not attribute much importance to their opinion (m is low), then the product nj.mj is low, indicating that the relative contribution to the SN is limited. Similarly, the descriptive normative beliefs n’ and motivation to comply m’ were assessed for social referents j’. The PBC was examined by listing for each control factor k the belief that it will be present c and the perceived power p of factor k to facilitate or impede performance of the behaviour (Fishbein and Ajzen, 2010, p. 129–178). Again, the correlation of ck.pk with intentions gives an indication of how important that particular belief was in the forming of intentions. In order to increase the variation in the results for ei and pk, farmers were asked to rank the possible outcomes and control factors according to their importance.
Training of enumerators
In Kenya and Madagascar local enumerators assisted in conducting the questionnaire. In Kenya, four local students were found who also had experience with doing surveys. They were all MSc students in agriculture and had ties with the research NGO CETRAD in Nanyuki. The enumerators, two women and two men, were trained for one day during which we did one test interview and discussed the outcome. In Madagascar, three young translators from Ambatondrazaka were hired who had worked with the ABACO project before and were familiar with agriculture.
In both countries, the interviews were done in groups of two, allowing one person to lead the conversation while the other could make notes of observations and explanations made by the farmer. At the end of each work day we came together to discuss and evaluate the days experiences. These were valuable moments in which hypotheses and observations could be discussed, and sometimes suggestions were made to include other questions.
Statistical analysis
Both intention and adoption were modelled as dichotomous variables. Therefore a binary logistic regression was used to understand the relative contribution of a set of independent variables to intentions and adoption of the selected CA practices. In a logistic regression model, the probability Pr that dependent variable 𝑌𝑖 takes the value 1 is given by
𝑃𝑟(𝑌𝑖 = 1|𝑋) = 𝑝𝑖 = 𝑒𝛼+𝛽𝑋𝑖 1 + 𝑒𝛼+𝛽𝑋𝑖
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where α is a constant, Xi represents the independent variables and β represents the regression coefficients. The odds that 𝑌𝑖 takes the value 1 is given by
𝑝𝑖
1 − 𝑝𝑖 = 𝑒𝛼+𝛽𝑋𝑖
which can be rewritten as
log ( 𝑝𝑖
1 − 𝑝𝑖) = 𝛼 + 𝛽𝑋𝑖 = 𝛼 + 𝛽1𝑋1+ 𝛽2𝑋2+ 𝛽3𝑋3+ ⋯ + 𝜀𝑖
where α is a constant, the X’s are the independent variables, the β’s are the regression coefficients, and ε is the error term. In the result section the specific regression models used in this study for intention and adoption of CA practices are further defined.
The relative contributions of attitudes (A), perceived norms (PN) and perceived behavioural control (PBC) in the prediction of intentions (I) to engage in CA practice j were tested with the following logistic regression model
𝐼𝑗= 𝛼 + 𝛽1𝐴𝑗+ 𝛽2𝑃𝑁𝑗+ 𝛽3𝑃𝐵𝐶𝑗+ 𝜀𝑗
The adoption, or actual behaviour (B) with respect to CA practices j was examined with the following logistic regression model:
𝐵𝑗 = 𝛽1𝐼𝑗+ 𝛽2𝐴𝐵𝐶𝑗+ 𝜀𝑗
where I is intention and ABC is actual behavioural control. PBC was used as a proxy for ABC, because no standard procedures for assessing actual control are currently available (Fishbein and Ajzen, 2010, p. 64), nor is it likely that a reliable, direct measure of ABC can be developed at all.
The significance of the difference between the -non-parametric- constructs’ mean values was established with a Mann-Whitney test, and for simple correlations Spearman’s rho (two-tailed) was used.
RAA and mixed methods
Implementing the RAA questionnaire involved a combination of qualitative and quantitative methods. Preparation of the questionnaire relied on quantitative methods of inquiry, including key informants and focus group discussions. While doing structured interviews with the farmers, farmers were generally commenting and explaining their answers. These comments were written down and helped understand in a different way what farmers wanted to say. The questionnaires had a structure that worked from general questions towards specific questions. For example, a farmer would be asked what “people whom (s)he respects and admires think about direct planting”.
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Although the answer in the questionnaire only required a value between -2 and 2, the issue would usually evoke a clarification or a comment on the side-line that proved very insightful. The questionnaire would continue with asking what the respective social normative ‘referents’, e.g.
neighbours, would think, and as such the qualitative and quantitative data proved opportunities for triangulation and an improved insight in the important topics.